A FEW FEET TO FAILURE: TWO ESSAYS ON ENHANCING THE SERVICE EXPERIENCE THROUGH CUSTOMER CONTACT EMPLOYEE PERFORMANCE By Ryan C. White A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Marketing 2011 ABSTRACT A FEW FEET TO FAILURE: TWO ESSAYS ON ENHANCING THE SERVICE EXPERIENCE THROUGH CUSTOMER CONTACT EMPLOYEE PERFORMANCE By Ryan C. White The importance of the customer contact employee in providing a successful service experience and ultimately generating customer satisfaction and customer loyalty is unequivocal. Even with research regarding the improvement of customer contact employee performance, managers still struggle with achieving the desired level of performance from their customer contact employees. This dissertation addresses the managerial concern of customer contact employee performance through two essays. Essay 1 uses 308 customer observations nested within 184 customer contact employees and 157 customer contact employee responses nested within 88 managers to examine the key antecedents and consequences of customer contact employee in-role and extra-role performance. Essay 2 uses 1000 daily observations nested within 100 customer contact employees to examine the consequences of customer interpersonal injustice on the emotions, attitudes, and counterproductive work behaviors of customer contact employees. The results from Essay 1 show that while formal marketing controls have no effect on customer contact employee performance, informal marketing controls do increase customer contact employee performance and the manager has an influence on the development of informal controls within an employee. The results from Essay 2 show that interpersonally unjust treatment from customers results in the emotions of anger and guilt within customer contact employees and these two negative emotions reduce job satisfaction and increase counterproductive work behaviors directed at various targets. The results from Essay 2 also show that job satisfaction is negatively related to counterproductive work behaviors directed at various targets and the customer orientation of the customer contact employee increases feelings of guilt following customer interpersonal injustice. Furthermore, the implications for researchers and practitioners offered by the results of both essays are identified and discussed. Copyright by RYAN C. WHITE 2011 To those who have helped me along the way v ACKNOWLEDGMENTS It is certain that this dissertation would not be completed without the support of many individuals. First, I would like to acknowledge the giants who graciously allowed me to stand on their shoulders – my committee. In particular, I would like to thank my co-chairs, Dr. Roger J. Calantone and Dr. Clay M. Voorhees, for providing the foundation that will serve me throughout my academic career as well as challenging me to achieve results I never imagined I was capable of achieving. I would also like to thank my other committee members, Dr. G. Tomas M. Hult and Dr. Brent A. Scott, for their insightful advice and expert guidance. Next, I would like to thank Stan, Tom, and Marge for taking a chance on a PhD student in granting me access to their organization and ensuring that everything went as smoothly as possible. I hope that the insight gained from this dissertation impacts your organization in the same way as your support has impacted me. Also, the staff of the Marketing department, especially Kathy, Sherry, Marlena, and Laurie, provided me with invaluable support over the past five years. Thank you for always helping me out of whatever predicament I got myself in. To my fellow doctoral students who provided a sounding board for my ideas as well as served as an informal support group – thank you. I will never forget the Three Musketeers, the sandwich night, the thermodynamic properties of melting snow, and ceteris paribus. Also, I would like to say thank you to my family for their unconditional support throughout this process. I am sure that I did not make it easy for you, but, I am thankful that you never gave up on me and never let me give up. vi Finally, I would like to recognize the support that I received from the one closest to me, my fiancée. I will never be able to fully express my gratitude for everything you did for me. You believed in me even when I did not and have never stopped making me a better person. Thank you. vii TABLE OF CONTENTS LIST OF TABLES xi LIST OF FIGURES xiii INTRODUCTION References 1 11 ESSAY 1 CUSTOMER CONTACT EMPLOYEES: THE EFFECT OF FORMAL AND INFORMAL MARKETING CONTROLS ON EMPLOYEE PERFORMANCE, TEAM PERFORMANCE, AND CUSTOME OUTCOMES Literature Review and Research Hypotheses Customer Satisfaction to Loyalty Employee Behavior to Customer Satisfaction Employee Authenticity Marketing Controls to Employee Behavior The Manager’s Social Power Study Methods Manager Data and Measures Employee Data and Measures Customer Data and Measures Analyses Overview Results Measurement Model Predictive Equations and Results at the Individual Data Levels Customer Outcomes Employee Outcomes Results of the Averaged Data Path Model Discussion The Effects of Customer Satisfaction The Effects of Employee Performance The Effects of Formal and Informal Marketing Controls The Effects of the Manager Implications Customer Satisfaction Employee Performance Formal and Informal Marketing Controls The Manager Limitations 14 15 19 23 26 27 38 40 40 42 44 45 46 46 47 47 56 62 67 68 69 69 70 70 70 71 74 75 77 viii Appendices Appendix A: Essay 1 Survey Items Appendix B: HLM Results of Non-Hypothesized Effects Appendix C: HLM Results of Baseline Effects Appendix D: PLS Results of Non-Hypothesized Effects References ESSAY 2 THE HIDDEN EFFECT OF RUDE CUSTOMERS: CUSTOMER CONTACT EMPLOYEE RESPONSES TO CUSTOMER INTERPERSONAL INJUSTICE Literature Review and Research Hypotheses Counterproductive Work Behavior Customer Interpersonal Injustice Affective Events Theory Work Events to Affective Reactions Work Events to Work Attitudes Work Events to Affect Driven Behaviors Work Events to Attitude Driven Behaviors The Effects of the Employee’s Disposition Study Methods Participants Procedure Measures Customer Interpersonal Injustice Anger Guilt Job Satisfaction Counterproductive Work Behavior Customer Orientation Control Variables Analyses Overview Results Correlations Partitioning of Variance Components Test of Hypotheses Main Effects Mediating Effects Cross-Level Moderating Effects Discussion Research Implications Managerial Implications Limitations Appendices Appendix E: Essay 2 Survey Items Appendix F: Essay 2 HLM Equations References ix 80 83 85 87 90 99 101 101 105 108 110 112 115 119 123 125 125 126 127 130 130 131 131 132 134 134 135 136 136 136 138 138 143 148 149 150 155 157 161 164 169 CONCLUSION References 180 184 x LIST OF TABLES Table 1: Means (M), Standard Deviations (SD), and Correlations Among Focal Constructs 48 Table 2: Measurement Testing - Mystery Shopper Ratings 49 Table 3: Latent Variable Correlations Between Mystery Shopper Ratings 50 Table 4: Correlations Between Mystery Shopper Ratings 50 Table 5: Measurement Testing – Employee Ratings 51 Table 6: Latent Variable Correlations Between Employee Ratings 52 Table 7: Correlations Between Employee Ratings 53 Table 8: HLM Results Predicting Customer Outcomes 56 Table 9: HLM Results Predicting Employee In-Role Performance 59 Table 10: HLM Results Predicting Employee Extra-Role Performance 60 Table 11: HLM Results Predicting Employee Orientations 63 Table 12: Averaged Data Path Model Estimates Predicting Customer Outcomes 65 Table 13: Averaged Data Path Model Estimates Predicting Employee Performance 66 Table 14: Averaged Data Path Model Estimates Predicting Employee Orientations 67 Table 15: Total Effects of Averaged Data Path Model Estimates at the Shift Level 68 Table 16: HLM Results of Non-Hypothesized Effects 83 Table 17: HLM Results of Baseline Effects 85 Table 18: PLS Results of Non-Hypothesized Effects 87 Table 19: Measurement Testing for Variables Between Individuals 128 Table 20: Measurement Testing for Variables Within Individuals 129 Table 21: Correlations Between CWB and Predictor Variables 137 xi Table 22: Parameter Estimates and Variance Components of Null Models 138 Table 23: HLM Results Predicting Anger 139 Table 24: HLM Results Predicting Guilt 139 Table 25: Total Effects of Anger and Guilt Predicting Job Satisfaction 140 Table 26: Total Effects of Anger and Guilt Predicting CWB 142 Table 27: Anger, Guilt, and Job Satisfaction Predicting CWB 145 Table 28: Mediating Effects of Anger and Guilt 146 Table 29: Sobel Test Results for the Mediating Effects of Anger and Guilt 147 Table 30: Sobel Test Results for the Mediating Effect of Job Satisfaction 148 Table 31: Potential Sources of Common Method Bias and Remedies Taken 158 xii LIST OF FIGURES Figure 1: Conceptual Model of Essay 1 4 Figure 2: Conceptual Model of Essay 2 6 Figure 3: The Service-Profit Chain 16 Figure 4: The Employee-Customer-Profit Chain 18 Figure 5: The Satisfaction Cycle 22 Figure 6: The 3M Model of Motivation and Personality 33 Figure 7: Robinson and Bennett’s (1995) Typology of Deviant Behavior 103 Figure 8: Affective Events Theory 109 Figure 9: The Theories of Reasoned Action and Planned Behavior 120 xiii INTRODUCTION In 2008, consumers in the U.S. spent approximately two thirds of all private consumption on services (BEA 2009). This figure, which is twice as large as the consumption expenditure on manufactured goods, represents nearly 6.7 trillion dollars or an amount greater than any other country’s gross domestic product in 2008 (IMF 2009). Additionally, this amount has grown by 54% in the past decade. Mirroring the steady increase in consumer spending on services is an increasing focus on services marketing in the academic literature, exemplified by the creation of the Journal of Service Research in 1998, a call for a new services based dominant logic for Marketing (Vargo and Lusch 2004), and a recent special issue focusing on competing through services in the Journal of Retailing (volume 83, issue 1). These trends confirm the unequivocal relevance of Services Marketing to academics and practitioners. Central to the total service offering is the service encounter, which is “the face-to-face interactions between a buyer and a seller” (Solomon, Surprenant, Czepiel, and Gutman 1985, pg. 100). This interaction is proposed to be evaluated by customers on two dimensions, a technical dimension and a functional dimension (Gronroos 1990). The technical dimension can be thought of as what customers receive in their interactions with the firm and can often be measured objectively by customers. The functional dimension, in contrast, is the how the customer receives the technical dimension and is often perceived subjectively. Comparing the two dimensions, the technical dimension is the outcome of the service encounter while the functional dimension is how the outcome was provided to the customer (Gronroos 1990). Parasuraman, Zeithaml, and Berry’s (1988) SERVQUAL and Haeckel, Carbone, and Berry’s (2003) mechanic and humanic clues reflect this notion that the customer evaluates the service encounter on more than one dimension. Notwithstanding these different, yet, similar 1 conceptualizations of how the customer evaluates the service offering, it is the personal interaction between the customer contact employee and the customer which is at the heart of many services (Czepiel et al. 1985). Further, the interaction with the customer contact employee is the service for many customers (Bitner, Booms, and Tetreault 1990). Empirically, the influence of the customer contact employee can be seen in studies such as van Dolen, de Ruyter, and Lemmink (2004) and Wall and Berry (2007) which agree that the performance of the customer contact employee strongly affects customer evaluations of the service encounter. In fact, Wall and Berry (2007) demonstrated that the performance, behavior, and appearance of customer contact employees dominated the technical clues in influencing customer service quality perceptions. Thus, of all the factors which compose the service encounter, the dyadic, human interaction between the customer contact employee and the customer is viewed as the most important contributor to the overall evaluation by the customer (Czepiel et al. 1985). The recognition of the importance and centrality of the customer contact employee’s performance in forming the service encounter to the customer has made the quality control of customer contact employees a prominent issue with managers (Czepiel et al. 1985). In addressing the issue of customer contact employee performance, researchers have tended to focus on two general areas: 1) the improvement of positive behaviors, such as extra-role performance, and 2) the reduction of negative behaviors, such as counterproductive work behaviors. For example, Bettencourt and Brown (1997) explored the effects of customer contact employee psychological outcomes on prosocial behaviors and customer evaluations, while Harris and Ogbonna (2002) examined the antecedents and consequences of service sabotage, the intentional behavior by the customer contact employee that negatively affects the service. 2 Despite research concerning how to improve customer contact employee performance, managers still have difficulty in achieving the desired performance levels from employees who are labeled as the least experienced, underappreciated, poorly paid, poorly trained, and of lowest status in the organization (Weatherly and Tansik 1993; Henkoff 1994; Surprenant and Solomon 1987). Consider the following cases from the popular press: Two Domino’s Pizza employees were arrested after uploading a video on the popular website YouTube which showed the employees tampering with customers’ orders (Murray 2009). A Kentucky Fried Chicken employee was arrested in 2002 for selling marijuana to drive through customers (CNN 2002). A Target customer service representative was arrested in 2006 for stealing a customer’s identity and charging more than $600 in credit card charges (Munson 2006). A McDonald’s employee was arrested in 2008 for double swiping customer’s credit cards and pocketing the cash of the second sale (Jones 2008). The two essays in this dissertation attempt to fill the gap in the literature and address how managers can increase the positive behaviors and decrease the negative behaviors of their customer contact employees. Essay 1 builds off of the service-profit chain and the employee-customer-profit chain (Heskett et al. 1994; Rucci, Kirn, and Quinn 1998) to examine the antecedents and consequences of positive customer contact employee behaviors. Specifically, Essay 1 links the effects of inrole and extra-role performance by customer contact employees to customer service encounter outcomes. In addition, Essay 1 includes the moderating effects of the employee’s perceived authenticity in behavior by the customer to better understand how customer perceptions influence evaluations of the service encounter. Essay 1 also explores key antecedents to in-role and extra-role behaviors in customer contact employees, such as the formal marketing controls advocated by agency theory and the informal marketing controls of customer orientation and 3 Figure 1: Conceptual Model of Essay 1 Manager Customer Orientation Employee Customer Orientation Manager Referent Power Manager Intrapreneurial Orientation Employee In-Role Performance Employee Authenticity Employee Intrapreneurial Orientation Employee Extra-Role Performance Formal Marketing Controls 4 Customer Satisfaction Customer Loyalty intrapreneurial orientation. Finally, Essay 1 incorporates the effect of the manager as a role model and the manager’s social power to explore how the manager can influence the identified key antecedents to in-role and extra-role behaviors in customer contact employees. Essay 2 uses affective events theory (Weiss and Cropanzano 1996) to examine how to reduce the negative customer contact employee behavior of counterproductive work behaviors. Specifically, Essay 2 builds off previous research which examined customer contact employee responses to customer interpersonal injustice (e.g. Rupp and Spencer 2006; Skarlicki, van Jaarsveld, and Walker 2008; Yang and Diefendorff 2009) by exploring how the customer contact employee responds emotionally, attitudinally, and behaviorally to customer interpersonal injustice. Essay 2 proposes that customer contact employees respond emotionally to customer interpersonal injustice with the negative emotions of anger and guilt and these negative emotions in turn influence work attitudes and negative (counterproductive) work behaviors. Additionally, Essay 2 examines the effects of work attitudes, specifically job satisfaction, on counterproductive work behaviors. Finally, and perhaps most importantly, Essay 2 demonstrates that customer interpersonal injustice affects counterproductive work behavior directed at targets other than the offending customer and the employee’s organization, such as the employee’s supervisor, the employee’s coworkers, and customers other than the offending customer. To accomplish these goals, this dissertation sampled data from 163 customer contact employees, 112 managers, and 414 mystery shopping evaluations performed by 4 certified mystery shoppers of a Midwestern U.S. convenience retail store chain in Essay 1 and from 146 customer contact employees of various Midwestern U.S. service organizations in Essay 2. A convenience retail store setting was chosen for Essay 1 as the employees of the store have regular interaction with customers and can thus be described as customer contact employees. 5 Figure 2: Conceptual Model of Essay 2 Counterproductive Work Behavior Anger Direct • Offending Customer Directed Customer Interpersonal Injustice Job Satisfaction Guilt Customer Orientation 6 Displaced • Organization Directed • Supervisor Directed • Coworker Directed • Other Customer Directed In Essay 1, employees and supervisors were provided with a paper and pencil version of the survey to complete either during time provided by the store or at their own leisure after work. Further, mystery shoppers were utilized instead of actual customers because of the lack of an opportunity to administer standard customer surveys in a convenience retail setting (Finn 2001) and also because actual customers often experience difficulty in recalling the service process (Wilson 1998). Employees followed an interval-contingent experience sampling methodology (ESM) and completed a one-time survey as well as one survey per work day for a total of ten work days in Essay 2. All surveys were completed online for Essay 2. For both essays, all items were asked using a 1 to 5 point scale as a preliminary optimal scaling analysis demonstrated no loss in information compared to a 1 to 10 point scale and all survey items are provided in the Appendices. Essay 1 uses data collected from the customer, the customer contact employee, the manager, as well as data aggregated at the shift level whereas Essay 2 uses multilevel data from customer contact employees (daily surveys were nested within individual customer contact employees). The hypothesized relationships in Essay 1 are analyzed using both hierarchical linear modeling (HLM) and partial least squares (PLS) path model analysis and the hypothesized relationships in Essay 2 are tested using HLM. Briefly, HLM is the appropriate method for examining cross level effects with data which is hierarchically nested (i.e. customers nested within employees who are nested within managers or days nested within employees) (Raudenbush and Bryk 2002). Further, PLS is appropriate due to its ability to provide robust estimates with smaller sample sizes compared to typical covariance-based procedures (Chin 1998; Hulland, Ryan and Rayner 2009). 7 The two essays in this dissertation aim to provide a comprehensive examination of two important issues faced by managers in effectively leading customer contact employees to maximize customer service experience evaluations. Through this comprehensive examination, this dissertation is expected to have specific contributions to both academic thought and marketing practice. Essay 1 provides insight into the relationship between extra-role performance and customer outcomes, as research shows both a positive, linear relationship (Netemeyer, Maxham, and Pullig 2005) and also a positive quadratic relationship (Netemeyer and Maxham 2007). Essay 1 also provides conclusions regarding which type of marketing controls, formal or informal controls, have stronger effects on employee in-role and extra-role performance and identifies key informal marketing controls that enhance the customer contact employee’s positive behaviors, such as the employee’s customer and intrapreneurial orientations, and thus enhance customer service experience evaluations. Finally, Essay 1 uses established theory to identify managerial actions which can develop the informal marketing controls within customer contact employees. For managers, Essay 1 provides insight into the best ways to motivate customer contact employees to not only provide the expected service, but, to also go above and beyond customer expectations to further enhance the customer experience. Essay 2 further underscores the customer as a source of negative influence on customer contact employee performance. By using affective events theory, Essay 2 builds off of Skarlicki, van Jaarsveld, and Walker (2008) and Yang and Diefendorff (2009), which identified customer interpersonal injustice as a cause of revengeful counterproductive work behaviors and counterproductive work behaviors directed at the employee’s organization, to identify the customer as a cause of supervisor, coworker, and other customer directed counterproductive work behaviors. Additionally, Essay 2 establishes the detrimental effect of customer 8 interpersonal injustice on the work attitude of job satisfaction through the positive effect of customer interpersonal injustice on the employee emotions of anger and guilt. Finally, Essay 2 empirically shows the moderating effects of informal marketing controls, specifically employee customer orientation, on customer contact employee affective reactions to customer interpersonal injustice. For managers, Essay 2 further clarifies the relationship between rude customers and the emotions, attitudes, and behaviors of customer contact employees, thus highlighting the need to develop formal and informal marketing controls to mitigate the harmful effects of customer interpersonal injustice on customer contact employee performance and ultimately firm performance. Overall, the demand for research which addresses the unique characteristics of services has grown greatly and resulted in the establishment of Services Marketing as a subdiscipline of the broader discipline of Marketing (Berry and Parasuraman 1993). This growth of Services Marketing research has paralleled the growth of services throughout the global economy, which has been described as “explosive” (Bitner, Brown, Goul, and Urban 2008). Within the expanding Services Marketing literature, however, calls for further research into the antecedents of customer contact employee performance remain unanswered (cf. Liao and Chuang 2004). The two studies conducted in this dissertation investigate antecedents to positive and negative customer contact employee behaviors in an effort to answer the call for further insight into customer contact employee performance. The results of this dissertation offer both research and managerial implications. 9 REFERENCES 10 REFERENCES “Bureau of Economic Analysis: National Economic Accounts,” (accessed September 8, 2009), [available at http://www.bea.gov]. Bettencourt, Lance A. and Stephen W. Brown (1997), "Contact Employees: Relationships Among Workplace Fairness, Job Satisfaction and Prosocial Service Behaviors," Journal of Retailing, 73 (1), 39-61. Bitner, Mary Jo, Bernard H. Booms, and Mary Stanfield Tetreault (1990), "The Service Encounter: Diagnosing Favorable and Unfavorable Incidents," Journal of Marketing, 54 (1), 7184. Bitner, Mary Jo, Stephen W. Brown, Michael Goul, and Susan Urban (2008), "Services Science Journey: Foundations, Progress, and Challenges," in Service Science, Management and Engineering Education for the 21st Century, Bill Hefley and Wendy Murphy, Eds. New York, NY: Springer US. Chin, Wynne W. (1998), "The Partial Least Squares Approach for Structural Equation Modeling," in Modern Methods for Business Research, George A. Marcoulides, Ed. Mahwah, NJ: Lawrence Erlbaum Associates, Inc. “Fast Food Worker Accused of Giving Marijuana to Customer,” (Accessed September 4, 2009), [available at http://archives.cnn.com/2002/LAW/10/10/ctv.scm/]. Czepiel, John A., Michael R. Solomon, and Carol F. Surprenant (1985), "Service Encounters: An Overview," in The Service Encounter: Managing Employee/Customer Interaction in Service Businesses, John A. Czepiel and Michael R. Solomon and Carol F. Surprenant, Eds. Lexington, MA: D.C. Heath and Company. Finn, Adam (2001), "Mystery Shopping Benchmarking of Durable-Goods Chains and Stores," Journal of Service Research, 3 (4), 310-20. Groonroos, Christian (1990), Service Management and Marketing: Managing the Moments of Truth in Service Competition. Lexington, MA: D.C. Heath and Company. Haeckel, Stephan H., Lewis P. Carbone, and Leonard L. Berry (2003), "How to Lead the Customer Experience," Marketing Management, 1 (2), 18-23. Harris, Lloyd C. and Emmanuel Ogbonna (2002), "Exploring Service Sabotage: The Antecedents, Types and Consequences of Frontline, Deviant, Antiservice Behaviors," Journal of Service Research, 4 (3), 163-83. 11 Henkoff, Ronald (1994), "Finding, Training, & Keeping the Best Service Workers," in Fortune Vol. 130. Heskett, James L., Thomas O. Jones, Gary W. Loveman, W. Earl Sasser Jr., and Leonard A. Schlesinger (1994), "Putting the Service-Profit Chain to Work," Harvard Business Review, 72 (2), 164-74. Hulland, John, Michael J. Ryan, and Robert K. Rayner (2009), "Modeling Customer Satisfaction: A Comparative Performance Evaluation of Covariance Structure Analysis Versus Partial Least Squares," in Handbook of Partial Least Squares: Concepts, Methods and Applications, Vincenzo Esposito Vinzi and Wynne W. Chin and Jorg Henseler and Huiwen Wang, Eds. New York, NY: Springer-Verlag Berlin Heidelberg. “IMF Data and Statistics,” (Accessed September 8, 2009), [available at http://www.imf.org/external/data.htm#data]. Jones, Ron (2008), “Local McDonald's Employee Arrested For Fraud – cbs13.com,” (Accessed September 4, 2009), [available at http://cbs13.com/local/mcdonalds.fraud.atm.2.731984.html]. Liao, Hui and Aichia Chuang (2004), "A Multilevel Investigation of Factors Influencing Employee Service Performance and Customer Outcomes," The Academy of Management Journal, 47 (1), 41-58. Munson, Kristen (2006), "Target Employee Arrested for ID Theft," in The Dispatch. Gilroy, CA. Murray, Stephanie (2009), "Ex-Domino's Employees Arrested for Tampering with Food After YouTube Video Goes Viral," in Ann Arbor News. Ann Arbor, MI. Netemeyer, Richard G. and James G. Maxham III (2007), "Employee Versus Supervisor Ratings of Performance in the Retail Customer Service Sector: Differences in Predictive Validity for Customer Outcomes," Journal of Retailing, 83 (1), 131-45. Netemeyer, Richard G., James G. Maxham III, and Chris Pullig (2005), "Conflicts in the WorkFamily Interface: Links to Job Stress, Customer Service Employee Performance, and Customer Purchase Intent," Journal of Marketing, 69 (2), 130-43. Parasuraman, A., Valarie A. Zeithaml, and Leonard L. Berry (1988), "SERVQUAL: A MultipleItem Scale for Measuring Consumer Perceptions of Service Quality," Journal of Retailing, 64 (1), 12-40. Raudenbush, Stephen W. and Anthony S. Bryk (2002), Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, CA: Sage Publications. Rucci, Anthony J., Steven P. Kirn, and Richard T. Quinn (1998), "The Employee-CustomerProfit Chain at Sears," Harvard Business Review, 76 (1), 82-97. 12 Rupp, Deborah E. and Sharmin Spencer (2006), "When Customers Lash Out: The Effects of Customer Interactional Injustice on Emotional Labor and the Mediating Role of Discrete Emotions," Journal of Applied Psychology, 91 (4), 971-78. Skarlicki, Daniel P., Danielle D. van Jaarsveld, and David D. Walker (2008), "Getting Even for Customer Mistreatment: The Role of Moral Identity in the Relationship Between Customer Interpersonal Injustice and Employee Sabotage," Journal of Applied Psychology, 93 (6), 133547. Solomon, Michael R., Carol F. Surprenant, John A. Czepiel, and Evelyn G. Gutman (1985), "A Role Theory Perspective on Dyadic Interactions: The Service Encounter," Journal of Marketing, 49 (1), 99-111. Surprenant, Carol F. and Michael R. Solomon (1987), "Predictability and Personalization in the Service Encounter," Journal of Marketing, 51 (2), 86-96. van Dolen, Willemijn, Ko de Ruyter, and Jos Lemmink (2004), "An Empirical Assessment of the Influence of Customer Emotions and Contact Employee Performance on Encounter and Relationship Satisfaction," Journal of Business Research, 57 (4), 437-44. Vargo, Stephen L. and Robert F. Lusch (2004), "Evolving to a New Dominant Logic for Marketing," Journal of Marketing, 68 (1), 1-17. Wall, Eileen A. and Leonard L. Berry (2007), "The Combined Effects of the Physical Environment and Employee Behavior on Customer Perception of Restaurant Quality," Cornell Hotel and Restaurant Administration Quarterly, 48 (1), 59-69. Weatherly, Kristopher A. and David A. Tansik (1993), "Managing Multiple Demands: A RoleTheory Examination of the Behaviors of Customer Contact Service Workers," in Advances in Service Marketing and Management, Teresa A. Swartz and David E. Bowen and Stephen W. Brown, Eds. Greenwich, CT: JAI Press Inc. Weiss, Howard M. and Russell Cropanzano (1996), "Affective Events Theory: A Theoretical Discussion of the Structure, Causes and Consequences of Affective Experiences at Work," in Research In Organizational Behavior, Barry M. Staw and L.L. Cummings, Eds. Vol. 18. Greenwich, CT: JAI Press. Wilson, Alan M. (1998), "The Role of Mystery Shopping in the Measurement of Service Performance," Managing Service Quality, 8 (6), 414-20. Yang, Jixia and James M. Diefendorff (2009), "The Relations of Daily Counterproductive Work Behavior with Emotions, Situational Antecedents, and Personality Moderators: A Diary Study in Hong Kong," Personnel Psychology, 62 (2), 259-95. 13 ESSAY 1 CUSTOMER CONTACT EMPLOYEES: THE EFFECT OF FORMAL AND INFORMAL MARKETING CONTROLS ON EMPLOYEE PERFORMANCE, TEAM PERFORMANCE, AND CUSTOMER OUTCOMES Approved by the American Marketing Association Board of Directors in 2007, Marketing is defined as “the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large” (AMA 2004). Contained within this definition of Marketing is the notion that the objective of a for-profit firm is to provide customers something of value in exchange for the realization of a profit (Hunt 2002). For a goods based firm, such as a consumer durables company, the provision to the customer of something of value is the result of the process to create a tangible piece of output (Vargo and Lusch 2004). With rare exception, this process to create the good occurs obscured from the customer’s view. For a service firm, however, the service offering is created and consumed on the spot by the customer (Regan 1963). This inseparability of production and consumption means that the customer contact employee and customer are involved in an interactive encounter wherein the customer tends to use the customer contact employee’s behavior as the basis for evaluating the service encounter (Bowen and Schneider 1985). Thus, for service firms the provision of service, the “offering of value” to exchange for revenue, is wholly dependent upon the performance of the customer contact employee. Yet, the customer contact employee is often characterized as the least experienced, underappreciated, poorly paid, poorly trained, and of lowest status in the organization (Weatherly and Tansik 1993; Henkoff 1994; Surprenant and Solomon 1987). In this study, the antecedents and consequences of customer contact employee performance are examined. The service-profit chain (Heskett, Jones, Loveman, Sasser, and 14 Schlesinger 1994) is built off of to link the effects of in-role and extra-role performance by customer contact employees to customer service encounter outcomes. In order to understand how customer perceptions influence the service encounter, the moderating effects of the employee’s perceived authenticity in behavior are included. In addition, key antecedents to inrole and extra-role behaviors in customer contact employees are explored. Finally, the manager’s social power is incorporated to explore how the manager can influence the identified key antecedents to in-role and extra-role behaviors in customer contact employees. LITERATURE REVIEW AND RESEARCH HYPOTHESES The service-profit chain establishes relationships between a service firm’s employees, customers, and profitability (Heskett et al. 1994). In the service-profit chain, Heskett and colleagues propose that firm profit and growth are driven principally by customer loyalty, which is in turn a direct result of customer satisfaction. Further, customer satisfaction is a result of the value in the service provided by the employee, which results from satisfied, loyal, and productive employees. Finally, employee satisfaction, loyalty, and productivity are engendered through high internal service quality, the internal working quality of the employee’s job environment. This model (see Figure 3) provides the basis for much of the focus in current sales and service management (Homburg, Wieseke, and Hoyer 2009). Using the logic of the service-profit chain, a more broad approach would lead to an employee-customer-profit model (Rucci, Kirn, and Quinn 1998). In this simplified approach, firm outcomes, such as return on assets, operating margin, and revenue growth, are proposed to be driven by customer behavior. Customer behavior is in turn driven by employee behavior, which is, in accordance with attitude theory, an outcome of the employee’s attitude toward the behavior. Thus, the chain of cause and effect in a service firm is simplified to “employee 15 Figure 3: The Service-Profit Chain Operating Strategy and Service Delivery System Employee Retention Internal Service Quality External Service Value Employee Satisfaction Customer Satisfaction Customer Loyalty Employee Productivity workplace design job design employee selection and development employee rewards and recognition tools for serving customers Revenue Growth Profitability service concept: results for customers retention repeat business referral Service designed and delivered to to Service designed and delivered meet targeted customers’ needs meet targeted customers’ needs Source: Heskett et al. (1994) 16 behavior to customer behavior to profits” (Rucci et al. 1998, pg 84). One such company which has embraced the notion that employee behavior is at the root of profit for companies, which is embedded in both –profit chains, is the renowned Ritz-Carlton Hotel Company. This focus is reflected in the wallet-sized copy of Ritz-Carlton’s Gold Standards distributed to every employee (corporate.ritzcarlton.com 2009). In its Gold Standards, Ritz-Carlton details the company’s Credo, Motto, Three Steps of Service, Service Values, 6th Diamond, and Employee Promise. Briefly, this wallet sized card instills the Ritz-Carlton philosophy of an unwavering commitment to service and provides employees with the performance expectations and procedure for interacting with customers and responding to customer needs. Examples of the commitment to the idea that employee behavior is essential to customer satisfaction and loyalty are reflected in every element contained on this card, including the Credo “…We pledge to provide the finest personal service and facilities for our guests who will always enjoy a warm, relaxed, yet refined ambience…”, the Motto, “We are Ladies and Gentlemen serving Ladies and Gentlemen”, and most demonstratively the Employee Promise, “At The Ritz-Carlton, our Ladies and Gentlemen are the most important resource in our service commitment to our guests…”. This commitment is further underscored through Ritz-Carlton’s mandatory 250 hours of training for first-year customer contact employees and daily five to ten minutes briefings at the start of every shift for all employees. The focus on employee behavior has led Ritz-Carlton Hotel Company to become the only two time winner of the Malcolm Baldridge National Quality Award in the service category (Mene 2000) as well as recognition in BusinessWeek’s “Customer Service Champs 2008” list (Gallo 2008). 17 Figure 4: The Employee-Customer-Profit Chain A COMPELLING PLACE TO WORK A COMPELLING PLACE TO SHOP Customer recommendations Service Attitude about the job Helpfulness Return on assets Employee behavior Customer impression Operating margin Revenue Growth Merchandise Attitude about the company A COMPELLING PLACE TO INVEST Value Customer retention Employee retention 5 UNIT INCREASE IN EMPLOYEE ATTITUDE DRIVES 1.3 UNIT INCREASE IN CUSTOMER IMPRESSION Source: Rucci et al. (1998) 18 DRIVES 0.5% INCREASE IN REVENUE GROWTH While the reflection of Ritz-Carlton’s focus on the contribution of individual employees to service firm profitability of both the service-profit chain and the employee-customer-profit chain is impressive, it may best be demonstrated by these two simple customer accounts: “One of your employees and I got on an elevator in your building. I pushed the sixth-floor button and he pushed none. Instead of getting off with me on the sixth floor, your employee simply said, ‘Have a nice day.’ Upon exiting the elevator, I asked, ‘Where are you going? Aren’t you getting off here?’ Your employee replied, ‘No, I’m going back down to the fifth floor.” (Michelli 2008, pg. 73) “We were sitting in the lounge having a couple of drinks. One of my peers commented about how nice it would be to have a cigar. A few minutes later, a security guard approached him and asked if he would like to check out the selection of cigars in the gift shop, which had been closed for the evening. The guard then escorted this gentleman to the shop, opened it up, and waited while he picked out a few cigars. Apparently our waitress had overheard us talking about cigars and asked the security guard to take care of us.” (Michelli 2008, pg. 177) Customer Satisfaction to Loyalty The ultimate effect of employee behavior in both profit chains is customer retention and the many benefits associated with customer loyalty. For example, loyal customers are more likely to repurchase from the firm (Anderson and Sullivan 1993), have lower sensitivity to price increases (Anderson 1996), will discount negative comparisons of the firm to the competition (Bolton, Kannan, and Bramlett 2000), and ensure a reliable stream of revenue for the firm (Reichheld and Sasser 1990). To understand how to create loyal customers, researchers have been interested in explaining why consumers have a tendency to prefer and purchase more of one brand than another since at least the 1950’s (Jacoby 1971). This continued interest in explaining why consumers buy one brand more than a competing brand led Jacoby (1971) to offer the first empirically substantiated full-scale conceptual definition of brand loyalty. Briefly, Jacoby (1971) defined brand loyalty as the biased behavioral response expressed over time by some decision making unit with respect to one or more alternative brands out of a set of such brands 19 and is a function of the psychological processes (Jacoby and Chestnut 1978). This definition of loyalty is important as it underscores the main component of behavioral loyalty as a preference for one brand over others that is expressed as biased purchasing behavior. Accordingly, the heart of behavioral loyalty is the continued preference for and purchase of one brand over others. Building upon the work of Jacoby (1971) and Jacoby and Chestnut (1978), Oliver (1997) addresses the issue of separating true brand loyalty from simple, repetitive purchase behaviors. To do this, Oliver (1997) argued that true brand loyalty can be explained through a four stage attitudinal phase model in which customers are able to become loyal, or according to Oliver (1999, p. 34) have a “deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or same brandset purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior.”. Where Oliver’s (1997) framework differs from previous conceptualizations of customer loyalty is that the customer is able to become loyal in each phase of the framework (Oliver 1999). This framework follows four stages, the cognitive stage, the affective stage, the conative stage, and the behavioral stage (Oliver 1999). In the first stage of Oliver’s (1997) framework, the cognitive stage, shallow customer loyalty is developed as available brand attribute information designates that one brand is more desirable than competitor brands. This cognition can be established upon previous knowledge or recent experience and if the transaction is routine and possibly not even processed as part of the experience (Oliver 1999). If the satisfaction from the transaction is processed, however, an attitude toward the brand can develop in the second stage, the affective stage. In this stage, which is less easily dislocated than cognitive loyalty, loyalty is the amount of affect for the brand (Oliver 1999). This amount of positive affect for the brand can then create a deeply held 20 commitment to purchase the brand, i.e. conative loyalty, the third stage. This conative loyalty may simply remain an intention like many behavioral intentions (Oliver 1999). Action inertia and action loyalty can develop if the intentions are realized repeatedly and the customer is satisfied. Therefore, the action of repurchase is enabled by the development of this inertia through customer satisfaction with each encounter. Just as loyalty develops through a cycle, satisfaction leads to loyalty through the cycle of satisfaction (Oliver 1997). This cycle begins with the first purchase of a service by a customer and proceeds following the expectancy disconfirmation model of satisfaction. As the customer completes one service encounter, the resulting satisfaction or dissatisfaction affects the consumer’s attitude toward the service. This revised attitude in turn affects intentions to purchase the service again in the future. This process repeats with the evaluation of each service encounter acting as a feedback loop that either strengthens or weakens the consumer’s attitude toward the service. Oliver (1997) proposes that the customer may experience the loyalty sensation (i.e. the deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future) as long as the service encounters are positive. Customer satisfaction is defined as the overall postpurchase evaluation of the service (Fornell 1992). In this manner, customer satisfaction is the summary psychological state resulting when the emotion surrounding disconfirmed expectations is coupled with the consumer’s prior feelings about the consumption experience (Oliver 1981, pg. 27). Customer satisfaction can thus be viewed as the result of the disconfirmation of expectations about the service held by the consumer (Oliver 1997). If the postpurchase evaluation of the service encounter exceeds expectations then positive disconfirmation and satisfaction will occur. If, on 21 the other hand, expectations exceeds the postpurchase evaluation of the service encounter negative disconfirmation and dissatisfaction will occur. Figure 5: The Satisfaction Cycle feedback Performance Expectations Disconfirmation Performance feedback Expectations of Satisfaction Satisfaction feedback Attitude Attitude feedback Intention Preconsumption Intention Consumption Source: Oliver (1997) 22 Postconsumption In their review on the previous empirically investigated outcomes of customer satisfaction, Luo and Homburg (2007) identified four main categories: customer-related, employee-related, efficiency-related, and overall performance related outcomes. Within the customer-related outcome category, the most predominant finding is that customer satisfaction increases customer loyalty (Luo and Homburg 2007). This assertion is supported by Fornell, Johnson, Anderson, Cha, and Bryant’s (1996) finding that customer satisfaction has a positive effect on loyalty for the largest firms within seven broad sectors of the U.S. economy, Mittal and Kamakura’s (2001) finding that satisfaction has monotonically increasing returns for repurchase behavior in 100,040 automotive customers, and Olsen’s (2002) finding that customer satisfaction positively influenced repurchase behavior for approximately 1,450 consumer packaged goods customers. Following the results of Fornell et al. (1996), Mittal and Kamakura (2001), and Olsen (2002) and because customer satisfaction leads to customer loyalty through the satisfaction cycle (Oliver 1997), it is hypothesized that: H1: Customer satisfaction has a positive effect on customer loyalty Employee Behavior to Customer Satisfaction When customers assess a service encounter, they rely upon the numerous clues that are embedded in each service encounter (Berry, Wall, and Carbone 2006). Embedded in each service encounter are three main categories of clues: functional, mechanic, and humanic clues. Functional clues are the technical quality of the service, mechanic clues relate to the actual objects or service environment, and humanic clues are the behavior and appearance of the service provider (Berry et al. 2006). Together, these three types of clues create the service experience, however, humanic clues have been found to dominate customer perceptions of 23 service encounters (Wall and Berry 2007). This finding is consistent with both the service-profit chain and the employee-customer-profit chain which maintain that the actions of the employee are the proximal cause of customer satisfaction. Further, research has consistently demonstrated the importance of customer service behavior in influencing customer evaluations of the service encounter (Hartline and Ferrell 1996; Bettencourt and Brown 1997; Netemeyer, Maxham, and Pullig 2005; Marinova, Ye, and Singh 2008). When discussing employee behavior it is important to note that not all employee behavior is the same. Employee behavior is typically categorized as either in-role behavior or extra-role behavior (e.g. Bettencourt and Brown 1997; MacKenzie, Podsakoff, and Ahearne 1998; MacKenzie, Podsakoff, and Rich 2001; Netemeyer and Maxham 2007; Maxham, Netemeyer, and Lichtenstein 2008). In this dichotomy, in-role behavior is the behavior required by the employee (i.e. specified in the job description) and extra-role behavior is the behavior of the employee which goes beyond the role requirements (thus “goes the extra mile” and “helps customers beyond job requirements”) (Bettencourt and Brown 1997; Netemeyer and Maxham 2007). The distinction between the two types of employee behavior is important as research has shown that the different types of employee behavior have different effects on customer satisfaction (Netemeyer et al. 2005; Netemeyer and Maxham 2007). As previously stated, the performance of in-role behaviors by customer contact employees refers to the performance of the behaviors which are formally required by the employee’s job description (Netemeyer and Maxham 2007). In essence, these behaviors are the behaviors which are necessary to constitute the core service offering. Without the successful performance of these behaviors the service cannot be provided and the customer cannot be satisfied. Therefore, it is hypothesized that: 24 H2: Employee in-role performance has a positive effect on customer satisfaction Extra-role behavior, on the other hand, is proposed to affect customer satisfaction in a different way than in-role behavior. This differential effect occurs because the performance of extra-role behaviors by customer contact employees is the performance of behaviors which go above and beyond job requirements. As such, these behaviors are not expected by customers and lead to the positive disconfirmation of service expectations. The result of this positive disconfirmation of expectations is satisfaction (Oliver 1997) and the positive disconfirmation of expectations is the reason for 43.8 percent of satisfactory encounters according to Bitner, Booms, and Tetreault (1990). Therefore, it is hypothesized that: H3a: Employee extra-role performance has a positive effect on customer satisfaction. H3b: The effect of employee extra-role performance on customer satisfaction is stronger than the effect of in-role performance on customer satisfaction Finally, the successful performance of both in-role behaviors and extra-role behaviors in a service encounter by an employee provides not only the core service offering, but, also exceeds expectations. In an encounter where both in-role and extra-role performance are high, the customer is satisfied not only because the core service was provided, but, also increasingly satisfied because the employee provided the core service and went above and beyond expectations in doing so. In other words, the positive effect of an employee going beyond role requirements to help a customer on customer satisfaction depends upon the level of in-role performance provided by the employee. On the other hand, a service encounter where in-role, extra-role performance, or both performance dimensions are low will satisfy the customer less because the core service was not provided, the employee did not go above or beyond job requirements while providing the service, or both. Therefore, it is hypothesized that: 25 H4: Employee in-role and extra-role performance have a positive interactive effect on customer satisfaction. Employee Authenticity The interaction between the customer contact employee and the customer is a dyadic, human interaction which plays out according to roles learned by both the customer contact employee and the customer (Solomon, Surprenant, Czepiel, and Gutman 1985). The encounter must be genuine and appear, “not that the provider is playing his or her role well, but that he or she is not playing a role at all” for this interaction to be successful from the customer’s perspective (Price, Arnould, and Tierney 1995, pg. 94). Following van Dolen, de Ruyter, and Lemmink (2004), authenticity is defined as the extent to which the employee is genuine and is his/her own person. Interestingly, Kennedy, Goolsby, and Arnould (2003) note that human beings appear especially adept at detecting the authenticity (i.e. differentiating between sincere and externally mandated efforts) behind other’s actions. Take for example the following two customer accounts which typify authentic and inauthentic employee behavior: At 2 a.m. waiter was very friendly and animated. Even though it was busy he was very efficient. Captured our attention and came across as genuine. (Price, Arnould, and Deibler 1995, pg. 56) The waitress didn’t seem real sincere. She was certainly civil, but her attitude seemed kind of affected. She used a friendly tone of voice but there didn’t seem to be any meaning behind it. The food took longer than usual, which was a bit irritating. (Price et al. 1995, pg. 56) These customer accounts underscore that the authenticity behind the employee’s behavior is critical to the success of the service encounter. This is because customers prefer to be treated in an honest and authentic way and also because the perception of authenticity behind an employee’s actions triggers positive emotions (Hennig-Thurau, Groth, Paul, and Gremler 2006). Furthermore, the positive emotions created from authentic treatment by employees serve an informational function in influencing consumer judgments (Schwarz and Clore 1983) and may 26 cause customers to be more satisfied with employee performance compared to evaluating the same performance while in a negative emotional state. Therefore, it is hypothesized that: H5: The perceived authenticity of the employee’s actions has a positive effect on customer satisfaction. H6a: The perceived authenticity of the employee’s actions positively moderates the relationship between in-role performance and customer satisfaction, such that when perceived authenticity is low the effect from in-role performance to customer satisfaction is reduced and when perceived authenticity is high the effect from in-role performance to customer satisfaction is enhanced. H6b: The perceived authenticity of the employee’s actions positively moderates the relationship between extra-role performance and customer satisfaction, such that when perceived authenticity is low the effect from extra-role performance to customer satisfaction is reduced and when perceived authenticity is high the effect from extra-role performance to customer satisfaction is enhanced. Marketing Controls to Employee Behavior The control of marketing activities (i.e. the control of employee behavior) can be thought of as falling into one of two broad groups, formal and informal controls (Jaworski 1988). First, formal controls are the written, management-initiated mechanisms that influence the probability that employees will behave in the manner desired by the firm (Jaworski 1988). Under this framework, Jaworski (1988) states that formal controls can be further categorized depending upon the timing of the intervention by management. In a customer contact employee setting, controls initiated before the employee is hired are input controls and seek to solve the hidden information or adverse selection problem. After the employee is hired, controls implemented when the firm tries to influence the means to achieve goals are process controls and output controls are when the firm sets specific performance standards and monitors the results. These latter two types of controls, process and output, are controls which seek to stop the hidden action or moral hazard problem. In examining which type of control system to use to influence the 27 behavior of employees, agency theory prescribes input, process, and output controls (Jaworski 1988; Anderson and Oliver 1987). In any situation where one party, the principal, assigns the responsibility to act in a certain manner to another party, the agent, an agency relationship exists. Perhaps the most common form of the agency relationship in the Marketing literature is the employment relationship. In this relationship, a specific manager or the firm hires an employee and expects the employee to perform his/her job as specified. Two types of agency problems, precontractual and postcontractual problems, are expected in this agency relationship, (Bergen, Dutta, and Walker 1992). In the customer contact personnel setting, precontractual problems occur before the employer decides to hire the employee. This problem, the hidden information problem or adverse selection problem, appears when the employer must determine whether or not the potential employee has the desired traits and abilities to perform the assigned responsibilities (Bergen et al. 1992; Eisenhardt 1989). The employer must undertake efforts to ascertain the potential employee’s true traits and abilities because the potential employee’s claims regarding specific traits and abilities cannot be completely verified. To acquire information about the potential employee, agency theory states that the employer may decide to undertake one or more of the following strategies: screening, examining the potential employee’s signals, and/or providing opportunities for self-selection. Wherein the employer can screen a potential employee by collecting further information (i.e. through job interviews, aptitude tests, etc.), can examine signals by looking at previous actions of the potential employee, and can provide opportunities for self-selection by giving employees chances to terminate the agency relationship before it starts (i.e. allowing employees to quit during a training program) (Bergen et al. 1992). 28 The employer must balance the tradeoff between the costs of these information acquisition strategies and the possible loss from poor performance by the employee. The postcontractual problem of hidden action or moral hazard arises after the employee has been hired if the employee shirks assigned responsibilities (Bergen et al. 1992; Eisenhardt 1989). In this problem, the employer and the employee are assumed to be motivated by self interest, such that they will both act to maximize their own utility. Further, the employer is assumed to have incomplete information regarding the behavior of the employee and the environment is also assumed to at least partly affect the employee’s job outcomes. These assumptions are especially true in the customer contact employee setting where frontline employees are continually seen to act against the desires of their employers, employers cannot monitor their employees for every minute of every shift, and the employer cannot write an employment contract which covers all possible service encounters. Given this situation, it is easy to see how the employee can and will act in his/her own best interest even if it may go against the wishes of the employer. While this problem is typically thought of as more serious shirking offenses, acting in one’s interest for frontline service employees may simply mean providing less than desired service or not going above and beyond requirements to satisfy a customer. To combat the hidden action problem, agency theory states that employers have two potential courses of action to ensure that the employee behaves in the desired manner. First, the employer can monitor the employee’s behavior and in conjunction create an employment contract which rewards and evaluates the employee based upon the employee’s observed behavior. For example, the customer contact employee’s job description could be written to include the number of customers that they have to personally greet each shift and the manager 29 could tally the number of greeted customers. The design and enforcement of behavior-based contracts, however, is difficult because monitoring employee behavior is expensive and often cannot comment on the quality of the behavior put in by the employee. Second, the employer can reward and evaluate the employee based upon performance outcomes. In this method, the customer contact employee’s job description could be drafted to include the number of items they must scan per hour at the check-out register or a specific sales quota could be established. A successful outcome-based contract, however, can be very difficult to create (Bergen et al. 1992). The difficulty arises because the outcome-based contract must be interesting enough for the employee to want to follow it and the incentive must be compatible to the employee. The aforementioned agency theory-based solutions both rely upon the creation of an employment contract which provides incentives for employees to behave in a certain fashion during a certain situation. These formal employment contracts are the employee’s job description and thus specify the employee’s in-role behaviors. As a result, it is hypothesized that: H7: The interaction between the agency theory based solutions of behavior-based contracting and employee monitoring has a positive effect on employee in-role performance. H8: The agency theory based solution of outcome-based contracting has a positive effect on employee in-role performance. Behavior-based contracts and employee monitoring as well as outcome-based contracts cannot by definition address employee extra-role behavior as extra-role behavior is the actions of the employee which goes beyond role requirements (Bettencourt and Brown 1997). These formal marketing controls may, however, serve as information which influences employee beliefs about and consequently employee attitudes toward performing extra-role behaviors (Eagly and Chaiken 1993). Therefore, it is hypothesized that: 30 H9: The agency theory based solution of behavior-based contracting and employee monitoring has a positive effect on employee extra-role performance. H10: The agency theory based solution of outcome-based contracting has a positive effect on employee extra-role performance. The formal controls of customer contact employees established by agency theory are not without limitations. It can be very easy for an employee to undertake actions which lead to inaccurate decisions by the employer. For example, a potential employee could lie in a job interview to misrepresent his/her true abilities and characteristics, an employee could appear to work hard while not actually expending much effort, and an employee could also falsify performance in formal reporting systems. Therefore, the formal systems of the traditional marketing control perspective need to be augmented with other forms of control, such as informal controls (Jaworski 1988), which may be more important than formal controls for customer contact employees (Mills 1985). As identified by Jaworski (1988), informal controls are the unwritten, usually workerbased mechanisms that influence the individual employee’s behavior. Informal controls differ from formal controls because they are not formally documented, may not match the firm’s goals, are initiated by the employees, and are usually only actively controlled by the employees. Further, these controls differ based upon the level of aggregation, such that self controls (controls within the individual), social controls (controls within work groups), and cultural controls (controls within divisions or organizations) exist. Examples of informal controls include individual goals, group norms, and organizational culture. While all three types of informal controls are proposed to influence the behavior of the customer contact employee, research has demonstrated that self controls affect both positive and negative behavior by the employee. For example, self controls were found to be critical to employee participation in strategic influence 31 activities (Pappas and Flaherty 2005) and self controls were also found to decrease dysfunctional employee behaviors, such as gaming, focusing, smoothing, and invalid reporting, which negatively affect the effectiveness of formal controls (Jaworski and MacInnis 1989). These empirical findings suggest that of the possible informal controls suggested by Jaworski (1988), self controls have the best potential to augment formal controls. Self controls are how individuals establish personal objectives, monitor their attainment of the objectives, and adjust their behavior if the objectives are not being met. Simply put, self controls describe how employees determine their own behavior in the specific work context. To understand how employees determine their own behavior, Mowen (2000) proposed the MetaTheoretic Model of Motivation and Personality or the 3M Model. The 3M model, shown in Figure 6, integrates both control theory and a hierarchical trait model to explain employee behavior in specific contexts. Briefly, a four level hierarchy of traits is linked to a comparator, to which the employee compares expected or experienced outcomes in relation to his/her values and goals. If the expected outcomes corresponds with the employee’s values and goals the employee proceeds with the behavior. If, on the other hand, the expected outcomes do not match the employee’s values and goals then an employee will undergo cognitive appraisal and possibly change behavior. Finally, Mowen (2000) proposes that the resources available to the employee affect the employee’s behavior and environmental factors affect outcomes of the employee’s behavior. From the 3M Model, employees refer to four levels of traits to determine their behavior. At the most basic level is elemental traits. Elemental traits are defined as the “basic, underlying predispositions of individuals that arise from genetics and a person’s early learning history” (Mowen 2000, pg. 20). These traits provide the most abstract values for determining employee 32 Figure 6: The 3M Model of Motivation and Personality Trait Hierarchy Elemental Traits R4 Compound Traits R3 R2 Situational Traits R1 C Interrupt Surface Traits Perceptual Inputs Cognitive Appraisal Outcomes Activities Environment Outcomes Notes: C = Comparator R = Reference Values Source: Mowen (2000) 33 Task Program behavior. At the next level, compound traits are the unified dimensional dispositions emerging from the interplay of elemental traits, the culture in which the employee lives, and the learning history of the employee (Mowen 2000). Third, Mowen (2000) lists situational traits, which are unidimensional predispositions to behave within a general situational context. Finally and most specifically are surface traits. Surface traits are highly specific dispositions that result from the effects of elemental, compound, and situational traits and from the pressures of the contextspecific environment (Mowen, Park, and Zablah 2007). These surface traits are an employee’s enduring tendencies to behave in specific ways in specific situational contexts (Brown, Mowen, Donavan, and Licata 2002). With regard to predicting customer contact employee in-role and extra-role behavior, the development of two enduring dispositions of the employee to behave in a specific manner are important to firms seeking to achieve a sustained competitive advantage through their customer contact employees, an employee’s customer orientation and an employee’s intrapreneurial orientation. In line with Brown et al. (2002) and Donavan, Brown, and Mowen (2004), customer orientation is defined as the enduring disposition of the customer contact employee to meet customer needs in a customer contact situation. Recent work by Donavan et al. (2004) has demonstrated that a four dimensional conceptualization of customer orientation is more appropriate compared to the 2 dimension conceptualization of Brown et al. (2002). Thus, an employee’s customer orientation is composed of the dimensions of need to pamper the customer, need to read the customer, need for a personal relationship with the customer, and need to deliver to the customer (Donavan et al. 2004). The need to pamper dimension concerns the employee’s need to make the customer feel special to the employee. The need to read the customer dimension is the employee’s need to recognize both verbal and nonverbal communication from 34 the customer. The need for a personal relationship with the customer is the employee’s need to develop a personal level relationship with the customer while the need to deliver is the employee’s desire to successfully perform the service. Since an employee’s customer orientation is the enduring disposition (i.e. stable over time) to meet customer needs, it is hypothesized that: H11: A customer contact employee’s customer orientation will have a positive effect on employee in-role performance. Additionally, because an employee who is inclined to meet customer needs (i.e. is customer orientated) will recognize when customer needs will not be met by in-role behaviors and then go above and beyond to help the customer, it is hypothesized that: H12: A customer contact employee’s customer orientation will have a positive effect on employee extra-role performance. As a construct, entrepreneurship has traditionally been handled at the firm level (e.g. Covin and Slevin 1991, Lumpkin and Dess 1996; Matsuno, Mentzer, and Ozsomer 2002; Griffith, Noble, and Chen 2006). Recent research, however, has examined the entrepreneurial behavior of individual employees (e.g. Wakkee, Elfring, and Monaghan 2010). In a customer contact employment situation, a customer contact employee who recognizes the manner in which the firm would like the employee to behave and then acts on their own ideas is an intrapreneur (Pinchot 1985). Thus, a customer contact employee who recognizes a novel opportunity to help a customer and develops and implements the method by which to successfully provide the service has acted as an intrapreneur. A customer contact employee’s intrapreneurial orientation is the employee’s enduring disposition to behave in an entrepreneurial manner, which is to pursue opportunities to be a successful customer contact employee without regard to the resources currently controlled. 35 Based upon previous research concerning entrepreneurial behavior at multiple levels (Stevenson and Jarillo 1990; Lumpkin and Dess 1996) an employee’s intrapreneurial orientation is proposed to be composed of five facets: autonomy, innovativeness, risk taking, proactiveness or initiative, and competitive aggressiveness. Autonomy is the customer contact employee’s ability and will to be self directed in serving customers. Innovativeness is the customer contact employee’s willingness to engage in new ways to serve customers. Risk taking is the customer contact employee’s willingness to accept the uncertainty that accompanies serving customer in a new way. Proactiveness or initiative is the customer contact employee’s need to continually seek out new ways to provide the service experience. Finally, competitive aggressiveness is the customer contact employee’s need to outperform coworkers at serving customers. The customer contact employee’s intrapreneurial orientation is a surface level trait. Accordingly, intrapreneurial orientation is a proximal determinant of how the employee will behave in the customer contact setting. As such, employees who have the enduring disposition to behave entrepreneurially in a customer contact setting will not only perform the behaviors required of them by their job description but also actively seek out and perform new methods to provide the service to customers. Therefore, it is hypothesized that: H13: A customer contact employee’s intrapreneurial orientation will have a positive effect on employee in-role performance. H14: A customer contact employee’s intrapreneurial orientation will have a positive effect on employee extra-role performance. Central to the theory of marketing controls is the notion that formal and informal controls influence employee behavior through the provision of rewards after attaining a goal, the completion of a behavior or set of behaviors (Deci 1975). For example, formal controls are often used for evaluating and rewarding employee performance (Ramaswami 1996). Self controls, on 36 the other hand, can be thought of as intrinsic motivation (Jaworski and MacInnis 1989) and reward employees with the performance of the behavior itself (Ryan and Deci 2000). In explaining which rewards individuals choose to pursue and thus how intrinsic and extrinsic motivation influences behavior, self-determination theory demonstrates that performance and long-term persistence increases as individuals pursue goals motivated by increasingly autonomous motivations (Deci and Ryan 2008). Therefore, because formal controls cannot explicitly reward extra-role behavior and because individuals exhibit increased performance and commitment to behaviors motivated by intrinsic motivation, it is hypothesized that: H15: The interaction between the agency theory based solutions of behavior-based contracting and employee monitoring has a relatively weaker effect on employee extra-role performance than self controls. H16: The agency theory based solution of outcome-based contracting has a relatively weaker effect on employee extra-role performance than self controls. Given the idea that self controls can strongly affect employee performance raises the question of how managers can influence the likelihood that employees will use self controls (Jaworski and MacInnis 1989) and more specifically how managers can influence the likelihood that employees will use the specific self controls desired by the manager. To influence the development and use of informal controls in employees, the manager cannot use formal controls because informal controls are created through interpersonal interactions between the individual and others (Jaworski 1988). Therefore, the manager must seek to use his/her social influence to affect the determinants of employee behavior which can be changed, such as compound, situational, and surface traits (Mowen 2000). Fortunately, managers play a significant role in molding, sculpting, and shaping the behavior and attitudes of their employees (Rich 1997). Following Rich (1997), a manager who acts as a role model is a manager that an employee perceives to be a suitable example to follow. If the employee determines that the 37 manager is an appropriate example to follow, the employee then will monitor and emulate the behavior of the manager. Due to role modeling by employees, it is hypothesized that: H17: The manager’s customer orientation will have a positive effect on the customer contact employee’s customer orientation. H18: The manager’s intrapreneurial orientation will have a positive effect on the customer contact employee’s intrapreneurial orientation. The Manager’s Social Power One way in which managers can achieve better interpersonal relationships with customer contact employees is through the effective use of social power (Busch 1980). A manager is defined as having power over an employee to the extent that the manager can get the employee to do something that he/she would not otherwise do (Dahl 1957). To have social power over an employee, the manager must be able to use the power bases embedded with his/her relationship with the employee to get the employee to alter his/her behavior (French and Raven 1959). Thus, social power can influence the informal control systems of employees as it is exercised through the relationship with the employee. French and Raven (1959) identify five power bases which are found within the relationship between the manager and the employee. First, reward power is the employee’s perception that the manager has the ability to provide rewards. An employee is proposed to change his/her behavior when he/she perceives the manager has reward power in an effort to get the reward. For example, the employee may perceive that the manager can reward their work with special recognition and thus work in the manner they think is desired by the manager. Second, coercive power is the employee’s perception that the manager has the ability to provide punishments, either though the reduction of positive valences or through the increase of negative valences. An employee is proposed to change his/her behavior when he/she perceives the 38 manager can provide something bad, such as being forced to work longer hours, or take away something good, such as a pay reduction. Third, legitimate power is the employee’s perception that the manager has the right to influence the employee’s behavior and the employee has the duty to obey this influence. Under this power base, an employee would change his/her behavior if he/she perceives that the manager has the right to tell him/her what to do because the manager occupies the managerial role. Fourth, referent power is the perception by the employee that the manager is a person with whom the employee would like to identify with or continue to identify with. For example, the employee would change their behavior because the employee would think “I like the manager and therefore I will behave like the manager does” or “I want to be like the manager and I will be more like him/her if I behave as he/she does” (French and Raven 1959). Finally, expert power is the perception by the employee that the manager has knowledge or skills in a certain subject area relevant to the employee. In this instance, the employee behavior would change if the employee perceived that he/she should listen to that the manager because the manager was competent to speak about that area. Given that that the successful exercise of referent power by the manager results in the emulation of the manager’s behavior by the employee and has been called the “power of a positive role model” (Wilkes and Raven 2002), it is hypothesized that: H19: The manager’s referent power has a positive effect on the customer contact employee’s customer orientation. H20: The manager’s referent power has a positive effect on the customer contact employee’s intrapreneurial orientation. H21: The manager’s referent power positively moderates the effect of the manager’s customer orientation on the customer contact employee’s customer orientation, such that when referent power is high the effect of the manager’s customer orientation on the customer contact employee’s customer orientation will be stronger. 39 H22: The manager’s referent power positively moderates the effect of the manager’s intrapreneurial orientation on the customer contact employee’s intrapreneurial orientation, such that when referent power is high the effect of the manager’s intrapreneurial orientation on the customer contact employee’s intrapreneurial orientation will be stronger. STUDY METHODS Survey responses were gathered from 112 convenience store managers and 163 convenience store employees from 31 locations of a Midwestern United States convenience store chain to test the proposed hypotheses. A convenience store setting was chosen because the employees of the store have regular interaction with customers and can thus be described as customer contact employees. All managers and employees received a survey packet which consisted of a cover letter, a written consent form, the questionnaire, and a self-addressed and pre-paid return envelope. Respondents who completed the survey questionnaire received a prepaid $10 Visa gift card. Additionally, 414 mystery shopping evaluations of employee performance were performed by 4 certified mystery shoppers. All constructs were assessed using a combination of extant and new scales, which were developed consistent with the procedure prescribed by Churchill (1979). For each of the new scales, an initial pool of items was developed through exploratory research, refined through expert feedback from academic researchers and customer contact employees, and then pretested with customer contact employees. All items were measured using a five-point Likert type scale and are provided in Appendix A. Manager Data and Measures Self –administered survey packets were provided to the 143 managers (approximately 4.6 managers per store) of the 31 stores. Following the procedure outlined by Dillman (2007), a thank you postcard was provided two weeks after the initial questionnaire to the managers and a 40 follow-up survey packet was provided two weeks after the thank you postcard. 112 managers returned completed responses, yielding a 78% response rate. Managers had an average job tenure of 5.68 years (σ = 5.58) with the organization and an average age of 37.50 years (σ = 12.19). Sixty-nine percent of managers were female, 61% had obtained some college education, and 86% had a household income below $59,999, with an ethnicity breakdown as follows: 82% Caucasian/White, 12% African American, 3% Hispanic, and 3% Other. Each manager provided ratings of the formal marketing control constructs of behaviorbased contracting (MBC), monitoring (MMON), and outcome-based contracts (MOC). Behavior-based contracting refers to the use of job contracts written with the purpose of rewarding or punishing an employee based upon the employee's behavior. To assess this construct, managers were surveyed using a new four item scale (α = 0.86) that asked managers to rate their emphasis on the behavior used to complete a job by employees under their supervision. Monitoring is the collection of information by the manager regarding the employee's behavior. Managers were surveyed using a new three item scale (α = 0.78) which measured the degree to which the manager observed employees at work. Outcome-based contracting refers to the use of job contracts written which evaluate the employee's actions on the basis of realized outcomes. To assess outcome-based contracting, managers were surveyed using a new four item scale (α = 0.84) that asked managers to rate the extent to which they stressed the importance of attaining specific outcomes or performance goals. Measures of formal marketing controls were obtained from managers because formal marketing controls are the written, management-initiated mechanisms that influence employee behavior (Jaworski 1988). 41 Employee Data and Measures At the same time as the manager survey packets, self-administered survey packets were provided to the 241 employees (approximately 7.8 employees per store) of the 31 stores. Again, following the procedure outlined by Dillman (2007), a thank you postcard was provided two weeks after the initial questionnaire to the managers and a follow-up survey packet was provided two weeks after the thank you postcard. 163 employees returned completed responses, yielding a 68% response rate. Employees had an average job tenure of 4.60 years (σ = 5.63) with the organization, an average age of 37.10 years (σ = 15.79), 74% were female, 51% received had obtained some college education, 54% had a household income of under $19,999, and had an ethnicity breakdown as follows: 74% Caucasian/White, 17% African American, 6% Hispanic, and 3% Other. Each employee provided ratings of their own in-role performance (EIRP), extra-role performance (EERP), and authenticity when helping customers (EAU) as well as ratings of the informal marketing controls of their own customer orientation (ECO) and intrapreneurial orientation (EIO). Employees also identified the manager which they worked with the most often and provided their perceptions of the identified manager’s customer orientation (MCO), intrapreneurial orientation (MIO), and referent power (MRP). In-role performance refers to the degree to which the employee performed the behaviors required in the job description in providing the experience while extra-role performance refers to the degree to which the employee exceed job requirements in helping customers. Both constructs were assessed using extant scales (Netemeyer and Maxham 2007) and had α values of 0.87 and 0.93, respectively. Employee authenticity refers to the extent to which an employee was genuine when helping customers and was assessed using a three item scale (α = 0.78) based 42 on items from van Dolen et al. (2004) and Grandey, Fisk, Matilla, Jansen, and Sideman (2005). Items for in-role performance, extra-role performance, and authenticity were all prefaced by the statement, “During the past 6 months”. Self-performance and authenticity ratings were used because the research models are interested in how employees’ surface traits, customer orientation and intrapreneurial orientation, guide their own behaviors to meet customer needs and the recognition that different rating sources may have different perceptions about the behaviors required to meet and/or exceed customer needs. This difference in perceptions is evidenced by the stronger effect of the surface trait of customer orientation on self performance ratings than supervisor performance ratings found by Brown et al. (2002). Customer orientation refers to the employee’s enduring disposition to meet customer needs while intrapreneurial orientation refers to the employee’s enduring disposition to behave in an entrepreneurial manner while in a customer contact setting. Customer orientation was measured using three items (α = 0.84) from the SOCO scale developed by Saxe and Weitz (1982). This three item scale correlated highly (correlation coefficient = 0.77, significant at p < 0.01) with the four dimensional customer orientation scale developed by Donavan et al. (2004). Intrapreneurial orientation was measured using a new three item scale, α = 0.78, which asked employees to rate how entrepreneurially they behaved in a customer contact setting (“I am an entrepreneur at my job”, “I behave at work as if this is my own store”, and “I try to do this job as if I am the owner of this store”). To obtain ratings for the manager’s customer orientation (α = 0.95) and intrapreneurial orientation (α = 0.87) the items were adapted to focus on the identified manager as opposed to the employee (e.g. “My manager is an entrepreneur at his/her job”). Finally, the manager’s referent power refers to the manager’s ability to influence the employee’s 43 behavior because of the employee’s identification with the manager. Referent power was measured using three items (α = 0.89) from the referent power scale developed by Rahim (1989). Customer Data and Measures Given the convenience store setting, mystery shopping was chosen as a viable method to obtain customer evaluations because of the lack of an opportunity to administer standard customer surveys (Finn 2001) and the difficulty posed to actual customers in recalling the service process (Wilson 1998). During mystery shopping, a trained observer evaluates the front-line operations of a business while posing as an average customer and completes a survey immediately following the encounter (Finn and Kayande 1999). It is this training and the opportunity to complete a survey that allows individual mystery shoppers to provide higher quality data than individual customers (Finn and Kayande 1999). Four mystery shoppers were employed to obtain the mystery shopping ratings of individual employees. All four mystery shoppers obtained Silver Certification by the Mystery Shopping Providers Association, which ensures the individual has the basic understanding and skills necessary to successfully complete a mystery shopping encounter. Once certified, all four mystery shoppers received additional training that covered how to engage in a typical convenience store encounter, the convenience store employee’s job description and duties, and the definition of the constructs asked in the survey. Mystery shoppers rated 414 service encounters. Mystery shoppers were asked to rate each employee’s in-role performance (α = 0.95), extra-role performance (α = 0.97), and authenticity (α = 0.95) using the same scales as identified above. Mystery shoppers were also asked to provide ratings of their own satisfaction with the employee (ESAT) and their own loyalty intentions (LOY) following the encounter. Satisfaction 44 was measured using a 5 item scale (α = 0.93) based off of items from Voss et al. (1998) and Keaveney and Parthsarathy (2001). Loyalty was measured using a 7 item composite scale composed of 4 items measuring repurchase intention and 3 items measuring favorable word of mouth (α = 0.97). Analyses Overview Given the objectives of the study and the structure of the data, a two-technique approach to analyze the data was used. First, hierarchical linear modeling (HLM) via HLM 6.08 (Raudenbush and Bryk 2002) was used to test the relationships at the individual data levels of the manager, the employee, and the customer. HLM is the appropriate method for examining data which is hierarchically nested (i.e. customers are nested within employees who are in turn nested within managers) (Raudenbush and Bryk 2002). The use of HLM allows researchers to interpret effects within single levels (i.e. what employee variables affect employee performance), formulate and test hypotheses regarding cross-level effects (i.e. what manager variables affect employee performance), and properly partition variance and covariance components among levels (Raudenbush and Bryk 2002). Following Aiken and West (1991) all possible interactions 1 and higher order terms were included in the HLM analyses . All interaction and quadratic terms were created by multiplying the mean centered variables of interest (Aiken and West 1991). Additionally, baseline effects were established by estimating regression relationships in HLM 1 To ensure the absence of the effects of multicollinearity, such as decreased statistical power, instability in regression coefficients, and anomalous results (Aiken and West 1991), variance inflation factors (VIFs) were obtained from multiple regressions for all relationships proposed at the first levels of analysis and correlations between variables were inspected at the second level of analysis. All VIFs were below the suggested cut-off of 10.0 (Kutner, Nachtsheim, and Neter 2004) and all correlations were below 0.90 (Hair, Black, Babin, Anderson, and Tatham 2006). Models were also run without the hypothesized interaction and quadratic terms. No appreciable changes in effect size, effect direction, or statistical significance were observed between the two sets of models. Results of the estimated interaction and quadratic terms can be found in Appendix B. 45 between the dependent variables of interest and median split independent variables of interest. The results of the baseline effects analysis is provided in Appendix C. Second, and in agreement with Maxham et al. (2008), a path model was employed to better understand the entire proposed system of relationships. To accomplish this, data was averaged at the shift level (n = 75) and a path model was estimated using partial least squares (PLS) via SmartPLS (Ringle, Wende, and Will 2005). The path model was estimated using PLS instead of structural equation modeling (SEM) because PLS accommodates smaller sample sizes compared to typical covariance-based procedures (Chin 1998). Averaging at the store level would allow for more easily interpretable findings, however, is not appropriate due to the small sample size (n = 31). RESULTS Measurement Model Table 1 shows the summary statistics of means, standard deviations, and correlations for all focal constructs in which measures were averaged at the construct level and then averaged at the shift level. The adequacy of the measurement model was assessed using individual item reliabilities, convergent validity, and discriminant validity (Hulland 1999). Item loadings, Cronbach’s α, composite reliability, average variance extracted, and correlations between constructs were calculated by the source and level of the constructs. For data provided by mystery shoppers, the constructs of customer loyalty, customer satisfaction, employee in-role performance, employee extra-role performance, and employee authenticity were measured at the customer level (Level 1). All individual item loadings were greater than 0.70, Cronbach’s α values ranged from 0.93 to 0.97, composite reliability values ranged from 0.94 to 0.97, and average variance extracted values ranged from 0.77 to 0.90. 46 Additionally, the square root of all average variance extracted values was greater than the correlations between the constructs. The result of measurement testing for the data provided by mystery shoppers is presented in Table 2, Table 3, and Table 4. For the data provided by the managers and the employees of the stores, the constructs of employee in-role performance, employee extra-role performance, employee authenticity, employee customer orientation, employee intrapreneurial orientation, manager customer orientation, and manager referent power were measured at the employee level (Level 1) while behavior-based contracting, monitoring, and outcome-based contracting were measured at the manager level (Level 2). For both the employee and manager level, all individual item loadings were larger than necessary to conclude sufficient individual item reliability (Hulland 1999). Cronbach’s α values ranged from 0.78 to 0.95 and composite reliability values ranged from 0.83 to 0.97. Convergent and discriminant validity was established as all average variance extracted values were greater than 0.50 and the square root of all average variance extracted values was greater than the construct correlations (Fornell and Larcker 1981). The result of measurement testing for the data provided by employees and managers is presented in Table 5, Table 6, and Table 7. Predictive Equations and Results at the Individual Data Levels Customer Outcomes Separate two-level models were estimated to predict the customer evaluations of loyalty and satisfaction. Customer evaluations were nested within employees to predict customer loyalty and customer satisfaction. 308 evaluations of the original 414 mystery shopping encounters were used for analysis as 106 evaluations could not be matched to an employee (i.e. the employee was 47 Table 1: Means (M), Standard Deviations (SD), and Correlations Among Focal Constructs M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Mystery Shopper Rated 1. LOY 3.17 0.64 2. ESAT 3.50 0.60 0.67** 3. EIRP 4.10 0.52 0.09 0.46** 4. EERP 2.52 0.41 0.58** 0.84** 0.51** 5. EAU 4.01 0.59 0.50** 0.60** -0.09 0.44** Employee Rated 6. EIRP 4.37 0.50 -0.12 -0.20 0.08 -0.30** -0.20 7. EERP 4.31 0.61 -0.08 -0.14 0.10 -0.23* -0.20 0.45** 8. EAU 4.20 0.52 0.07 -0.05 0.11 -0.13 0.04 0.62** 0.39** 9. ECO 4.36 0.56 0.13 0.02 0.17 0.01 0.09 0.57** 0.42** 0.62** 10. EIO 3.61 0.77 0.11 0.09 0.10 0.09 0.08 0.13 0.37** 0.25* 0.44** 11. MCO 4.06 0.89 0.09 0.00 -0.07 -0.13 0.13 0.16 0.12 0.16 0.22 0.04 12. MIO 3.57 0.91 0.24* 0.14 -0.02 0.05 0.18 -0.04 0.08 0.19 0.25* 0.29* 0.47** 13. MRP 3.88 0.87 -0.01 0.00 -0.07 -0.08 0.13 0.09 -0.10 0.11 0.07 -0.11 0.78** 0.48** Manager Rated 14. MBC 1.41 0.44 -0.03 0.14 0.11 0.14 0.12 0.08 0.13 0.19 0.13 -0.02 0.03 0.12 0.03 15. MMON 4.03 0.55 -0.05 0.09 0.10 0.10 0.12 -0.04 -0.10 0.05 0.01 -0.03 0.08 -0.04 -0.02 -0.18 16. MOC 3.70 0.65 -0.01 0.13 0.08 0.16 0.18 -0.15 -0.12 -0.09 -0.08 0.04 -0.04 -0.13 -0.07 -0.10 0.69** Note: Correlations among all variables are based on the averaged sample for the PLS path analysis (n=75). *Significant at p < 0.05, **significant at p < 0.01. LOY = loyalty, ESAT = satisfaction with the employee, EIRP = employee in-role performance, EERP = employee extra-role performance, EAU = employee authenticity, ECO = employee customer orientation, EIO = employee intrapreneurial orientation, MCO = manager customer orientation, MIO = manager intrapreneurial orientation, MRP = manager referent power, MBC = behavior-based contracting, MMON = monitoring, and MOC = outcome-based contracting. 48 Table 2: Measurement Testing - Mystery Shopper Ratings t-Value Cronbach’s Composite AVE Factor Construct Name Item Loading (Bootstrap) α Reliability Loyalty RP1 RP2 RP3 RP4 WOM1 WOM2 WOM3 0.84 0.87 0.86 0.89 0.96 0.94 0.96 5.05 5.17 5.12 5.16 5.20 5.57 5.32 0.97 0.97 0.82 F2 Satisfaction with the Employee ESAT1 ESAT2 ESAT3 ESAT4 ESAT5 0.89 0.88 0.85 0.91 0.85 4.62 4.38 3.80 4.74 4.50 0.93 0.94 0.77 F3 Employee In-Role Performance EIRP1 EIRP2 EIRP3 EIRP4 0.92 0.87 0.96 0.95 8.19 8.30 9.01 9.25 0.95 0.96 0.85 F4 Employee Extra-Role Performance EERP1 EERP2 EERP3 EERP4 0.94 0.94 0.96 0.96 6.41 6.67 5.54 6.10 0.97 0.97 0.90 Employee Authenticity EAU1 EAU2 EAU3 EAU4 EAU5 0.91 0.94 0.87 0.92 0.84 5.40 5.11 5.13 4.90 5.07 0.94 0.95 0.80 F1 F5 49 Table 3: Latent Variable Correlations Between Mystery Shopper Ratings F1 F2 F3 F4 F5 F1 0.90 F2 0.64** 0.88 F3 0.15* 0.51** 0.92 F4 0.66** 0.79** 0.46** 0.95 F5 0.44** 0.57** 0.16** 0.47** 0.90 Note: Diagonal elements are the square roots of the average variance extracted (AVE) and elements below the diagonal are the correlations between factors. *Significant at p < 0.05, **significant at p < 0.01. F1 = loyalty, F2 = satisfaction with the employee, F3 = employee in-role performance, F4 = employee extra-role performance, and F5 = employee authenticity. Table 4: Correlations Between Mystery Shopper Ratings F1 F2 F3 F4 F5 F1 0.74** 0.16* 0.69** 0.57** F2 0.64** 0.44** 0.83** 0.65** F3 0.11* 0.46** 0.42** 0.03 F4 0.61** 0.82** 0.45** 0.53** F5 0.41** 0.60** 0.13* 0.47** Note: Correlations above the diagonal represent between-individual (averaged) scores (n=184). Correlations below the diagonal represent within-individual scores (n=308) and were calculated by standardizing the regression coefficient obtained in hierarchical linear modeling Level 1 analyses between one predictor and one criterion. *Significant at p < 0.05, **significant at p < 0.01. F1 = loyalty, F2 = satisfaction with the employee, F3 = employee in-role performance, F4 = employee extra-role performance, and F5 = employee authenticity. not wearing a name tag, the evaluation was of a manager, etc.). These 308 evaluations covered 184 unique employees, an average of 1.67 evaluations per employee. Thus, customer loyalty and customer satisfaction were assessed at assessed at Level 1 (n = 308) while the Level 2 model was unconditional (n = 184). Consistent with Figure 1, the HLM equations for customer loyalty are shown below. Level 1 Model LOYij = β0j + β1j(ESAT) + rij Level 2 Model β0j = γ00 + u0j 50 β1j = γ10 where the i and j subscripts are the customer and the employee levels, respectively. β0j, the intercept, is the average level of loyalty across all employees and β1j is the average effect of customer satisfaction on customer loyalty across all employees. Table 5: Measurement Testing – Employee Ratings t-Value Cronbach’s Composite Factor Construct Item Loading AVE (Bootstrap) α Reliability Employee Level (n = 157) F1 Employee In-Role Performance EIRP1 EIRP2 EIRP3 EIRP4 0.82 0.85 0.88 0.82 5.45 5.47 6.66 5.49 0.87 0.91 0.71 F2 Employee Extra-Role Performance EERP1 EERP2 EERP3 EERP4 0.74 0.88 0.96 0.94 3.47 4.81 5.22 5.21 0.93 0.93 0.78 F3 Employee Authenticity EAU1 EAU2 EAU3 EAU4 EAU5 0.73 0.53 0.90 0.59 0.75 3.73 2.35 4.49 2.62 3.46 0.78 0.83 0.51 F4 Employee Customer Orientation ECO1 ECO2 ECO3 0.81 0.97 0.69 3.77 4.63 2.88 0.84 0.87 0.69 F5 Employee Intrapreneurial Orientation EIO1 EIO2 EIO3 0.80 0.84 0.84 3.60 3.79 3.77 0.78 0.87 0.69 F6 Manager Customer Orientation MCO1 MCO2 MCO3 0.97 0.97 0.94 11.03 9.98 10.34 0.95 0.97 0.91 51 Table 5 (cont’d) Factor Construct Item Loading t-Value Cronbach’s Composite AVE (Bootstrap) α Reliability F7 Manager Intrapreneurial Orientation MIO1 MIO2 MIO3 0.89 0.82 0.89 4.29 3.85 3.96 0.87 0.90 0.75 F8 Manager Referent Power MRP1 MRP2 MRP3 0.86 0.95 0.86 4.16 4.60 4.62 0.89 0.92 0.79 Manager Level (n = 88) F9 Monitoring F11 0.87 0.85 0.90 0.72 6.39 5.93 6.23 4.43 0.86 0.90 0.70 MMON1 MMON2 MMON3 0.83 0.87 0.79 4.58 4.09 3.51 0.78 0.87 0.69 MOC1 MOC2 MOC3 MOC4 0.83 0.77 0.81 0.87 4.69 4.09 4.83 5.58 0.84 0.89 0.67 Behavior-Based Contracting F10 MBC1 MBC2 MBC3 MBC4 Outcome-Based Contracting Table 6: Latent Variable Correlations Between Employee Ratings Employee Level (n = 157) F1 F2 F3 F4 F5 F6 F7 F8 F1 0.84 0.58** 0.59** 0.52** 0.19* 0.25** 0.12 0.08 F2 F3 F4 F5 F6 F7 F8 0.88 0.51** 0.51** 0.33** 0.20* 0.15 0.02 0.71 0.52** 0.21* 0.28** 0.17* 0.17* 0.83 0.35** 0.25** 0.21* 0.07 0.83 0.07 0.27** 0.03 0.95 0.51** 0.71** 0.87 0.49** 0.89 52 Table 6 (cont’d) Manager Level (n = 88) F9 F10 F11 F9 0.84 F10 - 0.20 0.83 F11 - 0.05 0.70** 0.82 Note: Diagonal elements are the square roots of the average variance extracted (AVE) and elements below the diagonal are the correlations between factors. *Significant at p < 0.05, **significant at p < 0.01. F1 = employee in-role performance, F2 = employee extra-role performance, F3 = employee authenticity, F4 = employee customer orientation, F5 = employee intrapreneurial orientation, F6 = manager customer orientation, F7 = manager intrapreneurial orientation, F8 = manager referent power, F9 = behavior-based contracting, F10 = monitoring, and F11 = outcome-based contracting. Table 7: Correlations Between Employee Ratings F1 F2 F3 F4 F5 F6 F7 F1 0.48** 0.61** 0.68** 0.10 0.29** 0.09 F8 0.09 F9 F10 F11 0.03 0.00 - 0.15 - 0.01 0.06 - 0.07 - 0.16 0.34** 0.23* 0.27* 0.08 - 0.05 - 0.22* 0.31** 0.14 0.11 0.02 0.06 - 0.11 0.09 - 0.05 - 0.08 0.01 F6 0.25** 0.20* 0.26** 0.26** 0.08 - F7 0.12 0.13 0.19* 0.20* 0.27** 0.47** F8 0.12 0.07 0.18* 0.11 0.05 F2 0.65** - F3 0.59** 0.50** 0.47** 0.61** 0.38** 0.24* - F4 0.61** 0.56** 0.57** 0.53** 0.23* - F5 0.21** 0.37** 0.28** 0.37** 0.26* 0.20 0.26* 0.56** 0.74** 0.04 - 0.10 - 0.14 - 0.72** 0.52** F9 0.59** 0.10 - 0.07 - 0.11 - 0.04 - 0.10 - 0.05 - F10 -0.21* - 0.05 - F11 - 0.09 0.65** - Note: Correlations above the diagonal represent manager level scores (averaged) scores (n=88). Correlations below the diagonal represent employee level scores (n=157) and were be calculated by standardizing the regression coefficient obtained in hierarchical linear modeling Level 1 analyses between one predictor and one criterion. *Significant at p < 0.05, **significant at p < 0.01. F1 = employee in-role performance, F2 = employee extra-role performance, F3 = employee authenticity, F4 = employee customer orientation, F5 = employee intrapreneurial orientation, F6 = manager customer orientation, F7 = manager intrapreneurial orientation, F8 = manager referent power, F9 = behavior-based contracting, F10 = monitoring, and F11 = outcome-based contracting. 53 As evidenced in the top half of Table 8, customer satisfaction was a significant predictor of customer loyalty, explaining about 41% of the variation in customer loyalty. The standardized customer satisfaction coefficient (t-ratio) was 0.75 (14.47, p < 0.00). This result provides support for the prediction that customer satisfaction has a positive effect on customer loyalty, supporting Hypothesis 1. To predict customer satisfaction, customer satisfaction was again assessed at Level 1 and nested within employees, Level 2. The effects of the each employee’s performance in the service encounter were taken into account as predictors at Level 1. Consistent with Figure 1, the HLM equations for customer satisfaction are shown below. Level 1 Model ESATij = β0j + β1j(EIRP) + β2j(EERP) + β3j(EAU) + β4j(EIRP*EERP) + β5j(EIRP*EAU) + β6j(EERP*EAU) + β7j(EIRP*EIRP) + β8j(EERP*EERP) + β9j(EAU*EAU) + β10j(EIRP*EERP*EAU) + rij Level 2 Model β0j = γ00 + u0j β1j = γ10 β2j = γ20 β3j = γ30 β4j = γ40 β5j = γ50 β6j = γ60 54 β7j = γ70 β8j = γ80 β9j = γ90 β10j = γ010 where the i and j subscripts are the customer and the employee levels, respectively. β0j, the intercept, is the average level of customer satisfaction across all employees and β1 through β10 are the average effects of the employee performance predictors of EIRP, EERP, and EAU, and their interactions on customer satisfaction across all employees. As shown in the bottom half of Table 8, EIRP, EERP, and EAU were significant predictors of customer satisfaction. Along with their interaction terms, the employee performance variables explained approximately 78% of the variation in customer satisfaction. The standardized coefficients (t-values) for EIRP, EERP, and EAU were 0.22 (5.89, p < 0.01), 0.57 (15.79, p < 0.01), 0.15 (3.30, p < 0.01), respectively. These results provide support for Hypothesis 2, Hypothesis 3a, and Hypothesis 5, which predicted positive effects of EIRP, EERP, and EAU on customer satisfaction, as well as support for Hypothesis 3b, which predicted that EERP would have a stronger effect on customer satisfaction than EIRP. Hypothesis 4, the prediction that in-role performance and extra-role performance would have a positive interaction effect on customer satisfaction, was also supported as the interaction between EIRP and EERP was positive (β4 = 0.16) and significant (t = 3.15, p < 0.01). Finally, the positive moderating effect of EAU on the employee performance to customer satisfaction relationship was supported 55 for EIRP (β5 = 0.11, t = 1.96, p < 0.05 one-tailed) but not for EERP (β6 = 0.01, t = 0.31, p < 0.76). Thus, support was found for Hypothesis 6a but not for Hypothesis 6b. Table 8: HLM Results Predicting Customer Outcomes ˆ βu SE t ˆ βs 0.83 0.18 4.74** 0.69 0.05 14.47** 1.03 0.22 4.79** 0.23 0.04 5.89** 0.22 0.44 0.03 15.79** 0.57 0.16 0.05 3.30** 0.15 0.16 0.05 3.15** 0.11 0.06 1.96^ 0.01 0.04 0.31 Customer Loyalty ˆ Intercept ( β 0 ) Predictor Variables ˆ H1: ESAT ( β1 ) R2 0.75 40.78% Customer Satisfaction ˆ Intercept ( β 0 ) Predictor Variables ˆ H2: EIRP ( β1 ) ˆ H3a: EERP ( β 2 ) ˆ H5: EAU ( β3 ) ˆ H4: EIRP * EERP ( β 4 ) ˆ H6a: EIRP * EAU ( β5 ) R2 ˆ H6b: EERP * EAU ( β 6 ) 77.92% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p <0.05 one-tailed. ESAT = satisfaction with the employee, EIRP = employee in-role performance, EERP = employee extra-role performance, and EAU = employee authenticity. Employee Outcomes Consistent with the hypotheses and Figure 1, separate hierarchical linear models were estimated for each of the four employee outcomes: employee in-role performance, employee extra-role performance, employee customer orientation, and employee intrapreneurial orientation. 56 For both in-role and extra-role performance, models were estimated that predicted main and interaction effects at both the employee level (n = 157), Level 1, and the manager level (n = 88), Level 2. Employees were nested within the manager that they identified that they had worked with the most. Twenty-four managers and five employees were excluded from the analysis as they could not be matched. Main and interaction effects of employee customer orientation and employee intrapreneurial orientation were predicted at Level 1, while main and interaction effects of behavior-based contracting, outcome-based contracting, monitoring were predicted at Level 2. The HLM equations for employee in-role performance and extra-role performance are presented below. Level 1 Models EIRPij = β0j + β1j(ECO) + β2j(EIO) + β3j(ECO*ECO) + β4j(EIO*EIO) + β5j(ECO*EIO) + rij EERPij = β0j + β1j(ECO) + β2j(EIO) + β3j(ECO*ECO) + β4j(EIO*EIO) + β5j(ECO*EIO) + rij Level 2 Model, which is the same for EIRP and EERP β0j = γ00 + γ01(MBC) + γ02(MMON) + γ03(MOC) + γ04(MBC*MBC) + γ05(MMON*MMON) + γ06(MOC*MOC) + γ07(MBC*MMON) + γ08(MOC*MMON) + γ09(MBC*MOC) + γ010(MBC*MOC*MMON) + u0j β1j = γ10 57 β2j = γ20 β3j = γ30 β4j = γ40 β5j = γ50 where the i and j subscripts are the employee and manager levels, respectively. β0j, the intercept, is the average level of employee performance across all managers and γ01 through γ010 are the effect of the formal marketing controls of behavior-based contracting, outcome-based contracting, and monitoring and their interactions on employee performance. β1j through β5j are the effects of the employee’s customer orientation, intrapreneurial orientation, and their interactions on employee performance. Table 9 shows the results for the prediction of employee in-role performance (EIRP) with the predictors and their interaction terms at both the employee and manager level explaining about 30% of the variance in EIRP. As predicted, the employee’s customer orientation had a significant positive effect on EIRP with a standardized coefficient (t-value) of 0.64 (7.15, p < 0.01). This result supports Hypothesis 11. The interaction between behavior-based contracting and monitoring as well as the main effects of outcome-based contracting and the employee’s intrapreneurial orientation did not have significant effects on EIRP. Thus, Hypothesis 7, Hypothesis 8, and Hypothesis 13 were not supported. Further and while not hypothesized, the interaction between the employee’s customer orientation and intrapreneurial orientation had a positive effect on EIRP (β5j = 0.21, t = 2.17, p < 0.05). 58 Table 9: HLM Results Predicting Employee In-Role Performance ˆ βu R2 8.44 1.16 -1.14 1.13 - 1.01 - 0.64 0.67 - 0.97 0.67 0.09 7.15** 0.64 - 0.07 H7: MBC*MMON ( γˆ02 ) ˆ H11: ECO ( β1 ) ˆ H13: EIO ( β 2 ) t 9.81 ˆ Intercept ( β 0 ) Predictor Variables H8: MOC ( γˆ01 ) SE ˆ βs 0.05 - 1.21 - 0.10 - 1.33 30.37% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. MOC = outcome-based contracting, MBC = behavior-based contracting, MMON = monitoring, ECO = employee customer orientation, and EIO = employee intrapreneurial orientation. Table 10 shows the results for the prediction of employee extra-role performance (EERP). The predictors and their interaction terms at both the employee and manager level explain approximately 39% of the variation in EERP. As hypothesized, both the employee’s customer orientation and intrepreneurial orientation have a positive, significant effect on EERP, supporting Hypothesis 12 and Hypothesis 14. The standardized coefficients (t-values) for the effect of customer orientation and intrepreneurial orientation on EERP are 0.63 (7.39, p < 0.01) and 0.18 (2.05, p < 0.05). Support for Hypothesis 9 was not found, however, as the effect of the interaction between behavior-based contracting and monitoring on EERP was insignificant (p < 0.40). Likewise, support for Hypothesis 10 was also not found as the effect of outcome-based contracting on EERP was insignificant (p < 0.21). This pattern of results for EERP provides support for Hypothesis 15 as well as Hypothesis 16, which stated that the informal self controls would have stronger effects on EERP than the formal, agency theory based controls. Finally, and similar to the model predicting EIRP, the interaction between the employee’s customer 59 orientation and intrapreneurial orientation had a positive effect on EERP (β = 0.18, t = 1.84, p < 0.05 one-tailed). Table 10: HLM Results Predicting Employee Extra-Role Performance ˆ βu R2 10.60 1.37 - 1.84 1.45 - 1.27 - 0.80 0.94 - 0.85 0.63 0.09 7.39** 0.63 0.13 H9: MBC*MMON ( γˆ02 ) ˆ H12: ECO ( β1 ) ˆ H14: EIO ( β 2 ) t 14.51 ˆ Intercept ( β 0 ) Predictor Variables H10: MOC ( γˆ01 ) SE ˆ βs 0.06 2.05* 0.18 - 2.07 38.57% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. MOC = outcome-based contracting, MBC = behavior-based contracting, MMON = monitoring, ECO = employee customer orientation, and EIO = employee intrapreneurial orientation. In the same manner as for the models predicting EIRP and EERP, employees were nested within the manager that they identified they had worked with the most for the models predicting the employee’s customer orientation and intrapreneurial orientation. Again, this nesting pattern excluded 24 managers and 5 employees because they could not be matched and resulted in a data set consisting of 157 employees (Level 1) and 88 managers (Level 2). For these two models, each employee rated their own manager’s customer orientation, intrapreneurial orientation, and referent power and these ratings were not averaged at the manager level. The ratings of the managers were not averaged to the manager level to recognize that employees may shape their own surface level traits based on their own perceptions of their manager and these individual perceptions may differ across employees. Empirical support for this assertion was found as the calculation of ICC(1) and ICC(2), indices of interrater agreement, resulted in values that were well below accepted cutoff points (Glick 1985, Bliese 2000). The resulting models predicted 60 main and interaction effects of the manager’s customer orientation, the manager’s intrapreneurial orientation, and the manager’s referent power at only the employee level. The HLM equations for employee customer orientation and employee intrapreneurial orientation are shown below. Level 1 Model ECOij = β0j + β1j(MCO) + β2j(MRP) + β3j(MCO*MRP) + β4j(MCO*MCO) + β5j(MRP*MRP) + rij EIOij = β0j + β1j(MIO) + β2j(MRP) + β3j(MIO*MRP) + β4j(MIO*MIO) + β5j(MRP*MRP) + rij Level 2 Model, which is the same for both Employee Customer Orientation and Employee Intrapreneurial Orientation β0j = γ00 + u0j β1j = γ10 β2j = γ20 β3j = γ30 β4j = γ40 β5j = γ50 where the i and j subscripts are the employee and manager levels, respectively. β0j, the intercept, is the average level of the employee customer or intrapreneurial orientation across all managers and γ01 through γ05 are the average regression slopes across managers. β1j through β5j are the 61 effects of the manager’s orientations, referent power, and their interactions on employee orientations. The top half of Table 11 shows the results for the prediction of employee customer orientation (ECO). The manager’s customer orientation, referent power, and the resulting interaction terms explain approximately 10% of the variance in ECO. As expected, Hypothesis 17 was supported as the manager’s customer orientation was significantly and positively related to ECO with a standardized coefficient (t value) of 0.56 (4.80, p < 0.01). Hypothesis 21 was also supported as the manager’s referent power positively moderated (β = 0.23, t = 1.69, p < 0.05 one-tailed) the effect of the manager’s customer orientation on the employee’s customer orientation. Support was not found, however, for Hypothesis 19 as the manager’s referent power did not have a significant effect on ECO (p < 0.22). The bottom half of Table 11 shows the results for the prediction of employee intrapreneurial orientation (EIO). In a similar pattern to the model predicting ECO, the manager’s intrapreneurial orientation was significantly related to EIO, standardized coefficient (t value) of 0.36 (4.20, p < 0.01), and this effect was positively moderated by the manager’s referent power (p < 0.01). Also, the manager’s referent power did not have a significant effect on EIO (p < 0.50). Thus, support was found for Hypothesis 18 and Hypothesis 22 but not for Hypothesis 20. Results of the Averaged Data Path Model To better understand the effects that antecedent variables, such as the manager’s customer orientation, have on consequent variables further down through the proposed system, such as EIRP and EERP, it is important to estimate the entire set of relationships and examine the total effects. Briefly, a total effect is the sum of the direct effect and all indirect effects of an 62 Table 11: HLM Results Predicting Employee Orientations ˆ βu SE t ˆ βs 3.17 0.35 9.01** 0.37 0.08 4.80** 0.56 - 0.10 0.08 - 1.25 - 0.16 0.23 0.13 1.69^ 2.72 0.39 7.02** 0.30 0.07 4.20** 0.36 - 0.06 0.09 - 0.68 - 0.07 0.22 0.67 3.23* Employee Customer Orientation ˆ Intercept ( β 0 ) Predictor Variables R2 ˆ H17: MCO ( β1 ) ˆ H19: MRP ( β 2 ) ˆ H21: MCO*MRP ( β3 ) 9.71% Employee Intrapreneurial Orientation ˆ Intercept ( β 0 ) Predictor Variables ˆ H18: MIO ( β1 ) ˆ H20: MRP ( β 2 ) ˆ H22: MIO*MRP ( β3 ) R2 7.45% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05, one-tailed. MCO = manager customer orientation, MRP = manager referent power, and MIO = manager intrapreneurial orientation. exogenous variable on an endogenous variable (Kaplan 2000) and shows the effect of a variable irrespective of the mechanism by which the effect occurs (Alwin and Hauser 1975). To calculate the total effects, all employee and customer data was averaged to the shift level at each store (n = 75) and a partial least squares (PLS) path model was estimated via SmartPLS (Ringle et al. 2005) to estimate the entire set of relationships. A PLS based approach to path analysis was selected due to distribution-free approach and the minimal sample size requirements of PLS (Chin 1998). All constructs were summed and averaged to create single item composite measures that were then mean centered. The mean centered variables were used 63 to create interaction effects. All main and interaction relationships were then estimated simultaneously. Bootstrap estimates for the significance of direct and total effects were calculated using the individual sign change option in SmartPLS as recommended by Henseler, Ringle, and Sinkovics (2009). Table 12 shows the results for the predicted paths for customer evaluations as outcomes, Table 13 shows the predicted paths for employee performance as outcomes, and Table 14 shows the results for employee orientations as outcomes. For the data provided by mystery shoppers, the results were similar to results from the individual HLM models in that customer satisfaction had a significant effect on customer loyalty and EIRP, EERP, and EAU all had significant main effects on customer satisfaction. The only differences are that EAU did not moderate the EIRP → customer satisfaction relationship and EIRP and EERP did not have an interactive effect on customer satisfaction. For the data provided by managers and employees, ECO was significantly related to EIRP and EERP, which is similar to the individual HLM results. Formal marketing controls were again found to have no influence on EIRP or EERP. The manager’s customer orientation and intrapreneurial orientation were significantly related to the respective employee orientation and support was found for the moderating effect of referent power on the MIO → EIO relationship. Interestingly and opposite as predicted, referent power was found to have a negative effect on ECO and EIO at the shift level. Contrary to the HLM models, support was not found for the moderating effect of referent power on the MCO → ECO relationship while EIO was found to have a negative effect on EIRP and no relationship with EERP. Overall, this pattern of results provides an image fairly consistent with the individual HLM models. 64 Regarding the total effects, Table 15 shows that EIRP, EERP, and EAU have significant total effects on customer loyalty. Thus, confirming the importance of employee performance in generating customer loyalty. Manager customer orientation has significant positive total effects Table 12: Averaged Data Path Model Estimates Predicting Customer Outcomes Standardized Path Estimates Predicted Paths ESAT → LOY R 2 – Customer Loyalty t-ratio 0.67 45.47% 5.55** 0.26 0.59 0.31 0.01 - 0.09 0.02 3.15** 5.67** 2.34* 0.08 0.83 0.15 - 0.02 0.11 0.02 - 0.08 - 0.01 0.02 0.28 1.35 0.26 1.21 0.09 0.74 Independent Variables Provided by Mystery Shoppers EIRP → ESAT EERP → ESAT EAU → ESAT EIRP*EAU → ESAT EERP*EAU → ESAT EIRP*EERP → ESAT Independent Variables Provided by Employees EIRP → ESAT EERP → ESAT EAU → ESAT EIRP*EAU → ESAT EERP*EAU → ESAT EIRP*EERP → ESAT R 2 – Customer Satisfaction 85.91% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. ESAT = satisfaction with the employee, LOY = loyalty, EIRP = employee in-role performance, EERP = employee extra-role performance, and EAU = employee authenticity. on employee in-role and extra-role performance, such that a 1 standard deviation increase in MCO increases EIRP and EERP by 0.46 and 0.25 standard deviations, respectively. Manager intrapreneurial orientation, however, does not have significant total effects on employee in-role and extra-role performance and manager referent power has a significant negative total effect on 65 employee extra-role performance. Still, these results demonstrate that the importance of the manager in influencing employee performance. Table 13: Averaged Data Path Model Estimates Predicting Employee Performance Standardized Predicted Paths t-ratio Path Estimates Dependent Variables Provided by Mystery Shoppers MBC*MMON → EIRP MOC → EIRP ECO → EIRP EIO → EIRP MBC*MON → EERP MOC → EERP ECO → EERP EIO → EERP 0.00 0.11 0.11 0.05 0.18 0.08 - 0.14 0.16 R 2 – Employee In-Role Performance R 2 – Employee Extra-Role Performance 1.05 0.65 0.97 0.56 0.24 0.64 1.18 1.45 11.25% 10.08% Dependent Variables Provided by Employees MBC*MMON → EIRP MOC → EIRP ECO → EIRP EIO → EIRP MBC*MMON → EERP MOC → EERP ECO → EERP EIO → EERP - 0.02 0.03 0.85 - 0.27 0.01 0.13 0.46 0.15 R 2 – Employee In-Role Performance R 2 – Employee Extra-Role Performance 0.04 0.30 7.72** 2.25* 0.09 1.34 3.11** 1.09 54.80% 42.27% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. MBC = behavior-based contracting, MMON = monitoring, EIRP = employee in-role performance, MOC = outcome-based contracting, ECO = employee customer orientation, EIO = employee intrapreneurial orientation, and EERP = employee extrarole performance. 66 Table 14: Averaged Data Path Model Estimates Predicting Employee Orientations Standardized Predicted Paths t-ratio Path Estimates MCO → ECO MRP → ECO MCO*MRP → ECO MIO → EIO MRP → EIO MIO*MRP → EIO 0.54 - 0.25 0.21 0.43 - 0.23 0.24 R 2 – Employee Customer Orientation R 2 – Employee Intrapreneurial Orientation 2.72** 1.87^ 1.26 3.08** 1.77^ 2.24* 9.85% 21.83% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. MCO = manager customer orientation, ECO = employee customer orientation, MRP = manager referent power, MIO = manager intrapreneurial orientation, and EIO = employee intrapreneurial orientation. It should be noted that the data provided by mystery shoppers and employees were included in the same path model in an attempt to link employee perceptions about themselves and their managers to customer provided evaluations. The results of this attempt, however, revealed that the employee provided measures of EIRP, EERP, and EAU had no relationship with the mystery shopper provided measure of customer satisfaction and the employee provided measures of ECO and EIO had no relationship with the mystery shopper provided measures of EIRP, EERP, and EAU. Thus, it appears that this model is unable to estimate the total effect of manager and customer orientations on customer evaluations because of the difference in perceptions of employee performance by employees and mystery shoppers. DISCUSSION Building off of the service-profit chain and the employee-customer-profit chain (Heskett et al. 1994; Rucci et al. 1998), a study was conducted that examined the antecedents and consequences of positive customer contact employee behaviors. 67 Specifically, this study Table 15: Total Effects of Averaged Data Path Model Estimates at the Shift Level Standardized Predicted Paths t-ratio Path Estimates Data Provided by Mystery Shoppers EIRP → LOY EERP → LOY EAU → LOY 0.18 0.40 0.31 2.99** 4.26** 2.34* 0.46 0.25 - 0.12 0.06 - 0.15 - 0.15 2.86** 1.90^ 1.64 0.78 1.06 1.67^ Data Provided by Employees MCO → EIRP MCO → EERP MIO → EIRP MIO → EERP MRP → EIRP MRP → EERP Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. EIRP = employee in-role performance, LOY = loyalty, EERP = employee extra-role performance, EAU = employee authenticity, MCO = manager customer orientation, MIO = manager intrapreneurial orientation, and MRP = manager referent power. examined the effectiveness of formal and informal marketing controls at increasing positive customer contact employee behaviors and the effects of positive customer contact employee behaviors on customer satisfaction and customer loyalty. The results of this study offer a more thorough understanding of the service-profit chain and insight into improving employee performance and ultimately customer satisfaction and loyalty. Key findings are highlighted and their implications for practitioners and academics are discussed below. The Effects of Customer Satisfaction At the individual and the shift level, customer satisfaction displayed a strong, positive effect on customer loyalty as predicted. 68 The Effects of Employee Performance Employee in-role performance and extra-role performance were both positively related to customer satisfaction. Despite both in-role performance and extra-role performance demonstrating positive effects on customer satisfaction, extra-role performance was approximately 2.6 times stronger at the individual level and approximately 2.3 times stronger at the shift level than in-role performance. In-role performance and extra-role performance also had a significant positive interaction at the individual level. The authenticity of the customer contact employee was positively related to customer satisfaction and had an effect on customer satisfaction that was approximately as 0.7 times as strong at the individual level and approximately 1.2 as strong as in-role performance at the shift level. At the individual level, employee in-role and extra-role performance both had decreasing returns on customer satisfaction (respective coefficients of – 0.16 and – 0.10, both significant at p < 0.01). Authenticity also exhibited decreasing returns on customer satisfaction (β = – 0.09, p < 0.05) and positively moderated the employee in-role performance to customer satisfaction relationship. Further, employee in-role performance, extra-role performance, and authenticity increased customer loyalty further down the service-profit chain. The Effects of Formal and Informal Marketing Controls At both the individual and shift levels, the formal marketing controls of behavior-based contracts accompanied by monitoring and outcome-based contracts had no effect on employee in-role or extra role performance. The employee’s customer orientation, on the other hand, increased both employee in-role and extra-role performance at both the individual and shift level while the employee’s intrapreneurial orientation increased employee extra-role performance only at the individual level. Furthermore, the employee’s customer orientation and intrapreneurial 69 orientation had a significant positive interaction when predicting both in-role and extra-role performance (β = 0.21 and p < 0.05, β = 1.84 and p < 0.05 one-tailed). This positive interaction shows that employees high on both self controls exhibited higher ratings across both performance dimensions. The Effects of the Manager In examining the ways to increase the employee’s customer orientation and intrapreneurial orientation, the manager’s customer orientation and intrepreneurial orientation were found transfer to the employee at both the individual and shift level. This transfer was enhanced at both levels through the manager’s referent power. At the individual level, the manager’s referent power positively moderated the transfer of both self controls while the transfer of the manager’s intrapreneurial orientation was positively moderated at the shift level. Despite enhancing the transfer of self controls at the individual and shift level, the manager’s referent power had a harmful main effect on the employee’s customer and intrapreneurial orientations at the shift level. Implications Customer Satisfaction The finding that satisfaction has a positive effect on customer loyalty is consistent with the Marketing literature (cf. Fornell et al. 1996, Cronin, Brady, and Hult 2000, Mittal and Kamakura 2001, Olsen 2002). Unlike many studies, however, this study measured and tested the effect of satisfaction with the individual employee on customer loyalty, as opposed to satisfaction with the store or the brand. Thus, this study demonstrates the importance of the customer contact employee in representing the service encounter to the customer (Czepiel, Solomon, and Surprenant 1985). In addition, the strength and direction of the effect that 70 satisfaction with a single customer contact employee had on customer loyalty makes the importance of the individual customer contact employee to the success of the organization hard to ignore. This should serve to call managers’ attention not only to satisfying customers with the overall encounter, but, also highlight the importance of the individual employee in creating the benefits of loyal customers for the organization, such as increased repurchase rates and favorable word of mouth. Employee Performance Managerially, this study agrees with Maxham et al. (2008) in that managers should concentrate on encouraging both employee in-role and extra-role performance. This study, however, disagrees with Maxham et al.’s (2008) recommendation that both performances should be encouraged equally. First, it was found that employee extra-role performance was approximately 2.5 times stronger at driving customer satisfaction than employee in-role performance. Second, mystery shoppers rated employee in-role performance on average 4.10 out of 5.00 at the shift level while employee extra-role performance was only rated on average 2.56 out of 5.00 at the shift level. Third, employee ratings of their own extra-role performance were, on average, 4.33 out of 5.00, which indicates that employees and customers had vastly different expectations (t = 19.34, p < 0.01) about the behaviors which constitute helping customers beyond job expectations. When these results are taken in combination, it appears as if employees are able to perform the basic behaviors required in their job description, but, struggle at going above and beyond requirements to satisfy customers. Therefore, managers can emphasize the importance of not only meeting basic customer needs but also exceeding customer expectations to reap the benefits of employee extra-role performance further down the serviceprofit chain. 71 The quadratic effects of employee in-role performance and employee extra-role performance were also investigated at the individual level following the example set by Netemeyer and Maxham (2007) to determine post-hoc if either in-role or extra-role performance were a “satisfaction-maintaining” attribute or a “satisfaction enhancing” attribute (Anderson and Mittal 2000). The identification of an attribute as either “satisfaction-maintaining” or “satisfaction-enhancing’ is useful to academics and managers as a more accurate, non-linear relationship is identified and better recommendations can be made to managers, who often have limited resources to allocate. Briefly, a “satisfaction-maintaining” attribute is a performance attribute that customers expect to receive and thus exhibits diminishing returns on customer satisfaction while a “satisfaction-enhancing” attribute is a performance attribute that customers do not anticipate and thus exhibits increasing returns on customer satisfaction (Anderson and Mittal 2000). While Netemeyer and Maxham (2007) found support for decreasing returns to in- role performance and increasing returns to extra-role performance in a service recovery setting, this study found support for decreasing returns to both in-role and extra-role performance in a typical service encounter. When coupled with the findings of Netemeyer and Maxham (2007) these findings suggest that while customers may not expect to be delighted in a service recovery, customers do expect to be delighted by employees in the original encounter. The findings of this study also provide clarification and additional insight into the benefits of authentic performance by employees that is relevant to managers and academics. Regarding employee authenticity, previous research has shown an inconclusive view on the direct effects of employee authenticity on customer satisfaction. For example, van Dolen et al. (2004) found that employee authenticity had a positive, direct effect on customer satisfaction with the encounter using a survey methodology and Grandey et al. (2005) found the same result 72 using an experimental design. Hennig-Thurau et al. (2006), on the other hand, found that authenticity had no effect on customer satisfaction with the transaction in an experimental setting. These studies, however, related employee authenticity to customer satisfaction with the overall experience or encounter. Consequently, factors outside of the individual employee’s control that affect the customer’s satisfaction with the overall experience or encounter could have confounded the relationship. To provide a more precise test and remove potential confounders, the effect of employee authenticity on customer satisfaction with the employee was tested in this study, which resulted in a positive, direct effect at the individual and shift level. These direct, positive effects provide clarification of the employee authenticity to customer satisfaction relationship to academics and urges managers to stress the importance of actually wanting to help customers (resulting in authentic displays) to employees. Further, this study also provides clarification concerning when employee authenticity matters at the individual level. Grandey et al. (2005) found a significant two-way interaction between authenticity and task performance, where authenticity increased customer satisfaction with the encounter when core task performance was high but not when performance was low. Since the Grandey et al. (2005) study was experimental, however, task performance was experimentally manipulated and no effort was made to distinguish between the performance of behaviors which constitute the employee’s job description and the performance of behaviors which went beyond the employee’s job description. To clarify the possible moderating effects of employee authenticity, this study investigated how employee authenticity altered the employee in-role and extra-role performance effects on customer satisfaction. 73 An examination of the interactions between authenticity and employee in-role and extrarole performance at the individual level revealed that employee authenticity positively moderated the employee in-role performance to customer satisfaction relationship but did not moderate the employee extra-role performance to customer satisfaction relationship. This suggests that whether or not an employee authentically goes above and beyond in brief shopping experiences is irrelevant to forming individual customer satisfaction judgments, rather, it is important for the employee to authentically perform the required core behaviors. When the employee performs the core service behaviors it is important to perform these behaviors authentically to enhance the positive effect these in-role behaviors have on customer satisfaction with the employee. Thus, it appears as though customers are indifferent to whether a behavior that exceeds expectations is authentic or inauthentic, but, are sensitive to whether or not the employee willingly performs the basic behaviors. Formal and Informal Marketing Controls This study also offers key implications for managers and academics regarding the influence of formal and informal marketing controls on employee performance. Of particular relevance to managers and academics are the relative ineffectiveness of formal marketing controls and the relative effectiveness of informal marketing controls at influencing employee performance. For example, while formal marketing controls may influence employee performance in isolation (cf. Evans, Landry, Li, and Zou 2007; Miao, Evans, and Zou 2007), this study found that the formal marketing controls espoused by agency theory, behavior-based contracts, monitoring, and outcome-based contracts (Bergen et al. 1992; Eisenhardt 1989) were ineffective at influencing employee performance when tested in conjunction with informal controls. Informal controls, on the other hand, were found to positively influence employee 74 performance when tested along with the agency theory advocated formal controls. In particular, the employee’s customer orientation was found to increase levels of in-role and extra-role performance at both the individual and shift level, while, the employee’s intrapreneurial orientation increased extra-role behavior at the individual level. Thus, for academics this study shows that informal controls are more influential than formal controls at influencing employee performance and for managers this study is a call to recognize the importance of the unwritten, informal rules which guide employee behavior. Also of importance to managers and academics alike is the identification of key informal controls which have a positive influence on employee performance. This study demonstrates the importance of the established informal control of customer orientation at influencing employee performance and introduces an additional informal control, intrapreneurial orientation. While the notion that behaving entrepreneurially influences performance is established, the majority of entrepreneurship research has been at the firm level (e.g. Covin and Slevin 1991, Lumpkin and Dess 1996; Matsuno et al. 2002; Griffith et al. 2006). Only recently, however, has the behavior of employees within service firms which can be characterized as entrepreneurial been investigated (Wakkee et al. 2010). For academics, this study is an important first step in identifying an informal control, other than customer orientation, which positively influences employee performance, while for managers this study provides two informal marketing controls which, if engendered within their employees, can have a positive effect on individual employee performance. The Manager While the identification of informal controls that have a positive effect on employee performance is important, it is also important for academics to identify key antecedents for 75 managers to know which “levers” are in their control that, if pulled, will successfully create the informal controls within employees. This study shows that one key antecedent to informal controls and important “lever” under managerial control is the manager him/herself. This is because employees use their managers as guides for their own behavior, such that the manager’s own informal controls, customer orientation and intrapreneurial orientation, positively influenced the employee’s own customer orientation and intrapreneurial orientation at both the individual and shift levels. For academics this finding confirms that managers are role models to employees (Rich 1997) and demonstrates that constructs at the manager level are a good source for potential antecedents to analogous constructs at the employee level. For managers, this study should call to attention a partial quote attributed to the former Baltimore Orioles third-baseman Brooks Robinson, “Whether you want to or not, you do serve as a role model”. Despite the fact that not all managers may willingly accept that they are a role model to employees, for the managers who do wish to take on role model status it is important to provide ways to more effectively transfer their own informal controls to their employees. For those managers, this study shows that referent power, the ability of the manager to influence the employee’s behavior because the employee would like to identify with or continue to identify with the manager (French and Raven 1959), positively enhances the transfer of informal controls from the manager to the employee. Specifically, at the individual level, the manager’s referent power positively moderated the transfer of both customer orientation and intrapreneurial orientation. For managers, referent power may be difficult to acquire as referent power often results from friendship, identification with an individual, or feelings of a shared identity (Busch 1980). Once acquired, however, the results of this study suggest that the adroit use of referent 76 power can be an effective tool for increasing the transfer of the manager’s informal controls to the employee. Limitations As with any study, this study is not without limitations. Specifically, two main limitations were created by averaging the individual data to the shift level. First, this study did not find support for a relationship between customer satisfaction, which was provided by mystery shoppers, and the employee provided measures of in-role performance, extra-role performance, and authenticity at the shift level. Previous research, such as Netemeyer and Maxham (2007), found support for this relationship at the individual level, but did not test for this relationship at the shift level. Second, the results of the model averaged at the shift level do not correspond exactly to the results of individual models. For example, support was not found for the moderating effect of referent power on the manager customer orientation to employee customer orientation relationship while the employee’s intrapreneurial orientation was found to have a significant negative effect on employee in-role performance and no relationship to employee extra-role performance at the shift level. Thus, while the averaged model largely agreed with the individual models, a few results were not found. One explanation for these two limitations lies in the statistical limitations of averaged data. In actuality, the hypothesized relationships may be present at the shift level, but, this study may not have had the ability to detect the relationships because of the limited sample size. This is because the sample size was reduced when the data was averaged to the shift level and thus statistical power and precision were also reduced (Harter, Schmidt, Hayes 2002). Further, a reduced sample size also can lead to the unsuccessful detection of moderating and quadratic effects (Jaccard et al. 1990). To prevent the reduced sample size from limiting this study, PLS, 77 which accommodates smaller sample sizes (Chin 1998), was used to analyze the averaged data. The use of PLS, however, may not have fully resolved the statistical limitations. A second explanation for these two limitations may be found in the reverse ecological fallacy. Briefly, the ecological fallacy, first put forth by Robinson (1950), is a situation where the researcher applies a relationship found at a higher level of analysis to a lower level of analysis. The reverse ecological fallacy, on the other hand, is a situation where the researcher applies a relationship at a lower level of analysis to a high level analysis (Hofstede 2001). In both cases, the relationships found at one level may not be applicable to the other level and thus conclusions may be fallacious or misleading (Rentfrow, Gosling and Potter 2008; Rottig 2009). Therefore, expecting to find results similar to the individual level by analyzing variables constructed by averaging the individual responses at the shift level may not have been reasonable as shifts, with regard to employee performance, are not necessarily homogenous. Finally, this study was unable to obtain any actual performance measures at any level of analysis from the partner company. Consequently, no attempt could be made to test the total effects of variables at the beginning of the model and thus this study cannot specify what effects variables such as manager customer orientation or employee intrapreneurial orientation have on the financial performance of the firm. The lack of financial measures presents a future opportunity to more strictly test the service-profit and employee-customer-profit chains as performance measures, e.g. profitability, revenue growth, and return on assets, are the endpoints of service-profit and employee-customer-profit chains. Moreover, the linking of manager, employee, and customer data to the financial performance of the firm will allow results to be more relevant to managers (Lehmann 2004). 78 APPENDICES 79 APPENDIX A Essay 1 Survey Items Manager Rated Measures Manager Level Behavior-Based Contracting 1. I only care that a customer service representative does a task and not about their performance of the task 2. It does not matter how well a customer service representative performs a job, only that the job gets done 3. It is more important that a customer service representative does a specific task than how well they do the task 4. I evaluate a customer service representative’s performance on whether or not they did something and not on the quality of what they did Monitoring 1. I watch my customer service representatives 2. I inspect my customer service representatives’ work closely 3. I watch my customer service representatives while they work Outcome-Based Contracting 1. I monitor the extent to which customer service representatives achieve performance goals 2. Customer service representatives have to reach all performance goals to be successful in my store 3. Customer service representatives must meet performance goals 4. I clearly communicate to customer service representatives that they are expected to meet performance goals Employee Rated Measures Employee Level Employee In-Role Performance 1. I met formal performance requirements when helping customers 2. I performed all tasks that customers required 3. I adequately completed all expected customer service behaviors 4. I did exactly what my job position required me to do Employee Extra-Role Performance 1. I went above and beyond the “call of duty” when helping customers 2. I willingly went out of my way to satisfy customers 3. I helped customers with problems beyond what they expected or required 80 4. I went the “extra mile” when helping a customer Employee Authenticity 1. Did you behave in a genuine manner when helping a customer? 2. Were you your own person while helping a customer? 3. Did you fake how you felt while a customer? 4. Were you sincere while helping a customer? 5. Did you seem to be pretending or putting on an act when helping a customer? Employee Customer Orientation 1. I try to help customers achieve their goals 2. I achieve my own goals by satisfying customers 3. I keep the best interests of the customer in mind Employee Intrapreneurial Orientation 1. I am an entrepreneur at my job 2. I behave at work as if this was my own store 3. I try to do my job as if I am the owner of this store Manager Customer Orientation 1. My manager tries to help customers achieve their goals 2. My manager achieves his/her own goals by satisfying customers 3. My manager keeps the best interests of the customer in mind Manager Intrapreneurial Orientation 1. My manager is an entrepreneur at his/her job 2. My manager behaves at work as if this was his/her own store 3. My manager tries to do his/her job as if he/she is the owner of this store Manager Referent Power 1. My manager has a pleasing personality 2. I admire my manager because he/she treats every person fairly 3. I like the personal qualities of my manager Mystery Shopper Rated Measures Customer Level Loyalty 1. I will visit [store name] stores again 2. I will do more business with [store name] in the future 3. The likelihood of me returning to [store name] again is high 4. I would seek out a [store name] location if I needed products from a convenience store 5. I will recommend visiting [store name] convenience stores to friends 6. I will say good things about [store name] convenience stores to others 7. I will encourage friends and relatives to visit [store name] convenience stores 81 Satisfaction 1. Overall, I was very satisfied with the service that I received from this employee 2. Overall, I am happy with the experience I had with this employee 3. I truly enjoyed my experience with this employee 4. I got the result I wanted when I interacted with this employee 5. The outcome of the interaction with this employee was not good for me Employee Level Employee In-Role Performance 1. The employee met formal performance requirements when helping me 2. The employee performed all tasks that I required 3. The employee adequately completed all expected customer service behaviors 4. The employee did exactly what his/her job position required him/her to do Employee Extra-Role Performance 1. The employee went above and beyond the “call of duty” when helping me 2. The employee willingly went out of his/her way to satisfy me 3. The employee helped me with problems beyond what I expected or required 4. The employee went the “extra mile” when helping me Employee Authenticity 1. The employee behaved in a genuine manner when helping me 2. The employee was his/her own person while helping me 3. The employee faked how he/she felt while helping me 4. The employee was sincere while helping me 5. The employee seemed to be pretending or putting on an act when helping me Notes: The employee rated measurement items of in-role performance, extra-role performance, and authenticity were preceded by the phrase “During the past six (6) months” and were rated on a five-point likert-type scale with endpoints of “never” and “always”. All other measurement items were rated on a five-point likert-type scale with endpoints of “strongly disagree” and “strongly agree”. 82 APPENDIX B HLM Results of Non-Hypothesized Effects Table 16: HLM Results of Non-Hypothesized Effects ˆ βs ˆ βu SE t - 0.16 0.05 - 3.47** - 0.10 0.03 - 3.74** - 0.09 0.04 - 2.35* 0.05 0.04 1.51 - 2.84 3.00 - 0.95 - 2.32 -0.99 1.08 - 0.92 - 0.99 0.27 0.23 1.19 0.07 0.09 0.80 0.13 0.16 0.83 - 0.50 0.43 - 1.17 - 0.88 0.92 - 0.96 0.19 0.20 0.94 0.05 0.11 0.44 - 0.05 0.05 - 1.02 0.21 0.10 2.17* Predicting Customer Satisfaction (Table 8) ˆ EIRP*EIRP ( β 7 ) ˆ EERP*EERP ( β8 ) ˆ EAU*EAU ( β9 ) ˆ EIRP*EERP*EAU ( β10 ) Predicting Employee In-Role Performance (Table 9) MBC ( γˆ03 ) MMON ( γˆ04 ) ˆ MBC*MBC ( γ 05 ) MMON*MMON ( γˆ06 ) ˆ MOC*MOC ( γ 07 ) MOC*MMON ( γˆ08 ) MBC*MOC ( γˆ09 ) ˆ MBC*MOC*MMON ( γ 010 ) ˆ ECO*ECO ( β 3 ) ˆ EIO*EIO ( β 4 ) ˆ ECO*EIO ( β 5 ) 83 Table 16 (cont’d) ˆ βu SE t ˆ βs - 4.62 3.73 - 1.24 - 3.65 - 1.66 1.35 - 1.23 - 1.60 0.30 0.24 1.25 - 0.08 0.13 - 0.67 - 0.02 0.14 - 0.18 - 0.46 0.48 - 0.95 - 1.47 1.04 - 1.41 0.31 0.25 1.23 0.11 0.12 0.91 - 0.01 0.05 - 0.17 0.18 0.10 1.84^ - 0.02 0.09 - 0.19 - 0.06 0.07 - 0.82 - 0.03 0.05 - 0.54 - 0.05 0.07 - 0.71 Predicting Employee Extra-Role Performance (Table 10) MBC ( γˆ03 ) MMON ( γˆ04 ) MBC*MBC ( γˆ05 ) MMON*MMON ( γˆ06 ) ˆ MOC*MOC ( γ 07 ) MOC*MMON ( γˆ08 ) MBC*MOC ( γˆ09 ) ˆ MBC*MOC *MMON ( γ 010 ) ˆ ECO*ECO ( β 3 ) ˆ EIO*EIO ( β 4 ) ˆ ECO*EIO ( β 5 ) Predicting Employee Customer Orientation (Table 11) ˆ MCO*MCO ( β 4 ) ˆ MRP*MRP ( β 5 ) Predicting Employee Customer Orientation (Table 11) ˆ MIO*MIO ( β 4 ) ˆ MRP*MRP ( β 5 ) Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. EIRP = employee in-role performance, EERP = employee extra-role performance, EAU = employee authenticity, MBC = behavior-based contracting, MMON = monitoring, MOC = outcome-based contracting, ECO = employee customer orientation, EIO = employee intrapreneurial orientation, and MRP = manager referent power. 84 APPENDIX C HLM Results of Baseline Effects Table 17: HLM Results of Baseline Effects ˆ βu SE t 2.73 0.06 43.67** 0.99 0.08 12.85** 2.69 0.06 44.85** 0.18 0.09 1.98* 0.90 0.08 11.82** 0.43 0.08 5.23** 3.97 0.09 46.56** Median Split MBC ( γˆ01 ) - 0.03 0.08 - 0.34 Median Split MMON ( γˆ02 ) - 0.02 0.09 - 0.25 Median Split MOC ( γˆ03 ) - 0.06 0.09 - 0.69 ˆ Median Split ECO ( β1 ) 0.77 0.09 8.72** ˆ Median Split EIO ( β 2 ) - 0.03 0.08 - 0.34 Predicting Customer Loyalty (Table 8) ˆ Intercept ( β 0 ) R2 ˆ Median Split ESAT ( β1 ) 22.76% Predicting Customer Satisfaction (Table 8) ˆ Intercept ( β 0 ) R2 ˆ Median Split EIRP ( β1 ) ˆ Median Split EERP ( β 2 ) ˆ Median Split EAU ( β3 ) 53.49% Predicting Employee In-Role Performance (Table 9) ˆ Intercept ( β 0 ) R2 28.91% 85 Table 17 (cont’d) ˆ βu SE t 3.81 0.11 36.22** 0.06 0.10 0.65 - 0.06 0.12 - 0.47 - 0.06 0.12 - 0.51 0.69 0.09 7.33** 0.27 0.09 2.89** 4.15 0.09 44.67** 0.60 0.15 3.89** - 0.28 0.15 - 1.88^ 3.42 0.12 27.45** 0.65 0.15 4.41** - 0.27 0.16 - 1.74^ Predicting Employee Extra-Role Performance (Table 10) ˆ Intercept ( β 0 ) Median Split MBC ( γˆ01 ) Median Split MMON ( γˆ02 ) R2 Median Split MOC ( γˆ03 ) ˆ Median Split ECO ( β1 ) ˆ Median Split EIO ( β 2 ) 38.18% Predicting Employee Customer Orientation (Table 11) ˆ Intercept ( β 0 ) R2 ˆ Median Split MCO ( β1 ) ˆ Median Split MRP ( β 2 ) 8.10% Predicting Employee Intrapreneurial Orientation (Table 11) ˆ Intercept ( β 0 ) ˆ Median Split MIO ( β1 ) ˆ Median Split MRP ( β 2 ) 11.56% R2 Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. ESAT = satisfaction with the employee, EIRP = employee in-role performance, EERP = employee extra-role performance, EAU = employee authenticity, MBC = behaviorbased contracting, MMON = monitoring, MOC = outcome-based contracting, ECO = employee customer orientation, and EIO = employee intrapreneurial orientation. 86 APPENDIX D PLS Results of Non-Hypothesized Effects Table 18: PLS Results of Non-Hypothesized Effects Standardized Path Estimate t-ratio Independent Variables Provided by Mystery Shoppers (Table 12) EIRP*EERP*EAU → ESAT - 0.05 0.52 Independent Variables Provided by Employees (Table 12) EIRP*EERP*EAU → ESAT - 0.02 1.08 Dependent Variables Provided by Mystery Shoppers (Table 13) ECO*EIO → EIRP MBC → EIRP MMON → EIRP MBC*MOC → EIRP MOC*MMON → EIRP MBC*MOC * MMON → EIRP ECO*EIO → EERP MBC → EERP MMON → EERP MBC*MOC → EERP MOC*MMON → EERP MBC*MOC*MMON → EERP - 0.07 0.00 0.07 0.11 - 0.14 0.18 - 0.21 0.18 0.10 0.04 0.05 - 0.06 0.77 0.05 0.48 0.52 1.08 1.14 1.53 1.36 0.63 0.20 0.48 0.47 Dependent Variables Provided by Employees (Table 13) ECO*EIO → EIRP MBC → EIRP MMON → EIRP MBC*MOC → EIRP MOC*MMON → EIRP MBC*MOC * MMON → EIRP ECO*EIO → EERP MBC → EERP MMON → EERP MBC*MOC → EERP MOC*MMON → EERP MBC*MOC * MMON → EERP 0.48 - 0.02 - 0.14 - 0.05 - 0.09 0.05 0.37 - 0.02 - 0.22 0.06 - 0.24 0.17 1.74^ 0.36 1.05 0.52 0.99 0.49 1.64 0.36 2.34* 0.63 2.26* 1.58 Path Customer Evaluations as Outcomes Employee Performance as Outcomes 87 Table 18 (cont’d) Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. EIRP = employee in-role performance, EERP = employee extra-role performance, EAU = employee authenticity, ESAT = satisfaction with the employee, ECO = employee customer orientation, EIO = employee intrapreneurial orientation, MBC = behavior-based contracting, MMON = monitoring, and MOC = outcome-based contracting. 88 REFERENCES 89 REFERENCES Aiken, Leona S. and Stephen G. West (1991), Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA: Sage Publications. Alwin, Duane F. and Robert M. Hauser (1975), "The Decomposition of Effects in Path Analysis," American Sociological Review, 40 (1), 37-47. American Marketing Association (2004), "Definition of Marketing - American Marketing Association," (Accessed September 5, 2009), [available at http://www.marketingpower.com/AboutAMA/Pages/DefinitionofMarketing.aspx]. Anderson, Erin and Richard L. Oliver (1987), "Perspectives on Behavior-Based Versus Outcome-Based Salesforce Control Systems," Journal of Marketing, 51 (4), 76-88. Anderson, Eugene W. (1996), "Customer Satisfaction and Price Tolerance," Marketing Letters, 7 (3), 265-74. ---- and Vikas Mittal (2000), "Strengthening the Satisfaction-Profit Chain," Journal of Service Research, 3 (2), 107-20. ---- and Mary W. Sullivan (1993), "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, 12 (2), 125-43. Bergen, Mark, Shantanu Dutta, and Orville C. Walker Jr. (1992), "Agency Relationships in Marketing: A Review of the Implications and Applications of Agency and Related Theories," Journal of Marketing, 56 (3), 1-24. Berry, Leonard L., Eileen A. Wall, and Lewis P. Carbone (2006), "Service Clues and Customer Assessment of the Service Experience: Lessons from Marketing," Academy of Management Perspectives, 20 (2), 43-57. Bettencourt, Lance A. and Stephen W. Brown (1997), "Contact Employees: Relationships Among Workplace Fairness, Job Satisfaction and Prosocial Service Behaviors," Journal of Retailing, 73 (1), 39-61. Bitner, Mary Jo, Bernard H. Booms, and Mary Stanfield Tetreault (1990), "The Service Encounter: Diagnosing Favorable and Unfavorable Incidents," Journal of Marketing, 54 (1), 7184. Bliese, Paul D. (2000), "Within-Group Agreement, Non-Independence, and Reliability: Implications for Data Aggregation and Analysis," in Multilevel Theory, Research, and Methods 90 in Organizations: Foundations, Extensions, and New Directions, Katherine J. Klein and Steve W. J. Kozlowski, Eds. San Francisco, CA: Jossey-Bass. Bolton, Ruth, P.K. Kannan, and Matthew D. Bramlett (2000), "Implications of Loyalty Program Membership and Service Experiences for Customer Retention and Value," Journal of the Academy of Marketing Science, 28 (1), 95-108. Bowen, David E. and Benjamin Schneider (1985), "Boundary-Spanning-Role Employees and the Service Encounter: Some Guidelines for Management and Research," in The Service Encounter: Managing Employee/Customer Interaction in Service Businesses, John A. Czepiel and Michael R. Solomon and Carol F. Surprenant, Eds. Lexington, MA: D.C. Heath and Company. Brown, Tom J., John C. Mowen, D. Todd Donavan, and Jane W. Licata (2002), "The Customer Orientation of Service Workers: Personality Trait Effects on Self-and Supervisor Performance Ratings," Journal of Marketing Research, 39 (1), 110-19. Busch, Paul (1980), "The Sales Manager's Bases of Social Power and Influence Upon the Sales Force," Journal of Marketing, 44 (3), 91-101. Chin, Wynne W. (1998), "The Partial Least Squares Approach for Structural Equation Modeling," in Modern Methods for Business Research, George A. Marcoulides, Ed. Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Churchill, Jr., Gilbert A (1979), "A Paradigm for Developing Better Measures of Marketing Constructs," Journal of Marketing Research, 16 (1), 64-73. Covin, Jeffrey G. and Dennis P. Slevin (1991), "A Conceptual Model of Entrepreneurship as Firm Behavior," Entrepreneurship: Theory & Practice, 16 (1), 7-25. Cronin, Jr., J. Joseph , Michael K. Brady, and G. Tomas M. Hult (2000), "Assessing the Effects of Quality, Value, and Customer Satisfaction on Consumer Behavioral Intentions in Service Environments," Journal of Retailing, 76 (2), 193-218. Czepiel, John A., Michael R. Solomon, and Carol F. Surprenant (1985), "Service Encounters: An Overview," in The Service Encounter: Managing Employee/Customer Interaction in Service Businesses, John A. Czepiel and Michael R. Solomon and Carol F. Surprenant, Eds. Lexington, MA: D.C. Heath and Company. Dahl, Robert A. (1957), "The Concept of Power," Behavioral Science, 2 (3), 201-15. Deci, Edward L. (1975), Intrinsic Motivation. New York, NY: Plenum Press. ---- and Richard M. Ryan (2008), "Self-Determination Theory: A Macrotheory of Human Motivation, Development, and Health," Canadian Psychology, 49 (3), 182-85. Dillman, Don A. (2007), Mail and Internet Surveys. Hoboken, NJ: John Wiley & Sons, Inc. 91 Donavan, D. Todd, Tom J. Brown, and John C. Mowen (2004), "Internal Benefits of ServiceWorker Customer Orientation: Job Satisfaction, Commitment, and Organizational Citizenship Behaviors," Journal of Marketing, 68 (1), 128-46. Eagly, Alice H. and Shelly Chaiken (1993), The Psychology of Attitudes. Fort Worth, TX: Harcourt Brace Jovanovich College Publishers. Eisenhardt, Kathleen M. (1989), "Agency Theory: An Assessment and Review," Academy of Management Review, 14 (1), 57-74. Evans, Kenneth R., Timothy D. Landry, Po-Chien Li, and Shaoming Zou (2007), "How Sales Controls Affect Job-Related Outcomes: The Role of Organizational Sales-Related Psychological Climate Perceptions," Journal of the Academy of Marketing Science, 35 (3), 445-59. Finn, Adam (2001), "Mystery Shopping Benchmarking of Durable-Goods Chains and Stores," Journal of Service Research, 3 (4), 310-20. ---- and Ujwal Kayande (1999), "Unmasking a Phantom: A Psychometric Assessment of Mystery Shopping," Journal of Retailing, 75 (2), 195-217. Fornell, Claes (1992), "A National Customer Satisfaction Barometer: The Swedish Experience," Journal of Marketing, 56 (1), 6-21. ----, Michael D. Johnson, Eugene W. Anderson, Jaesun Cha, and Barbara Everitt Bryant (1996), "The American Customer Satisfaction Index: Nature, Purpose, and Findings," Journal of Marketing, 60 (4), 7-18. ---- and David F. Larcker (1981), "Evaluating Structural Equation Models with Unobservable Variables and Measurement Error," Journal of Marketing Research, 18 (1), 39-50. French Jr., John R.P. and Bertram Raven (1959), "The Bases of Social Power," in Studies in Social Power. Ann Arbor, MI: Institute for Social Research, University of Michigan. Gallo, Carmine (2008), "Employee Motivation the Ritz-Carlton Way," (Accessed September 4, 2009), [available at http://www.businessweek.com/smallbiz/content/feb2008/sb20080229_347490.htm]. Glick, William H. (1985), "Conceptualizing and Measuring Organizational and Psychological Climate: Pitfalls in Multilevel Research," Academy of Management Review, 10 (3), 601-16. Grandey, Alicia A., Glenda M. Fisk, Anna S. Matilla, Karen J. Jansen, and Lori A. Sideman (2005), "Is "Service with a Smile" Enough? Authenticity of Positive Displays During Service Encounters," Organizational Behavior and Human Decision Processes, 96 (1), 38-55. 92 Griffith, David A., Stephanie M. Noble, and Qimei Chen (2006), "The Performance Implications of Entrepreneurial Proclivity: A Dynamic Capabilities Approach," Journal of Retailing, 82 (1), 51-62. Hair Jr., Joseph F., William C. Black, Barry J.. Babin, Rolph E. Anderson, and Ronald L. Tatham (2006). Multivariate Data Analysis. Upper Saddle River, NJ: Pearson Prentice Hall. Harter, James K., Frank L. Schmidt, and Theodore L. Hayes (2002), "Business-Unit-Level Relationship Between Employee Satisfaction, Employee Engagement, and Business Outcomes: A Meta-Analysis," Journal of Applied Psychology, 87 (2), 268-79. Hartline, Michael D. and O.C. Ferrell (1996), "The Management of Customer-Contact Service Employees: An Empirical Investigation," Journal of Marketing, 60 (4), 52-70. Henkoff, Ronald (1994), "Finding, Training, & Keeping the Best Service Workers," in Fortune Vol. 130. Hennig-Thurau, Thorsten, Markus Groth, Michael Paul, and Dwayne D. Gremler (2006), "Are All Smiles Created Equal? How Emotional Contagion and Emotional Labor Affect Service Relationships," Journal of Marketing, 70 (3), 58-73. Henseler, Jorg, Christian M. Ringle, and Rudolf R. Sinkovics (2009), "The Use of Partial Least Squares Path Modeling in International Marketing," in New Challenges to International Marketing (Advances in International Marketing), Shaoming Zou, Ed. Vol. 20: Emerald Group Publishing Limited. Heskett, James L., Thomas O. Jones, Gary W. Loveman, W. Earl Sasser Jr., and Leonard A. Schlesinger (1994), "Putting the Service-Profit Chain to Work," Harvard Business Review, 72 (2), 164-74. Hofstede, Geert (2001), Culture's Consequences: Comparing Values, Behaviors, Institutions, and Organizations Across Nations. Thousand Oaks, CA: Sage Publications. Homburg, Christian, Jan Wieseke, and Wayne D. Hoyer (2009), "Social Identity and the ServiceProfit Chain," Journal of Marketing, 73 (2), 38-54. Hulland, John (1999), "Use of Partial Least Squares (PLS) in Strategic Management Research: A Review of Four Recent Studies," Strategic Management Journal, 20 (2), 195-204. Hunt, Shelby D. (2002), Foundations of Marketing Theory: Toward a General Theory of Marketing. Armonk, NY: M.E. Sharpe Inc. Jaccard, James, Choi K. Wan, and Robert Turrisi (1990), "The Detection and Interpretation of Interaction Effects Between Continuous Variables in Multiple Regression," Multivariate Behavioral Research, 25 (4), 467-78. 93 Jacoby, Jacob (1971), "A Model of Multi-Brand Loyalty," Journal of Advertising Research, 11 (3), 25-31. ---- and Robert W. Chestnut (1978), Brand Loyalty Measurement and Management. New York, NY: Wiley. Jaworski, Bernard J. (1988), "Toward a Theory of Marketing Control: Environmental Context, Control Types, and Consequences," Journal of Marketing, 52 (3), 23-39. ---- and Deborah J. MacInnis (1989), "Marketing Jobs and Management Controls: Toward a Framework," Journal of Marketing Research, 26 (4), 406-19. Kaplan, David (2000), Structural Equation Modeling: Foundations and Extensions. Thousand Oaks, CA: Sage Publications. Keaveney, Susan M. and Madhavan Parthsarathy (2001), "Customer Switching Behavior in Online Services: An Exploratory Study of the Role of Selected Attitudinal, Behavioral, and Demographic Factors," Journal of the Academy of Marketing Science, 29 (4), 374-90. Kennedy, Karen Norman, Jerry R. Goolsby, and Eric J. Arnould (2003), "Implementing a Customer Orientation: Extension of Theory and Application," Journal of Marketing, 67 (4), 6781. Kutner, Michael A., Christopher J. Nachtsheim, and John Neter (2004). Applied Linear Regression Models. New York, NY: McGraw-Hill/Irwin. Lehmann, Donald R. (2004), "Metrics for Making Marketing Matter " Journal of Marketing, 68 (4), 73-75. Lumpkin, G.T. and Gregory G. Dess (1996), "Clarifying the Entrepreneurial Orientation Construct and Linking it to Performance," Academy of Management Review, 21 (1), 135-72. Luo, Xueming and Christian Homburg (2007), "Neglected Outcomes of Customer Satisfaction," Journal of Marketing, 71 (2), 133-49. MacKenzie, Scott B., Philip M. Podsakoff, and Michael Ahearne (1998), "Some Possible Antecedents and Consequences of In-Role and Extra-Role Salesperson Performance," Journal of Marketing, 62 (3), 87-98. ----, ----, and Gregory A. Rich (2001), "Transformational and Transactional Leadership and Salesperson Performance," Journal of the Academy of Marketing Science, 29 (2), 115-34. Marinova, Detelina, Jun Ye, and Jagdip Singh (2008), "Do Frontline Mechanisms Matter? Impact of Quality and Productivity Orientations on Unit Revenue, Efficiency, and Customer Satisfaction," Journal of Marketing, 72 (2), 28-45. 94 Matsuno, Ken, John T. Mentzer, and Aysegul Ozsomer (2002), "The Effects of Entrepreneurial Proclivity and Market Orientation on Business Performance," Journal of Marketing, 66 (3), 1832. Maxham III, James G., Richard G. Netemeyer, and Donald R. Lichtenstein (2008), "The Retail Value Chain: Linking Employee Perceptions to Employee Performance, Customer Evaluations, and Store Performance," Marketing Science, 27 (2), 147-67. Mene, Patrick (2000), "Malcolm Baldridge National Quality Award 1999 Award Recipient, Service Category," (Accessed September 4, 2009), [available at http://www.nist.gov/baldrige/ritz.cfm]. Miao, C. Fred, Kenneth R. Evans, and Shaoming Zou (2007), "The Role of Salesperson Motivation in Sales Control Systems - Intrinsic and Extrinsic Motivation Revisited," Journal of Business Research, 60 (5), 417-25. Michelli, Jospeh A. (2008), The New Gold Standard: 5 Leadership Principles for Creating a Legendary Customer Experience Courtesy of the Ritz-Carlton Hotel Company. New York, NY: McGraw-Hill. Mills, Peter K. (1985), "The Control Mechanisms of Employees at the Encounter of Service Organizations," in The Service Encounter: Managing Employee/Customer Interaction in Service Businesses, John A. Czepiel and Michael R. Solomon and Carol F. Surprenant, Eds. Lexington, MA: D.C. Heath and Company. Mittal, Vikas and Wagner A. Kamakura (2001), "Satisfaction, Repurchase Intent, and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics," Journal of Marketing, 38 (1), 131-42. Mowen, John C. (2000), The 3M Model of Motivation and Personality: Theory and Empirical Applications to Consumer Behavior. Norwell, MA: Kluwer Academic Publishers. ----, Sojin Park, and Alex Zablah (2007), "Toward a Theory of Motivation and Personality with Application to Word-of-Mouth Communications," Journal of Business Research, 60 (6), 590-96. Netemeyer, Richard G. and James G. Maxham III (2007), "Employee Versus Supervisor Ratings of Performance in the Retail Customer Service Sector: Differences in Predictive Validity for Customer Outcomes," Journal of Retailing, 83 (1), 131-45. ----, ----, and Chris Pullig (2005), "Conflicts in the Work-Family Interface: Links to Job Stress, Customer Service Employee Performance, and Customer Purchase Intent," Journal of Marketing, 69 (2), 130-43. Oliver, Richard L. (1981), "Measurement and Evaluation of Satisfaction Processes in Retail Settings," Journal of Retailing, 57 (3), 25-48. 95 ---- (1997), Satisfaction: A Behavioral Perspective on the Consumer. Boston, MA: The McGrawHill Companies, Inc. ---- (1999), "Whence Consumer Loyalty?," Journal of Marketing, 63 (4), 33-44. Olsen, Svein Ottar (2002), "Comparative Evaluation and the Relationship Between Quality, Satisfaction, and Repurchase Loyalty," Journal of the Academy of Marketing Science, 30 (3), 240-49. Pappas, James M. and Karen E. Flaherty (2005), "Informal Controls at Work: Affecting Behavior Amidst Uncertainty," in Innovating Strategy Process, Steven W. Floyd and Johan Roos and Claus D. Jacobs and Franz W. Kellerman, Eds. Malden, MA: Blackwell Publishing, Ltd. Pinchot III, Gilford (1985), "Introducing the 'Intrapreneur'," IEEE Spectrum, 22 (4), 74-79. Price, Linda L., Eric J. Arnould, and Sheila Deibler (1995), "Consumer's Emotional Responses to Service Encounters: The Influence of the Service Provider," International Journal of Service Industry Management, 6 (3), 34-63. ----, ----, and Patrick Tierney (1995), "Going to Extremes: Managing Service Encounters and Assessing Provider Performance," Journal of Marketing, 59 (2), 83-97. Rahim, Afzalur M. (1989), "Relationships of Leader Power to Compliance and Satisfaction with Supervision: Evidence from a National Sample of Managers," Journal of Management, 15 (4), 545-56. Ramaswami, Sridhar N. (1996), "Marketing Controls and Dysfunctional Employee Behaviors: A Test of Traditional and Contingency Theory Postulates," Journal of Marketing, 60 (2), 105-20. Raudenbush, Stephen W. and Anthony S. Bryk (2002), Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, CA: Sage Publications. Regan, William J. (1963), "The Service Revolution," Journal of Marketing, 27 (3), 57-62. Reichheld, Frederick F. and Earl W. Sasser Jr. (1990), "Zero Defections: Quality Comes to Services," Harvard Business Review, 68 (5), 105-11. Rentfrow, Peter J., Samuel D. Gosling, and Jeff Potter (2008), "A Theory of the Emergence, Persistence, and Expression of Geographic Variation in Psychological Characteristics," Perspectives of Psychological Science, 3 (5), 339-69. Rich, Gregory A. (1997), "The Sales Manager as a Role Model: Effects on Trust, Job Satisfaction, and Performance of Salespeople," Journal of the Academy of Marketing Science, 25 (4), 319-28. 96 Ringle, Christian Marc, Sven Wende, and Alexander Will (2005), "SmartPLS 2.0 (beta)." Hamburg, Germany. Robinson, W.S. (1950), "Ecological Correlations and the Behavior of Individuals," American Sociological Review, 15 (3), 351-57. Rottig, Daniel (2009), "Overcoming Common Pitfalls in Cross Cultural Management Research," International Business: Research, Teaching and Practice, 3 (1), 32-51. Rucci, Anthony J., Steven P. Kirn, and Richard T. Quinn (1998), "The Employee-CustomerProfit Chain at Sears," Harvard Business Review, 76 (1), 82-97. Ryan, Richard M. and Edward L. Deci (2000), "Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions," Contemporary Educational Psychology, 25 (1), 54-67. Saxe, Robert and Baron A. Weitz (1982), "The SOCO Scale: A Measure of the Customer Orientation of Salespeople," Journal of Marketing Research, 19 (3), 343-51. Schwarz, Norbert and Gerald L. Clore (1983), "Mood, Misattribution, and Judgments of WellBeing: Informative and Directive Functions of Affective States," Journal of Personality and Social Psychology, 45 (3), 513-23. Solomon, Michael R., Carol F. Surprenant, John A. Czepiel, and Evelyn G. Gutman (1985), "A Role Theory Perspective on Dyadic Interactions: The Service Encounter," Journal of Marketing, 49 (1), 99-111. Stevenson, Howard H. and J. Carlos Jarillo (1990), "A Paradigm of Entrepreneurship: Entrepreneurial Management," Strategic Management Journal, 11 (5), 17-27. Surprenant, Carol F. and Michael R. Solomon (1987), "Predictability and Personalization in the Service Encounter," Journal of Marketing, 51 (2), 86-96. "The Ritz-Carlton: About Us: Corporate Philosophy and Awards," (Accessed September 4, 2009), [available at http://corporate.ritzcarlton.com/en/About/Default.htm]. van Dolen, Willemijn, Ko de Ruyter, and Jos Lemmink (2004), "An Empirical Assessment of the Influence of Customer Emotions and Contact Employee Performance on Encounter and Relationship Satisfaction," Journal of Business Research, 57 (4), 437-44. Vargo, Stephen L. and Robert F. Lusch (2004), "Evolving to a New Dominant Logic for Marketing," Journal of Marketing, 68 (1), 1-17. Voss, Glenn B., A. Parasuraman, and Dhruv Grewal (1998), "The Roles of Price, Performance, and Expectations in Determining Satisfaction in Service Exchanges," Journal of Marketing, 62 (4), 46-61. 97 Wakkee, Ingrid, Tom Elfring, and Sylvia Monaghan (2010), "Creating Entrepreneurial Employees in Traditional Service Sectors: The Role of Coaching and Self-Efficacy," International Entrepreneurship Management Journal, 6 (1), 1-21. Wall, Eileen A. and Leonard L. Berry (2007), "The Combined Effects of the Physical Environment and Employee Behavior on Customer Perception of Restaurant Quality," Cornell Hotel and Restaurant Administration Quarterly, 48 (1), 59-69. Weatherly, Kristopher A. and David A. Tansik (1993), "Managing Multiple Demands: A RoleTheory Examination of the Behaviors of Customer Contact Service Workers," in Advances in Service Marketing and Management, Teresa A. Swartz and David E. Bowen and Stephen W. Brown, Eds. Greenwich, CT: JAI Press Inc. Wilkes, Michael and Bertram Raven (2002), "Understanding Social Influence in Medical Education," Academic Medicine, 77 (6), 481-88. Wilson, Alan M. (1998), "The Role of Mystery Shopping in the Measurement of Service Performance," Managing Service Quality, 8 (6), 414-20. 98 ESSAY 2 THE HIDDEN EFFECT OF RUDE CUSTOMERS: CUSTOMER CONTACT EMPLOYEE RESPONSES TO CUSTOMER INTERPERSONAL INJUSTICE The service encounter is generally characterized as a role performance, where both the customer and the employee proceed according to a generally accepted service “script” (Solomon, Surprenant, Czepiel, and Gutman 1985). For an employee, the expected role is determined by the often conflicting demands of the customer, the organization, and of the employee him/herself. This balancing act that the employee must perform has led researchers to label the service encounter as a three-cornered fight (Bateson 1985). Regarding the service encounter, most research has focused on the factors under the firm’s control (i.e. the factors within the employee and the firm) which can produce positive employee performance and job outcomes since the employee is considered a controllable element of the marketing strategy (e.g. Bettencourt and Brown 1997; MacKenzie, Podsakoff, and Ahearne 1998; Maxham and Netemeyer 2003; Bettencourt, Brown, and MacKenzie 2005). An examination of the literature, however, reveals that researchers have not fully explored the service encounter by failing to recognize the importance the customer has in shaping employee performance. The lack of research concerning the effects of the customer on the employee is surprising when one considers that the interaction between the customer contact employee and the customer is often a negative and stressful interaction for the employee (Browning 2008). Recently, research has started to explore this relationship (e.g. Grandey, Dickter, and Sin 2004; Rupp and Spencer 2006; Rupp, McCance, Spencer, and Sonntag 2008; Skarlicki, van Jaarsveld, and Walker 2008; Browning 2008; Yang and Diefendorff 2009). Unfortunately for managers, the effect of the customer on the employee has, for the most part, not been found to be positive. In 99 fact, findings show that the interaction with the customer can cause the employee to become stressed and emotionally exhausted (Grandey et al. 2004), to exact revenge against the offending customer (Skarlicki et al. 2008), and to engage in counterproductive work behaviors directed at the organization (Yang and Diefendorff 2009). The resulting view that customers cause employees to engage in emotional labor or counterproductive work behaviors directed only at the offending customer or displaced to the organization seems limited given how frustrationaggression theory explains human behavior in reaction to the perceived mistreatment by others and how displaced aggression increases as the target and instigator increase in similarity, such as from one human target to another (Marcus-Newhall, Pedersen, Carlson, and Miller 2000). Briefly, frustration-aggression theory (Dollard, Doob, Miller, Mowrer, and Sears 1939) concerns how individuals react after being prevented from achieving a goal. In the service encounter, customer interpersonal injustice acts as a frustration to the customer contact employee since the injustice prevents the customer contact employee from being treated in the expected manner. Further, frustration-aggression theory proposes that the employee will respond to the injustice with direct aggression, an act whose goal is to injure the perceived source of the frustration, the unjust customer, or displaced aggression, the changing of the form and/or target of aggression. Thus, a single rude customer may cause an employee to perform counterproductive work behaviors directed at a variety of targets. To provide a more complete picture of the effects of customer interpersonal injustice, this study develops a model, see Figure 2, to answer the research question of, “Other than counterproductive work behaviors directed at the offending customer and the organization, what effect does a rude customer have on employee behavior?”. Affective events theory (Weiss and Cropanzano 1996) is used to explore how customer interpersonal injustice can result in 100 counterproductive work behaviors directed at targets such as the offending customer, the employee’s organization, the employee’s supervisor, the employee’s coworkers, and customers other than the offending customer. Specifically, counterproductive work behaviors were modeled as consequences of the employee emotional responses of anger and guilt as well as the attitude of job satisfaction. In doing so, this study seeks to provide insight into the counterproductive work behaviors of employees which are estimated to be performed by eighty five percent of employees (Harris and Ogbonna 2002) and likely cost companies billions of dollars per year (Bennett and Robinson 2003). LITERATURE REVIEW AND RESEARCH HYPOTHESES Counterproductive Work Behavior Counterproductive work behaviors by employees are of particular interest to firms due to their pervasiveness and negative impact on firm performance (Henle, Giacalone, and Jurkiewicz 2005). For example, three out of four employees reported that they have stolen at least once from their employers (Applebaum, Iaconi, and Matousek 2007), ninety-four percent admitted to deviant behaviors at least once over the past six months (Slora 1989), and another ninety-five percent again admitted to engaging in deviant behaviors (Boye and Slora 1993). Further, a study of six hundred restaurant employees found that twelve percent admitted to either serving or preparing intentionally contaminated food, twenty-six percent admitted to touching a coworker in a sexually inappropriate manner, twenty-four percent took illegal drugs just before work, and twenty-one percent observed and did not report another employee stealing cash (Berta 2003). While estimates of the extent and impact of employee theft and fraud vary, one estimate shows that employee theft and fraud costs American businesses nearly fifty billion dollars annually and is the fastest growing category of crime in the U.S. (Coffin 2003). The fifty billion dollar 101 estimate only includes theft and fraud and does not include the often undetected counterproductive work behaviors of interpersonal and organizational deviance. Counterproductive work behavior, also known as deviant or antisocial behavior, is the voluntary behavior which violates significant organizational norms and in doing so threatens the well-being of the organization (Robinson and Bennett 1995). In creating a typology of counterproductive work behaviors Robinson and Bennett (1995) proposed that counterproductive work behaviors vary both in their target and their seriousness. Robinson and Bennett’s (1995) typology places counterproductive work behaviors into quadrants based upon the target (the organization versus another individual) and the seriousness of the behavior (minor versus serious). Accordingly, counterproductive work behaviors can take four possible forms: MinorOrganizational, Major-Organizational, Minor-Interpersonal, and Major-Interpersonal (see Figure 7). Minor-Organizational counterproductive work behavior is labeled production deviance and is defined, in accordance with Hollinger and Clark (1982), as the behaviors which violate the organization’s norms regarding the minimal quality and quantity of work to be finished. MajorOrganizational counterproductive work behavior is labeled property deviance and is defined, again in accordance with Hollinger and Clark (1982), as the acquisition or damage of the tangible property of the firm without authorization. Minor-Interpersonal counterproductive work behavior, or political deviance, is the behavior in a social interaction which creates a personal or political disadvantage for another individual. Finally, Major-Interpersonal counterproductive work behavior, personal aggression, is the behavior of one individual which is aggressive or hostile toward another individual. These behaviors can be seen in a service context, as examples of organization directed counterproductive work behaviors include taking a longer than allowed 102 break (production deviance) and breaking the cash register (property deviance). Examples of interpersonal directed counterproductive work behaviors include making fun of a coworker (political deviance) or stealing from a customer (personal aggression). Figure 7: Robinson and Bennett’s (1995) Typology of Deviant Behavior ORGANIZATIONAL Production Deviance Property Deviance • Leaving early • Taking excessive breaks • Intentionally working slow • Wasting resources • Sabotaging equipment • Accepting kickbacks • Lying about hours worked • Stealing from company MINOR SERIOUS Political Deviance Personal Aggression • Showing favoritism • Gossiping about co-workers • Blaming co-workers • Competing nonbeneficially • Sexual harassment • Verbal abuse • Stealing from co-workers • Endangering co-workers INTERPERSONAL In a services context, counterproductive work behavior has been treated as service sabotage (Harris and Ogbonna 2002; Harris and Ogbonna 2006) which is defined as the customer contact employee’s behaviors which are intentionally designed to have a detrimental effect on the service (Harris and Ogbonna 2002). While the behaviors captured by the service sabotage 103 construct are always counterproductive work behaviors, the reverse is not true. This is because the intent of service sabotage is to negatively affect the service encounter where the intent of counterproductive work behaviors, in general, can vary greatly. Robinson and Bennett’s (1995) and Bennett and Robinson’s (2000) more general conceptualization of counterproductive work behavior is therefore adopted. Just as Robinson and Bennett (1995) proposed that counterproductive work behaviors differ in terms of the form of behavior, researchers have also proposed a variety of antecedents to counterproductive work behavior. A review of the literature supports the conceptualization that counterproductive work behavior is caused by factors internal and external to the employee (e.g. Lau, Au, and Ho 2003; Dalal 2005; and Berry, Ones, and Sackett 2007). Counterproductive work behaviors have been identified as stemming from the employee’s own individual characteristics as well as from the employee’s relationships with the multiple entities present in the workplace (coworkers, supervisors, and the organization) and researchers have spent considerable effort to explore the effects of these antecedents on counterproductive work behaviors. For example, researchers have considered the effects of individual characteristics, such as gender (Hollinger and Clark 1983), attitude (Bolin and Heatherly 2001), cognitive moral development (Greenberg 2002), greed (Greenberg and Barling 1996), and personality (Skarlicki, Folger, and Tesluk 1999). Researchers have also examined the effect of coworkers through group socialization (MacLean 2001), group norms (Hollinger and Clark 1982), coworker support and antagonism (Chiaburu and Harrison 2008), and group cohesion (Wellen and Neale 2006). Supervisor related research includes, but is not limited to, the effects of the manager (Litzky, Eddleston, and Kidder 2006), top management’s ethical leadership (Mayer, Kuenzi, Greenbaum, Bardes, and Salvador 2009), and the individual manager’s leadership style (Brown and Trevino 104 2006). Finally, researchers have also examined factors at the organizational level, such as organizational fairness (Greenberg and Barling 1996), reward system and climate (Brass, Butterfield, Skaggs 1998), and employer psychological contract breach (Bordia, Restubog, and Tang (2008). Only recently, however, have researchers have begun to examine another entity with which the customer contact employee interacts with regularly, the customer, as a potential source of counterproductive work behaviors. This recent research stream began with the examination of the effect of negative treatment by customers on the emotions of employees. For example, Grandey et al. (2004) related customer aggression to service employee emotion regulation and Rupp and Spencer (2006) examined customer interactional injustice as an antecedent to employee emotional labor. Building off the notion that the customer can affect the employee, Reynolds and Harris (2006) proposed that dysfunctional behavior by customers not only affects the employee’s psychology but also increases the customer contact employee’s desire to retaliate against the customer. From this proposition, Skarlicki at al. (2008) demonstrated that customer interpersonal injustice was positively related to customer directed sabotage behavior and Yang and Diefendorff (2009) showed that customer interpersonal injustice was related to counterproductive work behavior directed at the organization. Customer Interpersonal Injustice The notion of justice within organizations typically involves the subjective perceptions of individuals about the fairness of the distribution of outcomes and the fairness of the procedures used to determine the distribution of outcomes (Colquitt, Conlon, Wesson, Porter, and Ng 2001). Research which concerns the fairness of the distribution of outcomes is focused on distributive justice, formally defined as the fair share of the rewards or that the ratio of one’s inputs to 105 outputs is equivalent to another’s ratio of inputs to outputs (Adams 1965). Whereas research that concerns the fairness of the procedures used to determine the distribution of the outcomes is focused on procedural justice (Thibaut and Walker 1975). While the focus on the fairness of the outcomes and procedures of interactions is important, Bies and Moag (1986) recognized that not only do people perceive the outcomes as fair or unfair, but, people also recognize the quality of the interpersonal treatment as fair or unfair. Greenberg (1993) further refined Bies and Moag’s (1986) interactional justice to include the dimensions of informational justice and interpersonal justice. Informational justice is the provision of knowledge about the enacted procedures that demonstrate regard for people’s concerns (Greenberg 1993). Informally, informational justice is fairness of the explanations provided to people that tell why certain procedures were used or why outcomes were distributed in a certain way (Colquitt et al. 2001). Interpersonal justice, on the other hand, is the show of concern for individuals regarding the outcomes they received (Greenberg 1993). In other words, interpersonal justice is the degree to which an individual is treated with politeness, respect, and dignity by another individual (Colquitt et al. 2001). When examining how employees form perceptions of justice, such as of interpersonal justice, it is important to note that employees form multiple relationships at work with multiple different parties (Lavelle, Rupp, and Brockner 2007). Consequently, this multifoci perspective of justice states that employees can and do judge the various forms of justice from the multiple parties separately (Lavelle et al. 2007). The multifoci perspective of justice limits the multiple parties that employees form justice perceptions to the organization, managers, and coworkers. Following Skarlicki et al. (2008), however, it is acknowledged that customer contact employees form relationships with customers in addition to the relationships identified by the multifoci 106 model of justice. As a result, customers represent an additional source of possible injustice for the customer contact employee. Implicit in the definitions of the different types of justice is the notion that the level of fairness is subjectively perceived by each individual. One employee may perceive an injustice whereas another employee in the same situation may feel that the situation is completely fair. Regardless of differences in perceptions of injustice across employees, an employee is proposed to react in a manner to recreate a fair situation if the employee perceives an injustice (Adams 1963). In a multifoci setting, the individual is proposed to react with a “target similarity effect” wherein perceptions of injustice from a source are proposed to best predict responses directed at the source of the perceived injustice (Lavelle et al. 2007). Skarlicki et al. (2008) empirically demonstrated this statement by showing that customer interpersonal injustice was positively associated with customer directed sabotage. Interestingly and in agreement with displaced aggression, the multifoci model of justice allows for “spillover effects” where perceptions of injustice from one source can affect the individual’s response to a completely different source (Lavelle et al. 2007). This notion is important as the relationship between the customer contact employee and the customer is considerably different than the relationship between the customer contact employee and other organizational parties (Skarlicki et al. 2008). The relationship between the customer contact employee and customer is different because it is often brief when compared to the relationships between other parties, such as the employee’s supervisor, the employee’s coworkers, and the organization, and also because the employee’s actions are guided by the display rules mandated by the organization (Ashforth and Humphrey 1993). 107 Consequently, a customer contact employee who perceives interpersonally unjust treatment from a customer may not have the ability to restore the fairness in the relationship as proposed by Adams (1963). If customer interpersonal injustice is viewed under the lens of frustration-aggression theory (Dollard et al. 1939), injustice can be seen as a frustration with an expected response of aggression directed at the perceived source of the frustration. This study uses affective events theory (Weiss and Cropanzano 1996) to answer the question of, “What happens if the customer contact employee cannot recreate a fair social relationship (i.e. respond to injustice with aggression) with the customer?”, and better understand what effects customer interpersonal injustice has on customer contact employee emotions, attitudes, and behaviors. Affective Events Theory Affective events theory (Weiss and Cropanzano 1996), see Figure 8, is based on the premise that things happen to employees in work settings and that employees often react emotionally to these events. At a macro level, affective events theory proposes that the things which happen to employees in a work setting, affective experiences, influence employee behaviors and attitudes. More specifically, these work events or affective experiences are proposed to have a direct effect on employee emotions which can be affected by employee dispositions. Employee emotions are then proposed to affect employee work attitudes, such as job satisfaction, and affective driven behaviors, such as coping or mood regulation behaviors (Weiss and Cropanzano 1996). Further, employee job attitudes are proposed to have a direct effect on judgment driven behaviors, or behaviors where attitudes are causally relevant, such as quitting from one’s job (Weiss 2002). Finally, work environment features, such as display rules, affect not only the likelihood of affective events at work but also employee work attitudes. 108 Figure 8: Affective Events Theory Work Environment Features Judgment Driven Behaviors Work Events Affective Reactions Work Attitudes Affect Driven Behaviors Dispositions Source: Weiss and Cropanzano (1996) Despite its relative infancy compared to other theoretical approaches, affective events theory has begun to gain popularity with researchers in the management and organizational behavior fields looking to explain how work experiences and emotions at work influence employee behavior and attitudes. For example, Paterson and Cary (2002) examined the mediating effects of justice perceptions and anxiety on the main effect of downsizing on employee behavior. Pirola-Merlo, Hartel, Mann, and Hirst (2002) used affective events theory to demonstrate the negative effect of obstacles on team climate and team performance and Judge, Scott, and Ilies (2006) used a model based on affective events theory to explore the relationships 109 between emotions at work, work attitudes, and deviant behavior. Regarding how employees respond to the affective event of customer based interactional injustice, Rupp et al. (2008) used affective events theory to show that employee anger mediated the effects of customer injustice on employee surface acting. Even with the recognition by scholars in other areas that work experiences influence an employee’s emotions, attitudes, and behaviors, the Marketing field has been slow to adopt the affective events theory framework. Despite its lack of popularity in the Marketing literature, affective events theory provides the most salient theoretical framework to explore the effects of the employee’s interaction with a rude or interpersonally unjust customer and is therefore used in this study. Work Events to Affective Reactions Throughout the duration of a customer contact employee’s work day, the employee can be expected to interact not only with coworkers, supervisors, and the organization itself, but also with customers. With each of these interactions, the customer contact employee makes judgments about the fairness of each interaction (Lavelle et al. 2007). As the employee forms judgments about the fairness of each interaction, the resultant equity or inequity has an effect on the employee’s emotions (Weiss, Suckow, and Cropanzano 1999) or the mental state of readiness in an individual that is caused by cognitive appraisals of events or thoughts (Bagozzi, Gopinath, and Nyer 1999). As a result, the perceived fairness of each of the customer contact employee’s daily work interactions can be construed as an affective event in that the appraisal of the (in)justice causes the employee to react emotionally. With regard to the daily interactions of a customer contact employee and of interest to this research study is how the customer contact employee reacts to the interpersonal mistreatment from customers. 110 Customer interpersonal justice is defined as the degree to which the customer treats the customer contact employee with politeness, respect, and dignity during the service encounter (Colquitt et al. 2001). Customer interpersonal injustice, on the other hand, occurs when a customer treats a customer contact employee impolitely, with disrespect, and with a lack of dignity. When an employee is confronted with a perceived injustice, such as rude treatment from a customer, research shows that the employee reacts with negative emotions (Fox, Spector, and Miles 2001). These emotions, the employee’s reaction to the event (Frijda 1993), are negative because the event was incongruent to the employee’s goal(s) and/or expectations (Lazarus 1991). Historically, the most associated emotions with injustice are the negative emotions of anger and guilt (Scher 1997). Anger is proposed to be the emotional reaction not merely to the hindrance of goal, which is frustration, but is the reaction to a perceived demeaning offense against an individual’s self (Lazarus 1991). Specifically, Lazarus (1991) proposes that anger arises in an individual when that individual is prevented from achieving a relevant goal, the prevention of achieving the goal harms the individual’s ego, and the individual blames an external source for the failure to achieve the goal. Anger can then be characterized as the attribution of blame to an external source. In a service encounter, customer interpersonal injustice leads to anger as the employee is prevented from being treated justly, the rudeness of the customer is demeaning to the employee, and the employee perceives the customer as the source of the injustice. In fact, anger was shown to be the most common emotional response by victims of an injustice (Mikula 1986; Clayton 1992). Therefore, it is hypothesized that: H1: Customer interpersonal injustice will have a positive effect on the customer contact employee’s emotional reaction of anger. 111 Guilt, on the other hand, occurs when an individual accepts responsibility for a negatively evaluated specific behavior (Tangney 1999). This condition is the resultant emotion in the individual when the individual focuses on and accepts responsibility for the actions of the self (Lewis 1993). In accordance with previous research, guilt is defined as the emotional state in an individual resulting when the individual performs a behavior or series of behaviors and experiences a sense of tension, regret, and remorse regarding his/her own behavior(s) (Tangney 1999). In a service encounter, customer interpersonal injustice leads to the emotion of guilt in a customer contact employee if the employee attributes the responsibility for the customer’s unjust treatment internally (i.e. perceives that his/her own behavior led to the customer’s unjust treatment of him/her and also views the precipitating behavior as negative). For example, a customer contact employee may feel guilty in response to customer interpersonal injustice if the employee thinks, “My behavior caused the customer to treat me rudely”. Therefore, because not all customer contact employees will attribute blame externally (anger), it is hypothesized that: H2: Customer interpersonal injustice will have a positive effect on the customer contact employee’s emotional reaction of guilt. Work Events to Work Attitudes The second basic premise of Affective Events Theory is that affective events at work influence an employee’s overall work attitudes, such as job satisfaction, and that this influence is mediated by the employee’s affective reactions to the work events (Weiss and Cropanzano 1996). An attitude is a psychological tendency that is expressed by evaluating a particular object with some degree of favor or disfavor (Eagly and Chaiken 1993). In particular, an attitude is a state internal to an individual that causes an individual to respond with either negative or positive responses to a certain stimulus (Eagly and Chaiken 1993). These responses can take one of three forms, either cognition, such as the changing of beliefs, behavior, such as avoiding the stimulus, 112 or affect, such as experiencing an emotion. In line with the view that attitudinal responses have three distinct classes, Eagly and Chaiken (1993) also propose that attitudes have three primary classes of antecedents: cognitive, behavioral, and affective. For example, attitudes are proposed to be influenced cognitively through a learning process whereby an individual gathers information, through direct or indirect experience, about the attitude object and then the favorability of this information influences an individual’s attitude toward the attitude object. Similarly, an individual’s behavior influences the individual’s attitude as the individual is proposed to form attitudes which are consistent with his/her past behavior. Finally, an individual’s emotions influence his/her attitude through classical conditioning. In this case, the attitude object, the conditioned stimulus, is paired with the emotion, the unconditioned stimulus. Through repeated association, the attitude object evokes the emotion in the individual and thus influences the attitude held regarding the attitude object. For that reason, the consistent pairing of an attitude object with a positive (negative) emotion, such as happiness or pride (anger or guilt), will result in a positive (negative) attitude toward the attitude object. One work attitude of relevant interest to Marketing scholars is job satisfaction. The interest in job satisfaction as a focal construct is evident in a meta-analysis of fifty-nine job satisfaction studies which examined the relationships between job satisfaction and twenty eight different correlates in four main categories, work outcomes, personal characteristics, role perceptions, and organizational variables (Brown and Peterson 1993). While much of the past research has defined job satisfaction according to Locke’s (1976) definition of a pleasurable or positive emotional state which results from the appraisal of an individual’s job or job experiences, Hulin and Judge (2003) define job satisfaction as a multidimensional psychological response to an individual’s job. In their conceptualization, the responses have cognitive, 113 behavioral, and affective components consistent with previous definitions of social attitudes (e.g. Eagly and Chaiken 1993). Hulin and Judge (2003) point out that the attitude of job satisfaction is formed in relation to an attitude object such as a particular aspect of the job or the job as a whole, which agrees with previous definitions of social attitudes. Therefore, overall job satisfaction is defined as the psychological tendency of an individual to evaluate the job, as a whole, with some degree of favor or disfavor. How then do a customer contact employee’s emotions influence the employee’s work attitude of job satisfaction and mediate the effect from customer interpersonal injustice to job satisfaction? Following Eagly and Chaiken (1993), the pairing of the attitude object of “work” with affective reaction producing work events (i.e. customer interpersonal injustice) will, through repeated association, cause the attitude object to elicit the affective response from the work events. In this way, the negative work experiences of interpersonally unjust treatment from customers will lead to a negative attitude of job satisfaction because the interpersonally unjust treatment by customers produces the negative emotions of anger and guilt. As the employee continues to experience these negative emotions while on the job, the employee’s psychological evaluation of the job will become unfavorable. Therefore, it is hypothesized that: H3: Anger will have a negative effect on the customer contact employee’s attitude of overall job satisfaction. H4: Guilt will have a negative effect on the customer contact employee’s attitude of overall job satisfaction. H5a: Anger will mediate the effect of customer interpersonal injustice on the customer contact employee’s attitude of overall job satisfaction. H5b: Guilt will mediate the effect of customer interpersonal injustice on the customer contact employee’s attitude of overall job satisfaction. 114 Work Events to Affect Driven Behaviors Affective Events Theory proposes that an individual’s emotions not only influence the attitudes of the individual, but, Weiss and Cropanzano (1996) also posit that the individual’s emotions have an effect on the job performance of the individual. The effect occurs because emotions affect an individual’s behavior and that the individual’s performance will be affected by the degree of match or mismatch between the emotion driven behavior and job requirements (Weiss and Cropanzano 1996). Consequently, both positive and negative emotions may have a negative effect on job performance if they cause the individual to behave in a manner which is inconsistent with performance standards. Nonetheless, affective events theory states that the performance consequences from negative emotions will be more prominent than the performance implications from positive emotions. This is due to the need for an individual to undertake primary and secondary appraisal, develop a coping strategy to remedy the situation, and also because the behavior generated by the coping strategy will tend to be more incongruent with performance standards than behavior resulting from positive emotions. When a customer contact employee is treated interpersonally unjust by a customer, the customer contact employee is proposed to follow the theory of stress and coping (Folkman and Lazarus 1985). Folkman and Lazarus (1985) propose that in this situation the customer contact employee first appraises whether the unjust treatment is irrelevant, benign-positive, or stressful. If the unjust treatment is perceived as a potential threat, a challenge, or as already having done harm, the encounter has created a stress, which is a relationship that is relevant to the employee but has taxed or exceeded the employee’s resources. In the situation of stress, the employee is proposed to examine the resources available to undertake some action to manage the stress. 115 In this way, emotions act as signals to individuals regarding the current status of ongoing relationships. For example, when a customer contact employee feels anger or guilt from an interpersonally unjust customer, the emotions act as an indicator to the employee that he/she is under stress. Lazarus (1991) allows for individuals to pursue either problem-focused coping, which is using action or behavior to change the situation, or emotion-focused coping, which involves the internal restructuring of thoughts. While research has examined the emotion-focused coping strategies of customer contact employees in response to interpersonal injustice from a customer, such as emotional labor, surface acting, deep acting, or venting (Grandey et al. 2004; Rupp and Spencer 2006; Rupp et al. 2008), only recently have researchers examined the problem-focused side of coping, in the form of customer-directed sabotage (Skarlicki et al. 2008). This form of coping, which confronts the source of the stress (Dewe and Guest 1990) in an effort to manage the negative emotion, is readily apparent in both frustration-aggression theory and equity theory. In frustrationaggression theory, Dollard et al. (1939, pg. 39) propose that, “the strongest instigation, aroused by a frustration, is to acts of aggression directed against the agent perceived to be the source of the frustration…”. From equity theory, anger is the most common emotional response to an injustice (Mikula 1986; Clayton 1992) and an individual who has perceived to be treated unjustly has a need to punish the offending party (Sheppard, Lewicki, and Minton 1992). Lazarus (1991) describes this urge for individuals to respond with an attack against the party perceived responsible for the offense as the action tendency of anger. The corresponding situation in a services context is for the customer contact employee to engage in counterproductive work behavior which is targeted at the customer who acted in an interpersonally unjust manner. Therefore, it is hypothesized that: 116 H6: Anger will have a positive effect on the customer contact employee’s counterproductive work behavior which is directed at the offending customer. Customer contact employees who experience guilt instead of anger can be expected to choose a problem focused coping strategy very different than counterproductive work behaviors directed at the offending customer. This different coping strategy is chosen because the emotion of guilt motivates reparative behavior (Tangney 1999). Examples of such behavior include confessions, apologies, and attempts to undo the harm done (Tangney, Miller, Flicker, and HillBarlow 1996). Thus, Tangney (1999) posits that guilt leads to more constructive, proactive, and future-oriented behaviors. In a service encounter, a customer contact employee who feels guilty will choose a problem focused coping strategy that does not further exacerbate the feeling of guilt unlike counterproductive work behaviors directed at the offending customer. Therefore, it is hypothesized that: H7: Guilt will have a negative effect on the customer contact employee’s counterproductive work behavior which is directed at the offending customer. Customer contact employees, however, are not always able to perform problem-focused coping in response to the customer interpersonal injustice triggered negative emotions of anger and guilt. Often, the duration of the service encounter is brief (Mattila and Enz 2002) and customer contact employees abide by display rules established by the organization to avoid punishment (Ashforth and Humphrey 1993). As a result, when a customer contact employee is developing a coping strategy to manage the stress created by the interpersonally unjust customer, the employee may reject problem-focused coping strategies. If so, the employee can then be expected to select an emotion-focused coping strategy, such as displacing aggression. Even though emotion-focused coping strategies are typically conceptualized as undertaking emotional labor, the displacement of aggression from the unjust customer to an innocent target also acts as 117 an emotion focused coping strategy to deal with the emotional discomfort of the negative emotions (Dewe and Guest 1990). Displaced aggression is an emotion-focused coping strategy because the displacement of aggression allows the employee to regulate distressing, negative emotions without directly addressing the customer interpersonal injustice (Folkman and Lazarus 1985). Therefore, it is hypothesized that: H8a: Anger will have a positive effect on the customer contact employee’s counterproductive work behavior which is directed at the employee’s organization. H8b: Anger will have a positive effect on the customer contact employee’s counterproductive work behavior which is directed at the employee’s supervisor. H8c: Anger will have a positive effect on the customer contact employee’s counterproductive work behavior which is directed at the employee’s coworkers. H8d: Anger will have a positive effect on the customer contact employee’s counterproductive work behavior which is directed at customers other than the offending customer. Customer contact employees who experience the emotion of guilt are not expected to select displaced aggression as an emotion focused coping strategy. Instead, individuals who experience guilt are expected to choose an emotion focused coping strategy which is more positive than displaced aggression, such as the internal restructuring of thoughts regarding the interpersonal injustice. This is because individuals who experience guilt are more likely to engage in constructive behaviors, such as non-hostile discussions, as opposed to destructive behaviors, such as yelling, in response to the guilt producing incident (Tangney et al. 1996). This is evidenced by the negative relationship between guilt and direct, indirect, and displaced aggression (Tangney et al. 1996). Therefore, it is hypothesized that: H9a: Guilt will have a negative effect on the customer contact employee’s counterproductive work behavior which is directed at the employee’s organization. 118 H9b: Guilt will have a negative effect on the customer contact employee’s counterproductive work behavior which is directed at the employee’s supervisor. H9c: Guilt will have a negative effect on the customer contact employee’s counterproductive work behavior which is directed at the employee’s coworkers. H9d: Guilt will have a negative effect on the customer contact employee’s counterproductive work behavior which is directed at customers other than the offending customer. Work Events to Attitude Driven Behaviors Emotions are not the only proposed antecedent to employee behavior in Affective Events Theory. Weiss and Cropanzano (1996) also posit that some employee behaviors are influenced by overall evaluations of the employee’s work. In their words, “These are likely to be behaviors which result from well considered decisions and specifically, those behaviors where the overall evaluation of the job enters into that decision.” (Weiss and Cropanzano 1996, pg. 52). While general or overall attitudes are not proposed to be the strongest predictor of any single behavior, Eagly and Chaiken (1993) do note that the general attitude is a relatively good predictor of the individual’s tendency to engage in behaviors relevant to the attitude object. This influence of attitudes on behavior can be shown through the theory of reasoned action and the theory of planned behavior, where attitudes along with other antecedents drive the behavioral intention of an individual (Ajzen and Fishbein 2005). As shown in the theories of reasoned action and planned behavior, an individual’s attitude toward a specific behavior has an effect, albeit mediated, on the individual’s specific behavior. This view is consistent with the definition of an attitude as a psychological tendency to respond with either negative or positive responses to the object of the attitude (Eagly and Chaiken 1993). Even though researchers must exercise caution when trying to relate general attitudes to specific behaviors, individuals with a positive attitude toward an attitude object tend 119 to respond to the attitude object with behaviors which promote the attitude object (Eagly and Chaiken 1993). Conversely, individuals with a negative attitude toward an attitude object tend to engage in behaviors which oppose the attitude object. Figure 9: The Theories of Reasoned Action and Planned Behavior Background factors Individual Personality Mood, emotion Intelligence Values, stereotypes General attitudes Experience Social Education Age, gender Income Religion Race, ethnicity Culture Information Knowledge Media Intervention Behavioral beliefs Attitude toward the behavior Normative beliefs Subjective norm Control beliefs Perceived behavioral control Intention Behavior Actual behavioral control Source: Ajzen and Fishbein (2005) One important attitude employees can have with regard to their performance on the job is overall job satisfaction, the psychological tendency of an individual to evaluate the job, as a whole, with some degree of favor or disfavor. It can be expected that a customer contact employee whose overall job satisfaction is negative will respond to the job with harmful behaviors and one form of behaviors which are not conducive to the goals of the job are 120 counterproductive work behaviors. Counterproductive work behaviors can take various forms and target different parties, such as the employee’s organization, the employee’s supervisor, the employee’s coworkers, and customers. Because a customer contact employee who has a negative attitude of overall job satisfaction will be more prone to respond to the attitude object of the job with the unfavorable behaviors of counterproductive work behaviors and because counterproductive work behaviors can have various targets, it is hypothesized that: H10a: Job satisfaction will have a negative effect on the customer contact employee’s counterproductive work behavior which is directed at the offending customer. H10b: Job satisfaction will have a negative effect on the customer contact employee’s counterproductive work behavior which is directed at the employee’s organization. H10c: Job satisfaction will have a negative effect on the customer contact employee’s counterproductive work behavior which is directed at the employee’s supervisor. H10d: Job satisfaction will have a negative effect on the customer contact employee’s counterproductive work behavior which is directed at the employee’s coworkers. H10e: Job satisfaction will have a negative effect on the customer contact employee’s counterproductive work behavior which is directed at customers other than the offending customer. When examined collectively, the effects of the emotions of anger and guilt on the attitude of job satisfaction, the effects of anger and guilt on direct and displaced counterproductive work behaviors, and the effects of job satisfaction on direct and displaced counterproductive work behaviors suggests that job satisfaction mediates the effects of anger and guilt on direct and displaced counterproductive work behaviors. The mediational chain (anger and guilt → job satisfaction → direct and displaced counterproductive work behaviors) is supported by the conceptualization of attitudes having three types of antecedents and consequences: cognition, behavior, and affect (Eagly and Chaiken 1993). Under Eagly and Chaiken’s (1983) conceptualization, individuals use their beliefs, actions, and emotions regarding an attitude object 121 to respond evaluatively to that object with beliefs, actions, and emotions. Thus, the individual’s emotions concerning the attitude object may be instrumental in determining how the individual chooses to behave with respect to that object because emotions influence attitudes which in turn influence behaviors. The notion that an individual’s emotional state influences the individual’s attitudes is further supported by the informative function of affective states in forming attitudes (Schwarz and Clore 1983). In the mood-as-information framework, individuals use their momentary affective states as information in making judgments of attitude objects. Consequently, when an individual has a positive (negative) affective experience the attitude object is evaluated more (less) favorably. For that reason, a customer contact employee who is experiencing a negative emotion, such as anger or guilt, while on the job can be expected to evaluate the job more unfavorably. Research has supported this conclusion by demonstrating a positive relationship between affective states and job satisfaction (Ilies and Judge 2002; Judge and Ilies 2004; Scott and Judge 2006). Therefore, it is hypothesized that: H11a: Job satisfaction mediates the relationship between anger and the customer contact employee’s counterproductive work behavior which is directed at the offending customer. H11b: Job satisfaction mediates the relationship between anger and the customer contact employee’s counterproductive work behavior which is directed at the employee’s organization. H11c: Job satisfaction mediates the relationship between anger and the customer contact employee’s counterproductive work behavior which is directed at the employee’s supervisor. H11d: Job satisfaction mediates the relationship between anger and the customer contact employee’s counterproductive work behavior which is directed at the employee’s coworkers. 122 H11e: Job satisfaction mediates the relationship between anger and the customer contact employee’s counterproductive work behavior which is directed at customers other than the offending customer. H12a: Job satisfaction mediates the relationship between guilt and the customer contact employee’s counterproductive work behavior which is directed at the offending customer. H12b: Job satisfaction mediates the relationship between guilt and the customer contact employee’s counterproductive work behavior which is directed at the employee’s organization. H12c: Job satisfaction mediates the relationship between guilt and the customer contact employee’s counterproductive work behavior which is directed at the employee’s supervisor. H12d: Job satisfaction mediates the relationship between guilt and the customer contact employee’s counterproductive work behavior which is directed at the employee’s coworkers. H12e: Job satisfaction mediates the relationship between guilt and the customer contact employee’s counterproductive work behavior which is directed at customers other than the offending customer. The Effects of the Employee’s Disposition Central to Affective Events Theory is the motivation to explain how and why individuals respond to events in the workplace. For that reason, affective events theory details how an event leads to emotions which ultimately lead to attitudes and behaviors. When faced with an event at work, however, individuals have predispositions to respond in a certain way emotionally to specific situational events. Hence, the same event may elicit a completely different emotional reaction for two different individuals. This is because an individual’s predisposition to respond in a particular manner, the individual’s disposition, can alter both how the individual perceives the event and the intensity of the emotional reaction (Weiss and Cropanzano 1996). One customer contact employee disposition that has received considerable attention is customer orientation (Kelley 1992; Brown, Mowen, Donavan, and Licata 2002; Donavan, 123 Brown, and Mowen 2004). Customer orientation is defined as the enduring disposition of the customer contact employee to meet customer needs in a customer contact situation (Donavan et al. 2004). Following the work by Donavan et al. (2004), which demonstrated that a four dimensional conceptualization of customer orientation is more appropriate than the 2 dimension conceptualization by Brown et al. (2002), customer orientation is proposed to have four dimensions. The dimensions of customer orientation are the need to pamper the customer, the need to read the customer, the need for a personal relationship with the customer, and the need to deliver to the customer (Donavan et al. 2004). The need to pamper dimension concerns the employee’s need to make the customer feel special to the employee and the need to read the customer dimension concerns the employee’s need to recognize both verbal and nonverbal communication from the customer. The need for a personal relationship with the customer concerns the employee’s need to develop a personal level relationship with the customer, while the need to deliver concerns the employee’s desire to successfully perform the service. A customer contact employee who has a high, enduring tendency to meet customer needs will perceive interactions with customers differently than a customer contact employee who is not customer oriented. This difference is particularly important when considering how a customer contact employee responds to the affective event of interpersonally unjust treatment from a customer. For example, a customer contact employee who is high on customer orientation can be expected to react with less anger than a customer contact employee who is low on customer orientation because the high customer orientation employee is predisposed to deliver the service while pampering, reading, and developing a relationship with the customer. As a result, the injustice may not be perceived as injustice at all for a customer oriented employee and may be perceived as a greater injustice for a non-customer oriented employee, 124 comparatively. On the other hand, a customer contact employee who is high on customer orientation can be expected to react with more guilt than a customer contact employee who is low on customer orientation. The increase in the amount of guilt felt by the customer contact employee occurs because an employee who is highly customer oriented will experience more empathy (Brown et al. 2002) and an employee with increased empathy will perceive that he/she was unable to meet the customer’s needs and accept responsibility for his/her behavior (i.e. to feel guilty for the customer interpersonal injustice). Thus, it is hypothesized that: H13a: The customer orientation of the customer contact employee moderates the relationship between customer interpersonal injustice and anger, such that the relationship is stronger (weaker) for individuals low (high) in customer orientation. H13b: The customer orientation of the customer contact employee moderates the relationship between customer interpersonal injustice and guilt, such that the relationship is stronger (weaker) for individuals high (low) in customer orientation. STUDY METHODS Participants Participants were 100 customer contact employees of organizations in the Midwestern United States. Participants had an average job tenure of 1.99 years (SD = 1.55 years) and an average age of 20.86 years (SD = 1.13 years). Participants were primarily Caucasian/White (71%), followed by Asian American (10%), African American (9%), Other (9%), and Hispanic (1%). Eighty-four percent of participants had completed at least some college and fifty-two percent of participants had an annual household income of $19,999 or less. The majority of participants were female (55%). 125 Procedure Participants were recruited from a junior level Marketing course at a university in the Midwestern United States. To be eligible to participate in the study, participants were required to work in a customer contact role and work at least 10 shifts during the data collection period. Overall, 146 individuals agreed to participate and the data collection took place over 6 weeks, beginning in February and ending in March of 2011. Participants received nominal extra credit upon their completion of the study Experience sampling methodology (ESM) was used to investigate the individuals’ emotions, attitudes, and behaviors during their work day (Larson and Csikszentmihalyi 1983). In ESM, a participant completes a survey at either a predetermined interval, after receiving a signal, or after an event (Wheeler and Reis 1991). An interval – contingent ESM was used, where participants recorded their responses on a Web-based survey at the completion of their shift (Wheeler and Reis 1991). A daily email (signal) was also sent to remind participants to complete the survey. Participants were also required to complete a one-time survey at the beginning of the data collection period. The daily survey measured customer interpersonal injustice, anger, guilt, job satisfaction, and counterproductive work behaviors while the one-time survey measured customer orientation and negative affectivity. The order in which constructs were assessed on daily surveys was randomized. To maintain confidentiality, participants entered a 5 digit number each time they completed a survey. Time stamps and Internet Protocol (IP) addresses recorded for each survey provided evidence that surveys were completed as instructed. 100 individuals completed all required surveys with 46 individuals either providing incomplete or unusable responses. A comparison of gender, age, job tenure, customer orientation, and negative affectivity revealed no 126 significant differences for gender, job tenure, customer orientation, and negative affectivity between those who completed the study and those who did not complete the study. Individuals who did not complete the study (average age = 21.64 years, SD = 2.06 years), however, were found to be significantly older (p < 0.01) than individuals who did complete the study (average age = 20.86 years, SD = 1.13 years). A closer look, however, at the average ages of those who completed the study and those who did not complete the study reveals a statistical, but, not a practical difference based on effect size (Martilla and Carvey 1975). Thus, no differences were judged to be found between those who completed the study and those who did not complete the study. 1,000 usable daily surveys were obtained out of a possible 1,460 responses, which corresponds to a response rate of 68.49% across individuals and time periods. Measures Measurement testing was performed at each level of analysis separately using partial least squares (PLS) as opposed to structural equation modeling (SEM) (cf. Singh, Fassott, Chao, and Hoffman 2006) because response level analysis from experience sampling violates the assumption of independence of observations made by structural equation modeling (Bollen 1989). This violation occurs as observations within each individual are not independent and observations adjacent in time are also likely to be interrelated (Hektner, Schmidt, Csíkszentmihályi 2007). Unlike maximum likelihood estimation techniques, partial least squares estimation makes no assumption regarding the independence of observations and thus offers robust estimates (Henseler, Ringle, and Sinkovics 2009). While latent curve modeling is able to estimate relationships between repeated variables with multiple indicators (Bollen and Curran 2006), recent Monte Carlo simulations have demonstrated the stated advantages of using partial least squares. Vilares, Almeida, and Coelho (2009) showed that while partial least squares and 127 maximum likelihood have comparable results in a reflective model with symmetric data, partial least squares exhibited reduced bias in the presence of skewed data (Vilares et al. 2009) and Hulland, Ryan, and Rayner (2009) showed that partial least squares exhibited greater robustness than maximum likelihood across the model characteristics of sample size, independent variable correlation, and the number of measures. For all constructs, all individual item loadings were greater than 0.60, cronbach’s alpha values ranged from 0.73 to 0.95, and composite reliability values ranged from 0.82 to 0.97. Convergent validity and discriminant validity was established for all constructs as the average variance extracted values for all constructs were greater than 0.50 and the average variance extracted values were greater than the squared correlations between constructs (Fornell and Larcker 1981). All item loadings, cronbach’s alpha values, composite reliabilities, and average variance extracted values are reported in Tables 19 and 20. All items were measured using a five-point Likert type scale and are provided in Appendix E. Table 19: Measurement Testing for Variables Between Individuals t-Value Cronbach’s Construct Name Item Loading (Bootstrap) α Composite AVE Reliability Customer Orientation CO 1 CO 2 CO 3 0.70 0.91 0.72 3.04 3.56 3.36 0.73 0.82 0.61 Negative Affectivity NA 1 NA 2 NA 3 NA 4 NA 5 NA 6 NA 7 NA 8 NA 9 NA 10 - - 0.82 - - 128 Table 20: Measurement Testing for Variables Within Individuals t-Value Cronbach’s Construct Name Item Loading (Bootstrap) α Composite AVE Reliability Customer Interpersonal Injustice CII 1 CII 2 CII 3 CII 4 0.82 0.81 0.77 0.80 12.53 14.10 10.77 9.97 0.82 0.88 0.64 Anger ANG 1 ANG 2 ANG 3 ANG 4 ANG 5 0.82 0.87 0.88 0.90 0.82 12.64 15.82 19.64 22.00 11.05 0.91 0.93 0.74 Guilt GLT 1 GLT 2 GLT 3 GLT 4 GLT 5 0.89 0.83 0.92 0.90 0.92 12.04 10.85 15.40 14.46 19.02 0.94 0.95 0.80 Job Satisfaction JS 1 JS 2 JS 3 JS 4 JS 5 0.85 0.85 0.62 0.82 0.72 19.50 17.94 5.71 13.96 6.50 0.83 0.88 0.60 CWB – Direct CWBD 1 CWBD 2 CWBD 3 CWBD 4 0.91 0.93 0.90 0.83 11.80 21.00 8.67 9.41 0.92 0.94 0.80 CWB – Organization CWBO 1 CWBO 2 CWBO 3 CWBO 4 CWBO 5 0.78 0.84 0.83 0.81 0.76 12.52 19.81 16.77 15.81 10.93 0.86 0.90 0.65 CWB – Supervisor CWBS 1 CWBS 2 CWBS 3 CWBS 4 CWBS 5 CWBS 6 CWBS 7 CWBS 8 CWBS 9 0.65 0.71 0.85 0.91 0.85 0.90 0.83 0.93 0.90 4.88 5.68 8.41 7.14 6.80 8.67 5.48 12.23 8.61 0.95 0.96 0.71 129 Table 20 (cont’d) t-Value Cronbach’s Composite AVE (Bootstrap) α Reliability Construct Name Item Loading CWB – Coworkers CWBC 1 CWBC 2 CWBC 3 CWBC 4 CWBC 5 CWBC 6 0.84 0.80 0.85 0.79 0.87 0.88 11.11 8.53 8.11 8.04 9.12 10.55 0.92 0.93 0.70 CWB – Other Customers CWBOC 1 CWBOC 2 CWBOC 3 CWBOC 4 0.93 0.94 0.94 0.95 6.54 8.55 7.87 6.70 0.95 0.97 0.88 Start Time TIME 1 - - - - - Customer Interpersonal Injustice Customer interpersonal injustice (CII) was measured using four items from the scale developed by Skarlicki et al. (2008). Instructions were modified to “To what extend today did customers” to reflect the daily nature of the survey. Participants responded to the four items using a 1 to 5 Likert type scale with endpoints of “Not at all” and “Extremely”. The items were “Refuse to listen to you”, “Interrupt you: Cut you off mid sentence”, “Make demands that you could not deliver”, and “Yell at you”. Items were averaged so that higher scores indicate greater customer interpersonal injustice. Cronbach’s alpha and composite reliability for this construct were 0.82 and 0.88, respectively. Anger Anger (ANG) was measured using five items from the state anger scaled developed by Speilberger, Jacobs, Russell, and Crane (1983). Instructions were modified to “Please indicate to what extent you felt the following feelings and emotions today at work”. Participants responded 130 to the five items using a 1 to 5 Likert type scale with endpoints of “Not at all” and “Extremely”. The items were “I felt mad”, “I felt angry”, “I felt like yelling at someone”, “I felt furious”, and “I felt like breaking things”. Items were averaged so that higher scores indicate greater anger. Cronbach’s alpha and composite reliability for this construct were 0.91 and 0.93, respectively. Guilt Guilt (GLT) was measured using the five item state guilt scale developed by Tangney and Dearing (2002). Instructions were modified to “Please indicate to what extent you felt the following feelings and emotions today at work”. Participants responded to the five items of “I felt remorse, regret”, “I felt tension about something I did”, “I could not stop thinking about something bad I did”, “I felt like apologizing, confessing”, and “I felt bad about something I did” using a 1 to 5 Likert type scale with endpoints of “Not at all” and “Extremely”. Items were averaged so that higher scores indicate greater guilt. Cronbach’s alpha and composite reliability for this construct were 0.94 and 0.95, respectively. Job Satisfaction Job satisfaction (JS) was measured using the five item version of the scale developed by Brayfield and Rothe (1951). Participants were asked, “How do you feel about your overall job right now?” and indicated to what extent they agreed with the five items using a five item scale with endpoints of 1 = “Strongly Disagree” and 5 = “Strongly Agree”. The five items were “I am enthusiastic about my work”, “I feel fairly satisfied with my present job”, “Each minute at work seems like it will never end” (reverse scored), “I am finding real enjoyment in my work”, and “I consider my job rather unpleasant” (reverse scored). Items were averaged so that higher scores indicate greater job satisfaction. Cronbach’s alpha and composite reliability for this construct were 0.83 and 0.88, respectively. 131 Counterproductive Work Behavior Five different counterproductive work behaviors were measured. For each counterproductive work behavior, participants were asked to, “Please indicate to what extent you did the following behaviors at work today” using a five item scale with endpoints of 1 = “Not at all” and 5 = “Extremely”. Items were averaged so that higher scores indicate greater counterproductive work behavior. Counterproductive work behavior directed at the offending customer (CWBD) was measured using four items adapted from the service sabotage scale developed by Harris and Ogbonna (2006). Participants were asked to indicate to what extent they agreed with the items of “Get revenge on the offending customer(s)”, “Get back at the offending customer(s)”, “Deliberately mess things up for the offending customer(s)”, and “Deliberately mistreat the offending customer(s)”. Cronbach’s alpha was 0.92 and composite reliability was 0.94. Counterproductive work behavior directed at the employee’s organization (CWBO) was measured using three items from the organizational deviance scale developed by Bennett and Robinson (2000) and two items from the organizational retaliatory behavior scale developed by Skarlicki and Folger (1997) (cf. Jones 2009). Participants were asked to indicate to what extent they agreed with the items of “Take an additional or longer break than is acceptable at your workplace”, “Try to look busy while wasting time”, “Put little effort into your work”, “Spend too much time fantasizing or daydreaming instead of working”, and “Spend time on personal matters while at work”. Cronbach’s alpha was 0.86 and composite reliability was 0.90. Counterproductive work behavior directed at the employee’s supervisor (CWBS) was measured using nine items. Four items were adapted from Bennett and Robinson (2000), two items were adapted from the service sabotage scale developed by Harris and Ogbonna (2006), 132 two items were adapted from Jones (2009), and one item was adapted from the organizational retaliatory behavior scale developed by Skarlicki and Folger (1997). Participants were asked to indicate to what extent they agreed with the items of “Purposely neglect to follow your supervisor's instructions”, “Act rudely toward your supervisor”, “Spread unconfirmed rumors about your supervisor”, “Do something to get your supervisor in trouble”, “Encourage your coworkers to get back at your supervisor”, “Say something hurtful to your supervisor”, “Curse at your supervisor”, “Get back at your supervisor”, and “Deliberately mess things up for your supervisor”. Cronbach’s alpha was 0.95 and composite reliability was 0.96. Counterproductive work behavior directed at the employee’s coworkers (CWBC) was measured using three items adapted from the interpersonal deviance scale developed by Bennett and Robinson (2000), two items adapted from the service sabotage scale developed by Harris and Ogbonna (2006), and one item adapted from Skarlicki and Folger (1997). Participants indicated to what they agreed with the items of “Say something hurtful to a coworker”, “Curse at a coworker”, “Publicly embarrass a coworker”, “Spread unconfirmed rumors about a coworker”, “Get back at a coworker”, and “Deliberately mess things up for a coworker”. Cronbach’s alpha was 0.92 and the composite reliability was 0.93. Finally, counterproductive work behavior directed at customers other than the offending customer (CWBOC) was measured using four items adapted from Harris and Ogbonna’s (2006) service sabotage scale. Participants were asked to indicate to what extent they agreed with the items of “Get revenge on a customer who did not offend you”, “Get back at a customer who did not offend you”, “Deliberately mess things up for a customer who did not offend you”, and “Deliberately mistreat a customer who did not offend you”. Cronbach’s alpha and composite reliability for this construct was 0.95 and 0.97, respectively. 133 Customer Orientation Participants’ customer orientation (CO) was assessed using three items adapted from the selling orientation – customer orientation (SOCO) scale developed by Saxe and Weitz (1982). Participants were asked, “Overall how do you feel about serving customers?”, and indicated the extent of their agreement to the items using a five item scale with endpoints of 1 = “Strongly Disagree” and 5 = “Strongly Agree”. The items were “I try to help customers achieve their goals”, “I achieve my own goals by helping customers”, and “I keep the best interests of the customer in mind”. This three item scale correlated highly (correlation coefficient = 0.73, p < 0.01) with the four dimensional customer orientation scale developed by Donavan et al. (2004). Items were averaged so that higher scores indicate greater customer orientation. Cronbach’s alpha and composite reliability for this construct was 0.73 and 0.82, respectively. Control Variables The theoretically relevant variables of the time that the participant started the daily survey, the participant’s customer orientation, and the participant’s negative affectivity were included when testing the model shown in Figure 2. The time that the participant started the daily survey (TIME) was controlled for as research has demonstrated that an individual’s positive affect displays significant diurnal variation, rising in the morning, remaining steady throughout the day, and falling at night (Clark, Watson, and Leeka, 1989; Watson 2000). Accordingly, this diurnal rhythm could artificially inflate relationships of interest and was therefore controlled for as a level 1 predictor in all analyses. Likewise, customer orientation was included as a control variable, when not included as a predictor variable, in Level 2 as research has demonstrated that customer orientation affects employee job satisfaction and organizational citizenship behaviors (Donavan et al. 2004) and failing to include its effects could bias results. 134 Negative affectivity (NA) was also controlled for as research has shown that individuals who have high negativity tend to be especially reactive to negative stimuli such as customer interpersonal injustice (Kaplan, Bradley, Luchman, and Haynes 2009) and that negative affectivity has a significant relationship with job stress, job satisfaction, and counterproductive work behaviors (Brief, Burke, George, Robinson, and Webster 1988; Penney and Spector 2005). Negative affectivity was included in all analyses as a level 2 predictor to eliminate the possibility that relationships of interest could be inflated due to the effects of negative affectivity. Negative affectivity was measured using the ten negative affectivity items (distressed, upset, guilty, scared, hostile, irritable, ashamed, nervous, jittery, and afraid) from the PANAS scale developed by Watson, Clark, and Tellegen (1988). Participants were instructed to indicate how well the word or phrase described themselves in general using a five item scale with endpoints of 1 = “Very slightly or not at all” and 5 = “Extremely”. Items were averaged so that higher scores indicate greater negative affectivity and cronbach’s alpha for this scale was 0.82. Analyses Overview Hierarchical linear modeling (HLM) via HLM 6.08 was used to analyze the relationships of interest due to the hierarchical nature of the data, days nested within employees (Raudenbush and Bryk 2002). HLM allows researchers to express relationships within each level of interest with their own submodels as well as to specify relationships across levels (Raudenbush and Bryk 2002). In the present study, the daily measures over time (Level 1) of customer interpersonal injustice, anger, guilt, job satisfaction, counterproductive work behaviors, and survey start time are at the within-individual level of analysis, while customer orientation and negative affectivity are at the between-individual level of analysis (Level 2). All variables at Level 1 were centered at each individual’s mean to remove the between-individual variance from the Level 1 variables 135 per Hoffman, Griffin, and Gavin (2000). Centering the Level 1 variables in this manner ensures that the relationships at Level 1 are not confounded by between-individual differences. RESULTS Correlations Correlations for this study, both within-individual and between-individual, are presented in Table 21. Correlations above the diagonal represent between-individual correlations while correlations below the diagonal represent within-individual correlations. Between-individual correlations were estimated by aggregating Level 1 variables across the 10 work days and then correlating the aggregated variables. Within-individual correlations were calculated by standardizing the regression coefficients obtained from simple regressions between one independent variable and one dependent variable in HLM. Partitioning of Variance Components Before analyzing the proposed relationships, the variance at the within-individual and between-individual levels was estimated to justify the use of HLM. If variance does not exist at the within-individual level of analysis then HLM is not necessary as the relationships can be explained without the use multiple levels (i.e. only at the between-individual level). To calculate the percentage of variation that lies at the within-individual level, a null model, or a model with no Level 1 or Level 2 predictors, was estimated for separately for each dependent variable. The results of the null models, shown in Table 22, demonstrate that a considerable percentage of variance for each dependent lies at the within-individual level. For the negative emotions of anger and guilt, 64.43% and 60.15% of the variation in is within-individuals, respectively, and 34.14% of the variation in job satisfaction is within-individuals. Additionally, within-individual variance percentages ranged from 37.41% to 44.50% for the different counterproductive work 136 Table 21: Correlations Between CWB and Predictor Variables Variable 1 2 3 4 5 6 7 8 9 10 11 12 1 CII 0.55** 0.68** -0.08 0.61** 0.24* 0.64** 0.60** 0.54** 0.13 0.04 0.01 2 ANG 0.47** 0.74** -0.37** 0.59** 0.37** 0.70** 0.67** 0.57** 0.11 -0.01 0.32** 3 GLT 0.31** 0.28** -0.19 0.65** 0.35** 0.76** 0.72** 0.67** 0.21* 0.02 0.17 4 JS -0.18** -0.23** -0.16** -0.08 -0.37** -0.19 -0.13 -0.11 0.05 0.37** -0.22* 5 CWBD 0.29** 0.26** 0.27** -0.19** 0.25* 0.87** 0.89** 0.89** 0.11 0.02 -0.01 6 CWBO 0.18** 0.11** 0.11** -0.24** 0.20** 0.32** 0.29** 0.20* -0.02 -0.31** 0.12 7 CWBS 0.19** 0.33** 0.45** -0.11** 0.45** 0.27** 0.95** 0.91** 0.12 -0.06 0.03 8 CWBC 0.15** 0.31** 0.32** -0.13** 0.45** 0.26** 0.70** 0.90** 0.12 0.00 0.04 9 CWBOC 0.11** 0.19** 0.18** -0.05^ 0.46** 0.07* 0.48** 0.81** 0.11 0.01 -0.08 10 TIME 0.02 0.04 0.05 -0.01 0.03 0.00 0.02 0.04 -0.02 0.14 -0.10 11 CO 0.08 12 NA Note: Correlations above the diagonal represent between-individual (aggregated) scores (n=100). Correlations below the diagonal represent within-individual scores (n=1000) and were calculated by standardizing the regression coefficient obtained in hierarchical linear modeling Level 1 analyses between one predictor and one criterion. Variables 1 through 10 were measured within-individuals and variables 11 and 12 were measured between individuals. *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. CII = customer interpersonal injustice, ANG = anger, GLT = guilt, JS = job satisfaction, CWBD = counterproductive work behaviors – direct, CWBO = counterproductive work behaviors – organization, CWBS = counterproductive work behaviors – supervisor, CWBC = counterproductive work behaviors – coworkers, CWBOC = counterproductive work behaviors – other customer, TIME = start time, CO = customer orientation, and NA = negative affectivity. 137 behaviors. These results suggest that the use of HLM is appropriate as appreciable variance exists at the within-individual level for predictors at Level 1 and Level 2 to explain. Table 22: Parameter Estimates and Variance Components of Null Models Intercept Within-Individual Between-Individual Dependent % Variability 2 Variable Within-Individual (γ00) Variance (σ ) Variance (τ00) ANG 1.32** 0.25 0.14 64.43% GLT 1.20** 0.16 0.11 60.15% JS 3.24** 0.21 0.41 34.14% CWBD 1.13** 0.08 0.09 44.50% CWBO 1.89** 0.29 0.54 34.85% CWBS 1.12** 0.05 0.08 37.41% CWBC 1.14** 0.07 0.09 44.01% CWBOC 1.07** 0.05 0.07 43.76% Note: γ00 is the pooled intercept representing the average level of the dependent variable 2 across individuals, σ is the within-individual variance in the dependent variable, and τ00 is the between-individual variance in the dependent variable. The percentage of 2 2 variability within-individual was computed as σ / (σ + τ00). **Significant at p < 0.01. ANG = anger, GLT = guilt, JS = job satisfaction, CWBD = counterproductive work behaviors – direct, CWBO = counterproductive work behaviors – organization, and CWBS = counterproductive work behaviors – supervisor, CWBC = counterproductive work behaviors – coworkers, CWBOC = counterproductive work behaviors – other customer Test of Hypotheses Main Effects A series of regressions was estimated in HLM where anger, guilt, job satisfaction, and counterproductive work behaviors were regressed upon the appropriate individual mean centered predictors to test the hypothesized main effects (Hypotheses 1, 2, 3, 4, 6, 7, 8a – 8d, 9a – 9d, and 10a – 10e). All equations are provided in Appendix F. Hypothesis 1 predicted that customer interpersonal injustice would have a positive effect on anger and Hypothesis 2 predicted that customer interpersonal injustice would have a positive effect on guilt. An examination of the results predicting anger in Table 23 shows that customer interpersonal injustice had a significant, positive effect on anger with a standardized coefficient (t-value) of 0.38 (t = 5.60, p < 0.01). 138 Similarly, an examination of the results predicting guilt in Table 24 shows that customer interpersonal injustice had a significant, positive effect on guilt with a standardized coefficient (tvalue) of 0.17 (t = 4.56, p < 0.01). These results provide support for Hypotheses 1 and 2. Table 23: HLM Results Predicting Anger Independent Variable ˆ Intercept ( β 0 ) CO ( γˆ01 ) NA ( γˆ02 ) ˆ CII ( β1 ) CO ( γˆ11 ) NA ( γˆ12 ) ˆ TIME ( β 2 ) R2 ˆ βu SE t ˆ βs 1.32 0.04 34.37** -0.04 0.06 -0.65 -0.06 0.24 0.08 3.01** 0.33 0.32 0.06 5.60** 0.38 -0.14 0.28 0.09 0.14 -1.50 1.98^ -0.21 0.39 0.09 0.06 1.52 0.05 24.78% Note: All Level 1 predictor scores were centered at the individuals’ means to eliminate between-individual variance and all Level 2 predictor scores were grand mean ˆ ˆ centered. β u = unstandardized coefficient and β s = standardized coefficient. R2 = variance explained and was calculated as the proportional reduction in the level 1 ˆ ˆ ˆ variance component: [( σ 2 unconditional - σ 2 conditional) / σ 2 unconditional]. *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05, one-tailed. CO = customer orientation, NA = negative affectivity, CII = customer interpersonal injustice, and TIME = start time. Table 24: HLM Results Predicting Guilt Independent Variable ˆ Intercept ( β 0 ) CO ( γˆ01 ) NA ( γˆ02 ) ˆ CII ( β1 ) CO ( γˆ11 ) NA ( γˆ12 ) ˆ TIME ( β 2 ) R2 ˆ βu SE t ˆ βs 1.20 0.03 35.08** 0.01 0.05 0.26 0.01 0.12 0.07 1.79^ 0.16 0.15 0.03 4.56** 0.17 0.11 0.09 0.07 0.08 1.66^ 1.14 0.16 0.12 0.03 0.05 0.66 0.02 12.40% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. CO = customer orientation, NA = negative affectivity, CII = customer interpersonal injustice, and TIME = start time. 139 Hypothesis 3 predicted that anger would have a negative effect on job satisfaction and Hypothesis 4 predicted that guilt would have a negative effect on job satisfaction. The regression results predicting the total effects of anger and guilt on job satisfaction are provided in Table 25. Both Hypothesis 3 and Hypothesis 4 were supported as anger had a significant, negative effect on job satisfaction with a standardized coefficient (t-value) of -0.26 (t = -5.85, p < 0.01) and guilt had a significant, negative effect on job satisfaction as well (standardized coefficient = -0.07, t = -1.87, p < 0.05, one-tailed). Table 25: Total Effects of Anger and Guilt Predicting Job Satisfaction Independent Variable Main Effects ˆ Intercept ( β 0 ) CO ( γˆ01 ) NA ( γˆ02 ) ˆ ANG ( β1 ) ˆ GLT ( β 2 ) ˆ TIME ( β3 ) R2 ˆ βu SE t ˆ βs 3.27 0.06 55.64** 0.41 0.10 3.89** 0.50 -0.33 0.13 -2.48* -0.37 -0.32 0.05 -5.85** -0.26 -0.08 0.04 -1.87^ -0.07 -0.01 0.05 -0.26 0.00 20.39% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. CO = customer orientation, NA = negative affectivity, ANG = anger, GLT = guilt, and TIME = start time. Hypotheses 6, 7, 8, and 9 predicted the effects of anger and guilt on counterproductive work behavior. Specifically, Hypotheses 6, 8a, 8b, 8c, and 8d predicted that anger would have a significant, positive effect on counterproductive work behavior directed at the offending customer, the employee’s organization, the employee’s supervisor, the employee’s coworkers, and customers other than the offending customer. Hypotheses 7, 9, 9b, 9c, and 9d predicted guilt would have a significant, negative effect on counterproductive work behavior directed at the offending customer, the employee’s organization, the employee’s supervisor, the employee’s 140 coworkers, and customers other than the offending customer. Table 26 presents the total effects of anger and guilt on the separate counterproductive work behaviors. Anger was found to have a significant, positive effect on counterproductive work behavior directed at the offending customer (standardized coefficient = 0.18, t = 2.89, p < 0.01), the employee’s organization (standardized coefficient = 0.10, t = 1.97, p < 0.05, one-tailed), the employee’s supervisor (standardized coefficient = 0.17, t = 3.90, p < 0.01), the employee’s coworkers (standardized coefficient = 0.18, t = 3.68, p < 0.01), and customers other than the offending customer (standardized coefficient = 0.13, t = 2.58, p < 0.05). These results provide support for Hypotheses 6, 8a, 8b, 8c, and 8d. Guilt, however, was not found to have a significant, negative effect on counterproductive work behavior directed at the offending customer (standardized coefficient = 0.03, t = 0.51, p < 0.61), the employee’s organization (standardized coefficient = 0.09, t = 1.75, p < 0.05, one-tailed), the employee’s supervisor (standardized coefficient = 0.16, t = 3.06, p < 0.01), the employee’s coworkers (standardized coefficient = 0.15, t = 2.35, p < 0.05), or customers other than the offending customer (standardized coefficient = -0.03, t = -1.07, p < 0.29). Thus, support was not found for Hypotheses 7, 9, 9b, 9c, and 9d. It should be noted that guilt did have significant effects on counterproductive work behavior directed at the employee’s organization, supervisor, and coworker, albeit in the opposite direction than hypothesized. Finally, Hypothesis 10 predicted that job satisfaction would have a significant, negative effect on counterproductive work behavior directed at the offending customer (Hypothesis 10a), the employee’s organization (Hypothesis 10b), the employee’s supervisor (Hypothesis 10c), the employee’s coworkers (Hypothesis 10d), and customers other than the offending customer (Hypothesis 10e). The results of the effect of job satisfaction on counterproductive work 141 Table 26: Total Effects of Anger and Guilt Predicting CWB Dependent Variable CWB – Direct CWB – Organization Independent Variable ˆ ˆ ˆ ˆ Intercept ( β 0 ) CO ( γˆ01 ) NA ( γˆ02 ) ˆ ANG ( β1 ) ˆ GLT ( β 2 ) ˆ TIME ( β3 ) R2 Independent Variable ˆ Intercept ( β 0 ) CO ( γˆ01 ) NA ( γˆ02 ) ˆ ANG ( β1 ) ˆ GLT ( β 2 ) ˆ TIME ( β3 ) R2 Independent Variable ˆ Intercept ( β 0 ) CO ( γˆ01 ) NA ( γˆ02 ) ˆ ANG ( β1 ) ˆ GLT ( β 2 ) ˆ TIME ( β3 ) R2 βu SE t -1.13 0.03 35.50** -0.01 0.04 -0.23 0.01 0.03 0.13 ˆ βs βu SE t 1.89 0.07 26.82** -0.02 -0.43 0.13 -3.21** -0.45 0.43 0.02 0.19 0.14 1.40 0.18 0.05 2.89** 0.18 0.14 0.07 1.97^ 0.10 0.02 0.04 0.51 0.03 0.12 0.07 1.75^ 0.09 0.02 0.02 1.15 0.02 -0.00 0.07 -0.04 0.00 βs 49.54% ˆ βu 18.10% CWB – Supervisor SE t 1.12 0.03 37.93** -0.03 0.03 -0.79 0.06 0.03 0.10 ˆ β ˆ βu CWB – Coworkers ˆ βs SE t 1.14 0.03 35.98** -0.08 -0.03 0.04 -0.80 -0.06 1.79^ 0.14 0.05 0.04 1.30 0.10 0.03 3.90** 0.17 0.13 0.03 3.68** 0.18 0.09 0.03 3.06** 0.16 0.10 0.04 2.35* 0.15 0.00 0.02 0.05 0.00 0.00 0.02 0.17 0.00 s 64.83% 52.10% CWB – Other Customers ˆ βs ˆ βu SE t 1.07 0.03 39.69** -0.03 0.02 -1.56 -0.07 0.01 0.02 0.33 0.02 0.08 0.03 2.58* 0.13 -0.02 0.02 -1.07 -0.03 -0.11 0.08 -1.39 -0.11 65.76% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. CO = customer orientation, NA = negative affectivity, ANG = anger, GLT = guilt, TIME = start time, and CWB = counterproductive work behaviors. 142 behaviors after controlling for the direct effects of anger and guilt are presented in Table 27. As expected, job satisfaction had a significant, negative effect on counterproductive work behavior directed at the offending customer (standardized coefficient = -0.08, t = -2.83, p < 0.01) and counterproductive work behavior directed at the employee’s organization (standardized coefficient = -0.16, t = -3.34, p < 0.01). Job satisfaction, however, was not found to have a significant, negative effect on counterproductive work behavior directed at the employee’s supervisor (standardized coefficient = -0.02, t = -0.85, p < 0.40), the employee’s coworkers (standardized coefficient = -0.03, t = -1.03, p < 0.31), or customers other than the offending customer (standardized coefficient = 0.04, t = 2.13, p < 0.05). Thus, support was found for Hypotheses 10a and 10b, whereas Hypotheses 10c, 10d, and 10e were not supported. Mediating Effects Following Baron and Kenny (1986), a series of three regressions were estimated to test all partial mediation hypotheses. The first regression estimated the effect of the independent variable on the dependent variable. This first equation was followed by regressing the mediator variable on the independent variable and then the effect of the mediator on the dependent variable after controlling for the independent variable was estimated. The results of these regressions were followed with Sobel (1982) tests to determine the statistical significance of the hypothesized indirect effects. All equations are provided in Appendix F. Hypothesis 5a predicted that anger would partially mediate the relationship between customer interpersonal injustice and job satisfaction while Hypothesis 5b predicted that guilt would partially mediate the relationship between customer interpersonal injustice and job satisfaction. To test these hypotheses, a regression predicting the total effect of customer interpersonal injustice on job satisfaction without the presence of the mediators (anger and guilt) 143 was estimated first. Next, the mediators of anger and guilt were regressed on customer interpersonal injustice. Finally, a regression of the effects of the mediators (anger and guilt) on job satisfaction controlling for customer interpersonal injustice was estimated. As shown by the standardized coefficient (t-value) of -0.16 (t = -3.75, p < 0.01) in Table 10, customer interpersonal injustice has a significant, negative effect on job satisfaction in the absence of anger and guilt. Customer interpersonal injustice also had significant effects on anger and guilt as discussed in Hypotheses 1 and 2 and shown in Tables 23 and 24. Additionally, anger (standardized coefficient = -0.23, t = -5.18, p < 0.01) and guilt (standardized coefficient = 0.06, t = -1.96, p < 0.05, one-tailed) both had a significant effect on job satisfaction in the presence of each other and customer interpersonal injustice (see Table 28). Finally, the results of Sobel (1982) tests, presented in Table 29, showed that the indirect effects of customer interpersonal injustice on job satisfaction through anger (Sobel’s Z = -3.82, p < 0.01) and through guilt (Sobel’s Z = -1.87, p < 0.05, one-tailed) were statistically significant. These results, when taken together with the comparison of the significant standardized effect of customer interpersonal injustice on job satisfaction to the insignificant effect of customer interpersonal injustice on job satisfaction after controlling for anger and guilt (standardized coefficient = -0.04, t = -0.97, p < 0.34), provide support for Hypotheses 5a and 5b. To test the hypotheses that job satisfaction partially mediates the relationship between anger and counterproductive work behaviors (Hypotheses 11a, 11b, 11c, 11d, and 11e) as well as the relationship between guilt and counterproductive work behaviors (Hypotheses 12a, 12b, 12c, 12d, and 12e) a similar series of regressions was estimated. First, the respective counterproductive work behaviors were regressed on anger and guilt. Job satisfaction was then 144 Table 27: Anger, Guilt, and Job Satisfaction Predicting CWB Dependent Variable CWB – Direct CWB – Organization Independent Variable ˆ ˆ ˆ ˆ Intercept ( β 0 ) CO ( γˆ01 ) NA ( γˆ02 ) ˆ ANG ( β1 ) ˆ GLT ( β 2 ) ˆ JS ( β3 ) ˆ TIME ( β 4 ) R2 Independent Variable ˆ Intercept ( β 0 ) CO ( γˆ01 ) NA ( γˆ02 ) ˆ ANG ( β1 ) ˆ GLT ( β 2 ) ˆ JS ( β3 ) ˆ TIME ( β 4 ) R2 Independent Variable ˆ Intercept ( β 0 ) CO ( γˆ01 ) NA ( γˆ02 ) ˆ ANG ( β1 ) ˆ GLT ( β 2 ) ˆ JS ( β3 ) ˆ TIME ( β 4 ) R2 βu SE t 1.13 0.03 35.48** -0.02 0.04 -0.55 0.01 0.03 0.12 ˆ βs βu SE t 1.89 0.07 26.82** -0.04 -0.43 0.13 -3.24** -0.45 0.37 0.02 0.18 0.14 1.32 0.17 0.04 2.68** 0.16 0.06 0.06 0.95 0.04 0.01 0.03 0.44 0.01 0.11 0.07 1.51 0.08 -0.05 0.02 -2.83** -0.08 -0.18 0.05 -3.34** -0.16 0.02 0.02 1.44 0.02 0.01 0.07 0.15 0.00 βs 49.84% ˆ βu 27.47% CWB – Supervisor SE 1.12 0.03 37.93** -0.02 0.03 -0.69 0.06 0.03 0.10 ˆ βs t ˆ βu CWB – Coworkers ˆ βs SE t 1.14 0.03 35.81** -0.05 -0.04 0.03 -1.41 -0.08 1.99* 0.14 -0.03 0.02 -1.37 -0.06 0.03 3.81** 0.17 0.12 0.03 3.51** 0.17 0.10 0.03 3.13** 0.18 0.10 0.04 2.37* 0.15 -0.01 0.01 -0.85 -0.02 -0.02 0.02 -1.03 -0.03 0.00 0.02 0.14 0.00 -0.00 0.02 -0.11 0.00 65.03% 55.50% CWB – Other Customers ˆ βu SE t 1.07 0.03 39.72** -0.03 0.02 -1.53 -0.07 0.00 0.02 0.24 0.00 0.08 0.03 2.64** 0.13 -0.01 0.02 -0.67 -0.02 0.02 0.01 2.13* 0.04 -0.10 0.08 -1.36 -0.10 ˆ βs 67.71% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. 145 regressed on anger and guilt. Third, regressions estimating the effect of job satisfaction on counterproductive work behaviors controlling for anger and guilt were conducted. Table 26 provides the results of anger and guilt predicting counterproductive work behaviors while Table 25 provides the results of anger and guilt predicting job satisfaction. As shown in the results for the main effects hypotheses, anger had a significant effect on all five counterproductive work behaviors and job satisfaction. Guilt on the other hand, while having a significant effect on job satisfaction, only had a significant effect on counterproductive work Table 28: Mediating Effects of Anger and Guilt Independent Variable ˆ βu Mediating Effects – Step 1: CII Predicting JS ˆ 3.27 Intercept ( β 0 ) CO ( γˆ01 ) 0.42 NA ( γˆ02 ) -0.32 ˆ -0.17 CII ( β1 ) ˆ -0.06 TIME ( β 2 ) 9.98% R2 Independent Variable ˆ βu t 0.06 55.68** 0.11 3.85** 0.51 0.14 -2.24* -0.36 0.05 -3.75** -0.16 0.06 -1.06 -0.03 SE t ˆ βs Mediating Effects – Step 2: CII, ANG, and GLT Predicting JS ˆ 3.27 0.06 Intercept ( β0 ) CO ( γˆ01 ) 0.42 0.11 NA ( γˆ02 ) R2 ˆ CII ( β1 ) ˆ ANG ( β 2 ) ˆ GLT ( β3 ) ˆ TIME ( β 4 ) ˆ βs SE 55.62** 3.96** 0.51 -0.27 0.14 -1.96^ -0.30 -0.04 0.05 -0.97 -0.04 -0.29 0.06 -5.18** -0.23 -0.07 0.04 -1.96^ -0.06 -0.01 0.05 -0.16 0.00 23.84% Note: *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. CO = customer orientation, NA = negative affectivity, CII = customer interpersonal injustice, TIME = start time, ANG = anger, and GLT = guilt. 146 Table 29: Sobel Test Results for the Mediating Effects of Anger and Guilt Variable Mediation Effect Sobel’s Z Anger -0.09 -3.82** Guilt -0.01 -1.87^ Note: The mediation effect (ab) for anger was calculated as (γ10 from Table 23 * γ20 from Table 28 – Step 2). The mediation effect (ab) for guilt was calculated as (γ10 from Table 24 * γ30 from Table 28 - Step 2). All coefficients used in the Sobel test calculation were unstandardized. *Significant at p < 0.05, **significant at p < 0.01, ab ^significant at p < 0.05 one-tailed. Sobel’s Z was calculated as z = , 2 2 2 2 b s a + a sb where s refers to the standard error of the corresponding parameter. behaviors aimed at the employee’s organization, the employee’s supervisor, and the employee’s coworkers. Moreover, the results of the effects of anger, guilt, and job satisfaction predicting counterproductive work behaviors (see Table 27) reveals that job satisfaction is significantly related to counterproductive work behavior directed at the offending customer (standardized coefficient = -0.08, t = -2.83, p < 0.01), the employee’s organization (standardized coefficient = 0.16, t = -3.34, p < 0.01), and other customers (standardized coefficient = 0.04, t = 2.13, p < 0.05). After controlling for anger and guilt, however, job satisfaction was not significantly related to counterproductive work behavior directed at the employee’s supervisor (standardized coefficient = -0.02, t = -0.85, p < 0.40) and the employee’s coworkers (standardized coefficient = -0.03, t = -1.03, p < 0.31). Lastly, the results of the Sobel (1982) tests, shown in Table 30, show that the only significant indirect effects through job satisfaction were from anger to counterproductive work behavior directed at the offending customer (Sobel’s Z = 2.55, p < 0.05), the employee’s organization (Sobel’s Z = 2.90, p < 0.01), and customers other than the offending customer (Sobel’s Z = -2.01, p < 0.05). These results, along with the effects of anger, after controlling for guilt and job satisfaction, on counterproductive work behavior directed at the offending customer (standardized coefficient = 0.16, t = 2.68, p < 0.01), the employee’s 147 organization (standardized coefficient = 0.04, t = 0.95, p < 0.35), and other customers (standardized coefficient = 0.13, t = 2.64, p < 0.01), provides support for Hypotheses 11a, 11b (full mediation), and 11e. Hypotheses 11c, 11d, 12a, 12b, 12c, 12d, and 12e, however, were not supported. Table 30: Sobel Test Results for the Mediating Effect of Job Satisfaction Mediated Path Mediation Effect Sobel’s Z Anger → CWB – Direct Anger → CWB – Organization Anger → CWB – Supervisor Anger → CWB – Coworkers Anger → CWB – Other Customers 0.01 0.06 0.00 0.01 -0.01 2.55* 2.90** 0.84 1.01 -2.01* Guilt → CWB – Direct Guilt → CWB – Organization Guilt → CWB – Supervisor Guilt → CWB – Coworkers Guilt → CWB – Other Customers 0.00 0.01 0.00 0.00 0.00 1.56 1.63 0.77 0.90 -1.41 Note: The mediation effect (ab) of Job Satisfaction for Anger → CWB was calculated as (γ 10 from Table 25 * γ30 from Table 27). The mediation effect (ab) of Job Satisfaction for Guilt → CWB was calculated as (γ20 from Table 25 * γ30 from Table 27). All coefficients used in the Sobel test calculation were unstandardized. *Significant at p < 0.05, **significant at p < 0.01, ^significant at p < 0.05 one-tailed. Sobel’s Z was ab , where s refers to the standard error of the calculated as z = 2 2 2 2 b s a + a sb corresponding parameter. CWB = counterproductive work behaviors. Cross-Level Moderating Effects Hypothesis 13 predicted that an employee’s customer orientation would moderate the relationship between customer interpersonal injustice and the emotions of anger (Hypothesis 13a) and guilt (Hypothesis 13b). To test this hypothesis, customer orientation was added as a Level 2 predictor of the intercept and slope of the relationships between customer interpersonal injustice and anger and customer interpersonal injustice and guilt. Table 23 and Table 24 show 148 that customer orientation did not significantly moderate the customer interpersonal injustice to anger relationship (standardized coefficient = -0.21, t = -1.50, p < 0.14), but, did significantly moderate the customer interpersonal injustice to guilt relationship (standardized coefficient = 0.16, t = 1.66, p < 0.05, one-tailed). Thus, Hypothesis 13b was supported whereas Hypothesis 13a was not supported. DISCUSSION Using Affective Events Theory (Weiss and Cropanzano 1996), a study was conducted that examined the consequences of customer interpersonal injustice on the emotions, attitudes, and behaviors of customer contact employees over 10 work days. This study explored how the behavior by customers which was lacking dignity, impolite, and disrespecting toward customer contact employees elicited the emotional responses of anger and guilt and how these emotional responses drove counterproductive work behaviors both directly and indirectly through job satisfaction. Further, this study included how the employee dispositional trait of customer orientation affected employee emotional responses to customer interpersonal injustice. The results of this study provide a more complete understanding of how customers can negatively affect employee performance through affecting employee emotions, attitudes, and behaviors. Key findings are discussed below. The results of this study revealed the detrimental ripple effect that interpersonally unjust treatment by customers has on customer contact employees. To begin, employees responded to interpersonally unjust treatment from customers by feeling both demeaned (anger) as well as a sense of tension, regret, and remorse regarding their behavior (guilt). This finding is in accordance with research that has demonstrated the link between customer interpersonal injustice and negative emotions as a whole (cf. Yang and Diefendorff 2009), however, this research 149 demonstrates customer interpersonal injustice as an antecedent to the separate negative emotions of anger and guilt. The results also showed that the individual emotional responses of anger and guilt reduced job satisfaction and together fully mediated the effect of customer interpersonal injustice on job satisfaction. This suggests that the effect of customer interpersonal injustice on job satisfaction is entirely indirect through anger and guilt. In addition to reducing job satisfaction, the other direct result of anger and guilt was increased counterproductive work behaviors. In particular, employee anger directly increased all five measured counterproductive work behaviors (i.e. counterproductive work behaviors directed at the offending customer, the employee’s organization, the employee’s supervisor, the employee’s coworkers, and customers other than the offending customer). Guilt, on the other hand, was not found to reduce counterproductive work behaviors by motivating employees to repair the situation, but, was a significant motivator of counterproductive work behaviors directed at the employee’s organization, the employee’s supervisor, and the employee’s coworkers. Additionally, part of the effect of anger on counterproductive work behavior directed at the offending customer, the employee’s organization, and customers other than the offending customer was mediated by job satisfaction. This finding is interesting because it indicates that the antecedents to counterproductive work behaviors vary depending upon the target. Finally, the employee’s predisposition to meet customer needs was not found to reduce the amount of anger felt by employees following interpersonally unjust treatment by customers but was found to found to increase the amount of guilt. Research Implications The results of this study have both broad and specific implications for Marketing academics. First, this study answers the call for research that investigates the subjective, 150 naturally occurring justice-related experiences of employees (Rupp 2011). Through the collection of responses via an ESM design and the use of affective events theory, this study increases the understanding of the justice experiences of employees over time and further clarifies how employees experience their lives while working (Rupp 2011). By doing this, however, this study shows that the current understanding of how customer contact employees perceive and react to the events in their workplace is incomplete and further research into the daily justice events of employees is warranted. The results of this study also speak to the development of a sustainable competitive advantage within service firms. Following Bharadwaj, Varadarajan, and Fahy (1993), service firms can achieve competitive positional advantages through the use of unique resources and skills to provide superior customer value. Central to the provision of customer value is the customer contact employee and, as a result, the role that the customer contact employee plays in building sources of competitive advantage, such as relationships with customers and brand equity, for the service firm cannot be overstated. For example, customer trust in employee behavior leads to increased perceptions of service quality and value (Brodie, Whittome, and Brush 2009), trust in service-based relationships can reduce customer uncertainty and vulnerability (Berry 1995), and the relationships customer contact employees form with customers are key to exceeding customer expectations and generating goodwill (Parasuraman, Berry, and Zeithaml 1991). Moreover, employee performance plays the most critical role in building brand equity (Berry 2000). This study shows how the customer, through negatively affecting customer contact employee performance, can damage marketing efforts to build to a sustainable competitive advantage. 151 With regard to specific implications, several sets of findings deserve further discussion. First, the findings that customer interpersonal injustice causes customer contact employees to react emotionally with anger and guilt and that these emotions were found to diminish job satisfaction and mediate the effect of customer interpersonal injustice on job satisfaction are worth noting. This result further clarifies the understanding of how customer interpersonal injustice affects the daily emotions and attitudes of employees and represents a contribution by identifying the relationships between customer interpersonal injustice, two individual emotions, one other-directed and one self-directed (de Rivera 1977), and the work attitude of overall job satisfaction. By showing that employees feel anger and guilt, two distinct emotions with different action tendencies, in response to interpersonally unjust treatment from customers, this set of findings presents an opportunity for researchers to investigate other possible emotions elicited by customer interpersonal injustice, such as pride and shame, and their resulting effect on job attitudes and job behaviors. In addition, the result that the emotions of anger and guilt fully mediate the effect of customer interpersonal injustice on job satisfaction in customer contact employees is important as it provides empirical support for the work event to affective reactions to work attitudes chain proposed by affective events theory (Weiss and Cropanzano 1996). Another set of important findings for academics and practitioners are the effects of anger and guilt on counterproductive work behaviors and the mediating effect of job satisfaction. The results showed that anger increased all counterproductive work behaviors (CWBD, CWBO, CWBS, CWBC, and CWBOC) while guilt was only found to increase certain counterproductive work behaviors, counterproductive work behaviors directed at the employee’s organization, the employee’s supervisor, and the employee’s coworkers. This is important as it demonstrates that customer interpersonal injustice leads to more than emotional labor (Grandey et al. 2004), 152 revenge (Skarlicki et al. 2008), and organization directed counterproductive work behavior (Yang and Diefendorff 2009). Indeed, the results confirm the “spillover effects” from customers as a source of justice to the employee’s organization and provide empirical support for “spillover effects” from customer interpersonal injustice to the employee’s supervisor, the employee’s coworkers, and customers other than the offending customer. Moreover, the positive direct effects of guilt on certain counterproductive behaviors, while counter than hypothesized, provide empirical evidence for the multiple component view of guilt (Caprara, Barbaranelli, Pastorelli, Cermak, and Rosza 2001) when considered with the use of marketing controls to influence employee behavior in service organizations (Jaworski 1988). Under the view that guilt has multiple components, guilt is proposed to have both a need for reparation component, which is negatively related to aggression, and a fear of punishment component, which is positively related to aggression (Caprara et al. 2001). Consequently, an employee who experiences guilt and focuses on the possible punishment for violating the performance standard prescribed by marketing controls can experience further feelings of anguish and distress that may ultimately result in aggression (Caprara et al. 2001). The results showed that employees may be concerned about punishment resulting from negatively evaluated interpersonal behaviors and present an opportunity for further research into the effects of marketing controls on employee responses to customer interpersonal injustice. Furthermore, the mediating effect of job satisfaction on the anger to counterproductive work behavior relationship contributes to the literature by demonstrating how both attitudes and emotions are significant antecedents to counterproductive work behavior. Researchers have typically approached the explanation of counterproductive work behaviors using either attitudes or emotions in the past. For example, Spector and Fox (2002) modeled counterproductive work 153 behavior and organizational citizenship behavior as results of employees’ emotional responses to the work environment and Yang and Diefendorff (2009) investigated the relationship between customer interpersonal injustice, negative emotions in general, and counterproductive work behaviors. Further, Judge et al. (2006) investigated the effect of interpersonal justice, trait and state hostility, and job satisfaction on workplace deviance while Mount, Ilies, and Johnson (2006) explored the relationships between personality traits, job satisfaction, and counterproductive work behaviors. Using affective events theory (Weiss and Cropanzano 1996), this study showed that counterproductive work behaviors within an individual can result from coping strategies necessitated by negative emotions or from thought out and well considered decisions. A post hoc examination of the pattern of direct and indirect effects of anger and guilt explicates the complexity of how work emotions and work attitudes interact to influence counterproductive work behavior. For instance, anger has significant direct effects on all five forms of counterproductive work behavior (CWBD, CWBO, CWBS, CWBC, CWBOC), and significant indirect effects on CWBD, CWBO, and CWBOC through job satisfaction. This suggests that counterproductive work behavior directed at the employee’s supervisor and coworkers results solely from coping strategies whereas counterproductive work behavior directed at the offending customer, the employee’s organization, and customers other than the offending customer is driven by emotions and attitudes. In addition, guilt has significant direct effects on counterproductive work behavior directed at the organization, the employee’s supervisor, and the employee’s coworkers, but, does not have any significant indirect effects on counterproductive work behaviors through job satisfaction. As a result, it appears that the 154 counterproductive work behaviors caused by guilt are emotional reactions directed at the sources able to punish the individual through marketing controls (Jaworski 1988). Finally, much of the customer orientation literature espouses the benefits of customer orientation, such as increased employee service performance (Brady and Cronin 2001), increased job satisfaction, organizational commitment, and organizational citizenship behaviors (Donavan et al. 2004), and increased usage of the emotion regulation strategy of deep acting (Allen, Pugh, Grandey, and Groth 2010). The results of this study, however, demonstrate that customer orientation has the potential to negatively affect employee performance through increasing counterproductive work behaviors. While this result may seem counterintuitive at first, this occurs as customer orientation positively moderates the customer interpersonal injustice to guilt relationship. Thus, customer oriented employees may experience more guilt and may focus on the possible punishment, resulting in increased aggressive behavior (Caprara et al. 2001). As a result, this represents an important step to establishing the boundary conditions of the positive effects of customer orientation and for managers this follows Homburg, Muller, and Klarmann (2011) in arguing for an optimal level of customer orientation in employees. Managerial Implications For managers, the collective results of this study provide a unique view of how customer contact employees respond to rude treatment from customers and how customers can affect the success of the company through influencing employee behavior. It should be noted that the finding that interacting with customers is stressful for customer contact employees and may cause customer contact employees to harbor feelings of antipathy and hostility is not unfamiliar (cf. Hollander 1985). Research into the specific emotional and cognitive processes customer contact employees undertake in response to rude treatment from customers, however, has been 155 relatively limited. In the broadest sense, this study reemphasizes to managers that customers are a source of stress to customer contact employees and adds that this stress causes customer contact employees to engage in counterproductive work behaviors as a result of coping with negative emotions and reduced job satisfaction. Consequently, managers need to undertake efforts to mitigate the ripple effect that rude customers have on customer contact employee emotions, attitudes, and behaviors. The results regarding the relationships between customer interpersonal injustice, anger, guilt, job satisfaction, and counterproductive work behaviors suggest that managers need to limit the amount of customer interpersonal injustice customer contact employees have to endure as well as reduce the intensity of emotional responses by customer contact employees to customer interpersonal injustice. For example, managers could empower employees to handle customer complaints to prevent customers from becoming irate (Hart, Heskett, and Sasser 1990) and have procedures in place to elevate the rude customer to a manager if necessary. These same strategies can also help in reducing intensity of the emotional response to rude customers by removing the customer contact employee from the source of the stress. Moreover, the fostering of effective emotion-focused coping strategies, such as humor and forgiveness, and problemfocused coping strategies, such as planning, in employees can reduce the amount of stress perceived by employees as well as the resulting counterproductive work behaviors. Similarly, the provision of realistic expectations of customer interactions to employees and the creation of a workplace with supervisor and coworker support (Grandey 2000) can also help managers temper the effect of rude customers on customer contact employees. Perhaps the most intriguing finding of the study with concern to managers is the result that customer orientation increased the amount of guilt felt by customer contact employees and 156 feelings of guilt were positively related to counterproductive work behaviors directed at the employee’s organization, the employee’s supervisor, and the employee’s coworkers. As discussed previously, this provides evidence for the existence of the fear of punishment component for guilt and indicates that as a customer contact employee ruminates on expected punishment for the negatively, self-evaluated behavior the employee may engage in aggressive behaviors as an emotion-focused coping mechanism. This is particularly relevant and possibly alarming to managers since the use of punishment and the threat of punishment is a common occurrence in organizations (Arvey and Ivancevich 1980). As a result, managers may walk a tightrope when it comes to punishing employees in that too little punishment may not have the desired effect, yet too much punishment may only make matters worse when it comes to employee guilt. This suggests that managers may need to rely more upon methods such as job enrichment (Herzberg 1968) and less upon punishment to motivate employees. Limitations As with any research, this study does have several limitations. First, all constructs were measured using self-reports and consequently may suffer from artificial covariance (Podsakoff, Mackenzie, Lee, and Podsakoff 2003). Steps were taken both in the design of the study as well as in the analyses to alleviate the harmful effects of this limitation. In designing the study, the question order for the daily studies was randomized to reduce item priming effects and, while anonymity was not possible due to the need to provide the incentive for completing the study, participants were assured that there were no correct answers and encouraged to answer all questions as honestly as possible to reduce any evaluation apprehension. Established scales were also used to reduce item complexity and ambiguity. Regarding the analyses, all daily measures were centered at each individual’s mean (Hoffman, et al. 2000) which removes the between- 157 individual variance and thus removes the effect of individual response biases. Additionally, mood state was statistically controlled for as negative affectivity and survey start time were included in all analyses. Table 31: Potential Sources of Common Method Bias and Remedies Taken Potential Source Remedy Taken Common rater Daily measures were centered at each individual’s mean Item priming Question order for all daily surveys was randomized Social desirability Participants were assured that there were no correct answers and encouraged to answer all questions as honestly as possible Item ambiguity Constructs were measured using established scales Mood state Negative affectivity and survey start time were included as control variables Recall Interval contingent experience sampling methodology was used Omitted variables Alternative explanations Data was gathered longitudinally and both within- and between effects were incorporated Second, the use of an interval contingent experience sampling methodology with a signal collected responses from employees after the end of their daily work shift. This design does not allow for the collection of responses immediately following interpersonally unjust treatment and may be subject to recall biases, which may influence how an employee remembers the events of that particular shift. One strategy to eliminate the potential recall biases is to use an event contingent experience sampling methodology, where employees respond every time they are treated interpersonally unjust by a customer. Nevertheless, an event contingent experience sampling methodology is intrusive and burdensome in nature (Wheeler and Reis 1991) and requires employees to stop work to complete a survey after each event. While this is not only impractical for many employees, it can potentially alter the relationships between customer 158 interpersonal injustice and employee emotions, attitudes, and behaviors as stopping work to complete a survey not only disrupts the natural flow of employee emotions, attitudes, and behaviors, but, also provides time for rumination, which has been shown to increase aggression (Bushman 2002). Therefore, the use of an interval contingent experience sampling methodology presents the more appropriate means to collect responses. Finally, while this study attempted to demonstrate causation through temporal precedence and covariation, alternative explanations were not tested. As such, omitted variables may have biased the results and failing to rule out alternative explanations by testing different configurations of the proposed model weakens claims for causal inference (Cook and Campbell 1979). The likelihood of alternative explanations due to omitted variables was reduced by gathering data longitudinally and incorporating both within- and between- effects (Rindfleisch, Malter, Ganesan, and Moorman 2008). Further, gathering data longitudinally increases confidence regarding conclusions about causal relationships among self-reported variables (Spector 1994). Still, alternative explanations may be possible and thus future researchers should investigate different explanations using a methodology, such as experimental design, which allows for greater confidence in establishing causation. 159 APPENDICES 160 APPENDIX E Essay 2 Survey Items Daily Measures Customer Interpersonal Injustice To what extent today did customers 1. Refuse to listen to you 2. Interrupt you: Cut you off mid sentence 3. Make demands that you could not deliver 4. Yell at you Anger Please indicate to what extent you felt the following feelings and emotions today at work 1. I felt mad 2. I felt angry 3. I felt like yelling at someone 4. I felt furious 5. I felt like breaking things Guilt Please indicate to what extent you felt the following feelings and emotions today at work 1. I felt remorse, regret 2. I felt tension about something I did 3. I could not stop thinking about something bad I did 4. I felt like apologizing, confessing 5. I felt bad about something I did Job Satisfaction How do you feel about your overall job right now? 1. I am enthusiastic about my work 2. I feel fairly satisfied with my present job 3. Each minute at work seems like it will never end (reverse scored) 4. I am finding real enjoyment in my work 5. I consider my job rather unpleasant (reverse scored) Counterproductive Work Behavior Directed at the Offending Customer Please indicate to what extent you did the following behaviors at work today 1. Get revenge on the offending customer(s) 2. Get back at the offending customer(s) 3. Deliberately mess things up for the offending customer(s) 4. Deliberately mistreat the offending customer(s) 161 Counterproductive Work Behavior Directed at the Employee’s Organization Please indicate to what extent you did the following behaviors at work today 1. Take an additional or longer break than is acceptable at your workplace 2. Try to look busy while wasting time 3. Put little effort into your work 4. Spend too much time fantasizing or daydreaming instead of working 5. Spend time on personal matters while at work Counterproductive Work Behavior Directed at the Employee’s Supervisor Please indicate to what extent you did the following behaviors at work today 1. Purposely neglect to follow your supervisor's instructions 2. Act rudely toward your supervisor 3. Spread unconfirmed rumors about your supervisor 4. Do something to get your supervisor in trouble 5. Encourage your coworkers to get back at your supervisor 6. Say something hurtful to your supervisor 7. Curse at your supervisor 8. Get back at your supervisor 9. Deliberately mess things up for your supervisor Counterproductive Work Behavior Directed at the Employee’s Coworkers Please indicate to what extent you did the following behaviors at work today 1. Say something hurtful to a coworker 2. Curse at a coworker 3. Publicly embarrass a coworker 4. Spread unconfirmed rumors about a coworker 5. Get back at a coworker 6. Deliberately mess things up for a coworker Counterproductive Work Behavior Directed at Customers Other than the Offending Customer Please indicate to what extent you did the following behaviors at work today 1. Get revenge on a customer who did not offend you 2. Get back at a customer who did not offend you 3. Deliberately mess things up for a customer who did not offend you 4. Deliberately mistreat a customer who did not offend you One-Time Measures Customer Orientation Overall, how do you feel about serving customers? 1. I try to help customers achieve their goals 2. I achieve my own goals by helping customers 3. I keep the best interests of the customer in mind 162 Negative Affectivity How well does the word or phrase describe you in general? 1. Distressed 2. Upset 3. Guilty 4. Scared 5. Hostile 6. Irritable 7. Ashamed 8. Nervous 9. Jittery 10. Afraid 163 APPENDIX F Essay 2 HLM Equations Hierarchical Linear Model Predicting Anger Level 1 Equation ANGij = β0j + β1j(CII) + β2j(TIME)+ rij Level 2 Equations β0j = γ00 + γ01(CO) + γ02(NA) + u0j β1j = γ10 + γ11(CO) + γ12(NA) + u1j β2j = γ20 + u2j Hierarchical Linear Model Predicting Guilt Level 1 Equation GLTij = β0j + β1j(CII) + β2j(TIME)+ rij Level 2 Equations β0j = γ00 + γ01(CO) + γ02(NA) + u0j β1j = γ10 + γ11(CO) + γ12(NA) + u1j β2j = γ20 + u2j Hierarchical Linear Model Predicting Job Satisfaction – Main and Mediating Effects Total Effects Level 1 Equation JSij = β0j + β1j(ANG) + β2j(GLT) + β3j(TIME)+ rij 164 Level 2 Equations β0j = γ00 + γ01(CO) + γ02(NA) +u0j β1j = γ10 + u1j β2j = γ20 + u2j β3j = γ30 + u3j Mediating Effects Step 1 Level 1 Equation JSij = β0j + β1j(CII) + β2j(TIME) + rij Level 2 Equations β0j = γ00 + γ01(CO) + γ02(NA) + u0j β1j = γ10 + u1j β2j = γ20 + u2j Step 2 Level 1 Equation JSij = β0j + β1j(CII) + β2j(ANG) + β3j(GLT) + β4j(TIME) + rij Level 2 Equations β0j = γ00 + γ01(CO) + γ02(NA) + u0j 165 β1j = γ10 + u1j β2j = γ20 + u2j β3j = γ30 + u3j β4j = γ40 + u4j Hierarchical Linear Model Predicting Counterproductive Work Behavior – Main and Mediating Effects Anger and Guilt Predicting Counterproductive Work Behavior Level 1 Equation CWBij = β0j + β1j(ANG) + β2j(GLT) + β3j(TIME) + rij Level 2 Equations β0j = γ00 + γ01(CO) + γ02(NA) + u0j β1j = γ10 + u1j β2j = γ20 + u2j β3j = γ30 + u3j Anger, Guilt, and Job Satisfaction Predicting Counterproductive Work Behavior Level 1 Equation CWBij = β0j + β1j(ANG) + β2j(GLT) + β3j(JS) + β4j(TIME) + rij Level 2 Equations β0j = γ00 + γ01(CO) + γ02(NA) + u0j 166 β1j = γ10 + u1j β2j = γ20 + u2j β3j = γ30 + u3j β4j = γ40 + u4j 167 REFERENCES 168 REFERENCES Adams, J. Stacy (1965), "Inequity in Social Exchange," in Advances in Experimental Social Psychology, Leonard Berkowitz, Ed. Vol. 2. New York, NY: Academic Press. ---- (1963), "Toward an Understanding of Inequity," Journal of Abnormal and Social Psychology, 67 (5), 422-36. Ajzen, Icek and Martin Fishbein (2005), "The Influence of Attitudes on Behavior," in The Handbook of Attitudes, Dolores Albarracin and Blair T. Johnson and Mark P. Zanna, Eds. Mahwah, NJ: Lawrence Erlbaum Associates. Allen, Joseph A., S. Douglas Pugh, Alicia A. Grandey, and Markus Groth (2010), "Following Display Rules in Good or Bad Faith?: Customer Orientation as a Moderator of the Display RuleEmotional Labor Relationship " Human Performance, 23 (2), 101-15. Applebaum, Steven H., Giulio David Iaconi, and Albert Matousek (2007), "Positive and Negative Deviant Workplace Behaviors: Causes, Impacts, and Solutions," Corporate Governance, 7 (5), 586-98. Arvey, Richard D. and John M. Ivancevich (1980), "Punishment in Organizations: A Review, Propositions, and Research Suggestions," Academy of Management Review, 5 (1), 123-32. Ashforth, Blake E. and Ronald H. Humphrey (1993), "Emotional Labor in Service Roles: The Influence of Identity," Academy of Management Review, 18 (1), 88-115. Bagozzi, Richard P., Mahesh Gopinath, and Prashanth U. Nyer (1999), "The Role of Emotions in Marketing," Journal of the Academy of Marketing Science, 27 (2), 184-206. Baron, Reuben M. and David A. Kenny (1986), "The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations," Journal of Personality and Social Psychology, 51 (6), 1173-82. Bateson, John E.G. (1985), "Perceived Control and the Service Encounter," in The Service Encounter: Managing Employee/Customer Interaction in Service Businesses, John A. Czepiel and Michael R. Solomon and Carol F. Surprenant, Eds. Lexington, MA: D.C. Heath and Company. Bennett, Rebecca J. and Sandra L. Robinson (2000), "Development of a Measure of Workplace Deviance," Journal of Applied Psychology, 85 (3), 349-60. 169 ---- (2003), "The Past, Present, and Future of Workplace Deviance Research," in Organizational Behavior: The State of the Science, Jerald Greenberg, Ed. Mahwah, MA: Lawrence Erlbaum Associates. Berry, Christopher M., Deniz S. Ones, and Paul R. Sackett (2007), "Interpersonal Deviance, Organizational Deviance, and Their Common Correlates: A Review and Meta-Analysis " Journal of Applied Psychology, 92 (2), 410-24. Berry, Leonard L. (2000), "Cultivating Service Brand Equity," Journal of the Academy of Marketing Science, 28 (1), 128-37. ---- (1995), "Relationship Marketing of Services - Growing Interest, Emerging Perspectives," Journal of the Academy of Marketing Science, 23 (4), 236-45. Berta, Dina (2003), "Employee Behavior Study Alarms Operators," Nation's Restaurant News, 37 (31), 1,16,99. Bettencourt, Lance A. and Stephen W. Brown (1997), "Contact Employees: Relationships among Workplace Fairness, Job Satisfaction and Prosocial Service Behaviors," Journal of Retailing, 73 (1), 39-61. Bettencourt, Lance A., Stephen W. Brown, and Scott B. MacKenzie (2005), "Customer-Oriented Boundary-Spanning Behaviors: Test of a Social Exchange Model of Antecedents," Journal of Retailing, 81 (2), 141-57. Bharadwaj, Sundar G., P. Rajan Varadarajan, and John Fahy (1993), "Sustainable Competitive Advantage in Service Industries: A Conceptual Model and Research Propositions," Journal of Marketing, 57 (4), 83-99. Bies, Robert J. and Joseph S. Moag (1986), "Interactional Justice: Communications Criteria of Fairness," in Research On Negotiations in Organizations, Roy J. Lewicki and Blair H. Sheppard and Max H. Bazerman, Eds. Greenwich, CT: JAI Press. Bolin, Aaron and Linette Heatherly (2001), "Predictors of Employee Deviance: The Relationship Between Bad Attitudes and Bad Behavior," Journal of Business and Psychology, 15 (3), 405-18. Bollen, Kenneth A. (1989), Structural Equations with Latent Variables. New York, NY: John Wiley & Sons, Inc. Bollen, Kenneth A. and Patrick J. Curran (2006), Latent Curve Models: A Structural Equation Perspective. Hoboken, NJ: John Wiley & Sons, Inc. Bordia, Prashant, Simon Lloyd D. Restubog, and Robert L. Tang (2008), "When Employees Strike Back: Investigating Mediating Mechanisms Between Psychological Contract Breach and Workplace Deviance," Journal of Applied Psychology, 93 (5), 1104-17. 170 Boye, Michael W. and Karen B. Slora (1993), "The Severity and Prevalence of Deviant Employee Activity within Supermarkets," Journal of Business and Psychology, 8 (2), 245-53. Brady, Michael K. and J. Joseph Cronin Jr. (2001), "Customer Orientation: Effects on Customer Service Perceptions and Outcome Behaviors," Journal of Service Research, 3 (3), 241-51. Brass, Daniel J., Kenneth D. Butterfield, and Bruce C. Skaggs (1998), "Relationships and Unethical Behavior: A Social Network Perspective," Academy of Management Review, 23 (1), 14-31. Brayfield, Arthur H. and Harold F. Rothe (1951), "An Index of Job Satisfaction," Journal of Applied Psychology, 35 (5), 307-11. Brief, Arthur P., Michael J. Burke, Jennifer M. George, Brian S. Robinson, and Jane Webster (1988), "Should Negative Affectivity Remain an Unmeasured Variable in the Study of Job Stress," Journal of Applied Psychology, 73 (2), 193-98. Brodie, Roderick J., James R.M. Whittome, and Gregory J. Brush (2009), "Investigating the Service Brand: A Customer Value Perspective," Journal of Business Research, 62 (3), 345-55. Brown, Michael E. and Linda K. Trevino (2006), "Socialized Charismatic Leadership, Values Congruence, and Deviance in Work Group," Journal of Applied Psychology, 91 (4), 954-62. Brown, Steven P. and Robert A. Peterson (1993), "Antecedents and Consequences of Salesperson Job Satisfaction: Meta-Analysis and Assessment of Causal Effects," Journal of Marketing Research, 30 (1), 63-77. Brown, Tom J., John C. Mowen, D. Todd Donavan, and Jane W. Licata (2002), "The Customer Orientation of Service Workers: Personality Trait Effects on Self- and Supervisor Performance Ratings," Journal of Marketing Research, 39 (1), 110-19. Browning, Victoria (2008), "An Exploratory Study into Deviant Behaviour in the Service Encounter: How and Why Front-Line Employees Engage in Deviant Behaviour," Journal of Management and Organization, 14 (4), 451-71. Bushman, Brad J. (2002), "Does Venting Anger Feed or Extinguish the Flame? Catharsis, Rumination, Distraction, Anger, and Aggressive Responding," Personality and Social Psychology Bulletin, 28 (6), 724-91. Caprara, Gian Vittorio, Claudio Barbaranelli, Concetta Pastorelli, Ivo Cermak, and Sandor Rosza (2001), "Facing Guilt: Role of Negative Affectivity, Need for Reparation, and Fear of Punishment in Leading to Prosocial Behaviour and Aggression," European Journal of Personality, 15 (3), 219-37. 171 Chiaburu, Dan S. and David A. Harrison (2008), "Do Peers Make the Place? Conceptual Synthesis and Meta-Analysis of Coworker Effects on Perceptions, Attitudes, OCBs, and Performance," Journal of Applied Psychology, 93 (5), 1082-103. Clark, Lee Anna, David Watson, and Jay Leeka (1989), "Diurnal Variation in the Positive Affects," Motivation and Emotion, 13 (3), 205-34. Clayton, Susan D. (1992), "The Experience of Injustice: Some Characteristics and Correlates," Social Justice Research, 5 (1), 71-91. Coffin, Bill (2003), "Breaking the Silence on White Collar Crime," Risk Management, 50 (9), 8. Colquitt, Jason A, Donald E. Conlon, Michael J. Wesson, Christopher O.L.H. Porter, and K. Yee Ng (2001), "Justice at the Millennium: A Meta-Analytic Review of 25 Years of Organizational Justice Research," Journal of Applied Psychology, 86 (3), 425-45. Cook, Thomas D. and Donald T. Campbell (1979), Quasi-Experimentation: Design & Analysis Issues for Field Settings. Boston, MA: Houghton Mifflin Company. Dalal, Reeshad S. (2005), "A Meta-Analysis of the Relationship Between Organizational Citizenship Behavior and Counterproductive Work Behavior," Journal of Applied Psychology, 90 (6), 1241-55. de Rivera, Joseph (1977), "A Structural Theory of Emotions," Psychological Issues, 10 (4), 11178. Dewe, Philip J. and David E. Guest (1990), "Methods of Coping with Stress at Work: A Conceptual Analysis and Empirical Study of Measurement Issues," Journal of Organizational Behavior, 11 (2), 135-50. Dollard, John, Leonard W. Doob, Neal E. Miller, O.H. Mowrer, and Robert R. Sears (1939), Frustration and Aggression. New Haven, CT: Yale University Press. Donavan, D. Todd, Tom J. Brown, and John C. Mowen (2004), "Internal Benefits of ServiceWorker Customer Orientation: Job Satisfaction, Commitment, and Organizational Citizenship Behaviors," Journal of Marketing, 68 (1), 128-46. Eagly, Alice H. and Shelly Chaiken (1993), The Psychology of Attitudes. Fort Worth, TX: Harcourt Brace Jovanovich. Folkman, Susan and Richard S. Lazarus (1985), "Coping and Emotion," in Psychological and Biological Approaches to Emotion, Nancy L. Stein and Bennett Leventhal and Tom Trabasso, Eds. Hillsdale, NJ: Lawrence Erlbaum Associates. Fornell, Claes and David F. Larcker (1981), "Evaluating Structural Equation Models with Unobservable Variables and Measurement Error," Journal of Marketing Research, 18 (1), 39-50. 172 Fox, Suzy, Paul E. Spector, and Don Miles (2001), "Counterproductive Work Behavior (CWB) in Response to Job Stressors and Organizational Justice: Some Mediator and Moderator Tests for Autonomy and Emotions," Journal of Vocational Behavior, 59 (3), 291-309. Frijda, Nico H. (1993), "Moods, Emotion Episodes, and Emotions," in Handbook of Emotions, Michael Lewis and Jeannette M. Haviland, Eds. New York, NY: Guilford Press. Grandey, Alicia A. (2000), "Emotion Regulation in the Workplace: A New Way to Conceptualize Emotional Labor," Journal of Occupational Health Psychology, 5 (1), 95-110. Grandey, Alicia A., David N. Dickter, and Hock-Peng Sin (2004), "The Customer is Not Always Right: Customer Aggression and Emotion Regulation of Service Employees," Journal of Organizational Behavior, 25 (3), 397-418. Greenberg, Jerald (1993), "The Social Side of Fairness: Interpersonal and Informational Classes of Organizational Justice," in Justice in the Workplace: Approaching Fairness in Human Resource Management, Russell Cropanzano, Ed. Hillsdale, NJ: Lawrence Erlbaum Associates. ---- (2002), "Who Stole the Money, and When? Individual and Situational Determinants of Employee Theft," Organizational Behavior and Human Decision Processes, 89 (1), 985-1003. Greenberg, Liane and Julian Barling (1996), "Employee Theft," in Trends in Organizational Behavior, C.L. Cooper and D.M. Rousseau, Eds. Vol. 3. Chichester, NY: John Wiley and Sons Ltd. Harris, Lloyd C. and Emmanuel Ogbonna (2002), "Exploring Service Sabotage: The Antecedents, Types and Consequences of Frontline, Deviant, Antiservice Behaviors," Journal of Service Research, 4 (3), 163-83. ---- (2006), "Service Sabotage: A Study of Antecedents and Consequences," Journal of the Academy of Marketing Science, 34 (4), 543-58. Hart, Christopher W.L., James L. Heskett, and W. Earl Sasser Jr. (1990), "The Profitable Art of Service Recovery," Harvard Business Review, 68 (4), 148-56. Hektner, Joel M., Jennifer A. Schmidt, and Mihaly Csikszentmihalyi (2007), Experience Sampling Method: Measuring the Quality of Everyday Life. Thousand Oaks, CA: Sage Publications, Inc. Henle, Christine A., Robert A. Giacalone, and Carole L. Jurkiewicz (2005), "The Role of Ethical Ideology in Workplace Deviance," Journal of Business Ethics, 56 (3), 219-30. Henseler, Jorg, Christian M. Ringle, and Rudolf R. Sinkovics (2009), "The Use of Partial Least Squares Path Modeling in International Marketing," Advances in International Marketing, 20, 277-319. 173 Herzberg, Frederick (1968), "One More Time: How Do You Motivate Employees?," Harvard Business Review, 46 (1), 53-62. Hoffman, David A., Mark A. Griffin, and Mark B. Gavin (2000), "The Application of Hierarchical Linear Modeling to Organizational Research," in Multilevel Theory, Research, and Methods in Organizations, Katherine J. Klein and Steve W.J. Kozlowski, Eds. San Francisco, CA: Jossey-Bass Inc. Hollander, Stanley C. (1985), "A Historical Perspective on the Service Encounter," in The Service Encounter: Managing Employee/Customer Interaction in Service Businesses, John A. Czepiel and Michael R. Solomon and Carol Surprenant, Eds. Lexington, MA: D.C. Heath and Company. Hollinger, Richard C. and John P. Clark (1983), "Deterrence in the Workplace: Perceived Certainty, Perceived Severity, and Employee Theft," Social Forces, 62 (2), 398-418. ---- (1982), "Formal and Informal Social Controls of Employee Deviance," The Sociological Quarterly, 23 (3), 333-43. Homburg, Christian, Michael Muller, and Martin Klarmann (2011), "When Should the Customer Really Be King? On the Optimum Level of Salesperson Customer Orientation in Sales Encounters," Journal of Marketing, 75 (2), 55-74. Hulin, Charles L. and Timothy A. Judge (2003), "Job Attitudes," in Handbook of Psychology, W. C. Borman and D. R. Ilgen and R. J. Klimoski, Eds. Hoboken, NJ: Wiley. Hulland, John, Michael J. Ryan, and Robert K. Rayner (2009), "Modeling Customer Satisfaction: A Comparative Performance Evaluation of Covariance Structure Analysis Versus Partial Least Squares," in Handbook of Partial Least Squares: Concepts, Methods and Applications, Vincenzo Esposito Vinzi and Wynne W. Chin and Jorg Henseler and Huiwen Wang, Eds. New York, NY: Springer-Verlag Berlin Heidelberg. Ilies, Remus and Timothy A. Judge (2002), "Understanding the Dynamic Relationships Among Personality, Mood, and Job Satisfaction: A Field Experience Sampling Study " Organizational Behavior and Human Decision Processes, 89 (2), 1119-39. Jaworski, Bernard J. (1988), "Toward a Theory of Marketing Control: Environmental Context, Control Types, and Consequences," Journal of Marketing, 52 (3), 23-39. Jones, David A. (2009), "Getting Even with One's Supervisor and One's Organization: Relationships Among Types of Injustice, Desires for Revenge, and Counterproductive Work Behaviors," Journal of Organizational Behavior, 30 (4), 525-42. Judge, Timothy A. and Remus Ilies (2004), "Affect and Job Satisfaction: A Study of Their Relationship at Work and at Home," Journal of Applied Psychology, 89 (4), 661-73. 174 Judge, Timothy A., Brent A. Scott, and Remus Ilies (2006), "Hostility, Job Attitudes, and Workplace Deviance: Test of A Multilevel Model," Journal of Applied Psychology, 91 (1), 12638. Kaplan, Seth, Jill C. Bradley, Joseph N. Luchman, and Douglas Haynes (2009), "On the Role of Positive and Negative Affectivity in Job Performance: A Meta-Analytic Investigation," Journal of Applied Psychology, 94 (1), 162-76. Kelley, Scott W. (1992), "Developing Customer Orientation Among Service Employees," Journal of the Academy of Marketing Science, 20 (1), 27-36. Larson, Reed and Mihaly Csikszentmihalyi (1983), "The Experience Sampling Method," in Naturalistic Approaches to Studying Social Interaction, Harry T. Reis, Ed. San Francisco: Jossey-Bass. Lau, Vivian C. S., Wing Tung Au, and Jane M.C. Ho (2003), "A Qualitative and Quantitative Review of Antecedents of Counterproductive Behavior in Organizations," Journal of Business and Psychology, 18 (1), 73-99. Lavelle, James J., Deborah E. Rupp, and Joel Brockner (2007), "Taking a Multifoci Approach to the Study of Justice, Social Exchange, and Citizenship Behavior: The Target Similarity Model," Journal of Management, 33 (6), 841-66. Lazarus, Richard S. (1991), Emotion and Adaptation. New York, NY: Oxford University Press. Lewis, Michael (1993), "Self-Conscious Emotion: Embarrassment, Pride, Shame, and Guilt," in Handbook of Emotions, Michael Lewis and Jeannette M. Haviland, Eds. New York, NY: Guilford Press. Litzky, Barrie E., Kimberly A. Eddleston, and Deborah L. Kidder (2006), "The Good, the Bad, and the Misguided: How Managers Inadvertently Encourage Deviant Behaviors," Academy of Management Perspectives, 20 (1), 91-103. Locke, E.A. (1976), "The Nature and Causes of Job Satisfaction," in Handbook of Industrial and Organizational Psychology, Marvin D. Dunnette, Ed. Vol. 1. Chicago, IL: Rand McNally. MacKenzie, Scott B., Philip M. Podsakoff, and Michael Ahearne (1998), "Some Possible Antecedents and Consequences of In-Role and Extra-Role Salesperson Performance," Journal of Marketing, 62 (3), 87-98. MacLean, Tammy L. (2001), "Thick as Thieves: A Social Embeddedness Model of Rule Breaking in Organizations," Business and Society, 40 (2), 167-96. 175 Marcus-Newhall, Amy, William C. Pedersen, Mike Carlson, and Norman Miller (2000), "Displaced Aggression is Alive and Well: A Meta-Analytic Review," Journal of Personality and Social Psychology, 78 (4), 670-89. Martilla, John A. and Davis W. Carvey (1975), "Four Subtle Sins in Marketing Research," Journal of Marketing, 39 (1), 8-15. Mattila, Anna S. and Cathy A. Enz (2002), "The Role of Emotions in Service Encounters," Journal of Service Research, 4 (4), 268-77. Maxham III, James G. and Richard G. Netemeyer (2003), "Firms Reap What They Sow: The Effects of Shared Values and Perceived Organizational Justice on Customers' Evaluations of Complaint Handling," Journal of Marketing, 67 (1), 46-62. Mayer, David M., Maribeth Kuenzi, Rebecca Greenbaum, Mary Bardes, and Rommel Salvador (2009), "How Low Does Ethical Leadership Flow? Test of a Trickle-Down Model." Mikula, Gerold (1986), "The Experience of Injustice: Toward a Better Understanding of its Phenomenology," in Justice in Social Relations, Hans Werner Bierhoff and Ronald L. Cohen and Jerald Greenberg, Eds. New York, NY: Plenum Press. Mount, Michael, Remus Ilies, and Erin Johnson (2006), "Relationship of Personality Traits and Counterproductive Work Behaviors: The Mediating Effects of Job Satisfaction," Personnel Psychology, 59 (3), 591-622. Parasuraman, A., Leonard L. Berry, and Valarie A. Zeithaml (1991), "Understanding Customer Expectations of Service," Sloan Management Review, 32 (3), 39-48. Paterson, Janice M. and Jane Cary (2002), "Organizational Justice, Change Anxiety, and Acceptance of Downsizing: Preliminary Tests of an AET-Based Model," Motivation and Emotion, 26 (1), 83-103. Penney, Lisa M. and Paul E. Spector (2005), "Job Stress, Incivility, and Counterproductive Work Behavior (CWB): the Moderating Role of Negative Affectivity," Journal of Organizational Behavior, 26 (7), 777-96. Pirolo-Merlo, Andrew, Charmine Härtel, Leon Mann, and Giles Hirst (2002), "How Leaders Influence the Impact of Affective Events on Team Climate and Performance in R&D Teams," The Leadership Quarterly, 13 (5), 561-81. Podsakoff, Philip M., Scott B. Mackenzie, Jeong-Yeon Lee, and Nathan P. Podsakoff (2003), "Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies," Journal of Applied Psychology, 88 (5), 879-903. Raudenbush, Stephen W. and Anthony S. Bryk (2002), Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, CA: Sage Publications, Inc. 176 Reynolds, Kate L. and Lloyd C. Harris (2006), "Deviant Customer Behavior: An Exploration of Frontline Employee Tactics," Journal of Marketing Theory and Practice, 14 (2), 95-111. Rindfleisch, Aric, Alan J. Malter, Shankar Ganesan, and Christine Moorman (2008), "CrossSectional Versus Longitudinal Survey Research: Concepts, Findings, and Guidelines," Journal of Marketing Research, 45 (3), 261-79. Robinson, Sandra L. and Rebecca J. Bennett (1995), "A Typology of Deviant Workplace Behaviors: A Multidimensional Scaling Study," Academy of Management Journal, 38 (2), 55572. Rupp, Deborah E. (2011), "An Employee-Centered Model of Organizational Justice and Social Responsibility," Organizational Psychology Review, 1 (1), 72-94. Rupp, Deborah E. and Sharmin Spencer (2006), "When Customers Lash Out: The Effects of Customer Interactional Injustice on Emotional Labor and the Mediating Role of Discrete Emotions," Journal of Applied Psychology, 91 (4), 971-78. Rupp, Deborah E., Sharmin Spencer, and Karlheinz Sonntag (2008), "Customer (In)Justice and Emotional Labor: The Role of Perspective Taking, Anger, and Emotional Regulation," Journal of Management, 34 (5), 903-24. Saxe, Robert and Baron A. Weitz (1982), "The SOCO Scale: A Measure of the Customer Orientation of Salespeople," Journal of Marketing Research, 19 (3), 343-51. Scher, Steven J. (1997), "Measuring the Consequences of Injustice," Personality and Social Psychology Bulletin, 23 (5), 482-97. Schwarz, Norbert and Gerald L. Clore (1983), "Mood, Misattribution, and Judgments of WellBeing: Informative and Directive Functions of Affective States," Journal of Personality and Social Psychology, 45 (3), 513-23. Scott, Brent A. and Timothy A. Judge (2006), "Insomnia, Emotions, and Job Satisfaction: A Multilevel Study," Journal of Management, 32 (5), 622-45. Sheppard, Blair H., Roy J. Lewicki, and John W. Minton (1992), Organizational Justice: The Search for Fairness in the Workplace. New York, NY: Lexington Books. Singh, Nitish, Georg Fassott, Mike C.H. Chao, and Jonas A. Hoffmann (2006), "Understanding International Web Site Usage: A Cross-National Study of German, Brazilian, and Taiwanese Online Consumers," International Marketing Review, 23 (1), 83-97. Skarlicki, Daniel P. and Robert Folger (1997), "Retaliation in the Workplace: The Roles of Distributive, Procedural, and Interactional Justice," Journal of Applied Psychology, 82 (3), 43443. 177 Skarlicki, Daniel P., Robert Folger, and Paul Tesluk (1999), "Personality as a Moderator in the Relationship Between Fairness and Retaliation," Academy of Management Journal, 42 (1), 10008. Skarlicki, Daniel P., Danielle D. van Jaarsveld, and David D. Walker (2008), "Getting Even for Customer Mistreatment: The Role of Moral Identity in the Relationship Between Customer Interpersonal Injustice and Employee Sabotage," Journal of Applied Psychology, 93 (6), 133547. Slora, Karen B. (1989), "An Empirical Approach to Determining Employee Deviance Base Rates," Journal of Business and Psychology, 4 (2), 199-219. Sobel, Michael E. (1982), "Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models," Sociological Methodology, 13, 290-312. Solomon, Michael R., Carol Surprenant, John A. Czepiel, and Evelyn G. Gutman (1985), "A Role Theory Perspective on Dyadic Interactions: The Service Encounter," Journal of Marketing, 49 (1), 99-111. Spector, Paul E. (1994), "Using Self-Report Questionnaires in OB Research: A Comment on the Use of a Controversial Method," Journal of Organizational Behavior, 15 (5), 385-92. Spector, Paul E. and Suzy Fox (2002), "An Emotion-Centered Model of Voluntary Work Behavior: Some Parallels Between Counterproductive Work Behavior and Organizational Citizenship Behavior," Human Resource Management Review, 12 (2), 269-92. Spielberger, C. D., G. Jacobs, S. Russell, and R.S. Crane (1983), "Assessment of Anger: The State-Trait Anger Scale," in Advances in Personality Assessment, James N. Butcher and Charles D. Spielberger, Eds. Vol. 2. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Tangney, June Price (1999), "The Self-Conscious Emotions: Shame, Guilt, Embarrassment, and Pride," in Handbook of Cognition and Emotion, Tim Dalgleish and Mick J. Power, Eds. New York, NY: John Wiley & Sons, Inc. Tangney, June Price and Ronda L. Dearing (2002), Shame and Guilt. New York, NY: The Guilford Press. Tangney, June Price, Rowland S. Miller, Laura Flicker, and Deborah Hill Barlow (1996), "Are Shame, Guilt, Embarrassment Distinct Emotions?," Journal of Personality and Social Psychology, 70 (6), 1256-69. Thibaut, John and Laurens Walker (1975), Procedural Justice: A Psychological Analysis. Hillsdale, NJ: Lawrence Erlbaum Associates. 178 Vilares, Manuel J., Maria H. Almeida, and Pedro S. Coelho (2009), "Comparison of Likelihood and PLS Estimators for Structural Equation Modeling: A Simulation with Customer Satisfaction Data," in Handbook of Partial Least Squares: Concepts, Methods and Applications. New York, NY: Springer-Verlag Berlin Heidelberg. Watson, David (2000), Mood and Temperament. New York, NY: The Guilford Press. Watson, David, Lee Anna Clark, and Auke Tellegen (1988), "Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS Scales," Journal of Personality and Social Psychology, 54 (6), 1063-70. Weiss, Howard M. (2002), "Deconstructing Job Satisfaction: Separating Evaluations, Beliefs and Affective Experiences," Human Resource Management Review, 12 (2), 173-94. Weiss, Howard M. and Russell Cropanzano (1996), "Affective Events Theory: A Theoretical Discussion of the Structure, Causes and Consequences of Affective Experiences at Work," in Research In Organizational Behavior, Barry M. Staw and L.L. Cummings, Eds. Vol. 18. Greenwich, CT: JAI Press. Weiss, Howard M., Kathleen Suckow, and Russell Cropanzano (1999), "Effects of Justice Conditions on Discrete Emotions," Journal of Applied Psychology, 84 (5), 786-94. Wheeler, Ladd and Harry T. Reis (1991), "Self-Recording of Everyday Life Events: Origins, Types, and Uses," Journal of Personality, 59 (3), 339-54. Yang, Jixia and James M. Diefendorff (2009), "The Relations of Daily Counterproductive Work Behavior with Emotions, Situational Antecedents, and Personality Moderators: A Diary Study in Hong Kong," Personnel Psychology, 62 (2), 259-95. 179 CONCLUSION With the worldwide growth of services, research efforts recognizing the characteristics of services that separate services from tangible goods, particularly intangibility, inseparability, heterogeneity, and perishability, have intensified. Within the Services Marketing subdomain, researchers have acknowledged that the unique characteristics of services places importance on the customer contact employee in providing the service to the customer. For instance, Bitner, Booms, and Tetreault (1990) stated that the interaction with the customer contact employee is the service from the customer’s perspective. As a result, the performance of the customer contact employee has considerable attention with researchers generally concentrating on the improvement of positive behaviors, such as in-role and extra-role performance, and the reduction of negative behaviors, such as counterproductive work behaviors. The two essays of this dissertation contribute to the literature and practice by examining methods by which managers can increase the positive behaviors and decrease the negative behaviors of their customer contact employees. To appropriately answer the research questions and contribute to the literature and practice, the two essays of the dissertation utilized a mixture of sampling and analysis methods. Using the framework established by the service-profit chain (Heskett, Jones, Loveman, Sasser, and Schlesinger 1994) and the employee-customer-profit model (Rucci, Kirn, and Quinn 1998), Essay 1 investigated the antecedents and consequences of the in-role and extra-role performance of customer contact employees with cross-sectional surveys from 163 customer contact employees and 112 managers of a convenience store chain as well as 414 mystery shopping evaluations of customer contact employee performance. The responses collected for Essay 1 from the customer, the customer contact employee, and the manager were analyzed using both 180 hierarchical linear modeling (HLM) and partial least squares (PLS) path model analysis. Meanwhile, Essay 2 used affective events theory to examine the emotional, attitudinal, and behavioral responses of customer contact employees to interpersonally unjust treatment from customers using a one-time survey as well as daily surveys from a panel of 146 customer contact employees over a ten workday time period. The hypothesized relationships of Essay 2 were tested using HLM. Several findings of interest emerged from the dissertation. Essay 1 demonstrated that, at both the individual and shift levels, the formal marketing controls of behavior-based contracts accompanied by monitoring and outcome-based contracts had no effect on customer contact employee in-role or extra role performance whereas customer contact employee customer orientation increased both employee in-role and extra-role performance at both the individual and shift level while customer contact employee intrapreneurial orientation increased extra-role performance at the individual level. Additionally, the manager was found to be a key influence on the customer contact employee self controls of customer orientation and intrepreneurial orientation and this influence increased as the manager’s referent power increased. Essay 2, on the other hand, showed that the counterproductive work behaviors of customer contact employees were the result of immediate emotional reactions to customer interpersonal injustice. Specifically, Essay 2 demonstrated that customer interpersonal injustice was positively related to the negative emotions of anger and guilt and these emotions mediated the effect of customer interpersonal injustice on job satisfaction. Counterproductive work behaviors directed at different targets ultimately resulted from coping mechanisms to handle the negative emotions and rational decisions. 181 For academics and managers, the results of this dissertation provide further clarification of the motivations for customer contact employees to engage in positive and negative behaviors. For example, Essay 1 provides academics with insight into the relative effectiveness of formal and informal controls at influencing customer contact employee behavior as well as the manager’s role in developing informal controls in customer contact employees. Managerially, Essay 1 provides recommendations regarding the relative effects of customer contact employee in-role performance, extra-role performance, and authenticity on satisfaction and also provides managers with methods to increase the informal marketing controls in customer contact employees, which increased customer contact employee performance. For academics, Essay 2 answered the call for research that investigates the subjective, naturally occurring justice-related experiences of employees (Rupp 2011) and demonstrated that customer interpersonal injustice was an antecedent to counterproductive work behavior that was directed at targets other than the offending customer and the employee’s organization, such as the employee’s supervisor, the employee’s coworkers, and customers other than the offending customer. For managers, Essay 2 further clarified the negative impact that rude customers have on customer contact employees and the necessity of empowering customer contact employees with methods to respond constructively to interpersonally unjust treatment from customers. 182 REFERENCES 183 REFERENCES Bitner, Mary Jo, Bernard H. Booms, and Mary Stanfield Tetreault (1990), "The Service Encounter: Diagnosing Favorable and Unfavorable Incidents," Journal of Marketing, 54 (1), 7184. Heskett, James L., Thomas O. Jones, Gary W. Loveman, W. Earl Sasser Jr., and Leonard A. Schlesinger (1994), "Putting the Service-Profit Chain to Work," Harvard Business Review, 72 (2), 164-74. Rucci, Anthony J., Steven P. Kirn, and Richard T. Quinn (1998), "The Employee-CustomerProfit Chain at Sears," Harvard Business Review, 76 (1), 82-97. Rupp, Deborah E. (2011), "An Employee-Centered Model of Organizational Justice and Social Responsibility," Organizational Psychology Review, 1 (1), 72-94. 184