THE STRUCTURAL EFFECTS OF TEAM DENSITY AND NORMATIVE STANDARDS ON NEWCOMER PERFORMANCE By Brian Manata A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Communication – Doctor of Philosophy 2015 ABSTRACT THE STRUCTURAL EFFECTS OF TEAM DENSITY AND NORMATIVE STANDARDS ON NEWCOMER PERFORMANCE By Brian Manata This dissertation investigates the impact of team density and team standards on newcomer performance. Data were collected from 204 newcomers, with results indicating that team density had a substantial negative effect on newcomer performance. Moreover, although that a team positive effect standards had was a predicted, trivial analyses impact on indicated newcomer performance. An interaction effect between team density and team standards was also predicted, but the hypothesis failed to receive any statistical support. This dissertation ends with a detailed discussion in which the contribution and implications of this research are research are offered. addressed, and directions for future ACKNOWLEDGMENTS I would like to thank Vernon Miller, Frankie B., Jim Dearing, and Elaine Yakura for serving on my committee; may you terrorize many other students throughout your academic careers. I would also like to thank Shannon Cruz for being a sweet BEH BEH and putting up with me throughout this godforsaken process. Lastly, I would like to thank Kyle, Bri, Ken, Min, and Vernon for collecting my data on that fateful Thursday morning (I don’t know, was it a Thursday?). iii TABLE OF CONTENTS LIST OF TABLES…………………………………………………………………………………………………………………… v LIST OF FIGURES………………………………………………………………………………………………………………… vi INTRODUCTION………………………………………………………………………………………………………………………… 1 LITERATURE REVIEW…………………………………………………………………………………………………………… Team Density…………………………………………………………………………………………………………… Normative Standards………………………………………………………………………………………… Team Density and Normative Standards…………………………………………… 3 5 6 8 METHOD………………………………………………………………………………………………………………………………………… Procedure…………………………………………………………………………………………………………………… Sample…………………………………………………………………………………………………………………………… Measures……………………………………………………………………………………………………………………… Team Density……………………………………………………………………………………………… Team Standards………………………………………………………………………………………… Newcomer Performance………………………………………………………………………… Control Variables………………………………………………………………………………… Task and Social Cohesion………………………………………………… Leader Member Exchange (LMX)……………………………………… Reception of Performance Evaluation…………………… Measurement Model……………………………………………………………………………………………… 11 11 12 13 13 15 15 16 16 17 17 18 RESULTS……………………………………………………………………………………………………………………………………… Control Variables……………………………………………………………………………………………… Reception of Performance Evaluation……………………………………………… Hypothesis Testing…………………………………………………………………………………………… Hypothesis 1……………………………………………………………………………………………… Hypothesis 2……………………………………………………………………………………………… Hypothesis 3……………………………………………………………………………………………… Post-Hoc Outlier Analysis………………………………………………………………………… 21 21 21 22 23 23 24 25 DISCUSSION……………………………………………………………………………………………………………………………… Team Density…………………………………………………………………………………………………………… Team Standards……………………………………………………………………………………………………… Limitations……………………………………………………………………………………………………………… 28 30 31 35 CONCLUSION……………………………………………………………………………………………………………………………… 39 APPENDIX…………………………………………………………………………………………………………………………………… 41 REFERENCES……………………………………………………………………………………………………………………………… 50 iv LIST OF TABLES Table 1: Factor Loadings, Reliabilities, Means, and SDs across each of the Four Factors……………………………………………………………………… 20 Table 2: Predictors of Newcomer Performance Scores (outliers included)……………………………………………………………………………………………………… 27 Table 3: Predictors of Newcomer Performance Scores (outliers exclude)………………………………………………………………………………………………………… 27 Table 4: Correlations between Factors……………………………………………………… 27 v LIST OF FIGURES Figure 1. Hypothesized interaction model between team density and team standards (H3)……………………………………………………………………… 10 Figure 2. Low versus high density group………………………………………………… 14 Figure 3. Visualized interaction term; outliers included…… 25 Figure 4. Post-hoc hypothesis and finding, in which team standards has a small indirect effect on newcomer performance (N = 45); outliers excluded………………………………………………… 32 vi INTRODUCTION Upon becoming organizational members, newcomers go through a period of socialization in which they are assimilated to the organization’s Schein, 1979). newcomers normative culture Specifically, “acquire the (Kramer, during knowledge, the 2010; Van Maanen socialization skills, & phase, attitudes, and behaviors” (Wanberg, 2012, p. 12) deemed essential to fulfilling their organizational roles. In the main, this period of adjustment is conceptualized as a concerted effort between both newcomers and organizational incumbents (Bauer & Erdogan, 2014). Organizational initiatives, in particular, may be used to guide newcomers through structured or unstructured experiences that facilitate their adoption of relevant organizational beliefs, values, and norms (Jones, 1986; Van Mannen & Schein, 1979). Conversely, newcomers may information integral to actively fulfilling seek out their or observe organizational expectations and outcomes (Chao, O’Leary-Kelly, Wolf, Klein, & Gardner, 1994; Miller & Jablin, 1991; Morrison, 1993). In both respects, the assimilation phase is a period of time in which newcomers acquire normative information that guides them through their adjustment period (Kramer & Miller, 2014; Stohl, 1986). Thus far, reviews of the socialization corpus generally recommend establishing and implementing socialization practices that facilitate newcomer learning and adjustment (e.g., Bauer & 1 Erdogan, 2014; Chao, 2012; Kramer & Miller, 2014). As van Vianen and Pater (2012) note, “a common understanding of organizational values and goals…advance[s] effective communication, smooth collaborations, and stability among organizational members” (p. 145). These conclusions analyses, which increases newcomer retention show (Bauer, are that buttressed facilitating performance, Bodner, by two recent newcomer role-clarity, Erdogan, Truxillo, adjustment and & meta- overall Tucker, 2007; Kramer, 2010; Saks, Uggerslev, & Fassina, 2007). Despite have been these empirical criticized multilevel theory for (MLT; advances, failing Kozlowski, to socialization apply 2012; the scholars tenets Kozlowski & of Klein, 2000) and the social network approach (SNA; Butts, 2009; Monge & Contractor, 2003; Newman, 2010). In the main, both perspectives suggest that failing to account for multilevel effects (i.e., group-level systems obfuscates relations (Bauer Kozlowski & In an & Bell, as our facilitate outcomes 2013). phenomena) they Erdogan, attempt to within understanding newcomer 2012; occur of performance 2014; Manata, allay how these team-member and Jokisaari Miller, organizational & assimilation Nurmi, DeAngelis, criticisms, 2012; & Paik, this study focuses on assessing the impact of variables deemed applicable to both multilevel theory and social network analysis (team density and standards). A literature review is provided below. 2 LITERATURE REVIEW Multilevel theory (MLT) postulates that organizations are complex, hierarchical systems comprised of interdependent teams and larger units (Kozlowski & Klein, 2000; Morgan, 2006). Hence, assuming extant between-unit variation in team culture and normative standards, it is implied that newcomer socialization differs as a function of the specific unit to which the newcomer is socialized (Kozlowski & Bell, 2012; Moreland & Levine, 1982, 2001). Similar to MLT, the SNA postulates that individuals are embedded within Erdogan, 2014; multilevel, Jokisaari relational & Nurmi, structures 2012; Monge & (Baurer & Contractor, 2003; Newman, 2010). Network level effects, for instance, may be modeled at Harrison, the team 2006), Contractor, level of analysis organizational Wasserman, & level Faust, (e.g., of 2006), Balkundi analysis and so on. & (e.g., Both theoretical perspectives thus suggest that newcomer assimilation outcomes are likely to vary as a function of one’s position and pattern of relationships (cf. Borgatti, Mehra, Brass, & Labianca, 2009; Crawford & Lepine, 2013). Strikingly, research guided by these two theoretical perspectives (i.e., MLT and SNA) differs substantially from past socialization investigations. Specifically, whereas past approaches have focused primarily on the importance of acquiring organizational-level information 3 (e.g., Chao et al., 1994; Jones, 1986; Miller & Jablin, 1994; Stohl, 1986; Van Maanen & Schein, 1979), investigating both MLT and group-level SNA peer focus on the interactions importance and of newcomers’ specific patterns of network relationships. Given the purported influence of newcomers’ immediate peers during socialization (cf. Jablin, 2001; Moreland & Levine, 1982, 2001; Louis, Posner, & Powell, 1983; Ostroff & Kozlowski, 1992; Salancik & Pfeffer, 1978), the general omission of these perspectives is somewhat unanticipated. Correspondingly, the incorporation of variables that help illume socialization the is complex, essential multilevel to nature uncovering of new newcomer and highly important aspects of newcomer socialization (Bauer & Erdogan, 2014; Jokisaari & Nurmi, 2012; Kozlowski & Bell, 2012; Manata et al., 2013; Moreland & Levine, 2001). Newcomer socialization studies guided by both theoretical perspectives (i.e., MLT and SNA) have helped illustrate how newcomers’ network positions and group-level relations affect integral assimilation outcomes. Recent work by Chen (2005) and Chen and Klimoski (2003), for instance, shows that being socialized to high performing teams with strong expectations is associated with substantial increases in performance. Additionally, in her pioneering work on newcomer social capital, Morrison (2002) found that being positioned within dense information networks was associated with increases in newcomer 4 role clarity strong and task mastery; friendship ties evidenced additionally, increases newcomers in role with clarity, social integration, and organizational commitment. Jokisaari and Vuori (2014) relatedly found that newcomers’ innovativeness increased as their informational resources became increasingly heterogeneous, and Jokisaari (2013) found that stronger ties to work colleagues transformed newcomers into more effective group members. Overall, these studies reinforce the practical and theoretical importance of assessing how group-level phenomena and network properties impact key assimilation outcomes like performance. Team Density Of the myriad team-level network variables available (see Hanneman & Riddle, 2005; Monge & Contractor, 2003; Newman, 2010), density is highly applicable to the multilevel nature of teams, members’ relational patterns, and socialization outcomes (e.g., performance; Balkundi & Harrison, 2006). Density is defined as the extent to which nodes found within a network are interconnected 2003). (Hanneman Accordingly, connectivity between & denser nodes Riddle, networks 2005; come increases Monge to & Contractor, fruition (Newman, as 2010). the When extrapolated to the team level of analysis, the density of the team increases as connections between team members are realized (Balkundi & Harrison, 2006). 5 Denser groups are typically characterized by increases in information exchange, collaboration, and overall member interaction (Coleman, 1988; Sparrowe, Liden, Wayne, & Kraimer, 2001; Zohar & Tenne-Gazit, 2008). Notably, in other literatures, these patterns of interaction have been shown to lead to increases in both team and member performance. For instance, in their recent meta-analysis of the hidden profile literature, Lu, Yuan, and McLeod (2012) showed that information sharing in groups was associated with substantial increases in decisionmaking accuracy. Moreover, recent reviews by Kozlowski and Ilgen (2006) and Kozlowski and Bell (2012) conclude that establishing shared mental schema of work-related activities aids with task completion and member coordination. Relatedly, in their metaanalysis, association Balkundi between and team Harrison density (2006) and found team a positive performance, and subsequent empirical investigations have since then buttressed their initial conclusions (e.g., Bizzi, 2013; Mehra, Dixon, Brass, & Robertson, 2006; Roberson & Williamson, 2012; Zohar & Tenne-Gazit, 2008). Given the overall positive effects of teamlevel density, the first hypothesis is offered. H1: Team density positively predicts newcomer performance. Normative Standards An additional mechanism by which team density influences member behavior is normative constraint and coordinated action 6 (Burt, 2000; 2001; Coleman, 1988). Within group contexts, norms are defined as established patterns of group member behavior to which other members of the group commonly adhere (Burgoon, 1978; Lapsinki & Rimal, 2005). Thus, as members enter networks that are highly clustered and dense, they are likely to be exposed to group-level normative standards that ultimately constrain their behavior (Centola, 2010; Shakya, Christakis, & Fowler, 2014). Barker (1993) and Gibson and Papa (2000), for instance, found that as newcomers entered their respective organizational units, they experienced pressure from organizational incumbents to adopt the team’s normative standards. This is in line with the theoretical musings members’ actions members, normative of Jones become (1984), increasingly pressures would who posited visible likely to that as other unit and thus ensue attenuate member “shirking or freeriding” (p. 686). Such norms are likely normative injunctive behaviors are in nature, met with where both violations social of sanctions said and member disapproval (Glynn & Huge, 2007; Jackson, 1966, 1975; Lapinski & Rimal, 2005; Manata & Miller, 2012; Miller & Form, 1964). In the absence of social sanctions, the mere espousal of normative standards likely conveys descriptive attitudinal information to which members assimilate (cf. de la Haye, Mohr, Robins, & Wilson, 2013; Lapinski & Rimal, 2005; Zohar & Hoffman, 7 2012). In his seminal work, Friedkin (1984) found that members’ attitudes were homogeneous with those of their direct social circle contacts. Within organizational settings, Fulk (1993) similarly found that workgroup attitudes and behaviors predicted those of individual attracted to the members, workgroup. as long Other as the members organizational were studies and reviews have also shown how organizational members’ attitudes and behaviors are typically predicted by the attitudes of those in their vicinity—generally, their work group (e.g., see Jokisaari & Nurmi, 2012; Rentsch, 1990; Stephens & Davis, 2009). Overall, these findings imply that performance norms and attitudes conveyed by newcomers’ peer groups are likely related to how newcomers ultimately perceive the importance of their task (cf. Zohar & Hoffman, 2012), and thus how they perform (cf. Kim & Hunter, 1993). In consequence: H2: Team standards positively predict newcomer performance. Team Density and Normative Standards Intriguingly, density teams the have argument the ability thus to far suggests constrain and that high- socialize newcomers to either high or low levels of performance (i.e., a team density x normative standards interaction). Presumably, units with high levels of team density are able to generate normative environments that constrain members’ actions (Burt, 2001; Coleman, 1988; Zohar & Tenne-Gazit, 2008). Thus, for teams 8 that are high in density and socialize newcomers to high performance norms, newcomer performance should increase (e.g., Chen, 2005; Chen & Klimoski, 2003; Katzenbach & Smith, 1993). Inversely, for teams that are high in density but evidence low performance standards, organizational teams and units may suppress the productivity of their members by either actively constraining their output (e.g., Cohen & Bailey, 1997; Roethlisberger & Dickson, 1939; Taylor, 1914; Zurcher, 1983) or by espousing and infecting newcomers with low-level performance standards (Monge & Contractor, 2003; Schein, 1968; cf. Zohar & Hoffman, constraint 2012). on In either newcomer case, performance the effect likely of normative depends on the strength and specific direction of the normative standard. In support of these assertions, Langfred (1998) found that workgroup standards moderated the cohesion-performance relationship, such that group cohesion enhanced team performance when normative standards were high, but attenuated it when they were low. Similarly, when studying the effects of latrine ownership, Shakya et al. (2014) found that latrine ownership was lowest when participants’ network interconnectedness (defined as transitivity) was high and others’ latrine ownership was low; notably, this effect disappeared as others’ latrine ownership decreased, thus suggesting that participants were less likely to 9 be subjected to normative peer pressures. Thus, in line with these empirical findings, it is predicted that: H3: Newcomer performance will be highest when team density and team standards are high, but lowest when team density is high and team standards are low. Moreover, when team density is low, the effect of team standards will be weaker when compared to these two conditions. Team Density Team Standards Newcomer Performance Figure 1. Hypothesized interaction model between team density and team standards (H3). 10 METHOD Participants were sampled from the Residence Education and Housing Services (REHS). REHS is a unique community of Resident Assistants (RA) who are charged with overseeing the living conditions and acclimation of undergraduate students. Moreover, RAs work in small teams, which are led by Assistant Community Directors (ACDs; graduate student advisors), that meet on a weekly basis in order to deal with work issues as they arise throughout the week. Because RAs typically live and work within close proximity to one another, and because hundreds of new RAs are socialized population effects was of to the deemed team a REHS community good density sample and team by each year, which to standards the RA assess the on newcomer performance. Procedure Data from this sample were collected during an REHS meeting that all RAs and ACDs were required to attend (before the start of the Spring 2015 semester). During this meeting, RAs and their corresponding ACDs were asked to split up into their respective sub-staffs, and customized survey then each packets. sub-staff These was survey given packets a set of contained a complete list of members assigned to each sub-staff. Thus, this procedure sought allowed participants work-related advice to from 11 report members on how assigned often they to their specific team. measures of Each survey packet also contained team standards, newcomer performance, general demographic information (gender, months worked, etc.), and other relevant control variables (see Appendix for full instrument). Sample In total, 340 RAs and ACDs across 45 different sub-staffs from REHS were sampled. Eighteen participants, however, had to be dropped from the subsequent analyses. Specifically, eight of these participants were brand new and thus had very limited or no experience. Additionally, 10 participants were transfers and thus did not have their names listed on the sub-staff’s customized survey packet. A decision was made to drop both types of individuals (i.e., brand new and transfers) because sub-staff members related were unable advice to from report on these whether members. they sought task- Additionally, for participants that were brand new, no connections were typically listed because they had yet to be integrated into network (i.e., they had no connections to report). the REHS These 18 participants were removed from the sample, as keeping them would have forced artificial, the zeroes introduction of (i.e., non-connections) false numerous, potentially into the density calculation, thus underestimating it. Of the remaining 322 REHS members, n = 204 were classified as newcomers by REHS because they 12 had been employed for 12 months or less. This sub-sample of n = 204 thus constituted the final sample of newcomers used in the subsequent analysis. Moreover, given the abundance of newcomers, teams were primarily composed of incoming RAs (M = 63%; SD = .15%) Of these available data, 88.7% (n = 180) of the participants were RAs, 10.8% (n = 22) were Assistant Community Directors, and 0.5% (n = 1) were Community Directors (supervisorial position). Subjects were mostly female (54.4%; n = 111), and identified as Caucasian (65.3%; n = 132), Black/African American (13.9%; n = 28), Asian (8.4%; n = 17), Multi-ethnic (5.4%; n = 11), and a range of other ethnicities (7%; n = 14). Additionally, participants were on average 20.67 years old (SD = 2.10), had been working for roughly 5.21 months (SD = 2.62), and identified as sophomores (34.3%; n = 70), juniors (37.3%; n = 76), seniors (15.2%, n = 31), and graduate students (13.2%, n = 27). Measures Team Density. To calculate team density, each team member was sent a list of their respective team members’ names and asked to check off the names of those from whom they sought work-related advice (e.g., Bizzi, 2013). Participants were also asked to report on how frequently these advice-seeking interactions occurred. Frequency of advice-seeking interactions was measured using a one-item measure that ranged from 1 = less 13 than once a week to 7 = several times a day. This addition helped differentiate between stronger and weaker advice-seeking ties, and also allowed for the network to be treated as a directed network (i.e., Member A may be tied to Member B, but Member B need not be tied to Member A). Team-level density ratios were calculated by dividing the sum of tie values by the total number of possible ties (Hanneman & Riddle, 2005). To produce team-level density ratios, the data matrix was partitioned into hypothesized blocks that represented each of the sub-staffs and their respective members. Following this, the density formula was applied to each of the partitioned blocks (UCINET, Borgatti, Everett, & Freeman, 2002; Hanneman & Riddle, 2005). Density scores ranged from 0 to 7, with higher scores representing stronger degrees of task-related advice- seeking activity (M = 1.83; SD = .52) (see Figure 2). LOW DENSITY GROUP HIGH DENSITY GROUP Figure 2. Low versus high density group. Thicker ties equate to stronger connections. 14 Team Standards. The extent to which RA teams had high standards of performance was measured using Taylor and Bower’s (1972) three-item peer goal emphasis scale. These items were positioned on 5-point Likert-type scales (1 = strongly disagree; 5 = strongly agree). Because team standards were theorized to be a group-level factor, within-group agreement in team standard scores was assessed using the intra-class correlation (Bliese, 2000). This analysis showed that subjects’ team standards responses evidenced substantial within-group agreement (ICC = .20, p < .001; cf. Kashy and Kenny, 2000; Maas & Cox, 2004a), thus providing validity to the claim that team standards construct was operating at the group-level of analysis (Kozlowski & Klein, 2000). As such, individual-level perceptions of team-level standards were aggregated to the team-level of analysis (M = 5.84; SD = .55). Newcomer Performance. Although ACDs are required to formally evaluate the performance of their respective RAs twice a year, a complete set of formal evaluations were not available at the time of data collection. In an attempt to assuage this limitation, newcomer performance scores were derived by soliciting subjective self-report evaluations using a one-item measure that ranged from 0%-100% (M = 84.28; SD = 7.59). 15 Control Variables. Task and Social Cohesion. A decision was made to control for the potentially confounding effects of group cohesion. Specifically, given the similarity of Langfred’s (1998) Group Cohesion x Team Standards hypothesis (i.e., network density has been previously conceptualized as a measure of structural cohesion; Hanneman & Riddle, 2005), it was deemed important to control for group cohesion in order to conclude that any of the team density effects were unique from the findings of Langfred (1998). To account for this, both task and social cohesion measures were created by adapting items from Carron et al.’s (1985) GEQ cohesion instrument and adding custom-made items. Task cohesion is defined as an attraction and unification toward the task, whereas social cohesion is defined as a general attraction to the group and its members (Castano et al., 2013; Dion, 2000). Similar to team standards, calculated in order to assess intra-class correlations the degree of were within-group agreement in both task and social cohesion scores. Analyses show that subjects’ task cohesion responses evidenced substantial within-group agreement (ICC = .27, p < .001; M = 5.91; SD = .55). Additionally, subjects’ social cohesion responses also evidenced within-group agreement, albeit not as substantially, and was thus also treated as a group-level factor (ICC = .12, p = .002; M = 6.34; SD = .51) (Bliese, 2000; see Kashy & Kenny, 16 2000 for recommendations on acceptable levels of ICCs). Thus, both factors were aggregated to the group-level of analysis. Leader Member Exchange (LMX). Given the integral role of the supervisor (ACD), the positive effects of LMX on job performance (Dulebohn, Bommer, Liden, Brouer, & Ferris, 2012) were controlled for statistically. High LMX relationships are typically classified as evidencing substantial trust, respect, and liking between both supervisor and subordinate. Conversely, low LMX relationships lack in these ostensibly important relational characteristics (Liden & Maslyn, 1998). To measure LMX, items were adapted from Graen & Uhl-Bien’s (1995) recommended LMX scale (items were positioned on 4-point scales). Because the purpose individual-level of this perceptions variable of was to leader-member control relations, for this factor was kept at the individual-level of analysis (M = 3.36; SD = .59). Reception of Performance Evaluation. As mentioned above, ACDs are tasked with formally evaluating their respective RAs’ performance twice a year (once in December, and once in May). Despite this, the exact time at which RAs receive their performance evaluations from their ACDs is not held constant across sub-staffs. Thus, it may be the case that in some substaffs RAs received their performance evaluations in December (i.e., pre data collection), whereas in other sub-staffs, RAs 17 had yet to receive their performance evaluations before participating in this study (i.e., post data collection). Given that these data were collected during the first week of January, and given that all formal evaluations had yet to be completed, this created a condition in which some RAs had received their performance evaluations (n = 125, 63.78%), and others had not. Depending on the nature of the evaluation (i.e., negative, neutral, positive), it is not unreasonable to expect the existence or absence of such an evaluation to impact the number of connections levels of reported self-rated instance, may by the subjects, performance. either attenuate A or negative their reported evaluation, perceptions of for performance ability, or alter RAs’ advice-seeking patterns. Thus, in order to control for this potentially confounding factor, a one-item measure asking subjects if their performance evaluations had already been received was included in the survey. Measurement Model The structural validity of the proposed four-factor model (i.e., team standards, task cohesion, social cohesion, LMX) was assessed using confirmatory factor analysis (cf. Hunter, 1980; Hunter & Gerbing, 1982). Factor loadings were derived using the centroid method of estimation, parallelism theorems were correlations for of each and used the 18 internal to consistency generate indicators. Items and predicted evidencing consistently large residuals were deemed invalid and removed from the analysis. Upon removal of items with exceedingly large residuals, inspection of the root mean squared error term suggested good model fit (RMSE = .05). Moreover, alpha levels across each of the four factors suggested good to adequate levels of reliability. The four-factor model was thus retained. For a full list of items, factor loadings, means, standard deviations, and reliability coefficients attributable factors, see Table 1, below. 19 to each of the four Table 1. Factor Loadings, Factors Reliabilities, Means, and SDs across each TS Team Standards (α = .89) (M = 5.84, SD = .55) Members on my sub-staff maintain high standards of performance. Members on my sub-staff set an example by working hard themselves. Members on my sub-staff encourage each other to give their best efforts. Task Cohesion (α = .88) (M = 5.91, SD = .55) When members of my sub-staff work together, it feels like an integrated experience. Members on my sub-staff are unified when working together. Members on my sub-staff work well with each other. Members on my sub-staff get along with each other when working together. Members on my sub-staff have conflicting aspirations Social Cohesion (α =.86) (M = 6.34, SD = .51) I do not enjoy being a part of my sub-staff. I do not want to be friends with those on my sub-staff. I would have rather preferred being in a sub-staff with other people. Members in my sub-staff make me feel uncomfortable. If given the chance to work with my sub-staff again, I would take it. LMX (α =.86) (M = 3.36, SD = .59) Do you usually know how satisfied your ACD is with what you do? How well do you feel that your ACD understands your problems and needs? How well do you feel that your ACD recognizes your potential? Regardless of how much formal authority your ACD has built into his or her position, what are the chances that he or she would be personally included to use power to help you solve problems in your work? Again, regardless of the amount of formal authority your ACD has, to what extent can you count on him or her to “bail you out” at his or her expense when you really need it? I have enough confidence in my ACD that I would defend and justify his or her decision if he or she was not present to do so. How would you characterize your working relationship with your ACD? 20 of TC the SC Four LMX .89 .89 .78 .70 .87 .84 .83 -.74 .78 .80 .80 -- .65 .85 .81 .60 -- -.84 RESULTS Control Variables Before conducting any of the main analyses, the effects of the control reception variables of (task performance cohesion, evaluation) social on cohesion, newcomer LMX, performance were assessed using a hierarchical linear model (HLM; Raudenbush & Bryk, 2002; Singer, 1998; Singer & Willet, 2003). Substantial group-level Increases effects in task emerged for cohesion task predicted and social increases cohesion. in newcomer performance scores, γ = 3.22, z = 1.99, 95% CI [0.05, 6.39]. Moreover, and quite interestingly, increases in social cohesion predicted substantial decreases in newcomer performance scores, γ = -3.57, z = -2.21, 95% CI [-6.75, -0.40]. Both of these variables were thus controlled for statistically when performing subsequent analyses. Conversely, LMX and reception of performance evaluation evidenced trivial effects on newcomer performance, and were thus dropped from subsequent analyses (Singer & Willet, 2003). Reception of Performance Evaluation Whether or not the previous reception of a formal evaluation altered the amount of reported connections was also of concern. To assess whether this factor impacted the number of reported work-related advice-seeking connections, a measure of out-degree centrality (i.e., reported 21 outward connections; Newman, 2010) was computed for each of the subjects. Analyses indicated that there was not a substantial difference in the number of reported outward connections when comparing those that had received their performance evaluations (M = 10.96, SD = 7.33) to those that had not yet received it (M = 11.60, SD = 7.92), t(308) = .72, p = .48. As such, and in conjunction with the initial regression model stipulated above, this variable was no longer considered during analysis. Hypothesis Testing In ascertaining the main effects of team density (H1) and team standards (H2), both variables were added to the previously stipulated HLM model. Formally, Newcomer_Performance𝑖𝑗 = π0𝑗 + ε𝑖𝑗 and π0𝑗 = γ00 + γ01 Task Cohesion𝑗 + γ02 Social Cohesion𝑗 + γ03 Team Density𝑗 + γ04 Team Standards𝑗 + ζ0𝑗 thus leaving us with the combined model of Newcomer_Performance𝑖𝑗 = γ00 + γ01 Task Cohesion𝑗 + γ02 Social Cohesion𝑗 + γ03 Team Density𝑗 + γ04 Team Standards𝑗 + (ε𝑖𝑗 + ζ ) 0𝑗 where ε𝑖𝑗 = within-group residual ζ0𝑗 = between-group residual γ00 = grand mean 22 π0𝑗 = group-level mean γ01 = group-level effect of task cohesion γ02 = group-level effect of social cohesion γ03 = group-level effect of team density γ04 = group-level effect of team standards Upon running this model, inspection of the residuals for the stochastic component of the model evidenced substantial departure from normality. Departures from normality are known to result in 2004a; 2004b), intervals biased second-level which used to affect assess standard errors the accuracy the fixed of (Maas the effects & Cox, confidence at Level-2 (Raudenbush & Bryk, 2002). Robust standard errors were thus used to assess the validity of Hypotheses 1-3, as they are less affected by this violation (Maas & Cox, 2004a; 2004b). Hypothesis 1. Counter to expectations, team density emerged as a substantial negative predictor of newcomer performance (γ = -1.67, z = -1.85, 95% CI [-3.46, 0.10]). Explicitly, increases in team density were performance. Thus, effect, proposed the associated despite the positive with decreases emergence effects of of team a in newcomer substantial density (H1) failed to receive statistical support. Hypothesis 2. In terms of team standards, although the effect appears somewhat negative (γ = -1.34, z = -0.98, 95% CI 23 [-4.02, 1.34]), the confidence interval is quite wide, thus indicating that the effect is decidedly weak and potentially due to sampling error. Thus, team standards did not have a substantial direct effect on newcomer performance. As such, H2 also failed to receive statistical support. Hypothesis 3. To assess the validity of H3, an interaction term, which was designated as the multiplicative term between team density and team standards, was added to the previously stipulated model. Formally, Newcomer_Performance𝑖𝑗 = γ00 + γ01 Task Cohesion𝑗 + γ02 Social Cohesion𝑗 + γ03 Team Density𝑗 + γ04 Team Standards𝑗 + γ05 Team Density𝑗 × Team Standards𝑗 + (ε𝑖𝑗 + ζ0𝑖 ) where γ05 Team Density𝑗 × Team Standards𝑗 = the group-level interaction effect between team density and team standards This model produced an interaction estimate in the predicted direction (γ = 1.24, z = 0.95, 95% CI [-1.33, 3.81]), but, given the small group-level N, also included 0 in its confidence interval. To visualize this effect, the regression equation was modeled at +1, 0, and -1 SD of team standard’s mean. As is shown in Figure 3, newcomer performance appears lowest when team density is high and team standards are low. Conversely, when team density is high and team standards are 24 high, the negative effects of team density are mitigated. Thus, a visualization of the interaction provides some support for the Newcomer Performance tenets that underlie H3. Figure 3. Visualized interaction term; outliers included. Post-Hoc Outlier Analysis The above regression model flagged three participants as substantial statistical outliers (i.e., standards residuals were more than three standard deviations away from the newcomer performance regression line). Inspection of these individuals indicated that they evaluated their own performance as exceptionally low (i.e., 50%, 50%, and 65%, respectively). In an 25 attempt to assess whether these outliers altered the previously reported conclusions, these individuals were excluded and the regression models were re-run. The negative effect of team density (H1) remained negative and became substantial, γ = -2.67, z = -3.86, 95% CI [-4.03, 1.31]. Moreover, the insignificant effects of team standards (H2) became substantially weaker, γ = -.11, z = -0.09, 95% CI [2.55, 2.33], thus corroborating the notion that team standards had little to no direct effect on newcomer performance. Lastly, and perhaps most importantly, removal of the outliers negated the interaction effect reported above, γ = .24, z = -0.24, 95% CI [-2.24, 1.75]. This occurred because the three outliers reported high levels of team standards and low levels of team density, thus making the high standards regression line appear comparatively less steep than the low standards regression line (i.e., artificial non-additivity). As such, upon removal of the outliers, H3 also failed to receive any statistical support. The coefficients attributable to each of the three multilevel regression models are found in Tables 2 (outliers included) and 3 (outliers removed). Moreover, correlation coefficients for each of the factors are reported in Table 4. 26 Table 2. Predictors of Newcomer Performance Scores (outliers included) Model 1 B Model 2 B Model 3 B Constant 82.19*** 88.39*** 101.28*** Task cohesion 3.22* 4.57** 4.44** Social cohesion -3.57* -3.18* -3.11* LMX 0.84 --Evaluation 1.86 --receipt Team density -1.67† -8.81 Team standards -1.34 -3.53 TS x TD (H3) 1.24 Note. Coefficients are unstandardized coefficients. †p < .10 *p < .05. **p < .01. ***p < .001 Table 3. Predictors of Newcomer Performance Scores (outliers excluded) Model 1 B Model 2 B Model 3 B Constant 80.24*** 84.68*** 82.15*** Task cohesion 3.39* 3.99** 4.01** Social cohesion -3.17* -2.81† -2.82† LMX 0.63 --Evaluation 1.60 --receipt Team density -2.67*** -1.28 Team standards -0.11 -0.32 TS x TD (H3) -0.24 Note. Coefficients are unstandardized coefficients. †p < .10 *p < .05. **p < .01. ***p < .001 Table 4. Correlations between Factors NP TD Newcomer performance Team density -.11 Team standards .13 .26 Task cohesion .18 .21 Social cohesion .01 .14 LMX .06 -.02 Evaluation received .16 -.16 Note. Group N = 45, Newcomer Listwise n abbreviations of the corresponding row .15; outliers excluded. 27 TS TC SC LMX EVAL .72 .48 .70 .11 .18 .29 .07 .26 .23 .14 = 178. Column labels are labels; p < .05. if r > DISCUSSION Analyses suggest that team density had a negative effect on newcomer performance, whereas team standards had a negligible effect on newcomer performance. Moreover, these effects remained consistent despite excluding the three outliers. The exclusion of the three outliers, however, did impact the substantive conclusions regarding H3. The predicted interaction effect received partial support when the outliers were included in the analyses, but failed to receive any statistical support when they were excluded. This means one of two things: (1) the newcomers flagged as outliers were true outliers and thus produced artificial non-additivity, or (2) a larger sample with additional low-performing newcomers would negate the outlier status of the three outliers and thus clarify the nature of the proposed interaction effect (H3). In either case, it is evident that additional research is required. Despite failing to find statistical support for all three hypotheses, the results and nature of this study shed light on newcomer socialization experiences. Specifically, the previously unexplored concepts of team density (structural cohesion) and standards were integrated into the socialization corpus, which differs markedly instead focused from on past studies assessing the in which impact of scholars have organizational context (Van Maanen & Schein, 1979), informational content (Chao 28 et al., 1994), and memorable messages (Stohl, 1986). The main difference lies traditionally in been the locus focused at of the analysis, which individual-level. has Thus, whereas past studies have focused on the impact of individuallevel predictors, this study instead examined how group-level phenomena (e.g., team standards) and structural variables (team density) impacted newcomer performance. As such, an overarching framework is interactions provided (Jablin, in which 2001) the and influence multilevel of peer-level organizational structure (Monge & Contrator, 2003) are now assessable within the context of newcomer socialization (Bauer & Erdogan, 2014). This study is also unique from previous newcomer socialization works in which the impact of myriad structural variables were investigated. Past work has examined the impact of ego-network densities (Jokisaari, 2013; Jokisaari & Vuori, 2014; Morrison, 2002), which is an individual-level property and thus different from team-level density, which is a group-level variable. Doing so forced the differentiation between personal and team-level networks, which are conceptually different and thus hold the potential to have different effects (Kozlowski & Klein, 2000). Future research may attempt to conduct newcomer socialization research in which both networks are accounted for, thus allowing for the comparison of both types of networks. 29 Team density psychological was further cohesion (task distinguished and social from measures cohesion), which of was important given that the different types of cohesion evidenced disparate effects. The effects of task cohesion on newcomer performance, for instance, were positive, but the effects of team density negative. cohesion (structural Using cohesion) measures interchangeably of and social structural should thus cohesion and be were psychological implemented with caution, as their effects appear non-parallel (Dion, 2000). Team Density The negative team density effect contradicts the findings found in produced Balkundi a performance. finding by and positive One may Harrison’s effect between attempt considering the (2006) to RA team account role, meta-analysis, density for which this one may and which team unexpected argue is primarily independent. When roles are independent, team density may thus hamper, as oppose to foster, effective role performance. To wit, if RAs are forced to work in teams despite having independent roles, higher levels of density may mean that RAs are not spending enough time performing their duties. This information may be especially pertinent to RA administrators, as they, in this instance, have appeared to force the creation of teams that may be directly responsible performance of their employees. 30 for stifling the Future investigations of this ilk may make great use of RA teams, as they present a considerable advantage in that they are primarily composed of independent members (a rare occurrence; cf. Kozlowski & Bell, 2012). In conjunction with assessing the impact of team density on other similar groups (e.g., faculty departments), similar evidence in line with what was produced here may important emerge, and moderators thus that begin to account illume for the variance presence in the of team density  team performance relationship (cf. Hunter & Schmidt, 2004). Considering the nature of the network tie (i.e., adviceseeking ties) may also help explain the negative team density effect. In particular, members that spend great amounts of time seeking advice from others may be doing so because they believe that they performance are was not performing measured using their a jobs well. one-item Given that self-evaluation measure, this explanation is also quite sensible. Team Standards This study revisited the role of team standards, a concept which has been long forgotten and essentially neglected in newcomer socialization research (see Roethlisberger & Dickson, 1939; Taylor, 1914). Despite predicting a strong positive effect on newcomer performance, the HLM revealed that team standards had a trivial effect on newcomer performance after controlling 31 .72 .17 Team Standards Newcomer Performance Task Cohesion .10 Figure 4. Post-hoc hypothesis and finding, in which team standards has a small indirect effect on newcomer performance (N = 45); outliers excluded. for both task and social cohesion. This is not to say, however, that team standards do not play a critical role in the eventual performance of newcomers. Strong team standards, for instance, may be essential to establishing strong levels of task cohesion (Hoigaard, Safvenbom, & Tonnessen, 2006), which then impact newcomer performance (Castano et al., 2013). Inspection of the correlation matrix conjecture. Indeed, provides a some simple support causal for model this in initial which team standards predict task cohesion, which then predicts newcomer performance, fits the data quite well (see Figure 4). Future research that attempts to evaluate the tenability of this post-hoc hypothesis may begin to shed light on the proposed effects of team standards. In doing so, RA administrators (as well as other organizational 32 executives) may use this information to generate team compositions in which a predominant proportion of members have high standards of performance (cf. Bell, 2007). Researchers pertinent to might the also study begin of to team address other standards and aspects newcomer socialization. For example, future research may attempt to focus precisely on from whom the newcomer is gleaning normative information. In this study, newcomers were able to indicate that they sought advice from both peers and supervisors. It may be, however, that some newcomers place greater value on information received from peers (Ostroff & Kozlowski, 1993), whereas others place greater value on information received from supervisors (cf. Ostroff & Kozlowski, 1992). Under circumstances in which the standards of peers differ from supervisorial standards, divisive faultlines may divide the team in two, which may allow for divergent standards of performance to exist concurrently within a single team (Lau & Murnighan, 1998; Taylor, 1914). When these scenarios arise, exactly whom the newcomer retrieves information from, or precisely why one acclimates to one subgroup over another, remains an interesting question. Differentiating between different measures of density-like constructs (e.g., constraint, Hanneman & Riddle, 2005), as well as slightly different conceptualizations of the network density idea (e.g., ego-network density), may also help shed additional 33 light on the negligible team standards effect. For instance, and as noted above, ego-network density does not focus on the team, but rather focuses on the member’s personal network (e.g., Jokisaari & Vuori, 2014; Morrison, 2002). Thus, researchers must contend not only with the standards of a newcomer’s specific team (team-level variable), but also with the standards of the newcomer’s own personal network (individual-level variable). This distinction is an interesting one to make, as it suggests that some newcomers may retrieve normative information from members on their team, whereas others may retrieve normative information from members deemed external to their team (or both). Ultimately, it may be that normative information culled from a members preferred network acts as the main predictor of their eventual behavior. Finally, exactly how normative standards are conveyed to newcomers remains unclear, and will likely illume the processes by which both team standards and team density operate. Normative constraint to performance standards, for instance, can occur because (a) members explicitly communicate normative information to the newcomer (injunctive influence) (Hackman, 1992), or because (b) newcomers simply observe the normative behaviors of others over time (descriptive influence) (Miller & Jablin, 1991). The effects of standards may thus operate via injunctive or descriptive influence (or both), which raises the possibility 34 of different effects (cf. Lapinski & Rimal, 2005). Understanding which of these normative influences impacts newcomer behavior will be integral to understanding how both team standards and team density operate during newcomer socialization. Limitations The ranges in scores of the three main variables (i.e., team density, team standards, newcomer performance) were restricted to either high or low levels, and were skewed either positively or negatively. This claim is based on the comparison made between observed and maximum possible variances, as well as the skewness statistics produced in the analysis. Given that team density (skewness = .90; SE = .35) and team standards (skewness = -.64; SE = .35) scales were positioned on 1-7 point scales, maximum SD was roughly 3. On the other hand, observed SDs for team respectively. density and Moreover, team standards given that were .52 newcomer and .55, performance (skewness = -1.16; SE = .17) was measured on a 0-100 point scale, maximum SD was roughly 50, whereas observed SD for newcomer performance was 7.58. The restriction of both team standards and newcomer performance to high levels is likely due to the ego-centric bias (i.e., consistent standards; Harris overestimation & Schaubroek, of performance 1988). Future levels and research can assuage this limitation by having outsiders rate both levels of 35 newcomer performance and team standards (or, when applicable, utilizing objective measures of performance and standards). Newcomers’ peers and colleagues, for instance, may be able to provide reliable estimates of how well newcomers are performing. Additionally, ratings from multiple top executives/managers may be able to provide more objective ratings of team standards. This approach may help increase variance in both newcomer performance and team standards scores (cf. Hunter & Schmidt, 2004), and thus reduce the risk of attenuating coefficients. Replicating newcomer these performance results will be with alternative particularly measures important, as of past meta-analyses have shown that self-evaluations do not correlate highly with the ratings of others (Conway & Huffcut, 1997; Harris & Schaubroek, 1988; Heidemeier & Moser, 2009). If it is the case that self- and other-ratings do not correlate highly, then alternative measures of newcomer performance might yield different results. The use of self-ratings is an obvious limitation here, which, as similarly recommended above, could be allayed by implementing other, more objective measures of newcomer performance. Subsequent empirical attempts would also benefit from using measures with multiple indicators of newcomer performance (as opposed to a one-item measure), as this would contribute to reliability, and thus attenuate measurement error (Nunnally, Bernstein, & Berge, 1967). 36 Despite the limitation of the one-item performance measure, it is important measurement is keep in validity. mind To that wit, the the sine optimal qua non of measurement of newcomer performance will depend on how accurately the construct of newcomer performance is represented. Thus, the focus is not so much on how much agreement there exists between self- versus other-ratings (e.g., Atwater, Ostroff, Yammarino, & Fleenor, 1998), but rather on which of the two is deemed the most valid approximation of performance. Given the nature of RA work, one might question the validity of evaluations that come from others that do not see them perform (ACDs). Indeed, given the somewhat independent their nature performance of the may RA be better performance scores than researchers should consider evaluations instances, are but better poorer role, self-evaluations indicators others’ the indicators indicators RAs’ in of their true As such, that self- evaluations. possibility of performance others. of in some Ultimately, the nature of the member’s role, as well as the team’s level of task-interdependency, will likely guide this question. The substantive reason responsible for restriction in team density scores raises both interesting and potentially fruitful exploratory think of questions. any parsimoniously Specifically, immediate explain why the psychological responses 37 author reason about is unable that to might advice-seeking activity would be biased in any specific direction. Instead, restriction in team density scores may be due to a previously raised issue: for some teams, team density is not a property considered integral to the effectiveness of its members. To wit, if it is to be argued that RAs are primarily independent during task completion, then it follows that the formation of compact teams would presumably be stifled. Moreover, if density is not a property essential performance, additional, then to fostering introducing beneficial properties both this may team property foster, mitigate, lower levels of newcomer performance. 38 and newcomer without as opposed any to CONCLUSION It should be clear to the reader that future investigations of this ilk will undoubtedly need to rely on the theoretical underpinnings offered by both multilevel theory and the social network approach. In this study, for instance, density was conceptualized as a team-level factor. Consider, however, that the inclusion of ego-network density forces the consideration of density as an consideration individual-level of multilevel property, relationships. and thus Moreover, the these complex relationships are further expounded when variables at higher (or lower) levels of analysis are added to one’s conceptual model (e.g., departments at a higher level; time at a lower level). Indeed, as these rich theoretical notions begin to creep into newcomer socialization research (cf. Manata et al., 2013), multilevel aspects of organizational networks will undoubtedly force this type of theoretical thinking (Borgatti et al., 2009; Kozlowski & Bell, 2012). It should be recognized, however, that the implementation of these two approaches leaves us with the uncomfortable notion that newcomer success is in part a function of factors that one has little control over. For instance, given the negative effects of team density evidenced here, one is left with the question: do newcomers (or organizations) have the ability to change extant team network patterns? 39 Indeed, being able to accomplish this task constitutes a formidable challenge and thus seems unlikely. Specifically, such a drastic change would require either (a) complete overhauls in personnel (Schneider, 1987), or operation (b) a (e.g., change Barker, in team 1993). structure Newcomers accomplish either of these on their own, attempt to acclimate to organizational beliefs. 40 or patterns of are unlikely to especially as they values, norms, and APPENDIX 41 APPENDIX: Survey Instrument RA NETWORK STUDY Name_____________________________ Last Six Digits of your PID_____________________ 42 1. What is your position within the RA network? a. Resident Assistant b. Assistant Community Director c. Community Director 2. Roughly how long (in months) have you worked in your position? a. ____________________________ 3. Are you returning to your sub-staff this year, or are you new to your substaff? a. I am a returning RA/member b. I am a first-year RA/member 4. What is your sex? (circle one) Male / Female 5. What is your age in years? ____________________ years 6. What year are you in school? (circle one) a. 1st year (Freshman) b. 2nd year (Sophomore) c. 3rd year (Junior) d. 4+ years (Senior) e. Graduate Student (M.A. or Ph.D.) 7. Please indicate your ethnicity by placing a checkmark next to one (or more): ____ African ____ Black/African American ____ Asian ____ Hispanic ____ Caucasian/White ____ Indian sub-continent ____ Latino/Latina ____ Middle-Eastern ____ Multi ____ Native American/First Nation ____ Pacific Islander/Native Hawaiian ____ Other 8. Have you already received your December performance evaluations from your ACD? a. NO, I have not received my December performance evaluations from my ACD b. YES, I have received my December performance evaluations from my ACD 43 Below you will find a list of RAs that are in your sub-staff (ACD included). If, between weekly staff meetings, you seek advice from and communicate with any of these individuals about work-related issues, place an X next to their names and indicate how frequently these communicative interactions occur. Everyone on this list could receive an X, or no one could receive an X. Please ignore your own name. 1. [insert member name here] _____ a. How frequently do you seek advice about work-related information from this individual? Less Than Once a Week 1 2 3 4 5 6 7 Several Times a Day 2. [insert member name here] _____ a. How frequently do you seek advice about work-related information from this individual? Less Than Once a Week 1 2 3 4 5 6 7 Several Times a Day 3. [insert member name here] _____ a. How frequently do you seek advice about work-related information from this individual? Less Than Once a Week 1 2 3 4 5 6 7 Several Times a Day 4. [insert member name here] _____ a. How frequently do you seek advice about work-related information from this individual? Less Than Once a Week 1 2 3 4 5 6 7 Several Times a Day 5. [insert member name here] _____ a. How frequently do you seek advice about work-related information from this individual? Less Than Once a Week 1 2 3 4 5 6 7 Several Times a Day 6. [insert member name here] _____ a. How frequently do you seek advice about work-related information from this individual? Less Than Once a Week 1 2 3 4 5 6 7 Several Times a Day 7. [insert member name here] _____ a. How frequently do you seek advice about work-related information from this individual? Less Than Once a Week 1 2 3 4 44 5 6 7 Several Times a Day 8. [insert member name here] _____ a. How frequently do you seek advice about work-related information from this individual? Less Than Once a Week 1 2 3 4 5 6 7 Several Times a Day 9. [insert member name here] _____ a. How frequently do you seek advice from about work-related information from this individual? Less Than Once a Week 1 2 3 4 45 5 6 7 Several Times a Day When answering categories: these     next questions about your sub-staff,    Genuine Connections w/ Residents Developing Community Safety & Security Educator consider the REHS performance Team Player Leader Administrator 1. Members on my sub-staff maintain high standards of performance. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 7 Strongly Agree 2. Members on my sub-staff set an example by working hard themselves. Strongly Disagree 1 2 3 4 5 6 3. Members on my sub-staff encourage each other to give their best efforts. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 4. Of the performance feedback I have received thus far, I think my ACD is a harsh evaluator. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 5. When members on my sub-staff work together, it feels like an integrated experience. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 4 5 6 7 Strongly Agree 4 5 6 7 Strongly Agree 6. Members on my sub-staff are unified when working together. Strongly Disagree 1 2 3 7. Members on my sub-staff work well with each other. Strongly Disagree 1 2 3 8. Members on my sub-staff get along with each other when working together. Strongly Disagree 1 2 3 4 46 5 6 7 Strongly Agree evaluation 9. Members on my sub-staff have conflicting aspirations for the sub-staff’s performance. Strongly Disagree 10. 5 6 7 Strongly Agree 1 2 3 4 5 6 7 Strongly Agree 1 2 5 6 7 Strongly Agree 3 4 1 2 3 4 5 6 7 Strongly Agree 5 6 7 Strongly Agree Members in my sub-staff make me feel uncomfortable. Strongly Disagree 14. 4 I would have rather preferred being in a sub-staff with other people. Strongly Disagree 13. 3 I do not want to be friends with those on my sub-staff. Strongly Disagree 12. 2 I do not enjoy being a part of my sub-staff. Strongly Disagree 11. 1 1 2 3 4 If given the chance to work with my sub-staff again, I would take it. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree Instructions: The following question asks you to think about and rate the performance quality of RAs in your sub-staff. It is important that you respond to this question honestly, being as accurate as possible. Please the question carefully and write your numerical response in the space provided after the question text. As noted in the question text, rate the RA’s performance quality using a percentage-based scale that ranges from 0-100%. Please round your response to the nearest whole number (i.e., you may use any integer between 0-100, e.g., 79 or 82, do not use decimals). 1. If I were to rate my personal performance as an RA on a scale that ranges from 0% (low quality) to 100% (high quality), I would rate my own performance as: My personal performance: ___________ % 47 THE FOLLOWING QUESTIONS ARE FOR RAS ONLY. ACDs: do not answer these questions. 1. Do you usually know how satisfied your ACD is with what you do? a. Never know where I stand b. Seldom know where I stand c. Usually know where I stand d. Always know where I stand 2. How well do you feel that your ACD understands your problems and needs? a. Not at all b. Some but not enough c. As much as the next person d. Fully 3. How well do you feel that your ACD recognizes your potential? a. Not at all b. Some but not enough c. As much as the next person d. Fully 4. Regardless of how much formal authority your ACD has built into his or her position, what are the chances that he or she would be personally inclined to use power to help you solve problems in your work? a. No chance b. Might or might not c. Probably would d. Certainly would 5. Again, regardless of the amount of formal authority your ACD has, to what extent can you count on him or her to ‘‘bail you out’’ at his or her expense when you really need it? a. b. c. d. 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