THE IMPACT OF SOCIAL NETWORKS ON SALES TRAINING TRANSFER AND PERFORMANCE By Blake A. Runnalls A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Business Administration – Marketing – Doctor of Philosophy 2017 ABSTRACT THE IMPACT OF SOCIAL NETWORKS ON SALES TRAINING TRANSFER AND PERFORMANCE By Blake A. Runnalls Sales training provides salespeople with additional knowledge, skills, and abilities to help improve selling capabilities and subsequently firm performance. Although selling firms continue to invest billions of dollars in sales training, sales academics and practitioners are unaware of the many mechanisms that inhibit or promote the link between sales training and salesperson performance. Prior research has shown that social support in the form of both supervisor and peer support is a major factor that influences the effectiveness of training initiatives. In addition, it is widely acknowledged that as compared to passive learning, active learning enhances individual learning. To date there has been no research that has focused on the link between sales training and sales performance when considering supervisor and peer support, along with continuous individual learning. Adopting views from social capital theory and the theory of situated learning, I develop and test a comprehensive sales training transfer model, focused on identifying the interactive influence of sales training and social support in the form of knowledge and friendship networks on individual salesperson training transfer performance. In addition, I report how findings from this research can be utilized by sales organizations to increase individual learning through the identification, development, and support of intra-organizational social networks. Copyright by BLAKE A. RUNNALLS 2017 This dissertation is dedicated to my parents, Lee and Elizabeth Runnalls, loving parents and dedicated teachers. iv ACKNOWLEDGEMENTS I would like to take this opportunity to acknowledge and thank my committee members for helping me during the conceptualization and execution of this dissertation, along with developing me into the scholar that I am today. To my chair, Dr. Douglas Hughes, thank you for believing in me and for your ongoing support. To Dr. Roger Calantone, thank you for all of the opportunities that you provided to me throughout my time at Michigan State University, including the opportunity to teach Pricing at the MBA level, in addition to your guidance during the statistical analysis portion of this project. To Dr. Tomas Hult, thank you for always being there to listen to me and for coining the term “Blake-ism”. To Dr. Clay Voorhees, your honesty and candid comments helped to ensure that I put my best foot forward during this ever so important time of career development. And last, but certainly not least, special thanks to Dr. Kevin Ford. I am honored and privileged to be associated with the preeminent scholar on the topic of training transfer. Dr. Ford, thank you for meeting with me on multiple occasions and for your patience while I worked toward understanding how I might contribute someday to the topic of training transfer. v TABLE OF CONTENTS LIST OF TABLES ........................................................................................................................ vii LIST OF FIGURES ..................................................................................................................... viii INTRODUCTION .......................................................................................................................... 1 LITERATURE REVIEW ............................................................................................................... 9 Training in Organizations ............................................................................................................ 9 Training Transfer ....................................................................................................................... 13 Sales Training ............................................................................................................................ 17 CONCEPTUAL BACKGROUND ............................................................................................... 32 Social Capital Theory ................................................................................................................ 33 Theory of Situated Learning ...................................................................................................... 36 HYPOTHESES DEVELOPMENT .............................................................................................. 40 Training and Training Transfer Performance ............................................................................ 40 The Impact of Sales Manager Support on Training Transfer .................................................... 41 The Impact of Network Peer Support on Training Transfer ..................................................... 42 METHODOLOGY ....................................................................................................................... 46 Social Network Analysis ........................................................................................................... 46 Sample and Data Collection ...................................................................................................... 47 Measurement ............................................................................................................................. 48 Statistical Approach ................................................................................................................... 50 Results ....................................................................................................................................... 52 DISCUSSION ............................................................................................................................... 60 Theoretical Implications ............................................................................................................ 60 Managerial Implications ............................................................................................................ 63 Limitations and Future Research ............................................................................................... 64 APPENDIX ................................................................................................................................... 67 REFERENCES ............................................................................................................................. 98 vi LIST OF TABLES TABLE 1: Network Survey Questionnaire……………………………………………………....86 TABLE 2: Results for Hypothesis H1………………………………………………………...…87 TABLE 3: Results for Hypothesis H2…………………………………………………………...88 TABLE 4: Results for Hypothesis H3…………………………………………………………...89 TABLE 5: Results for Hypothesis H4a………………………………………………………….90 TABLE 6: Results for Hypothesis H4b………………………………………………………….94 vii LIST OF FIGURES FIGURE 1: Conceptual Model……………………………………………………………….….68 FIGURE 2: Combined Product and Cross-Selling Revenue for All Trained Salespeople……....69 FIGURE 3: Combined Product and Cross-Selling as a Percent of Total Revenue for All Trained Salespeople……………………………………………………………………………………....70 FIGURE 4: Interaction Graph for Hypothesis H2.………………………………………………71 FIGURE 5: Interaction Graph for Hypothesis H2.…………...………………………………….72 FIGURE 6: Interaction Graph for Hypothesis H3.………………………………………………73 FIGURE 7: Interaction Graph for Hypothesis H3..…………..…………………………….……74 FIGURE 8: Interaction Graph for Hypothesis H3………………………………………….……75 FIGURE 9: Interaction Graph for Hypothesis H3………………………………………….……76 FIGURE 10: Interaction Graph for Hypothesis H4a……….…………………………….……...77 FIGURE 11: Interaction Graph for Hypothesis H4a…………………………………………….78 FIGURE 12: Interaction Graph for Hypothesis H4a…………………………………………….79 FIGURE 13: Interaction Graph for Hypothesis H4a…………………………………………….80 FIGURE 14: Interaction Graph for Hypothesis H4a…………………………………………….81 FIGURE 15: Interaction Graph for Hypothesis H4b……………………………………….……82 FIGURE 16: Interaction Graph for Hypothesis H4b…………………………………………….83 FIGURE 17: Interaction Graph for Hypothesis H4b…………………………………………….84 FIGURE 18: Interaction Graph for Hypothesis H4b…………………………………………….85 viii INTRODUCTION Due to constant technological changes, increased competition, and for the fact that selling knowledge is quickly outdated, firms are investing considerable amount of money on training their salesforces (Kauffeld and Lehmann-Willenbrock 2010). Training is no longer seen as an event, but rather a process and research has begun to focus on understanding how training processes can be optimized (Holton, Bates, and Ruona 2000). In addition, there has been a shift within the academic community from identifying if training is successful to understanding the question of why training works (Holton et al. 2000). Once factors are identified that suggest why training works, it is the responsibility of sales training researchers to make suggestions on how academic findings can help sales training organizations better organize, structure, and implement effective programs. Sales organizations invest in a variety of sales training initiatives to address firm needs and attain organizational objectives (Leach and Liu 2003). The expectations of training investments are that training will increase employee performance and potentially lead to a competitive advantage through the development of human capital (Cannon-Bowers and Salas 2001). Sales training is well recognized as a large expense faced by sales organizations. According to a study conducted by the American Society for Training & Development (ASTD), now the Association for Talent Development (ATD), U.S organizations on average spend about $135 billion annually on training and developing employees. Of this $135 billion, it is estimated that 63% of training investments are for internal services (e.g. in-house training programs), 27% for external services (e.g. out-of-house training programs) and the remaining 10% is invested in tuition reimbursement (Patel 2010). U.S. companies spend around $10 billion dollars annually on sales training (Hair et al. 2009) and this figure doesn’t take into account the time spent on 1 training and developing salespeople before and after a training intervention (Dubinsky 1996). In addition, because of the high cost of training a salesperson (upwards of $100,000 per individual (Dubinsky 1996)), it may take years before any profit is realized from a new salesperson (Johnston and Marshall 2016; Ricks, Williams, and Weeks 2008). As the role of a salesperson becomes increasingly complex, and selling organizations transition from a product to a solution focused selling strategy, sales organizations continue to invest substantial amounts of resources into the development of salesforces through learning and behavioral change initiatives. Although sales executives often see training as a means to enhance the performance of the salesforce through behavioral change, training may be met with skepticism by salespeople. It is estimated that 90% of salespeople are dissatisfied with the training they receive (Spiro, Stanton, and Gregory 2003). This is especially likely for top-performers, who may believe they know much of the training material or that their time within a formal classroom setting could be better used out in the field selling (Powell 2001). Additional criticism is that sales training too often focuses on developing a sales force based on previously successful salespeople as compared to developing a sales force based on future market environments (Cespedes 1995; Pelham 2002). Furthermore, there is common assumption among salespeople that the only way to learn how to sell is to sell (Wilson, Strutton, and Farris 2002). This evidence further suggests a need to uncover how to effectively design, implement, and manage sales training programs. In order for sales training to be effective, salespeople need to implement the training content back to the workplace and apply what they have learned (Hatala and Fleming 2007). The topic of training transfer has long been a focus of researchers and practitioners, but has received little attention in the sales field. Transfer of training refers to the application, generalization, and maintenance of learning, trained skills, and behaviors from the training environment to the work 2 environment (Baldwin and Ford 1988). The transfer of training “is considered the primary leverage point by which training influences organizational-level outcomes and results” (Saks and Belcourt 2006, p.629). Training investments do not provide intended effects if knowledge, skills, and abilities acquired in training are not transferred to job-related activities (Aquinis and Kraiger 2009). Training transfer processes are affected by individual and contextual characteristics (Baldwin and Ford 1988; Burke and Hutchins 2007). These processes influence motivational, behavioral, cognitive, and emotional engagement in training transfer (Jacot, Raemdonck, and Frenay 2015). Transfer may be positive (e.g. increases performance), negative (e.g. decrease performance) or neutral. Negative transfer is counterproductive in that it is costly and directly impacts the return of training investment by reducing performance. For training transfer to occur, learned behavior must be generalized and maintained on the job over a period of time (Baldwin and Ford 1988). It is widely acknowledged that as compared to passive learning, active learning enhances learning. Passive learning such as lectures and reading restrict the learning process as the flow of information is one-way from source to student (Wright, Bitner, and Zeithaml 1994). Passive learning does not promote student involvement and the knowledge domain is specified by the instructor. In comparison, active learning promotes student involvement, including an information discovery process. Student involvement in a cognitive learning process results in the formation of new information, while simultaneously investigating existing information. This two-way learning requires increased student involvement, and alters the instructor’s role from the sole source of information provider to learning facilitator. Active learning techniques include group discussions, role-playing, simulations and experiential exercises. Furthermore, individual learning processes support the incorporation of newly learned behaviors into a trainee’s regular behavior within the work 3 environment (Blanchard and Thacker 2010). It is suggested that individual’s must apply what they learned in training, be prepared to learn from new experiences, in addition to structuring their environment to promote ongoing learning (Bransford and Schwartz 1999). As autonomous individuals, salespeople are provided with relative independence when striving for individual and organizational sales goals (Johnston and Marshall 2016). Current training researchers describe training as a three stage motivation process in which training participants decide whether to participate in training from a cognitive standpoint, to expend effort on learning during training, and to transfer the knowledge and skills they learned in training on an ongoing basis (Kozlowski and Salas 2009). Subsequently, research has transitioned from considering training participants as passive to active learners who are self-directed toward professional development, but also rely on peer and manager support to aid in successfully implementing training material into their selling process (Kozlowski and Salas 2009). Researchers agree that the work environment is a powerful factor in the training transfer process (Blume et al. 2010; Cheng and Ho 2001; Holton et al. 2000). Work environment refers to the features of the workplace perceived by employees that support or hinder the use of the knowledge, skills, or abilities derived from training (Burke and Saks 2009). Social support, including supervisor and peer support, is vital in enhancing the transfer of training (Chiaburu and Lindsay 2008; Lim and Morris 2006; Russ-Eft 2002). Supervisor support focuses on providing reinforcement for learning on the job, providing opportunity to use training on the job, and creating a supportive transfer environment, including reducing transfer obstacles. Additionally, peer support plays a vital role in the transfer of training through the dissemination of information and by providing emotional support and guidance. Social support has increasingly become a focus of training transfer research (Van den Bossche, Segers, and Jansen 2010). Researchers 4 have proposed a need for a better understanding of how social support in the work environment contributes to learning and overall training effectiveness (Clarke 2002; Van den Bossche and Segers 2013; Weisweiler et al. 2013). Adopting a social network approach may provide an avenue for sales researchers to study the structural, relational, and behavioral aspects of social networks and the effects that these have on the training transfer process (Van den Bossche and Segers 2013). It has been suggested that social networks are a “crucial mechanism in explaining transfer” (Van den Bossche and Segers 2013). Understanding the pattern of relationships between peers within an organization is likely to uncover factors within peer support systems that impact training transfer (Hatala and Fleming 2007). Bransford and Schwartz (1999) broadened the concept of training transfer by considering a trainee’s preparation for future learning. Within a social network context, this implies that a trainee must be prepared to learn post training and structure their social environment to promote such learning. Furthermore, Bransford and Schwartz (1999) note that training cannot make someone an expert; it can only place them on a path toward expertise (Van den Bossche and Segers 2013). Social networks, specifically knowledge networks, are important to consider when investigating the flow of information throughout social networks. Social networks within a sales organization likely influences the transmission and use of training related information through ongoing interactions between sales personnel. It is well established that employees use information obtained from individuals within their networks to work more effectively (Borgatti and Cross 2003; Cross et al. 2001). Problem-solving, learning and decision-making are be enhanced through social networks (Tynjälä 2008). This is achieved through the transmission of knowledge and information within social networks (Kali and Reyes 2007). Individuals that have personal ties increase the chance of being exposed to different kinds of information, increasing 5 the potential for knowledge transfer and learning (Dal Fiore 2007). In addition discussion among group members assist individuals to appraise the scope and limits of their knowledge and evaluate the prospects of its transfer to novel tasks and new circumstances (Billett 2002). Although the topic of training transfer, including the role that social support plays in enhancing training transfer, has been extensively studied in the fields of industrial psychology and management, there are no studies that focus specifically on the role that social support plays in increasing the return of investment of sales training initiatives. Sales organizations represent an interesting context to consider the role that social support plays on training transfer as selling organizations rely on salespeople as boundary-spanning agents to strive for organizational excellence (Johnston and Marshall 2016). Salespeople form personal networks in order to obtain valuable resources (e.g. information, emotional support, etc.) that are subsequently used in the pursuit of individual and organizational sales goals (Bolander et al. 2015). As the selling process becomes increasingly complex and dependent on additional functional units and for the fact that selling organizations continue to spend billions of dollars annually on training, it is important for researchers to identify factors that influence the relationship between sales training and performance. The role of social networks in selling organizations has been a focus of sales related research topics and has been shown to impact performance (Ahearne et al. 2013; Bolander et al. 2015; Gonzalez, Claro, and Palmatier 2014; Üstüner and Iacobucci 2012). Specifically, both formal and informal networks within sales organizations provide access to sales resources that impact salesperson’s effectiveness, such as opportunity-identification, solution-creation, and closing a deal (Üstüner and Iacobucci 2012). Additionally, a sales manager’s network position impacts both the quality and diversity of sales specific information (Ahearne et al. 2013). 6 Furthermore, network characteristics of relationship managers, especially non-overlapping ties in informal networks and densely interconnected formal ties, improve sales performance based on cross-network synergies (Gonzalez et al. 2014). Recently, Bolander et al. (2015) provided additional findings regarding the impact of social networks on sales performance and found that salespeople’s political skill is an antecedent to relational centrality and that both relational and positional centrality of a salesperson impact performance. This dissertation focuses on the influence that social networks have on the learning processes within a sales organization and how friendships and the exchange of knowledge among intraorganizational sales members impact training transfer performance. More specifically, this dissertation answers a call for more research focused on understanding the influence that manager and peer support has on shaping employee training experiences, learning, and behaviors (Grant and Parker 2009; Van den Bossche and Segers 2013). My focus and intended contribution is to establish whether social networks in the form of friendship and knowledge networks provide access to valued resources that enhance the link between learning and sales training transfer performance. To support my inquiry, I blend social capital theory (Lin 1999; Nahapiet and Ghoshal 1998) and the theory of situated learning (Lave and Wenger 1991) to investigate how training, individual learning, and both supervisor and peer support impact sales training success. Given the well-recognized problem of measuring and evaluating sales training investments, in addition to the fact that post training work environment is a contributing success factor for training programs, there is a critical need to better understand how training, sales managers’ support (e.g. leadership style), and intra-organizational social support impact the return on investments (ROI) of sales training programs. In the absence of such an understanding, the development, execution, management, and evaluation of sales training programs will likely 7 remain problematic. This study can be expected to have a significant positive impact, not only on improving the efficacy of sales training programs, but also on the sales education field. I aim to make 5 contributions with this dissertation. First, I provide an extensive review on the topic of training and training transfer. Findings from this body of work have yet to adopted and assimilated into the sales training field and provide the basis for which I build upon in this dissertation. Second, I review the sales training literature to report on the key areas of sales training that have been previously researched and make suggestions for why there is limited research aimed specifically at measuring and evaluating sales training. Third, by adopting social capital theory and the theory of situated learning, I develop and test a sales training model that focuses on the impact that managerial and peer support play in the successful transfer of training overtime. Fourth, I discuss the findings from this research and provide theoretical and managerial implications pertaining to the influence that social support has on the training transfer performance. Lastly, I acknowledge the limitations of this study and suggest avenues for future researchers to consider to increase the impact of future sales training research projects. 8 LITERATURE REVIEW Training in Organizations The topic of training has been extensively researched in a variety of disciplines, across diverse settings. Topics related to the design, delivery, and implementation of training and how these impact training effectiveness have been investigated in the fields of psychology, sociology, and business. The importance of training is reflected in the continuous publication of training reviews in the Annual Review of Psychology (Aguinis and Kraiger 2009; Campbell 1971; Cannon-Bowers and Salas 2001; Goldstein 1980; Latham 1988; Tannenbaum and Yukl 1992; Wexley 1984). Training is defined as “the acquisition of skills, concepts, or attitudes that results in improved performance in an on-the-job environment” (Goldstein 1980, p. 230). Organizations invest in training for a variety of reasons, including to provide employees with additional knowledge, skills, and abilities (KSA) to improve performance. Such an investment in human capital is considered an avenue that may lead to a competitive advantage (Salas et al. 2012) There has been considerable development and advancement in training research over the last 60 years. Early training research was “voluminous, non-empirical, non-theoretical, poorly written and dull” (Campbell 1971, p. 565). Much of the research in the 1950s and 1960s avoided the topic of what is to be learned from training and focused almost exclusively on topics related to hardware training techniques (Campbell 1971). In addition, research at this time did not consider training participant behavior and how content of training programs may or may not lead to trainee learning and behavioral change (Campbell 1971, p. 594). More research was needed that focused on observing training related behaviors in combination with empirical work that could help to isolate methods and concepts to aid behavior modification. Early training researchers stressed that learning tasks associated with training should not be viewed as a 9 “mechanistic behavior” and suggested the existence of interactive effects between behavioral outcomes and “subsystems” within an organization (Campbell 1971). Over the course of the next decade (1970s), training research remained non-theoretical and non-empirical. Researchers that did attempt to contribute substantively and empirically offered “little that was new or thoughtful” (Goldstein 1980, pp. 231). On the other hand, authors that did perform empirical investigations began to develop important conceptual and theoretical material related to the instructional process (Goldstein 1980). Researchers considered training topics that focused more on criterion development, need assessments, and instructional systems. Along with these advancements, Goldstein (1980) suggested that more research be conducted on need assessment techniques, focusing on what behaviors are essential to perform needed tasks, along with the necessary learning and instructional content best suited to accomplish the desired learning (Goldstein 1980, p. 262). There was a need to better understand how to develop valid training evaluation models, utilizing individual difference methodology. Training began to be viewed as an intervention within an overall organizational development process. This led to a “desperate need for high quality empirical investigations that examine the usefulness of training techniques” (Goldstein 1980, p. 263). Incorporating the revealed importance of learning in the overall training process, Wexley (1984) defined training “as a planned effort by an organization to facilitate the learning of jobrelated behavior on part of its employees” (Wexley 1984, p. 519). Technology usage was increasing and there was a need for retraining. In attending to prior calls for research (Goldstein 1980), researchers focused on investigating topics related to needs assessment, maximizing trainees’ learning, training methods, and evaluating training programs. Wexley (1984) encouraged researchers to investigate training topics at the organizational level, specifically how 10 organizational internal and external forces impact the choice of training and subsequent performance. Additionally, Wexley (1984) stressed future research that linked task analysis and training design, in addition to suggesting the use of multiple source survey responses. At the time, it was unknown if “individual performance deficiencies are caused by inability or motivational and/or environmental causes” (Wexley 1984, p. 544). Future researchers were encouraged to match individual differences (i.e. aptitudes) between trainees and instructional strategies (i.e. treatments), along with investigating how to facilitate positive transfer through such mechanisms as goal-setting (Wexley 1984). As explicated by Latham’s (1988) review, reiterating Campbell (1971), Goldstein (1980) and Wexley (1984), there was a need to develop training based theories. Researchers were concerned with the inability for training research to promote permanent change in the behavior of the practitioner. Theories pertaining to Bandura’s social learning theory as well as theories explaining underlying cognitive mechanisms such as self-regulation and self-efficacy were ideal starting points for this need. In addition, because of economical and societal issues pertaining to labor needs and organizational and employee relations, research that focused on cross-cultural training was stressed. Furthermore, emphasis was placed on methodological issues pertaining to training research, such as issues related to rater reliability, sample size and experimental design (Latham 1988). During the mid-1980s through the early 1990s, training researchers investigated training needs analysis from an organizational, task, and personal level, along with topics related to the design of training, including influences of cognitive mechanisms such as automatic processing, mental models, and metacognition and learning skills (Tannenbaum and Yukl 1992). Simulations and games, along with high-tech methods continued to build upon behavioral modeling 11 frameworks and topics such as trainee characteristics and the pre-training and post-training environments were identified as key factors of influence during training processes. Researchers focused on trainees’ abilities and skills, motivation, attitudes, and expectations, along with considering how aptitude-treatment interactions influenced training effectiveness. Pre-training topics such as environmental cues and signals, trainee input and choice, and preparation for change were discussed, along with suggesting that post-training environments played a key role in the successful transfer of training Tannenbaum & Yukl (1992). In 2001, Salas and Canon reviewed the progress over the preceding decade across five major areas including training theory, training needs analysis, antecedent training conditions, training methods and strategies, and post-training conditions. The authors concluded that in response to the suggestion by Tannenbaum and Yukl (1992), there was an “explosion” of theoretical, methodological, and empirical work in training research. Topics focusing on cognitive and organizational concepts had advanced the field, and the authors suggested that the topic of training did not belong to any one discipline. Work in fields such as engineering, human factors, instructional psychology, and military development extended the reach and contribution of training research. Technology would take a significant role in future training development and training was considered a system embedded in organizational contexts. It was during this time that there was a clear distinction made between training effectiveness and training evaluations. As clarified by Salas and Canon (2001, p. 490), Training effectiveness “is concerned with why training works and is much more “macro” in nature” as compared to training evaluation which “examines what works in training, taking a much more “micro” view (i.e. focused on measurement)” (Salas and Canon 2001, p. 491). More specifically, training effectiveness focuses on training interventions from a system perspective and considers the training method, the 12 motivation of trainees, and the mechanisms to ensure transfer, while training evaluation attempts to identify learning at various levels as the basis for determining if an intervention was effective. In the most recent training review published in the Annual Review of Psychology, Aguinis and Kraiger (2009) emphasized the benefits that training produces for individuals, teams, organization and society. Adopting a multi-level, multi-disciplinary view, Aguinis and Kraiger (2009) stressed the importance of needs assessment and pre-training states of trainees (e.g. motivation), in addition to effective training design and delivery incorporating error training and training evaluations. Essential to training success is understanding both the goals of stakeholders, and the impact of interpersonal factors on the transfer of training. Aguinis and Kraiger (2009) suggested that future researchers study cross-level transfer, such as vertical transfer which refers to how training at the individual level promotes “better functioning at the team and organizational level” (Aguinis and Kraiger 2009, p. 467) Training Transfer As discussed in the introduction of this dissertation, for a training initiative to approach success, training must be transferred successfully. Training transfer is defined “as the degree to which trainees effectively apply the knowledge, skills, and attitudes gained in the context to the job” (Baldwin and Ford 1988, p. 63). For transfer to occur, training content must be generalized and maintained over a period of time on the job. Baldwin and Ford (1988) describe the training transfer process in terms of training inputs, outcomes, and conditions of transfer. Training inputs consist of trainee characteristics, training design, and work environment. Trainee characteristics such as ability, personality, and motivation impact learning and retention, which are necessary for the generalization and maintenance of training material. Training design refers to the principles of learning, sequencing, and training content, which aid in predicting learning and 13 retention. In addition, a work environment consisting of support and the opportunity to utilize training impact learning and retention. Since Baldwin and Ford’s initial 1988 piece, the definition of training transfer has been expanded to “mean that learners successfully use in their jobs the knowledge and skills gained in training in ways that fulfill work needs” (Yelon, Ford, and Bhatia 2014, p.28). Research expanded the notion of training transfer from “the extent to which knowledge and skills acquired during a training setting are generalized and maintained over a period of time in the job setting” (Ford and Weissbein 1997, p. 34), to include a trainee’s ability to adapt to novel or changing situational demands. Holyoak (1991) differentiated between adaptive versus routine expertise, routine expertise being similar to training generalization. On the other hand, adaptability is described as the extent to which an individual views trained methods as not appropriate or effective, leading to a consideration for new methods or novel strategies in light of added complexity. Regarding learning principles, topics in cognitive and instructional psychology were influential in adding a level of understanding to the role that guided discovery, error-based instruction, and metacognition play in training design. Guided discovery, which takes an inductive approach to learning, allows trainees to explore and experiment with the training tasks. This process promotes learning rules and principles underlying performance advances, leading to greater transfer due to increased motivation to learn. In addition, error based learning (Ivancic and Hesketh 1995) brings about attentional resources within the trainee and alerts them to incorrect assumptions, promoting better “mental models” of the learned task. Additionally, training designs, metacognition, which promotes planning, monitoring, and regulated learning strategies have been shown to increase trainees (e.g. students) performance (e.g. grades) (Volet 1991). Regarding trainee characteristics, Ford and Weissbein (1997) discussed the topic of goal 14 orientation and theories relating to motivation. The construct of goal orientation, an individual combination construct of the level of mastery versus performance based orientation, influences the effort of continual learning by trainees. Individuals high in mastery orientation tend to focus on developing new skills, learning the trained task, and determining appropriate learning strategies to master the trained material. Individuals with a high-performance orientation often focus on outperforming others, independent of whether they expend the effort on the trained task (Ford and Weissbein 1997). Cheng and Ho (2001) provided the field with a comprehensive training transfer review, specifically developing a conceptual framework presenting the “popular” constructs that had been empirically tested during their targeted review timeframe. The authors based their framework on Kirkpatrick’s four-level taxonomy of training evaluation including trainee reactions, learning, behavior, and organizational results (Kirkpatrick 1967) and adapted Kirkpatrick’s framework to address the topic of training transfer. Cheng and Ho (2001) defined four stages of training transfer as 1) trainee pre-training motivation to master training content 2) learning as a means to master the content of training 3) measuring performance as the extent of trainee achievement in a training context and 4) transfer outcomes that may benefit both the trainee and the organization (e.g. behavior change, job performance, skill maintenance, etc.). Three factors that have been demonstrated to influence training transfer are learning characteristics, intervention design and delivery, and work environment influences (Burke and Hutchins 2007). Regarding learner characteristics, studies have shown that cognitive ability, selfefficacy, pre-training motivation, anxiety, openness to experience, perceived utility of training, career planning, and organizational commitment have strong or moderate relationships with training transfer (Burke and Hutchins 2007). Additionally, neuroticism, learning goal orientation, 15 conscientiousness, and voluntary participation have moderate relationships with training transfer (Blume et al. 2010). Regarding work environment, research has shown that transfer climate, supervisory support, peer support and opportunity to perform have strong or moderate relationships with transfer (Burke and Hutchins 2007). More specifically, transfer climate followed by support had the highest relative relationship with transfer (Blume et al. 2010). Since the year 2000, research has been repetitive in examining previous models and little has been discovered regarding new variables that may forecast the actual transfer of training behavior (Cheng and Hampson 2008). Furthermore, there is a prevalence of inconsistent and unexpected findings from previous studies. Researchers have encouraged the use of other schools of thought to study the transfer process in order to attend to inconsistency in research findings (Cheng and Hampson 2008). Progress has been made in 4 key training transfer areas. More specifically, empirical work has demonstrated that 1) researchers have moved past training related to simple motor tasks to study the transfer of complex training content 2) there has been an increase in the use of actual interventions designed to enhance transfer 3) there has been progress made to better understand the influence that pre- and post-training influences have on transfer and 4) there is a variety of transfer measures, including evaluating transfer across multiple time intervals (Baldwin, Ford, and Blume 2009). In addition, research has been expanded to study complex and authentic learning content, along with complex learning tasks (e.g. interpersonal skills), including samples of business employees, health professionals, and computer specialist as opposed to student samples (Baldwin et al. 2009). As I have demonstrated, there has been advancement in our understanding of the factors that influence training success in general. Unfortunately, I cannot say the same for the field of sales training. To begin to update and move the sales training field forward to the level of rigor 16 and completeness shown by our colleagues in other discipline, it is essential to first understand where the sales training research presently is. To achieve this, I conducted a literature review on the topic of sales training utilizing Web of Science with advanced search methods including operators, fields, and special characters to identify prior sales training research. As suspected, I conclude that the sales training literature is vast and extremely fragmented. The following section of this dissertation attempts to provide an overview of the work that has been conducted on the topic of sales training. It is my hope that by combining findings on the topic of training and training transfer with the work conducted within the sales training field, the topic of sales training can be nurtured and developed to a point that aids practitioners in the design and execution of impactful sales training programs. Sales Training The preceding training and training transfer reviews demonstrate the plethora of research topics that have been reviewed over the past many decades. Although the topic of sales training has been studied, the extent of sales training research with regard to theoretical development, empirical studies, and methodological rigor are dwarfed by the topic of training in other disciplines. This is not to be a criticism of the prior work in sales training, but rather a recognition and call for action to incorporate findings and methodologies from our colleagues in other disciplines. Much of the sales training research to date has been qualitative (e.g. case studies) and descriptive studies, with very little empirical work and theoretical development (Singh, Manrai, and Manrai 2015). I suggest that the lack of attention provided to sales training research by our training colleagues in other disciplines is a function of three factors inherent to the selling role and selling organizations. First, researchers have concluded that the measurement of training is a complex task, especially when the skills to be trained are open skills as compared 17 to closed skills (Yelon and Ford 1999). Closed skills, such as how to fill-out an expense report, are those where there is one correct way to complete a task (Yelon et al. 2014). Open skills training are often “intangible tools – ideas, rules, principles, or procedures to guide action” (Yelon et al. 2014, p. 28). Open skills do not have clearly defined boundaries for which an individual may act. Sales training programs often focus on open skills and as such trainees may elect to use training content in different ways (Baldwin et al. 2009), leading to measurement difficulties in evaluating if sales training content is being used. Second, salespeople are provided with relatively high levels of autonomy to conduct their job (Johnston and Marshall 2016). The dynamics and motivation to transfer training are different for closely supervised compared to relatively autonomous employees (Yelon and Ford 1999). Generally speaking, salespeople are not forced by sales managers and the selling organization to follow standard procedures (i.e. utilize training). Third, selling is a rich environment in which there are many factors that inhibit or promote the success of selling efforts by salespeople and sales teams (Johnston and Marshall 2016). Attributing individual performance to sales training assumes that salespeople work in isolation. This is clearly not the case, as sales success is a function of not only individual attributes, but also resources exchanged among selling members (Bolander et al. 2015) and help and guidance from sales managers (Boichuk et al. 2014). The topic of sales training has been of interest to sales researchers and practitioners over the past many decades. From the early work measuring performance differences between sales training participants and sales training non-participants (Harris and Vincent 1967), to connecting the effect of sales training to product life cycles (Kortge 1993), to the more recent work linking sales training to a salesperson’s future value to a selling firm (Kumar, Sunder, and Leone 2014), the scope of sales training research continuously evolves. Furthermore, research related to topics 18 of ethics in selling (Valentine 2009), salesperson self-regulation (Leach, Liu, and Johnston 2005) and selling in a global context (Attia, Honeycutt, and Jantan 2008) provide diversity to accurately describe the evolving state of field-based training programs. The topic of sales training and the influence that sales training initiatives have on a salesperson’s knowledge, skills, and abilities (KSAs) remain at the forefront of academic research as a means to test sales training effectiveness under a variety of behavioral conditions (Cron et al. 2005). Lassk et al. (2012) stress that future research in sales training focus on training content development, delivery, and evaluation. As previously stated, training in general is defined as the “systematic acquisition of skills, rules, concepts, attitudes that results in performance in another environment” (Goldstein 1993, p.3). Sales training provides salespeople with knowledge (e.g. product, market, competition, company policies, etc.) and selling skills (e.g. how to match offerings with customer needs) (Dubinsky and Staples 1982), in an effort to enhance salesperson performance (Wilson et al. 2002). The accumulated knowledge and learning of individual salespeople to the selling organization level may be an organization’s only source of competitive advantage (Kohli, Shervani, and Challagalla 1998; Pelham 2002). Most sales training efforts focus on inducing behavioral change that promote performance (Wilson et al. 2002). Behaviors are considered to be more controllable than outputs (Churchill et al. 1985; Pelham 2002), which may be affected by outside factors, uncontrollable to the selling organization. Although research suggests that properly designed sales training programs impact salesperson performance (Frayne and Geringer 2000; Harris and Vincent 1967) some managers see training as a necessary investment that can yield significant performance results, while others view it as an expense that must be controlled (Pettijohn, Pettijohn, and Taylor 2009). 19 In order to organize and provide a concise review of the sales training literature, I organize this section according the three fundamental stages of the sales training process, namely needs identification, training delivery methods, and training evaluations (Goldstein 1993). The first stage relates to sales training assessment and overall program design in which a selling organization determines training needs and sets training objectives. The second stage focuses on the delivery of the sales training material. Sales training can be delivered through a variety of mediums including training manuals, online learning modules, in-class instruction, or through a combination of methods. The final stage focuses on sales training evaluation and assesses the success of a sales training program. Sales training programs may focus on a variety of needs including influence strategies ((Harris and Spiro 1981), problem solving (Fan and Cheng 2006), impression management (King and Booze 1986) and self-management skills (Frayne and Geringer 2000). Prior to initiating a sales training program, a through needs analysis must be conducted to determine the knowledge, skills, and abilities (KSAs) to be trained. During this stage an assessment of the current skills of the sales force is conducted (Borna and Sharma 2011), identifying gaps in skills across departments, selling roles, and other individual and organizational characteristics. Unfortunately, this crucial step is often overlooked, and training needs assessments are often conducted in unsystematic manners (Clarke 2003; Ford and Noe 1987; Wexley 1984). Dubinsky and Hansen (1981) found that 36 percent of large and 63 percent of small companies fail to set training objectives. Additionally, systematic needs assessments are seldom undertaken (Erffmeyer, Russ, and Hair 1991). Appropriate sales training needs assessments should focus on three primary areas; the organization, the task, and the person (Goldstein 1993). From an organizational standpoint, this 20 needs assessment phase ensures that sales training programs align with the available resources and support systems, along with organizational culture (Goldstein 1980). The task assessment specifies the necessary behavioral change to accomplish desired sales outcomes and ensures that the KSAs are developed (Goldstein 1993). Lastly, firms identify which individuals within the salesforce are likely to benefit from training. Formalized needs assessment practices create more effective sales training processes, leading to performance increases through the identified behavioral change (Erffmeyer et al. 1991). Honeycutt, Ford and Rao (1995) surveyed sales executives and found that customer surveys regarding their expectations along with information pertaining to competitors are valuable sources to identify training needs. Furthermore, through observations, sales managers are in the position to identify decencies within salespeople that may be addressed by a training intervention (Honeycutt et al. 1995). Another source of identifying sales training needs is by comparing sales objectives with performance to identify shortfalls in the sales force’s KSAs (Goldstein 1993). Dubinsky (1981) advocates the use of a “sales force management audit” to assist sales executives in identifying existing and potential problems in order to take corrective action. The author presents four elements to consider when conducting an audit. First, the sales management environment focuses on intra- and extra-organizational factors the affect sales management actions. Second, the sales management planning system is audited to determine if the sales functions objectives are clearly stated, realistic, measurable, and appropriate when taking into considerations a firm’s resources and strategic objectives. Third, the sales management organization evaluation assesses the capability of the sales management team and determines if additional training is needed and if compensation and incentives align with training goals. 21 Finally, the sales management function audit focuses on various management functions such as the recruitment and selection, supervision, motivation, sales forecasting, and budgeting. Overall, this holistic approach presented by Dubinsky (1981) increases the breadth and depth of conducting a needs assessment, taking into account a variety of factors that impact the selling function’s performance. Attia, Honeycutt, and Leach (2005) offer a 3-stage model that ensures there is alignment between training objectives and organizations strategies. The first stage of this comprehensive model focuses exclusively on the assessment of training needs. More specifically, the authors stress the importance of collaborative efforts among multiple levels within the selling function to identify what behavioral changes are expecting to improve performance. Additionally, sales executives must identify those individuals who are deficient in desired competencies, while simultaneously consider whether some salespeople will benefit at all from training. In theory, this practice would focus on one individual salesperson at a time, but the authors note that this process become increasingly difficult as the size of the salesforce grows. A solution to this scope problem is to segment trainees based on geographic location, market/customer characteristics, and individual performance data (Attia et al. 2005). There are a variety of methods for which sales training content is delivered, including traditional reading mediums, in-class lectures, role-plays, interactives videos, and simulations. A single method use is unlikely to accomplish a company’s training needs and within each method there are inherent strengths and weaknesses (Erffmeyer and Johnson 1997). Although, early adoption of technology to enhance sales training was a slow process, today, technology plays a major role in the delivery of sales training material. Russ et al. (1989) surveyed sales managers from a diverse set of industries on their companies’ current usage of high-tech sales training 22 approaches and the perceived effectiveness of various methods. Included in their study were responses to questions pertaining to the use of computer aided instruction methods, computer managed instruction methods, tele-training methods, and computer conferencing. Although hightech training methods were shown to be effective in meeting training objectives (Erffmeyer, Russ, and Hair 1992), firms remained reluctant to adopt such methods due to the perceived high start-up costs and uncertain benefits associated with high-tech training methods (Russ et al. 1989). Adoption eventually began to increase for technology based sales training methods that encouraged high participation and that focused on six training objectives: knowledge acquisition, knowledge retention, change in attitude, development of interpersonal skills, development of problem solving-skills, and participant acceptance of training goals (Erffmeyer et al. 1992). Academic researchers continued to uncover meaningful use of sales training that required active participation such as role playing, interactive video, and computer-assisted or –managed instruction. These active methods were shown to increase participation, leading to increases in trainee learning (Erffmeyer et al. 1992). In addition, this research demonstrated the benefits of case studies, business games, and discussions when the size of the training cohort increases. On the other hand, passive participation in the form of viewing video and attending lectures did not promote advanced processing of training material (Erffmeyer et al. 1992). A primary source of learning is derived from the reading of training material. Much of today’s information on product specifications are available to salespeople through company online documents and websites. The “readability” of sales training material must be easy, which lessens trainee effort and reduces frustration and anger. Readability is “concerned with the ease of understanding or comprehension due to the style of writing” (Kaminski and Clark 1987). Readability is a function of sentence length, word length, and vocabulary used. Furthermore, 23 well written training material such as product specifications, and company sales strategies and policies, reduces the time needed for managers to explain the content, thus increasing training material value (Kaminski and Clark 1987). Firms can measure the readability of training material by computing a composite score based on sentence length and the number of difficult words within written training material. Gunning’s (1968) Fox Index provides a measure of reading difficulty of sales training material to ensure sales training material aligns with the reading comprehension of its audience. Early research on technology use in sales training investigated computer based sales training packages, such as Sell! Sell! Sell! and Customer Oriented Selling – Computer- Based Training Edition (Collins 1986). These “canned” sales training program provided a knowledge base, which salespeople could build through continuous individual learning and coaching to approach competency (Collins 1986). It has been shown that combining the benefits of expert personal computer and sales training programs create synergistic effects between ongoing sales training and monitoring by supervisors that enhances salesperson development and coordination of sales efforts (Steinberg and Plank 1987). One of the attractive aspects of computer based training is its cost effectiveness (Collins 1986). Computer based training may cost 10% of conventional sales training, when considering the cost of instructors, travel, and use of facilities (Collins 1986). Furthermore, computer based sales training programs allow participants the flexibility of when to engage with course material and to review concepts when further instruction and learning is needed. Computer based sales training also increases trainer control and leads to higher participant acceptance (Clopton 1986). Additionally, computer based sales training allows for the development of interactive simulation exercises to reflect realistic scenarios, while also providing immediate feedback. 24 Another method for which sales training material is provided to trainees is in video format. Early research showed the benefits of incorporating some aspect of video within sales training. In an experimental study conducted by Gehring and Toglia (1988), participants were shown a video segment from a pharmaceutical sales training program under a variety of treatment conditions, including a combination of video and audio, lecture, and lecture-plus slides. Comparing pre- and post-training testing across treatments, video training was resistant to forgetting when compared to lectures with and without visual slides. Interactive videos provide the benefits of visual learning with immediate feedback. Furthermore, interactive videos can be combined with artificial intelligence to enhance the learning experience (Martin and Collins 1991). Early interactive video systems were merged with computers, laser discs, and touch screens to provide an interactive environment for training participants. Prescribed knowledge base and inference engines allowed for artificial intelligence to direct participants during their decision-making steps. Artificial intelligence programs evaluate trainee answers and determine what material will be presented next. The selection of incorrect responses allows for the system to prescribe alternative methods and, if deemed necessary, require trainees to review lesson material before advancing to the next phase of training. In their research, Martin and Collins (1991) collaborated with BellSouth Services on measuring the effectiveness of such a system. The authors found that interactive video users reported satisfaction based on the opportunity to practice in private and receive timely feedback. Training participants reported learning more in less time, in a comfortable environment. With the adoption of video home systems (VHS), commercial sales training organizations began offering sales training programs in a convenient format that increased the flexibility of delivering sales training content to selected individuals. Video enhanced sales training programs 25 provided sales managers with the opportunity to train salespeople that would benefit from additional training (Honeycutt, McCarty, and Howe 1993). Participants studied written material and watched videos at their own pace. Once training participants felt that they had acquired the intended knowledge, they would call a toll-free number to take a multiple-choice test. The participant was immediately provided with his or her test score and if necessary a second round of testing was conducted to ensure competency in the training material. This form of sales training enhanced organizations’ ability to communicate consistent messages to decentralized organizations, along with saving time and reducing the need of trainers to conduct multiple training sessions (Honeycutt et al. 1993). The introduction of the Internet in the 1990s provided additional capabilities to offer sales training across distances and blend computer-based training with the interactive online capabilities. Web-based training was an innovative approach to distance learning, where the capabilities inherent to the Internet are blended with computer-based training to increase the interaction among sales trainers and trainees (Barron 1998). Benefits of web-based training include the ability to dynamically update information, reach a dispersed audience, incorporate both audio and visual content, and communicate among attendees. In addition, web-based training allows sales organizations to design learning environments that combine the benefits of tutorials, games, and simulations, while simultaneously increasing learner attention through website design (i.e. removing internet browser bars). In addition, web-based training can deliver just-in-time learning modules that provide electronic job aids to continually support salespeople throughout the sales process. Users of online training have reported that training within an online environment is enjoyable and easy to use, and report that web-based training improves their work performance (Abbott et al. 2009). Ritchie et al. (2011) tested a technology acceptance 26 model (TAM) within the context of a global technology firm that employed salesforce software training and support program using a learning management system. Respondents showed favorable acceptance of the learning management system based on perceived ease of use and perceived usefulness. The authors found that acceptance was moderated by geographic proximity to the country of company origin. Sales training simulations are another common method to train salespeople. During the early 1990s there was a growth of sales simulation offerings in the marketplace (Faria and Dickinson 1994). Simulations offer enhanced, interactive technology for sales organizations to train new salespeople, identify potential sales managers, and provide ongoing training. Simulations provide a method to train across multiple areas of sales including goal setting, strategy formation, and tactical decision making. Simulations provide an active learning environment, where participants can gain experience without real-world penalties. Furthermore, simulations provide immediate feedback, along with adding excitement to learning (Faria and Dickinson 1994). Although technology greatly enhanced the reach and efficacy of sales training, classroom based training offers additional factors that enhance learning. Research demonstrates that skill development that requires real-time two-way interaction are best learned in formal classroom settings. In response to organizations considering alternatives to costly, in-class sales training, such as computer assisted distance learning, Graham et al. (1998) investigated whether benefits existed for classroom based sales training. The author conducted interviews with individuals three months after a classroom sales training event and found that topics including negotiations and product and technical knowledge application within the sales process were enhanced through classroom training. Respondents reported increases in their ability to differentiate their product 27 from competitor products, along their ability to communicate effectively with buyers and develop targeted sales plans. Participants reported that classroom based sales training provided the environment to interact and apply concepts to real life scenarios, learn new approaches to sales, and gain feedback through interaction and participation (Graham et al. 1998), elements not fully supported through computer based sales training. Another classroom based sales training method is role-playing. Role-playing has been shown to be a powerful vehicle for behavioral change (Robinson 1987). Unlike passive sales training content such as manuals and lectures, role-playing is active. Role playing requires active involvement in peer-review settings, leading to higher learning (Robinson 1987). Research suggests that role-play sessions should focus on one specific subject, the buyer should be somewhat resistant to making a purchase, and debriefing sessions should focus on what the salesperson could have done better, thus reinforcing the learning process (Robinson 1987). Furthermore, to reduce defensiveness of the seller, comments should also include aspects of the role-play that the participants did well (Robinson 1987). Firms that utilize multiple methods of sales training tend to perform better than firms that limit the scope of training engagement (El-Ansary 1993). In addition, firms that utilize technology enhanced sales training programs are more engaged and focused on attending to evaluating training programs. El-Ansary (1993) studied differences between sales training practices between top and low performing sales organizations. The author found that top performing sales organizations did not spend significantly more on training, but did offer more training initially to new hires along with offering more methods, content, and sources for training delivery. The author suggests that sales organizations rebalance training budgets to address strategy needs and to expand training sources and methods. Honeycutt (2002) conducted a study 28 between high-tech and low-tech sales training programs within Malaysia and found that firms that utilized high-tech sales training programs engage in higher levels of activities across 4 key sales training practices (namely needs assessment, objective setting, program content, and evaluation) as compared to firm that utilize low-tech sales training programs. Additional research has examined the effectiveness of sales training methodologies to train geographically dispersed salesforce members. Erffmeyer and Johnson (1997) investigated distance education sales training efforts to identify effective methods and uncover the processes that enhance learning across distance. In their research, the authors assigned 6 treatment groups within a given insurance firm to various distance learning methods, including lecture, manual, manual with videotape, audio-graphics, computer tutorial and video-conferencing. Results indicated that selfscheduling methods (i.e. manual plus videotape, computer tutorial, written manual) were least disruptive with regard to salesperson time. Regarding enhancing learning processes, trainees indicated that lectures were most effective and written manuals were least effective. The authors conclude that sales trainers’ reluctance to incorporate high-tech distance methods because of perceived low interaction and quality are unfounded. There have been numerous training evaluation methods introduced in the academic literature (Singh et al. 2015). The most commonly used evaluation model is Kirkpatrick’s 4 levels of training evaluation (Kirkpatrick 1967). Although academics point to deficiencies within the Kirkpatrick model (Alliger and Janak 1989), academics and practitioners still use this model as the basis for training evaluations based on its simplicity and comprehensiveness. Specific to sales training, Leach and Liu (2003) do find support for Kirkpatrick’s model when considering the hierarchical relationship between each level. At the first level, trainee’s reactions are measured regarding the perceived value of training, whether the training aligns with the sales 29 context, and their perceived ability to incorporate the training into their selling techniques. The second level of Kirkpatrick’s model focuses on learning. At this level, trainees’ recall of pertinent training material is measured through testing and observations. The third level, behavioral change, focuses on whether the learning that was obtained through training is successfully applied within the field. Understanding what motivates an individual to make the critical step to implement what has been learned to actual changes in behavior is essential and represents “training transfer”. The last level focuses on results. More specifically, this stage identifies the results obtained through behavioral change of trainees. This stage is often the hardest to measure and frequently overlooked (Attia, Honeycut, and Attia 2002). Extending Kirkpatrick’s training evaluation model, Phillips, Phillips, and Robinson (2013) provide a rigorous methodology for calculating the return on investment (ROI) of sales training programs. The authors introduce a ROI methodology that includes 4 processes to improve the calculation of sales training program evaluation. The first process focuses on evaluation planning, which includes developing training objectives and evaluating the training plan. The second process entails collecting the appropriate data during and after the sales training program. Next, the data that are collected are analyzed to isolate the effects of the training program and convert the data to monetary value. Finally, the fourth process relates to the reporting of the results to assess the overall success of the program and to identify areas for improvement. Research has demonstrated that most evaluation measures are simple and that complex approaches are used infrequently (Erffmeyer et al. 1991). In addition, managerial perceptions, evaluation restrictions, methodological problems, and lack of empirical evidence are cited as sources of sales training evaluation difficulties (Attia et al. 2002). Progress has been made 30 regarding sales training evaluation. Researchers have formally conceptualized the sales training evaluation process (Attia et al. 2005; Lupton, Weiss, and Peterson 1999), while others have provided calculation methods to isolate the return on investments of training programs through utility analysis (Honeycutt et al. 2001). Sales training evaluation is still arguably the most difficult stage within the sales training process. In summary, previous sales training research does provide substantial findings and recommendations for practitioners to consider when designing, implementing, and measuring a sales training program. Inherently missing are theoretical frameworks and empirical testing that aim to uncover phenomena within sales training environments. It is my goal of this dissertation to begin the process of introducing theory and empirically modeling a sales training process to provide the impetus for future sales training projects. In the remaining sections of this dissertation I develop and report the findings of a sales training model that focuses on uncovering the effects that social support in the forms of instrumental leadership and peer support have on the sales training transfer performance. More specifically, by blending social capital theory with the theory of situated learning, I theorize how resources that are exchanged within intraorganizational networks enhance situational learning and how ongoing learning post sales training impacts training transfer performance overtime. 31 CONCEPTUAL BACKGROUND Figure 1 provides an illustration of my hypothesized model. I focus on studying the impact that training has on training transfer performance overtime. As such, the event of a sales training intervention is the independent variable in my model and training transfer performance is the dependent variable. Training transfer performance is measured as the level of sales achieved by individual salespeople that align with the sales training objective. As sales training is a process, not a static event, I measure individual training transfer performance before and after the sales training intervention. I will explain the calculation of this measure in more in detail later on in the methodology section. As previously discussed, transfer climate and peer support are key factors that promote training transfer. I investigate the interactive effects of three sets of variables. First, I propose that managerial support in the form of instrumental leadership provides salespeople with strategic guidance and performance monitoring to strive to achieve sales training objectives. Second, peer support in the form of friendships promote trust, provide emotional support, and reduce stress, factors that will promote positive behavioral change after a sales training intervention. Third, knowledge networks, which include both knowledge sharing and knowledge receiving networks, enhance individual learning, allowing salespeople to more effectively apply sales training overtime and subsequently increase training transfer performance. The theoretical basis of my model includes both social capital theory and the theory of situated learning. More specifically, I utilize social capital theory to suggest that the exchange of friendship and knowledge resources within a selling organization’s intraorganizational social network help in the effective implementation of training content into salespeople selling processes. In addition, I incorporate the theory of situated learning to explain how social network knowledge transfer within the job environment promotes continuous 32 learning that is essential to achieve successful behavioral change. In the remainder of this section, I describe in detail both social capital theory and the theory of situated learning, which provide the foundations for my research hypotheses. Social Capital Theory Social capital theory describes how the access and use of resources obtained by individuals within social networks affects performance (Burt 2000; Lin 1999). Social capital theory subsumes network theory in that it describes social investments made by individuals and how these investments are connected within a networked environment. At the broadest level, society can be viewed as a market in which individuals exchange various resources in pursuit of their interests (Burt 2000). Based on the location within a market, some individuals are provided with better access to resources based on a positional advantage. In essence, “better connected people enjoy higher returns” (Burt 2000, p.348). Social capital theory describes the existence of macro-level (e.g. structural) and micro-level (e.g. individual) action through social ties (Lin 2001). Lin (1999) defines social capital as “resources embedded in a social structure which are accessed and/or mobilized in purposive actions” (Lin 1999, p. 35). According to Lin (1999), “social capital contains three elements intersecting structure and action: the structural (embeddedness), opportunity (accessibility) and action-oriented (use) aspects” (p. 35). Lin (1999) describes three elements within a social capital framework. First, social capital involves an investment. Second, an individual’s location within a social structure influences that individual’s access to and mobilization of social capital. Third, social capital specifies two types of expected returns within corresponding networks; instrumental and expressive. Instrumental returns refer to the opportunity to obtain resources (e.g. knowledge) not possessed by a focal individual. Returns from instrumental actions may constitute economic return, political return, 33 and social returns (Lin 1999). Expressive returns focus on maintaining, possessing, and subsequently consolidating resources and defending against possible resource losses. According to Nahapiet and Ghoshal, social capital is defined as the “sum of actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit” (p. 243). The authors describe three distinct aspects of social capital, namely structural, relational, and cognitive dimensions. The structural dimension of social capital encapsulates the location of social interaction within an individual’s social structure and provides certain advantages to that individual (Tsai and Ghoshal 1998). The relational dimension refers to assets such as trust, norms, and reciprocity present within relationships (Nahapiet and Ghoshal 1998). The cognitive dimension includes attributes such as a shared code or shared paradigm that promote collective goals and prescribe proper ways of acting in a social system. Social capital can be conceptualized at different levels of analysis including individuals (Üstüner and Godes 2006), groups (Burt 2000), intraorganizational (Bolander et al. 2015), interorganizational (Chung, Singh, and Lee 2000), and industry (Walker, Kogut, and Shan 1997). Social networks are groups of collaborating entities that are related to each other (Håkansson, Havila, and Pedersen 1999). Social networks are informal structures through which salespeople build social capital that allows them to be more effective in their selling role (Bolander et al. 2015). Analysis of organizational social networks focuses on understanding the interpersonal mechanisms and social structures among employees. The pattern of ties among individuals provides opportunities for individuals to receive information, seek advice and obtain social support (Borgatti et al. 2009). Social network theory distinguishes between two important organizational based networks, expressive (i.e. friendship) networks, and instrumental (i.e. 34 advice) networks (Tichy, Tushman, and Fombrun 1979). Expressive networks include interactions among members focused mainly on exchanging social support and friendship (Lincoln and Miller 1979). Friendships are mechanisms for open communication often in the form of stories, and are associated with high social support, especially during times of instability (e.g. strategic change) or personal hardship (Vardaman et al. 2012). Friendships promote wellbeing, provide reassurance and encouragement during adverse work events (Cohen and Wills 1985), and include the element of trust (Van der Horst and Coffé 2012). Research has shown that supportive friendship relationships diminish exposure to stress (Halpern 1999). Additionally, friendships tend to have stronger ties within dense clusters and are better suited to provide social support than access to information (Granovetter 1973). Network ties in expressive networks tend to be stronger and involve more intimate interaction among members (Marsden 1988). As compared to expressive ties, instrumental ties are generally weaker and focus more on task-related interactions (Ibarra 1992). Instrumental networks are a collection of individuals that share relationships based on the exchange of information, advice, and guidance on work related tasks (Lincoln and Miller, 1979). Inside an organization, employees establish connections to exchange resources and transfer knowledge (Tsai 2000). A knowledge network, a form of social network, is a “set of nodes-individuals or higher level collectives that serve as heterogeneously distributed repositories of knowledge and agents that search for, transmit, and create knowledgeinterconnected by social relationships that enable and constrain nodes’ efforts to acquire, transfer, and create knowledge” (Phelps, Heidl, and Wadhwa 2012 , p. 1117). Knowledge networks provide paths for the transmission of knowledge and information, may be defined through formal and/or informal ties, and be described under various flow conditions (e.g. knowledge sharing, knowledge receiving, etc.). Knowledge is an import resource that can be 35 accessed through social networks and is assumed to improve performance (Wong 2008). Employees collect and incorporate knowledge and information from their social networks to work more effectively (Borgatti and Cross 2003), including learning, solving problems, and making decisions (Cross and Thomas 2008). Through access to knowledge, in addition to shared values and common language, individuals learn from each other (Tsai 2000). I propose that centrality in both friendship and knowledge networks will enhance the relationship between training and training transfer performance, through different network mechanisms. To examine network structures an appropriate centrality measure must be identified. Based on the focus of my research, I believe that degree centrality is a network measure that aligns well with the relationships under investigation. Degree centrality represents the extent to which an individual is connected to others in a network and quantifies an individual’s relative number of relationships in the organization (Sparrowe et al. 2001). Individual degree centrality has been shown to be positively related to a variety of outcomes including organizational commitment (Hartman and Johnson 1989) and job satisfaction (Dean and Brass 1985). Based on my data collection encompassing a complete network with clear boundaries and an established structure, degree centrality appears to be a valid representation for a measure of connectedness (Borgatti, Everett, and Johnson 2013). Theory of Situated Learning Situated learning provides opportunities for adult learners to participate in learning practice (Greeno 1997). Wilson (1993) suggests that adults do not simply “learn from experience, they learn in it, as they act in situations and are acted upon by situations” (p. 75). Greeno (1997) suggests that as adults form identities as learners, they “take initiative and responsibility for their learning, including active formulation of goals and criteria for their own 36 success” (p. 9). As such, the theory of situated learning can be applied to describe the active learning process necessary to affect positive behavioral change toward achieving sales training program objectives. The separation between knowledge and practice is considered “unsound” both in theory and practice (Brown and Duguid 1991; Lave and Wenger 1991). Learners acquire conceptual knowledge when learning is “situated” (Brown, Collins, and Duguid 1989). Situations coproduce knowledge through activity. Researchers suggest that a composite concept of “learningin-working” more accurately portrays the fluid evolution of learning through practice (Brown and Duguid 1991). Brown, Collins and Duguid (1989) suggest that “knowledge is situated, being in part a product of the activity, context, and culture in which it is developed and used” (p.32). Additionally, Hendricks (2001) recognizes that “learning and doing are inseparable and that learning is a process of enculturation” (p.1). As such, the theory of situated learning (also termed situated cognition) (Lave and Wenger 1991), posits that learning will occur when three conditions are met. First, the learning context must be authentic and similar to aspects of the job situation. Second, the theory of situated learning suggests that learning occurs in a social context (Lave and Wenger 1991). Third, learning should consist of multiple contexts and perspectives, which will promote transfer across situations (Greeno 1997). With regard to authenticity of the learning context, real world relevance leads to heightened engagement and deep learning (Biggs 2011; Prosser and Trigwell 1999). Deep learning approaches require sales trainees to work at a more conceptual level to achieve challenging learning outcomes. Furthermore, sales trainees engage in synthesizing new information with prior knowledge to apply new understandings to novel situations. These novel situations are subsequently authentic and related to professional practice (Bridges, Chan, and 37 Hmelo-Silver 2016). Unlike learning that takes place outside of authentic situations, situated learning develops through continued situated use and requires complex social negotiations. Intraorganizational selling networks provide such an environment (Bolander et al. 2015). The social dimension within authentic, context-based learning enhances the process of collaborative knowledge building and creation (Scardemalia and Bereiter 2014). The appropriate and successful use of knowledge is a function “of the culture and the activities in which the concept has been developed” (Brown et al. 1989). Communities and culture of practice are bound by socially constructed webs (Geertz 1983) and as such, practicing with members of a learning community allows for users to observe knowledge use and adopt knowledge with great success (Brown et al. 1989). Conceptual knowledge is understood through use, which continuously changes the user’s view and results in the adoption of the belief system of the contextual culture in which the knowledge is used (Brown et al. 1989). In addition, when learning is applied through socially constructive acts, learners can acquire explicit and implicit knowledge (Brown and Duguid 1996). Knowledge as a tool requires ongoing usage, increasing the understanding for how the knowledge can be applied. Learning and cognition are fundamentally situated and learning concepts continually evolve with the occasion and context of use. As a result, new learning experiences and activities create new, more textured forms of knowledge (Brown et al. 1989). The theory of situated learning focuses on the collective, rather than an individual as the level of analysis (Lave and Wenger 1991). The theory posits that individuals learn by being in social relations to others and proposes that “persons acting and the social world of activity cannot be separated” (Lave 1993, p. 5). Learning through social interactions promotes idea exchange and modification, in addition to developing belief systems through ongoing 38 conversations (Brown et al. 1989). The key to understanding learning resides in relational networks in which people learn by participating in shared practices. These shared practices in the form of knowledge sharing and knowledge receiving form a community of learning that enhances the identity of the group. In addition, group members are held accountable. This accountability is sustained through norms, collaboration, shared language, and a communal repository of resources, routines, tools, and stories. Friendship networks within intraorganizational sales networks are likely to provide such an environment (Vardaman et al. 2012). As a result, members within these communities learn identity formation, rather than knowledge (Lave and Wenger 1991). As members within the learning community participate, skill and knowledge emerge through co-engagement of the practice community. Member roles range from novices to experts. Novices are not passive recipients of knowledge and experts are not necessarily expert teachers. Learning is often two-way between newcomers and established practitioners (Fuller and Unwin 2004). Furthermore, communities of practice do not have rigid boundaries and can span across teams, departments, institutions, and organizations (McLeod, O'Donohoe, and Townley 2011). A novice in one learning environment may be an export in another learning environment and vice versa. As such, multiple communities of practice are able to move through a series of inter-related networks (McLeod et al. 2011). In summary, it is reasonable to assume that selling environments provide an authentic scenario for individuals to learn among each other, by considering multiple perspectives on how sales training can promote individual training transfer performance. 39 HYPOTHESES DEVELOPMENT Training and Training Transfer Performance Marketing Strategy “refers to an organization’s integrated pattern of decisions that specify its crucial choices concerning products, markets, and marketing activities and marketing resources in the creation, communication and/or delivery of products that offer value to customers in exchanges with the organization and thereby enables the organization to achieve specific objectives” (Varadarajan 2010, p. 128). Implementing a marketing strategy requires the organization and deployment of the necessary resources required to achieve strategic objectives. Sales training programs help firms to strive toward organizational objectives (Leach and Liu 2003). Research has shown that sales training increases performance (Frayne and Geringer 2000; Harris and Vincent 1967). By conducting a thorough needs analysis, in addition to identifying the necessary behavioral changes needed to positively impact sales performance, selling organizations are likely to develop the necessary human capital to compete in a given marketplace. An investment in sales training within a larger strategic framework requires support and development to maximize the chance that a sales training will lead to sustained behavioral change. As previously discussed, organizations invest substantial resources to develop the knowledge, skills, and abilities of their salespeople, with the expectation that such investments will achieve intended results. Well planned sales training programs contribute to higher salesperson performance (Honeycutt 1996). From a social capital standpoint, human capital that resides in trainees is further developed through sales training programs. As it relates to the theory of situated learning, a sales training intervention creates communities of practice within an authentic learning environment that provides a context and opportunities to learn how to effectively reach sales training 40 objectives. Additionally, norms within learning communities hold members accountable through a shared identity. Trust and reciprocity further promote ongoing learning through the exchange of knowledge and social support. A sales training intervention promotes the development of group experiences, leading to behavioral change which subsequently impacts training transfer performance. Therefore, I expect the following: H1: Training is positively related to training transfer performance The Impact of Sales Manager Support on Training Transfer Instrumental leadership is defined “as the application of leader expert knowledge on monitoring of the environment and of performance, and the implementation of strategic and tactical solutions” (Antonakis and House 2014). The two main facets of instrumental leadership are strategic leadership and follower work facilitation. Strategic leaders understand the nature of the organization and are able to set structured tasks and provide guidance on task completion (Antonakis and House 2014). Factors included in strategic leadership are environmental monitoring and strategy formulation. Environmental monitoring refers to leader’s actions focused on scanning internal and external environments. Strategy formulation focuses on developing policies, goals and objectives to support the firm’s strategy and mission. Strategic leaders focus on knowing the capabilities of the organization, designing appropriate strategies and setting attainable objectives (Antonakis and House 2014). Trainee’s willingness and ability to successfully transfer training may not lead to performance if there are situational constraints present. Situational constraints are characteristics of the environment that interfere or restrict individuals’ performance (Peters and O'Connor 1980). These include factors such as adequacy of job-related information, tools and equipment, and time availability. Situational constraints can directly or indirectly affect performance through 41 the mediating effects of motivation (Guzzo and Gannett 1988). Situational factors impact a trainees’ opportunity to perform trained skills, which moderate the extent of training transfer (Ford et al. 1992). Instrumental leaders facilitate path-goal activities, give direction, support, and resources and help ease situational constraints during the transfer process. Having sufficient resources to perform one’s job increase motivation (Kopelman, Brief, and Guzzo 1990) and provides the opportunity to engage within the learning environment. Furthermore, outcome monitoring provided by instrumental leaders promotes goal attainment and is a positive avenue to enhance learning. From a social capital perspective, instrumental leaders facilitate resource consolidation through strategic guidance and prevent resource erosion by easing situational constraints. Additionally, instrumental leaders are provided with the opportunity to structure transfer environments to help salespeople achieve sales training performance objectives. As members of intraorganizational learning communities, instrumental leaders are also likely to be held accountable in accommodating group norms. Therefore, I expect the following: H2: There is a positive and significant interaction between sales manager’s level of instrumental leadership and training on training transfer performance, such that as a sales manager’s instrumental leadership increases, the effect of training on training transfer performance increases. The Impact of Network Peer Support on Training Transfer Friendship networks consist of interaction that provide social and emotional support (Vardaman et al. 2015). Social relations in the forms of friendships are important sources for subjective well-being (Van der Horst and Coffé 2012). In addition, friendships are mechanisms for open communication often in the form of gossip and stories and are associated with high social support during times of instability or personal hardship (Vardaman et al. 2012). 42 Friendships also provide reassurance and encouragement during adverse work events, such as organizational change (Cohen and Wills 1985). There are additional benefits derived from friendship networks, including increased trust (Van der Horst and Coffé 2012). A trust relationship implies that common goals and values have brought and kept individuals together (Barber 1983). Trust is an antecedent of cooperation (Gulati 1995), which promotes individuals’ willingness to share resources. Research has also shown that supportive relationships diminish the exposure to stress (Halpern 1999). Conversely, individuals that occupy peripheral positions in friendship networks may feel isolated and lack a sense of belonging (Williams 2007). In addition, research has shown that anxiety has a large negative effect on motivation to learn and subsequently decreases learning and transfer (Colquitt, LePine, and Noe 2000). Friendships have been shown to help employees make sense of change and perceive change as controllable (Vardaman et al. 2012). Furthermore, Vardaman et al. (2014), found social support in the form of friendships are important during change as increases in controllability is linked to effective implementation. By being centrally located in a friendship network, salespeople will be provided with emotional resources that help in the adjustment of behavioral change, by reducing the levels of stress and anxiety experienced by the focal individual. In addition, trust, norms, and reciprocity which are inherent in friendships, enhance accountability within situated learning environments, thus promoting the exchange of valuable social capital and enhancing overall member learning. Therefore, I expect the following: H3: There is a positive and significant interaction between training and an individual’s degree centrality in friendship networks on training transfer performance, such that as an individual’s degree centrality in friendship networks increases, the effect of training on training transfer performance increases. 43 Learning is enhanced when the context in which learning occurs is authentic (Lave and Wenger 1991). Salespeople with high degree centrality have control over information flow and access to unique knowledge and information (Bolander et al. 2015). Being in a position to access information from others within a network provide individuals with “… timelier access to more, richer, and more diverse information, increasing the extent to which they learn from their network and their potential to synthesize and recombine this information into novel ideas” (Phelps et al. 2012). As a result of being exposed to multiple perspectives across multiple contexts, centrally located individuals within knowledge networks will be more successful at transferring training across situations (Greeno 1997). Additionally, frequent interaction allow actors to know one another, share important information, and create a shared vision (Tsai and Ghoshal 1998). High levels of interaction allow for the development of different aspects of cognitive social capital (Boisot 1995). A key to harvesting resources within knowledge networks relates to the idea of frequency. Learning and cognition are enhanced when learning continuously evolves over time and through use. As frequency increases, so does the development and depth of knowledge (Brown et al. 1989). Salespeople that frequently engage in knowledge sharing and knowledge receiving communication are more likely to develop an increased understanding of how training knowledge can be applied. Conversely, low frequency of knowledge sharing and knowledge receiving will not enhance learning. In low frequency knowledge sharing and knowledge receiving networks, learning and cognition will remain stagnant, prohibiting the development and depth of knowledge. Therefore, I expect the following: H4a: There is a positive and significant interaction between training and an individual’s degree centrality in high knowledge sharing networks on training transfer performance, 44 such that as an individual’s degree centrality in high knowledge sharing networks increases, the effect of training on training transfer performance increases. H4b: There is a positive and significant interaction between training and an individual’s degree centrality in high knowledge receiving networks on training transfer performance, such that as an individual’s degree centrality in high knowledge receiving networks increases, the effect of training on training transfer performance increases. 45 METHODOLOGY Social Network Analysis Social network analysis refers to a set of methodological tools that can be used to map relationships among employees and identify dynamics specific to the exchange of resources. More specifically, social network analysis “translate core concepts in social and behavioral theories into formal definitions expressed in relational terms” (Wasserman and Faust 1994, p. 21). Social network analysis views individuals and their actions as interdependent. Additionally, ties between individuals provide avenues for the transmission of resources which can be used to enhance the training transfer process. These relational patterns create an environment that promote or inhibit individual action based on the utilization of sales process information. Carpenter, Li, and Jiang (2012) reviewed methodological and choice issues pertaining to the topic of social networks. The authors provide a 2x2 categorization scheme for researchers to use when deciding upon valid design methodology for a network study. Broadly speaking, network analysis studies are designed for interpersonal, intraorganizational, and interorganizational analysis with a focus on social capital or network development. Interpersonal level research focuses on individual actors within a network as opposed to interorganizational level research in which the focal actors are organizations. Social capital research focuses on the “instrumental utility and beneficial consequences of a social network to its participants” (Carpenter et al. 2012), as opposed to network development research that focuses on the “development and evolution of networks as social phenomena, mainly concentrating on patterns and precursors of network formation and change” (Carpenter et al. 2012, p. 1340). Based on the focus of my research, I conduct social capital research focusing on the impact that friendships, 46 and knowledge sharing and knowledge receiving network affiliation have on training transfer performance within an intraorganizational network. Sample and Data Collection Data were collected from a sales force of a U.S. based division of multi-national consumer manufacturer. The firm sells business solution technology in a business-to-business (B2B) setting and recently conducted sales training initiatives focused on transitioning a productfocused salesforce to a solution-focused salesforce. The firm has undertaken extensive training, providing advanced sales methodologies to its salesforce to transition from a product specific conversation, to a conversation focused on identifying and accelerating the buying firm’s strategic goals. The first phase of training focused on salespeople that had met or exceeded quota for two consecutive years. This training was classroom based and the trainer was hired from an outside sales training firm. The second phase of training included all remaining salesforce members and was administered by the company’s in-house training department. The material presented to both groups was similar in nature, although higher expectations were set forth by upper-management for the advanced group to learn and implement the training immediately following the training. The second group, which consisted of those individuals with less tenure and/or lower sales performance were encouraged to master the material and work alongside their managers to effectively implement the course material. As compared to B2C sales, B2B sales often have longer cycle times and require multiple meetings and many rounds of negotiations. Through in-depth conversations among myself, the president of sales, vice presidents of sales and branch leaders, we determined that an appropriate time to distribute the network questionnaire would be approximately 12 months after the completion of the first phase of the sales training interventions. We assumed that 12-month post 47 training was an adequate timeframe for the salespeople to learn how to effectively incorporate the training methodology into their selling processes. As mentioned by Bolander et al. (2015), there are several benefits to working with a single firm when undertaking sales research, specifically when the focus is on the impact of social networks. First, by attending formal face-to-face training workshops over the course of two years and working with sales executives, I could clarify the goals and measurement needs for this study. Secondly, this interaction allowed me to build a strong collaborative relationship with the firm, gaining access to the complete network of the sales division, leading to a strong response rate. Of the 316 sales and non-sales members, I obtained 277 complete responses, representing an 87.65% response rate. Lastly, as it is necessary to set boundaries when undertaking a network study (Borgatti et al. 2013), collecting data from a clearly defined sales division provided a natural boundary line for the data collection portion of this study. Measurement The focal firm provided a complete list of all members of the sales division including salespeople, sales managers, product specialists, and branch leaders, which was subsequently used to collect network data through a roster method. Roster method reduces recall error and allows researchers to disaggregate data to calculate individual network measures (Borgatti et al. 2013). Respondents were asked to report who over the past 12-months they received sales related knowledge from, who they shared sales related knowledge with, and who they considered a friend. These questions allowed me to identify the impact that knowledge exchange and friendship networks had on training transfer performance, increasing temporal separation and permitting me to make causal inferences when combined with future performance data (Bolander et al. 2015; Hui et al. 2013). Since a main contribution of this dissertation is to investigate how 48 the frequency of knowledge sharing and receiving interacts to enhance sales training transfer performance, it was imperative to collect data on how often respondents exchanged knowledge among one another over the 12-month period. Table 1 provides the social network questionnaire and corresponding response items. Degree Centrality represents the number of ties that an individual has in a given network. Given that my focus is on investigating the exchange of knowledge, along with friendships within the sales organizations, degree centrality accurately accounts for all possible conduits among salespeople for which knowledge and emotional support resources may flow. Degree centrality was calculated by the following formula: 𝑔 𝐶𝐷 (𝑁𝑖 ) = ∑ 𝑥𝑖𝑗 (𝑖 ≠ 𝑗) 𝑗=1 Where i = an individual actor in the network 𝐶𝐷 (𝑁𝑖 ) = a degree centrality measure for individual i ∑𝑔𝑗=1 𝑥𝑖𝑗 = is the number of direct ties that actor i has to the g-1 other j nodes The computation of 𝐶𝐷 (𝑁𝑖 ) involves adding all the cell entries in either actor i’s row or its column of a sociomatrix. As previously mentioned, my focal dependent variable is training transfer performance. The objective of the sales training initiative was to transition the salesforce from a productfocused selling strategy to a solutions-focused selling strategy. Salespeople were expected to cross-sell into the firm’s solution-based product line. In order to measure training transfer performance, I recoded performance data to mirror the goals of the training, which was the movement of salespeople from focusing on selling one specific product to cross-selling across 49 product lines. These data represent the successful transfer of training. The response variable was calculated as: 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 − 𝐶𝑟𝑜𝑠𝑠 𝑆𝐸𝑙𝑙𝑖𝑛𝑔 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑇𝑜𝑡𝑎𝑙 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 Missing values occurred if no sales were made in a given month and as such were replaced with 0s where applicable. Although the focus of training was to eventually transition the organization’s salesforce from a product-focused to only a solution-focused salesforce, I included cross-selling revenue as an additional dependent variable as it was not expected that cross-selling would constitute all sales revenue during the research timeperiod. Any improvement in cross-selling sales was seen as a positive according to upper management at the focal firm. All models were tested using both performance measures, cross-selling revenue and cross-selling revenue as a percent of total revenue. To account for factors that may influence the successful implementation of training into a salesperson’s selling process, I included variables for salesperson age, company tenure, sales team, and job title when testing my hypotheses. These data were retrieved from company records. Statistical Approach I utilized UCINET to calculate salesperson degree centrality in both the knowledge (e.g. receiving and sharing) and friendship networks. Although I collected valued data regarding frequency of knowledge receiving and knowledge sharing, analyzing valued data is computationally intensive. As such, it is suggested that valued data be transformed to binary data (Borgatti et al. 2013). To identify if a relationship exists between individuals in the knowledge network and how these networks impact the training transfer process, I recoded all valued data into binary data to create a complete knowledge network. I transformed the binary data (Borgatti et al. 2013) by determining appropriate cut-off values. As Table 1 illustrates, respondents were 50 asked the level of knowledge sharing and receiving that they participated in among identified sales members. Data described as either “above average” or “far above average” was coded as “high”. Data described as “average” “below average” or “far below average” was coded as “low”. These designations were further transformed into individual matrices representing networks of Low//High knowledge sharing and knowledge receiving. To begin to investigate the impact that sales training has on training transfer performance, I focused intially on analyzing performance overtime of training participants. I was provided with performance data from April 2014 (time 1) through January 2017 (time 34). Prior to the analysis, the data were organized to align with the research context and allow for valid testing at this initial stage. I tested for selection bias between the training groups at time 17 and time 26 by utilizing genearlized analysis of covariance and confirmed that there was no significant differences between the training groups. Next, I dummy coded the 34 months of data to indicate which training a salesperson and his/her manager attended. I included managerial training data, as manager’s were included in the network questionnaire and are formidable participants in the sharing and receiving of knowledge. In addition, I created a group variable for each salesteam to control for potential territory effects. Lastly, I merged the sales performance data, salesperson age, tenure, and selling role to complete the dataset. In order to test whether training was effective at increasing the occurrence of sales across product lines, I utilized generalized estimating equation (GEE). GEE allows for the modeling of within-panel correlation, along with specifying response variable distributions and link functions. As such, I assumed normal response variables with an identity (y=y) link function, incorporating an autoregressive of order one for the correlation structure. Before beginning the analysis, I grand mean centered the degree centrality measures variables and computed the interaction terms 51 as hypothesized. By centering the data, I improved the interpretability of main effects coefficients when the interactions were modeled. Results As Table 2 shows, salesperson training at time 17 was positively related to training transfer performance (cross-selling revenue (β=7231.8, p<.001) and cross-selling revenue as a percent of total revenue (β=.0379, p < .05)). Salesperson training at time 26 was not related to training transfer performance (cross-selling revenue (β=1082.4, p>.05) or cross-selling revenue as a percent of total revenue (β=.0264, p > .05)). Suprisingly, manager training at time 17 was negatively related to training transfer performance (cross-selling revenue (β= -4201.7, p<.01) and cross-selling revenue as a percent of total revenue (β=-.0379, p < .01)). Finally, manager training at time 26 was not related to training transfer performance (cross-selling revenue (β=1778.8, p >.05) or cross-selling revenue as a percent of total revenue (β=.0264, p > .05)). It appears that training at time 17 was successful in transitioning salespeople from a productfocused conversation to a more solution-based focused conversation. As a follow-up, I plotted cross-selling revenue and cross-selling revenue as a percent of total revenue along with product-selling revenue and product-selling as a percent of total revenue across the data period to visually inspect the results that overall, training did influence performance overtime. As Figures 2 illustrates, there appears to be visual evidence that crossselling revenue has increased over the longer term. In addition, Table 3 shows that cross-selling as a percent of total revenue appears to be converging with product-revenue as a percent of total revenue. As Table 3 shows, there is no main effect found of instrumental leadership on training transfer performance (i.e. cross-selling revenue or cross-selling revenue as a percent of total 52 revenue). There is, however, a significant positive interaction found between instrumental leadership and salesperson training at time 17 on training transfer performance (i.e. cross-selling revenue) (β= 3449.6, p<.05). More specifically, as Figure 4 shows, for salespeople that attended training at 17, the positive effect of sales manager instrumental leadership and salesperson training at time 17 on training transfer performance (i.e. cross-selling revenue) is stronger for sales managers who demonstrate high levels of instrumental leadership as compared to sales managers who demonstrate low levels of instrumental leadership. In addition, there was a significant negative interaction found between instrumental leadership and salesperson training at time 26 on training transfer performance (i.e. cross-selling revenue as a percent of total revenue) (β= -0.0540, p<.001). More specifically, as Figure 5 shows, for salespeople that attended training at time 26, the negative interactive effect of sales manager instrumental leadership and salesperson training at time 26 on salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) is stronger for sales managers that demonstrate high levels of instrumental leadership as compared to sales managers that demonstrate low levels of instrumental leadership. As Table 4 shows, there is a positive main effect of salesperson friendship network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue (β= 578.8, p<.01) and cross-selling as a percent of total revenue (β= .00507, p<.01). There is a positive interaction found between salesperson training at time 17 and friendship network degree centrality on training transfer performance (i.e. cross-selling revenue) (β= 1438.3, p<.001). More specifically, as Figure 6 shows, for those salespeople that attended training at time 17, the positive interactive effect of friendship network degree centrality and salesperson training at time 17 on training transfer performance (i.e. cross-selling revenue) is stronger for salespeople that are more centrally located in friendship networks as compared to salespeople that are less 53 centrally located in friendship networks. In addition, there is a negative interaction found between sales manager training at time 17 and salesperson friendship network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue) (β= -1101.5, p<.01). As Figure 7 shows, for sales managers that attended training at time 17, the negative interactive effect of salesperson friendship network degree centrality and sales manager training time 17 on salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) is stronger for salespeople that are more centrally located in friendship networks as compared to salespeople that are less centrally located in friendship networks. Additionally, there is a positive interaction between salesperson training at time 26 and salesperson friendship network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) (β= 0.0127, p<.01). As Figure 8 shows, for those salespeople that attended training at time 26, the positive interactive relationship between salesperson friendship network degree centrality and salesperson training at time 26 on salesperson training transfer person (i.e. crossselling as a percent of total revenue) is stronger for salespeople that are more centrally located in friendship networks as compared to salespeople that are less centrally located in friendship networks. Furthermore, there is a negative interaction between sales manager training at time 26 and salesperson friendship network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) (β= -0.0185, p<.05). As Figure 9 shows, for sales managers that attended training at time 26, the negative interactive relationship between a salesperson’s friendship network degree centrality and sales manager training at time 26 on salesperson training transfer performance (i.e. cross-selling as a percent of total revenue) is stronger for salespeople that are more centrally located in friendship networks as compared to salespeople that are less centrally located in friendship networks. 54 As Table 5 shows, there is a positive main effect of high knowledge sharing network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue (β= 2137.5, p<.001). There is a positive interaction between salesperson training at time 17 and salesperson high knowledge sharing network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue) (β= 6745.3, p<.01). As Figure 10 shows, for salespeople that attended training at time 17, there is a strong positive interaction between high levels of salesperson high knowledge sharing network degree centrality and salesperson training at time 17 as compared to a non-existent interactive relationship between low levels of salesperson high knowledge sharing network degree centrality and salesperson training at time 17 on salesperson training transfer performance (i.e. cross-selling revenue). There is a positive interaction between salesperson training at time 26 and salesperson high knowledge sharing network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue) (β= 3192.3, p<.05). As Figure 11 shows, for those salespeople that attended training at time 26, there is a strong positive interaction between high levels of salesperson high knowledge sharing network degree centrality and salesperson training at time 26 and salesperson training transfer performance (i.e. cross-selling revenue) as compared to a negative interactive relationship between low levels of salesperson high knowledge sharing network degree centrality and salesperson training at time 26 and salesperson training transfer performance (i.e. cross-selling revenue). Additionally, there is a negative interaction between sales manager training at time 17 and salesperson high knowledge network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue) (β= -6388.5, p<.001). As Figure 12 shows, for those sales managers that attended training at time 17, there is a strong negative interaction between high levels of salesperson high knowledge sharing network degree centrality and sales manager 55 training at time 17 and salesperson training transfer performance (i.e. cross-selling revenue) as compared to a slight positive interactive relationship between low levels of salesperson high knowledge sharing network degree centrality and sales manager training at time 17 and salesperson training transfer performance (i.e. cross-selling revenue). There is no main effect and no significant interactions found between salesperson low knowledge sharing degree centrality and salesperson training transfer performance (i.e. cross-selling revenue). In addition, Table 5 shows that there is a positive main effect of high knowledge sharing network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) (β= 0.0130, p<.001). There is a positive interaction between salesperson training at time 26 and high salesperson knowledge sharing network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) (β= 0.0452, p<.001). As Figure 12 shows, for those salespeople that attended training at time 26, there is a positive interactive relationship between high salesperson high knowledge sharing degree centrality and salesperson training at time 26 and salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) as compared to a negative interactive relationships between low salesperson high knowledge sharing degree centrality and salesperson training at time 26 and salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue). There is a negative interaction between sales manager training at time 17 and high knowledge sharing centrality on salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) (β= -0.0176, p<.01). As Figure 14 shows, for those sales managers that attended training at time 26, there is a strong negative interactive relationship between high salesperson high knowledge sharing degree centrality and sales manager training at time 17 and salesperson training transfer performance 56 (i.e. cross-selling revenue as a percent of total revenue) as compared to a slight negative interactive relationship between low salesperson high knowledge sharing degree centrality and sales manager training at time 17 and salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue). There is no main effect and no significant interactions found between salesperson low knowledge sharing degree centrality and salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue). As Table 6 shows, there is a positive main effect of high knowledge receiving network degree centrality on training transfer performance (i.e. cross-selling revenue) (β= 652.9, p<.05). There is a positive interaction between salesperson training at time 17 and high knowledge receiving network degree centrality on salesperson training transfer performance (i.e. crossselling revenue) (β= 2995.9, p<.01). As Figure 15 shows, for those salespeople that attended training at 17, there is a strong positive interactive relationship between high salesperson high knowledge receiving degree centrality and salesperson training at time 17 and salesperson training transfer performance (i.e. cross-selling revenue) as compared to a slight negative interactive relationship between low salesperson high knowledge receiving degree centrality and salesperson training at time 17 and salesperson training transfer performance (i.e. cross-selling revenue). There is a negative interaction between sales manager training at time 17 and high knowledge receiving network degree centrality on cross-selling revenue (β= -2947.5, p<.001). As Figure 16 shows, for those sales managers that attended training at time 17, there is a strong negative interactive relationship between high salesperson high knowledge receiving degree centrality and sales manager training at time 17 and salesperson training transfer performance (i.e. cross-selling revenue) as compare to a slight positive interactive relationships between low salesperson high knowledge receiving degree centrality and sales manager training at time 17 57 and salesperson training transfer performance (i.e. cross-selling revenue). There is no main effect found between training and low knowledge receiving degree centrality on training transfer performance (i.e. cross-selling revenue). In addition, Table 6 shows a positive main effect of high knowledge receiving network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) (β= 0.00415, p<.05). There is a positive interaction between sales manager training at time 26 and high knowledge receiving network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) (β= 0.0278, p<.05). As Figure 17 shows, for those sales managers that attended training at time 26, there is a strong positive interactive relationship between salesperson high knowledge receiving degree centrality and sales manager training at time 26 and salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) as compared to slight positive interactive relationship low salesperson high knowledge receiving degree centrality and sales manager training at time 26 and salesperson training transfer performance (i.e. cross-selling as a percent of total revenue). There is a positive interaction between sales manager training at time 17 and low knowledge receiving network degree centrality on salesperson training transfer performance (i.e. cross-selling revenue as a percent of total revenue) (β= 0.0117, p<.05). As Figure 18 shows, for those sales managers that attended training at time 17, there is a positive interactive effect between high salesperson low knowledge receiving degree centrality and sales manager training at time 17 and salesperson training transfer performance (i.e. cross-selling as a percent of total revenue) as compared to a negative interactive relationship between low salesperson low knowledge receiving degree centrality and sales managers training at time 17 and salesperson training transfer performance (i.e. cross- 58 selling as a percent of total revenue). There is no main effect found between training and low knowledge receiving degree centrality on training transfer performance (i.e. cross-selling revenue as a percent of total revenue). Control variables yield significant effects on salesperson training transfer performance (i.e. cross-selling revenue and cross-selling revenue as a percent of total revenue). As shown in Tables 2-9, salesperson tenure is negatively related to training transfer performance (i.e. crossselling revenue and cross-selling revenue as a percent of total revenue). The one exception is represented Table 8 where no main effect is found for salesperson tenure (β= -247.9, t=-1.58) when considering the interaction effects between high knowledge receiving network degree centrality and training and salesperson training transfer performance (i.e. cross-selling revenue). Both salesperson age and salesperson selling role are positively related to salesperson training transfer performance (i.e. cross-selling revenue and cross-selling revenue as a percent of total revenue). 59 DISCUSSION My central hypothesis is that social support in the form of sales manager leadership and peer knowledge networks positively impacts individual learning processes of salespeople to increase the performance of sales training programs. I combined training data with manager level and social network data to investigate the impact that social support has on the training transfer performance overtime. This research addresses three important gaps in the literature. First, I address whether managerial instrumental leadership influence the successful implementation of training overtime. Second, I address whether the frequency of knowledge sharing and knowledge receiving, in addition to friendship networks are useful in predicting training transfer. Third, I decompose knowledge sharing and knowledge receiving networks into high and low frequency networks, thus further uncovering networks characteristics that impact training transfer overtime. I next address these key issues in more detail. Theoretical Implications As previously noted, I found some support that instrumental managers interact to strengthen the relationship between training and training transfer performance. Instrumental leaders provide strategic guidance to salespeople and may be influential in reducing obstacles faced by salespeople. Obstacles are likely to reduce the ability for salespeople to spend adequate time, implementing and learning from experiences during the training transfer process. The results of this study do not find strong support that instrumental managers impact the ongoing learning and successful transfer of training. This may be the result of an underlying competing theoretical perspective, not uncovered in this work. More specifically, social capital theory posits that valuable resources (i.e. knowledge) are exchanged among individuals in a network to provide value to network members. In addition, the theory of situated learning suggests that 60 learning takes place under 3 conditions, namely when the learning aligns with the job context, consists of multiple perspectives, and occurs within a social context. As previously mentioned, training is defined as “the acquisition of skills, concepts, or attitudes that results in improved performance in an on-the-job environment” (Goldstein 1980, p. 230). Organizations invest in training to provide employees with additional knowledge, skills, and abilities (KSA). It is likely that instrumental leaders impact the training transfer performance in a manner not captured within the theoretical frameworks that I employ. This is an avenue for future research. As they relate to learning, friendships are mechanisms for open communication often in the form of stories and are associated with high social support during times of instability (Vardaman et al. 2012). From a social capital theory perspective, it appears that emotional support transmitted through open communication between friends may provide opportunities for friends to “lend helping hands” to one another to achieve individual performance goals. When considering the theory of situated learning, communication within friendship networks may provide additional perspectives on overcoming learning hurdles during the transfer process. The significance of friendship network interactions is inconsistent across the two training transfer performance measures (i.e. cross-selling revenue and cross-selling revenue as a percent of total revenue). Specific to this research, training was focused on instilling a new selling methodology within the sales force. As such, individuals were expected to alter the conversations that they held between buyers and themselves, a likely challenging task for some newly tenured sales people. The inconsistent, but significant findings regarding the interaction between friendship network centrality and sales training may demonstrate that as a whole, friendship networks are not as effective at improving learning as compared to knowledge networks, but nonetheless, may offer additional support through trust, norms, and reciprocity. 61 Results demonstrate significant relationships of the interaction of knowledge sharing and knowledge receiving networks and training on training transfer performance. These results were further delineated by separating both knowledge sharing and knowledge receiving networks into high and low levels of knowledge exchange frequency. As a whole knowledge sharing networks are highly significant during the post-training phase and appear to contribute substantially to training transfer performance. Unfortunately, there may be a dark side to high knowledge sharing and receiving networks. Results showed a significant negative relationship between sales managers that attended training at time 17 and their salespeople’s training transfer performance. Furthermore, results showed a highly significant negative interaction between both high knowledge sharing and high knowledge receiving and sales manager training at time 17. This suggests a potential contagion effect in which managers who did not adopt the training methodology subsequently reduced the effort put forth by their salespeople to incorporate the training into their selling processes. On a brighter note, salespeople that attended training at time 17 without their managers experienced a highly significant positive interaction from both high knowledge sharing and high knowledge receiving on their individual training transfer performance. This suggests that individual learning is enhanced through high frequency knowledge exchange, when negative contagion effects are isolated and removed. Furthermore, as compared to high levels of knowledge receiving, high levels of knowledge sharing demonstrate a stronger interactive effect between training and training transfer performance. This result may best be summarized by a quote by the spiritual leader Yogi Bhajan … “If you want to learn something, read about it. If you want to understand something, write about it. If you want to master something, teach it.” 62 By frequently sharing knowledge, salespeople may be in effect enhancing their own individual learning. By verbalizing and explaining to others ways in which training has been successfully transferred, high knowledge sharing individuals may in fact be encouraging and nurturing their own internal learning cognitions. Managerial Implications Kotler et al. (2015) suggest that internal networks allow for the exchange of ideas and increases a salesperson’s understanding of the firm’s competencies and resources. Networks should be managed based on what people know in addition to how open people are to learning and change. Adopting this suggestion may allow sales managers and executives to select certain individuals for training session and to actively engage with the internal networks to identify points of influence that may help in disseminating strategic level information that support necessary change. This dissertation provides three key points that sales managers should consider when designing and executing sales training programs First, based on the strong, consistent positive relationship between age and training transfer performance, it does appear that you “can teach old dogs new tricks”. Prior research has suggested that as people grow older, they are less willing to engage in self-development (McEnrue 1989). This research demonstrates that in a sales training context this may not be the case. Salespeople are interested in strengthening their knowledge, skills, and abilities to increase their performance. Generally speaking, the more someone sells, the more income a person earns. As autonomous individuals, salespeople are more likely to accept and utilize training if training is perceived as credible, practical, and fills a selling need (Yelon et al. 2004). As such sales managers should consider surveying their salespeople to identify training topics that are most relevant to the largest audience. 63 Second, managers should focus on promoting knowledge exchange, specifically knowledge sharing, among all members of a sales division, including those members that do not hold a formal selling position. Sales training is a process and does not cease once instruction is complete. Informal learning promotes individual development by sharing experiences. Sales managers should expend resources toward creating and supporting learning networks (Van den Bossche and Segers 2013). This may be achieved through a variety of ways including the rearranging of office environments, reducing silos between team that are all too common within selling organizations, and promoting the exchange of knowledge not only through verbal communication, but also through technology (e.g. email, text, instant messaging, CRM systems). Third, it has been well established in the literature that managerial and peer support along with transfer climate are essential to promote training transfer (Baldwin et al. 2009; Blume et al. 2010). Using social network analysis managers can identify individuals that are central actors within a knowledge network. By analyzing these relationships, managers may approach these focal individuals and ask for their endorsement and subsequent support in ensuring that training transfer is met with enthusiasm and utilized overtime. These “ambassadors of training” could be highly effective in their ability to communicate beyond formal reporting lines, promoting ongoing training utilization through the processes of adoption and diffusion. Limitations and Future Research As with any research, it is necessary to identify shortcomings within the methodology employed. First, although there are benefits from working with a single firm (Bolander et al. 2015), these results were not tested across organizations and therefore the generalizability of the findings come into question. The focal firm is in a highly-technical, highly knowledge intensive environment. As the results show, the mere act of sharing and receiving knowledge is not a core 64 driver of learning, but rather it is the frequency that knowledge is shared and received among salespeople. Conducting this research in a B2C setting or other B2B industries may not generate the same results if knowledge exchange is not a core driver for successful sales calls. Future researchers may attempt to work with a sales training firm to access data across multiple firms to test the generalizability of these results. This would be a huge undertaking as longitudinal data would need to be collected and standardized across firms, along with collecting complete network data from all firms in the sample. If successful, this type of research project would be extremely valuable in validating the findings of my dissertation. Second, although I assumed that training directly leads to training transfer, I did not include antecedents to suggest why a salesperson would decide to incorporate training into his or her selling process. For example, I do not know the factors that caused managers that attended training at time 17 to negatively impact individual salesperson training transfer performance over time. I conducted interviews with key informants at sales training organizations and it was suggested that managers themselves shouldn’t necessarily attend training, but rather attend training that teaches them how to coach to the training. An additional possibility may reside in the fact that training at time 17 was administered by an outside trainer. This may have caused some managers to question the credentials of the trainer and as a result limited the manager’s attention and motivation during and after the training session. Furthermore, training at time 26 was administered by the focal firm’s trainer. There was limited support showing this training was significantly related to training transfer performance and without knowing exactly what characteristics of this training were effective and/or ineffective, I am unable to conclude what led to many insignificant results within the model regarding training at time 26. Without theorizing and testing potential antecedents to the actual training transfer process, this remains a question 65 that I unfortunately cannot answer through my research. Future research could focus on how people form intentions to transfer sales training. Adopting a research methodology similar to Fu et al. (2010) might provide such an avenue. By incorporating the theory of planned behavior, the authors tested and confirmed key antecedents responsible for creating salespeople intentions to sell new products. I see this methodology as a natural fit when researching the factors that contribute to a salesperson’s intentions to utilize training material in his or her selling process. Third, I assume that increases in cross-selling revenue and cross-selling revenue as a percent of total revenue represent training transfer performance. I provide no evidence regarding what behaviors individual salespeople transferred that led to the increase in the dependent variables. Additionally, although I find strong support on the impact that social support in the form of peer networks have on strengthening the relationship between training and training transfer performance, I fall short in identifying the specific forms and types of knowledge that are most impactful in enhancing the learning process. Future research could address this issue by attempting to merge training data with CRM systems to identify the phases within buying cycles where training transfer behavior increases the success of various phases of the sales cycle. There are several learning management system modules that can now be linked to CRM systems such as salesforce.com that provide a rich environment to identify specific training transfer behaviors that are successful during various stages of the buying and selling cycle. 66 APPENDIX 67 FIGURE 1: Conceptual Model Time 0 Time N Instrumental Leadership Level 2 Level 1 Training Transfer Performance Training Friendship Network Degree Centrality Knowledge Network Degree Centrality 68 Revenue FIGURE 2: Combined Product and Cross-Selling Revenue for All Trained Salespeople 0 10 17 20 26 30 Month Product Cross-Selling 69 34 40 .6 .4 .2 0 Proportion of Revenue .8 1 FIGURE 3: Combined Product and Cross-Selling as a Percent of Total Revenue for All Trained Salespeople 0 10 17 20 26 30 Month Product 70 Cross-Selling 34 40 FIGURE 4: Interaction Graph for Hypothesis H2 High Sales Manager Instrumental Leadership Low Sales Manager Instrumental Leadership Training Transfer Performance (i.e. Cross-Selling Revenue) No Salesperson Training (Time 17) 71 Yes FIGURE 5: Interaction Graph for Hypothesis H2 High Sales Manager Instrumental Leadership Low Sales Manager Instrumental Leadership Training Transfer Performance (i.e. Cross-Selling as a Percent of Total Revenue) No Salesperson Training (Time 26) 72 Yes FIGURE 6: Interaction Graph for Hypothesis H3 High Friendship Degree Centrality Low Friendship Degree Centrality Training Transfer Performance (i.e. Cross-Selling Revenue) No Salesperson Training (Time 17) 73 Yes FIGURE 7: Interaction Graph for Hypothesis H3 High Friendship Degree Centrality Low Friendship Degree Centrality Training Transfer Performance (i.e. Cross-Selling as a Percent of Total Revenue) No Sales Manager Training (Time 17) 74 Yes FIGURE 8: Interaction Graph for Hypothesis H3 High Friendship Degree Centrality Low Friendship Degree Centrality Training Transfer Performance (i.e. Cross-Selling as a Percent of Total Revenue No Salesperson Training (Time 26) 75 Yes FIGURE 9: Interaction Graph for Hypothesis H3 High Friendship Degree Centrality Low Friendship Degree Centrality Training Transfer Performance (i.e. Cross-Selling as a Percent of Total Revenue) No Sales Manager Training (Time 26) 76 Yes FIGURE 10: Interaction Graph for Hypothesis H4a High-High Knowledge Sharing Degree Centrality Low-High Knowledge Sharing Degree Centrality Training Transfer Performance (i.e. Cross-Selling Revenue) No Salesperson Training (Time 17) 77 Yes FIGURE 11: Interaction Graph for Hypothesis H4a High-High Knowledge Sharing Degree Centrality Low-High Knowledge Sharing Degree Centrality Training Transfer Performance (i.e. Cross-Selling Revenue) No Salesperson Training (Time 26) 78 Yes FIGURE 12: Interaction Graph for Hypothesis H4a Training Transfer Performance (i.e. Cross-Selling Revenue) High-High Knowledge Sharing Degree Centrality Low-High Knowledge Sharing Degree Centrality No Sales Manager Training (Time 17) 79 Yes FIGURE 13: Interaction Graph for Hypothesis H4a High-High Knowledge Sharing Degree Centrality Low-High Knowledge Sharing Degree Centrality Training Transfer Performance (i.e. Cross-Selling as a Percent of Total Revenue) No Salesperson Training (Time 26) 80 Yes FIGURE 14: Interaction Graph for Hypothesis H4a High-High Knowledge Sharing Degree Centrality Low-High Knowledge Sharing Degree Centrality Training Transfer Performance (i.e. Cross-Selling as a Percent of Total Revenue) No Sales Manager Training (Time 17) 81 Yes FIGURE 15: Interaction Graph for Hypothesis H4b High-High Knowledge Receiving Degree Centrality Low-High Knowledge Receiving Degree Centrality Training Transfer Performance (i.e. Cross-Selling Revenue) No Salesperson Training (Time 17) 82 Yes FIGURE 16: Interaction Graph for Hypothesis H4b Training Transfer Performance (i.e. Cross-Selling Revenue) High-High Knowledge Receiving Degree Centrality Low-High Knowledge Receiving Degree Centrality No Sales Manager Training (Time 17) 83 Yes FIGURE 17: Interaction Graph for Hypothesis H4b High-High Knowledge Receiving Degree Centrality Low-High Knowledge Receiving Degree Centrality Training Transfer Performance (i.e. Cross-Selling as a Percent of Total Revenue) No Sales Manager Training (Time 26) 84 Yes FIGURE 18: Interaction Graph for Hypothesis H4b Training Transfer Performance (i.e. Cross-Selling as a Percent of Total Revenue) High-Low Knowledge Receiving Degree Centrality Low-Low Knowledge Receiving Degree Centrality No Sales Manager Training (Time 17) 85 Yes TABLE 1: Network Survey Questionnaire Items Responses Your Interactions: Work Information Receiving Below, you will see the list of people that you indicated interacting with to some extent previously. For each person, please indicate how much information or knowledge on selling related topics, techniques, experience this person provides you, relative to other people you receive sales related information from. In many cases, a person may also come to you for advice at about sales issues. However, please only indicate the EXTENT to which THIS PERSON PROVIDES INFORMATION TO YOU. Your Interactions: Work Information Sharing Below, you will see the list of people that you indicated interacting with to some extent previously. For each person, please indicate how much information or knowledge on selling related topics, techniques, experience, you provide to this person, relative to other people you provide with sales related information and knowledge. In many cases, you may have already indicated that a particular person provides you with sales-related advice. However, it is quite common for one individual in a relationship to provide more advice than the other due to differences in experience, seniority, personality, etc. When answering these questions, please only indicate the EXTENT to which a YOU PROVIDE INFORMATION TO THIS PERSON. Your Interactions: Friendship Below, you will see the list of people that you indicated interacting with to some extent previously. For each person, please indicate whether or not this is a person you like to spend breaks with OR with whom you like to take part in different social activities (i.e. a friend). While you may have a lot of acquaintances or colleagues at work or at other [XYZ] branches, for this question, a friend is defined as a person you like to spend breaks with OR with whom you would enjoy taking part in different social activities. 86       None (Note: Recoded/Remained as “None”) Far Below Average (Note: Recoded as “Low”) Below Average (Note: Recoded as “Low”) Average (Note: Recoded/Remained as “Low”) Above Average (Note: Recoded as “High”) Far Above Average (Note: Recoded as “High”)       None (Note: Recoded/Remained as “None”) Far Below Average (Note: Recoded as “Low”) Below Average (Note: Recoded as “Low”) Average (Note: Recoded/Remained as “Low”) Above Average (Note: Recoded as “High”) Far Above Average (Note: Recoded as “High”)   Yes No TABLE 2: Results for Hypothesis H1 Training Transfer Performance Dependent Variable Cross-Selling Revenue Salesperson Training (Time 17) 7231.8*** Cross-Selling as a % of Total Revenue 0.0379* (3.50) (1.99) 1082.4 0.0264 (0.43) (1.11) -4201.7** -0.0379** (-2.45) (-2.39) 1778.8 0.0762* (0.47) (2.16) -43.46 0.0000401 (-1.12) (0.12) -280.8** -0.00749*** (-2.35) (-7.01) 240.0*** 0.00290*** (3.18) (4.30) 3354.4*** 0.0341*** (3.33) (3.77) Observations 3220 3220 Salespeople 127 127 Salesperson Training (Time 26) Manager Training (Time 17) Manager Training (Time 26) Control Variables Sales Team Salesperson Tenure Salesperson Age Salesperson Selling Role Note: * = p<0.05 ** = p<0.01 *** = p<.001. All tests are one-tailed tests based on direction of hypotheses (t statistics in parentheses). 87 TABLE 3: Results for Hypothesis H2 Training Transfer Performance Dependent Variable Cross-Selling Revenue Cross-Selling Revenue 8159.0** (3.03) Cross-Selling as a % of Total Revenue 0.0251 (1.15) Cross-Selling as a % of Total Revenue 0.0230 (1.06) Salesperson Training (Time 17) 8257.9** (3.07) Salesperson Training (Time 26) 390.1 (0.12) 100.7 (0.03) 0.00209 (0.07) -0.000634 (-0.02) Manager Training (Time 17) -5485.6* (-2.23) -5378.1* (-2.16) -0.0326 (-1.64) -0.0317 (-1.58) Manager Training (Time 26) 2981.6 (0.59) 3927.1 (0.55) 0.128** (3.04) 0.195*** (3.28) Manager Instrumental Leadership 477.6 (0.48) 30.01 (0.02) -0.00609 (-0.79) -0.00235 (-0.23) -80.89 (-1.40) -83.05 (-1.43) -0.000187 (-0.42) -0.000189 (-0.41) Salesperson Tenure -413.3** (-2.57) -430.6** (-2.67) -0.00882*** (-7.03) -0.00885*** (-7.01) Salesperson Age 366.3*** (3.57) 358.5*** (3.36) 0.00414*** (5.17) 0.00399*** (4.77) Salesperson Selling Role 3726.5** (2.70) 3789.9** (2.74) 0.0309** (2.87) 0.0321** (2.97) Control Variables Sales Team Interaction Terms Salesperson Training (Time 17) x Manager Instrumental Leadership 3449.6* (1.65) 0.0177 (1.05) Salesperson Training (Time 26) x Manager Instrumental Leadership -1020.6 (-0.38) -0.0540** (-2.43) Manager Training (Time 17) x Manager Instrumental Leadership -1879.0 (-0.85) -0.00475 (-0.27) Manager Training (Time 26) x Manager Instrumental Leadership Observations Salespeople 694.6 (0.09) 2254 75 -0.0478 (-0.74) 2254 75 2254 75 2254 75 Note: * = p<0.05 ** = p<0.01 *** = p<.001. All tests are one-tailed tests based on direction of hypotheses (t statistics in parentheses). 88 TABLE 4: Results for Hypothesis H3 8271.5** (3.07) Training Transfer Performance Cross-Selling Cross-Selling as a Revenue % of Total Revenue 12260.0*** 0.0312 (4.22) (1.46) Cross-Selling as a % of Total Revenue 0.0480* (2.06) Salesperson Training (Time 26) 1193.8 (0.36) 4312.4 (1.13) 0.0111 (0.41) 0.0555 (1.76) Manager Training (Time 17) -6339.1** (-2.60) -9806.0*** (-3.72) -0.0487** (-2.52) -0.0670*** (-3.18) Manager Training (Time 26) 2913.0 (0.60) -356.4 (-0.07) 0.125** (3.16) 0.0793 (1.87) Friendship Network Degree Centrality Control Variables Sales Team 578.8** (2.84) 511.7 (1.86) 0.00507** (3.23) 0.00408 (1.88) -59.96 (-1.10) -40.09 (-0.74) 0.000128 (0.30) 0.000234 (0.55) Salesperson Tenure -424.4** (-2.67) -407.0** (-2.60) -0.00865*** (-7.07) -0.00860*** (-7.08) Salesperson Age 409.9*** (4.05) 354.1*** (3.50) 0.00449*** (5.78) 0.00422*** (5.39) Salesperson Selling Role 4101.9** (2.97) 3874.2** (2.84) 0.0315** (2.97) 0.0306** (2.90) Dependent Variables Cross-Selling Revenue Salesperson Training (Time 17) Interaction Terms Salesperson Training (Time 17) X Friendship Degree Centrality 1438.3*** (3.11) 0.00476 (1.28) Salesperson Training (Time 26) X Friendship Degree Centrality 821.2 (1.41) 0.0127** (2.64) Manager Training (Time 17) X Friendship Degree Centrality -1101.5** (-2.51) -0.00433 (-1.23) Manager Training (Time 26) X Friendship Degree Centrality Observation Salespeople -1279.5 (-1.25) 2254 75 -0.0185* (-2.22) 2254 75 2254 75 2254 75 Note: * = p<0.05 ** = p<0.01 *** = p<.001. All tests are one-tailed tests based on direction of hypotheses (t statistics in parentheses). 89 TABLE 5: Results for Hypothesis H4a Training Transfer Performance Dependent Variable Salesperson Training (Time 17) Cross-Selling Revenue 53487.5*** (8.89) 8331.2*** (3.14) 9500.9*** (3.29) Salesperson Training (Time 26) 1642.5 (0.50) 21819.4* (2.10) 2979.5 (0.74) Manager Training (Time 17) -6546.4** (-2.74) -48419.9*** (-8.21) -6956.0** (-2.63) Manager Training (Time 26) 2340.5 (0.49) 2190.6 (0.17) -445.0 (-0.07) High Knowledge Network Sharing Centrality 2137.5*** (5.23) 1295.8** (2.56) Low Knowledge Network Sharing Centrality Control Variables Sales Team -4.289 (-0.01) -72.60 (-1.37) -61.07 (-1.25) -82.45 (-1.51) Salesperson Tenure -366.0* (-2.33) -265.8* (-1.85) -426.3** (-2.64) Salesperson Age 416.8*** (4.21) 277.8** (2.96) 384.8*** (3.77) Salesperson Selling Role 3877.7** (2.76) 3636.4** (2.92) 4145.9** (2.87) 255.1 (0.65) Note: * = p<0.05 ** = p<0.01 *** = p<.001. All tests are one-tailed tests based on direction of hypotheses (t statistics in parentheses). 90 TABLE 5 (cont’d) Training Transfer Performance Dependent Variable Interaction Terms Salesperson Training (Time 17) X High Knowledge Sharing Centrality Cross-Selling Revenue 6745.3*** (7.94) Salesperson Training (Time 26) X High Knowledge Sharing Centrality 3192.3* (1.99) Manager Training (Time 17) X High Knowledge Sharing Centrality -6388.5*** (-7.14) Manager Training (Time 26) X High Knowledge Sharing Centrality -51.72 (-0.03) Salesperson Training (Time 17) X Low Knowledge Sharing Centrality 671.3 (1.02) Salesperson Training (Time 26) X Low Knowledge Sharing Centrality 633.6 (0.77) Manager Training (Time 17) X Low Knowledge Sharing Centrality -478.3 (-0.74) Manager Training (Time 26) X Low Knowledge Sharing Centrality Observations Salespeople -1364.4 (-0.79) 2254 75 2254 75 2254 75 Note: * = p<0.05 ** = p<0.01 *** = p<.001. All tests are one-tailed tests based on direction of hypotheses (t statistics in parentheses). 91 TABLE 5 (cont’d) Training Transfer Performance Dependent Variable Salesperson Training (Time 17) Cross-Selling as a % of Total Revenue 0.0329 (1.54) 0.0738 (1.44) 0.0284 (1.24) Salesperson Training (Time 26) 0.0111 (0.41) 0.288** (3.19) -0.000948 (-0.03) Manager Training (Time 17) -0.0484* (-2.52) -0.145** (-2.89) -0.0308 (-1.46) Manager Training (Time 26) 0.120** (3.06) 0.119 (1.08) 0.106* (2.17) High Knowledge Network Sharing Centrality 0.0130*** (4.08) 0.0121** (2.83) Low Knowledge Network Sharing Centrality Control Variables Sales Team -.000727 (-0.31) -0.0000136 (-0.03) 0.0000948 (0.23) -0.000187 (-0.44) Salesperson Tenure -0.00821*** (-6.69) -0.00852*** (-7.07) -0.00851*** (-6.87) Salesperson Age 0.00444*** (5.74) 0.00489*** (6.23) 0.00426*** (5.43) Salesperson Selling Role 0.0278* (2.53) 0.0339** (3.25) 0.0287** (2.58) -.00158 (-.052) Note: * = p<0.05 ** = p<0.01 *** = p<.001. All tests are one-tailed tests based on direction of hypotheses (t statistics in parentheses). 92 TABLE 5 (cont’d) Training Transfer Performance Dependent Variable Interaction Terms Salesperson Training (Time 17) X High Knowledge Sharing Centrality Cross-Selling as a % of Total Revenue 0.00575 (0.79) Salesperson Training (Time 26) X High Knowledge Sharing Centrality 0.0452*** (3.24) Manager Training (Time 17) X High Knowledge Sharing Centrality -0.0176** (-2.31) Manager Training (Time 26) X High Knowledge Sharing Centrality 0.00342 (0.19) Salesperson Training (Time 17) X Low Knowledge Sharing Centrality 671.3 (1.02) Salesperson Training (Time 26) X Low Knowledge Sharing Centrality 633.6 (0.77) Manager Training (Time 17) X Low Knowledge Sharing Centrality -478.3 (-0.74) Manager Training (Time 26) X Low Knowledge Sharing Centrality Observations Salespeople -1364.4 (-0.79) 2254 75 2254 75 2254 75 Note: * = p<0.05 ** = p<0.01 *** = p<.001. All tests are one-tailed tests based on direction of hypotheses (t statistics in parentheses). 93 TABLE 6: Results for Hypothesis H4b Training Transfer Performance Dependent Variable Salesperson Training (Time 17) Cross-Selling Revenue 7652.6** (2.81) 19807.6*** (5.63) 9500.9*** (3.29) Salesperson Training (Time 26) 1457.8 (0.43) 10475.4 (1.53) 2979.5 (0.74) Manager Training (Time 17) -5769.5** (-2.37) -18222.5*** (-5.41) -6956.0** (-2.63) Manager Training (Time 26) 2453.0 (0.51) -476.7 (-0.06) -445.0 (-0.07) High Knowledge Network Receiving Centrality 652.9* (2.30) 568.0 (1.71) Low Knowledge Network Receiving Centrality Control Variables Sales Team 6.179 (0.02) -74.34 (-1.37) -75.60 (-1.44) -82.45 (-1.51) Salesperson Tenure -349.3* (-2.13) -247.9 (-1.58) -426.3** (-2.64) Salesperson Age 367.3*** (3.60) 281.7** (2.83) 384.8*** (3.77) Salesperson Selling Role 3807.2** (2.63) 4527.3*** (3.38) 4145.9** (2.87) 255.1 (0.65) Note: * = p<0.05 ** = p<0.01 *** = p<.001. All tests are one-tailed tests based on direction of hypotheses (t statistics in parentheses). 94 TABLE 6 (cont’d) Training Transfer Performance Dependent Variable Interaction Terms Salesperson Training (Time 17) X High Knowledge Receiving Centrality Cross-Selling Revenue 2995.9*** (4.84) Salesperson Training (Time 26) X High Knowledge Receiving Centrality 2013.9 (1.57) Manager Training (Time 17) X High Knowledge Receiving Centrality -2947.5*** (-4.78) Manager Training (Time 26) X High Knowledge Receiving Centrality -538.0 (-0.35) Salesperson Training (Time 17) X Low Knowledge Receiving Centrality 671.3 (1.02) Salesperson Training (Time 26) X Low Knowledge Receiving Centrality 633.6 (0.77) Manager Training (Time 17) X Low Knowledge Receiving Centrality -478.3 (-0.74) Manager Training (Time 26) X Low Knowledge Receiving Centrality Observations Salespeople -1364.4 (-0.79) 2254 75 2254 75 2254 75 Note: * = p<0.05 ** = p<0.01 *** = p<.001. All tests are one-tailed tests based on direction of hypotheses (t statistics in parentheses). 95 TABLE 6 (cont’d) Training Transfer Performance Dependent Variable Salesperson Training (Time 17) Cross-Selling as a % of Total Revenue 0.0287 (1.32) 0.0230 (0.80) 0.0284 (1.24) Salesperson Training (Time 26) 0.00983 (0.36) -0.0152 (-0.26) -0.000948 (-0.03) Manager Training (Time 17) -0.0436* (-2.25) -0.0330 (-1.20) -0.0308 (-1.46) Manager Training (Time 26) 0.121** (3.05) 0.213*** (3.27) 0.106* (2.17) High Knowledge Network Receiving Centrality 0.00415* (1.89) 0.00253 (0.94) Low Knowledge Network Receiving Centrality Control Variables Sales Team -0.000764 (-0.30) -0.0000179 (-0.04) -0.0000334 (-0.08) -0.000187 (-0.44) Salesperson Tenure -0.00809*** (-6.38) -0.00825*** (-6.61) -0.00851*** (-6.87) Salesperson Age 0.00414*** (5.25) 0.00411*** (5.19) 0.00426*** (5.43) Salesperson Selling Role 0.0273** (2.45) 0.0276** (2.59) 0.0287** (2.58) -0.00158 (-0.52) Note: * = p<0.05 ** = p<0.01 *** = p<.001. All tests are one-tailed tests based on direction of hypotheses (t statistics in parentheses). 96 TABLE 6 (cont’d) Training Transfer Performance Dependent Variable Interaction Terms Salesperson Training (Time 17) X High Knowledge Receiving Centrality Cross-Selling as a % of Total Revenue -0.00138 (-0.27) Salesperson Training (Time 26) X High Knowledge Receiving Centrality 0.0424 (-0.39) Manager Training (Time 17) X High Knowledge Receiving Centrality 0.00195 (0.39) Manager Training (Time 26) X High Knowledge Receiving Centrality 0.0278* (2.17) Salesperson Training (Time 17) X Low Knowledge Receiving Centrality -0.00161 (-0.31) Salesperson Training (Time 26) X Low Knowledge Receiving Centrality -0.00338 (-0.50) Manager Training (Time 17) X Low Knowledge Receiving Centrality 0.0117* (2.29) Manager Training (Time 26) X Low Knowledge Receiving Centrality Observations Salespeople -0.0139 (-1.00) 2254 75 2254 75 2254 75 Note: * = p<0.05 ** = p<0.01 *** = p<.001. 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