Deploying “connectors”: A control to manage employee turnover intentions?

Accounting, Organizations and Society xxx (xxxx) xxx

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Deploying “connectors”: A control to manage employee turnover intentions?* Romana L. Autrey a, Tim D. Bauer b, *, Kevin E. Jackson c, Elena Klevsky d a

Atkinson Graduate School of Management, Willamette University, 900 State Street, Salem, OR, 97301, USA School of Accounting and Finance, University of Waterloo, 200 University Avenue W., Waterloo, ON, N2L 3G1, Canada c Department of Accountancy, Gies College of Business, University of Illinois at Urbana-Champaign, 1206 S. Sixth St., Champaign, IL, 61820, USA d Anderson School of Management, University of New Mexico, 1922 Las Lomas NE, Albuquerque, NM, 87106, USA b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 February 2017 Received in revised form 5 July 2019 Accepted 11 August 2019 Available online xxx

This paper investigates whether individuals that we identify as “connectors”dwho possess a blend of innate traits and skills that predispose them to be personable, willing to relate to others, and able to influence others’ relationshipsdcan serve as a catalyst for improving group outcomes. More specifically, we explore whether identifying connectors and placing them in work groups can serve as a control to help firms manage undesirable voluntary employee turnover by improving the group experience and reducing their fellow group members’ turnover intentions. We conduct an experiment to test our hypotheses that members in a group with a connector (versus without) have lower turnover intentions because their experiences are perceived as more positive, and that this turnover intention effect is more pronounced for group members who are demographically distinct from others in their group. Results are consistent with predictions, although the effect of connectors on lowering group members’ turnover intentions is driven by members who are distinct. Our findings broaden the understanding of who connectors are and how they affect group interactions, and further suggest that hiring and deploying connectors in work groups can be an effective component of a more comprehensive retention strategy. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Connector Turnover intentions Employee retention Personnel controls Work group dynamics

1. Introduction Organizations strive to manage voluntary employee turnover to prevent it and its costs (e.g., identifying and training new employees, loss of experienced or productive employees) from becoming excessive (Dess & Shaw, 2001; Shaw, Gupta, & Delery, 2005). Given the proliferation of work groups and workforce diversity that has “unalterably changed organizational work” (Van Knippenberg & Mell, 2016, 135), we consider how organizations

* We thank Ranjani Krishnan (Editor), two anonymous reviewers, Carrie Baumken, Clara Chen, Willie Choi, Nadine Fladd, Brent Garza, Sean Hillison, Jessen Hobson, Joe Mahoney, Tracie Majors, Jennifer Nichol, Brad Pomeroy, Adam Presslee, Tom Vance, Laura Wang, Alan Webb, Michael Williamson, Amanda Winn, and participants at workshops at Willamette University, University of Illinois, University of Pittsburgh, and University of Waterloo for their helpful comments. This work was funded by the Accountancy Fund for Excellence, an unrestricted account at the University of Illinois at Urbana-Champaign. The data reported in the paper are available from the authors. * Corresponding author. E-mail addresses: [email protected] (R.L. Autrey), [email protected] (T.D. Bauer), [email protected] (K.E. Jackson), [email protected] (E. Klevsky).

might manage voluntary turnover in light of these two trends. A strong predictor of voluntary turnover is employees’ connection with their work group (Dess & Shaw, 2001; Mowday, Porter, & Steers, 1982; Riketta & Van Dick, 2005); group members are more apt to leave when they feel disconnected from other members and this is particularly true for dissimilar or distinct members (Jackson et al., 1991; Riordan & Shore, 1997). Moreover, given the richness of ideas, outputs, and solutions that arise from combining the varied experiences of distinct group members (Van Knippenberg, van Ginkel, & Homan, 2013), losing diversity to turnover can be especially damaging to an organization (Dess & Shaw, 2001). As such, organizations who wish to lessen the risk of excessive voluntary turnover, including from distinct employees, may be wise to do so using mechanisms within their management control system (MCS) that influence work groups. In this study, we examine a unique set of individuals, “connectors,” who possess group-oriented traits and skills and whom organizations can hire and deploy into groups as a form of personnel control. Personnel controls, such as employee selection and placement, typically function by hiring employees with required traits or skills (e.g., high engagement or commitment) or assigning

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Please cite this article as: Autrey, R. L et al., Deploying “connectors”: A control to manage employee turnover intentions?, Accounting, Organizations and Society, https://doi.org/10.1016/j.aos.2019.101059

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them to groups with norms or culture that help them do their jobs well (Merchant & Van der Stede, 2017). We emphasize that organizations can hire connector employees with specific traits and skills that help others to be engaged or committed, and assign connectors to groups in which these traits and skills will offer the most benefit. While connectors are presumably affected by the existing norms and culture of their assigned group, this paper focuses not on how the group impacts connectors, but on how deployed connectors influence the group and its other members. We define connectors as individuals with an identifiable blend of innate traits and skills that predispose them to be personable, willing to relate to others, and/or able to influence others’ relationships. As their name implies, connectors link or build relationships between individuals (Gladwell, 2002). Within groups, connectors facilitate a more positive group experience (or culture) and strengthen connections among all group members, in part by encouraging equal turn-taking (Pentland, 2010) and inclusivity. In short, connectors create a positive social culture of togetherness, cooperation, open communication, and fair treatment within groups that should lead members to be committed to and engaged in their jobs. We examine whether placing individuals, who we identify as connectors, into groups increases group members’ positive experience and reduces their turnover intentions relative to groups that have no connector. Given the importance of work group attachment to turnover intentions (Dess & Shaw, 2001; Mowday et al., 1982) and given that a connector’s influence occurs via socialization and strengthening relational environments, connectors likely reduce turnover intentions within groups. We predict members of work groups that include a connector will have lower turnover intentions than members of groups with no connector, and this effect will be mediated by members’ group experience. Further, controls that operate via socialization are often more effective than formal controls in settings that involve ambiguity (e.g., in trying to incentivize group cohesion) or within groups whose members have diverse backgrounds (Abernethy & Brownell, 1997; Govindarajan & Fisher, 1990; Kachelmeier & Shehata, 1997). As such, we also predict the effect of connectors on turnover intentions of fellow work group members in our ambiguous setting will be more pronounced for group members who are demographically distinct from others in the group. To test our predictions, we employ a three-step approach where 1) we use an out-of-sample pool of participants to create a 31-item “connector survey” designed to identify connectors (derived from a longer survey of multiple scales and subsequently validated), 2) we ask a different set of participants, whom we intend to include in our experiment, to complete the connector survey, and 3) we invite step two respondents to participate in a follow-up controlled experiment. Our connector survey consists of 31 questions drawn from 11 previously validated constituent scales that relate to the three facets of our connector construct. These questions measure traits and skills comprising (1) being personable (as captured by the Big Five personality traits and affect), (2) desire to relate to others (as captured by interpersonal orientation and relational identity), and (3) ability to influence others’ relationships (as captured by connectivity, political skill, and self-monitoring). After participants in step two (undergraduate students) completed the connector survey, we used their responses to compute each participant’s connector score. Next, we assigned all participants to experimental groups; we seeded some groups with a connector, while the remaining groups had no connector. Each assigned group met alone in a small room where members completed a short task together, brainstorming cartoon captions. After completing the caption task, each group member completed a post-experiment survey about his or her turnover intentions (i.e., desire to remain with or leave group), and

about his or her perceptions of the group experience. Findings from our experiment largely support our predictions. Specifically, participants expressed significantly lower turnover intentions when their group included a connector than when it did not. This result, however, is driven by group members who are demographically distinct due to their gender or ethnicity. In addition, the connectors’ effect on turnover intentions occurred through their impact on the group experience of group members. Our study makes several contributions. Broadly, we show that individuals who we identify as “connectors” have a positive influence on group members’ experience and their desire to stay with or committed to their group. While their influence on group members who have shared demographic characteristics (i.e., who are more homogeneous) may be less substantial, connectors have a marked impact on group members who are distinct (i.e., heterogeneous) and more likely to feel out of place or leave the group. These results have important implications for groups in a multitude of settings from sports teams to student study groups, but especially so for organizations, particularly those heavily organized around work groups, who find themselves vying for talent in today’s competitive environment. Opportunities to improve the likelihood of retaining the most desirable employees represent a tangible competitive advantage to these organizations. Organizations may already have connectors or can select them in their hiring process to strategically place in work groups as a form of personnel control, allowing such organizations to better manage voluntary employee turnover. Regarding the selection aspect of personnel controls, we introduce a simple survey that identifies individuals with traits and skills that, on balance, capture a connector’s predispositions. Having this brief survey to administer and score gives organizations an opportunity to efficiently identify or hire connector employees. More importantly, regarding the placement aspect of personnel controls, results of our experiment show that connectors do indeed influence group members’ turnover intentions; placing connectors into groups enhances the experience of those groups and reduces the turnover intentions of those group members, relative to groups with no connector. Our results also suggest connectors are especially useful in work groups with distinct members. This is important for organizations to consider because, while they may reap some retention benefits by placing connectors in groups with homogenous members, they would likely see greater benefits by placing connectors in groups with heterogeneous members who, due to their distinctness and dissimilarity to others, might feel less motivated or commited. Enhancing an organization’s ability to retain distinct members is especially important given recent trends in globalization and worker mobility. Overall, while hiring and placing connectors in certain groups may not address all voluntary turnover challenges organizations face, our findings suggest identifying and deploying connectors can be an effective component of a more comprehensive retention strategy. Our theory and results also extend prior research in important ways. We find connectors operate informally at a social level by creating harmony and a more positive experience in work groups, which, from a control standpoint, inspires (rather than coerces) members to work together toward organizational goals and remain in their groups and organization. Our study also responds to recent concerns that research on MCS, including human relations controls, has overly focused on formal, coercive, and singular controls, and is therefore out of touch with the realities of today’s organizations (Cardinal, Kreutzer, & Miller, 2017). As a control, hiring and deploying connectors is but one potential informal, non-coercive way forward for practice and research, particularly in diverse work groups that are a frequent reality for many modern organizations (Van Knippenberg & Mell, 2016). Finally, we extend prior literature that has largely identified connectors based on ex-post

Please cite this article as: Autrey, R. L et al., Deploying “connectors”: A control to manage employee turnover intentions?, Accounting, Organizations and Society, https://doi.org/10.1016/j.aos.2019.101059

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observations of what they do, how they act, or how well-connected they are (e.g., Boster, Kotowski, Andrews, & Serota, 2011; Gladwell, 2002; Pentland, 2010) by also looking at who they are, ex-ante. We suggest being a connector is more than an ability to make connections; it is also a desire to get along with and relate to others on a more personal level and an ability to influence others. We further suggest being a connector requires possessing a holistic set of traits and skills measured in our connector survey, but not necessarily high levels of all of them. 2. Theory and hypotheses 2.1. Background 2.1.1. Voluntary employee turnover in organizations While organizations expect or even desire some level of voluntary employee turnover (e.g., to promote innovation or avoid stagnation), they are motivated to prevent it and its costs from becoming excessive (Dess & Shaw, 2001; Shaw et al., 2005). Such costs include losing specialized human capital, replacing personnel, and training new or reassigned personnel (Regts & Molleman, 2013; Siebert & Zubanov, 2009; Wright & Bonett, 2007). Furthermore, turnover-reducing control mechanisms, such as selection, placement, promotion, and reward systems, can be costly and can have varied success (McEvoy & Cascio, 1987; Parker, 2014; Sheridan, 1992). A key variable related to turnover intentions and voluntary turnover is job satisfaction (Bullen & Flamholtz, 1985; Porter & Steers, 1973; Wright & Bonett, 2007). Moreover, employees’ job satisfaction and desire to stay in an organization are both heavily influenced by connections with their work group and its members (Dess & Shaw, 2001; Ehrhardt & Ragins, 2019; Mowday et al., 1982; Riketta & Van Dick, 2005). Because organizational work is increasingly structured around work groups (Van Knippenberg & Mell, 2016) and organizations have a strong financial interest to avoid excessive voluntary turnover, we examine a potential control for reducing turnover intentions that organizations can employ as part of their broader management control system (MCS): hiring individuals identified as connectors and deploying them in work groups. 2.1.2. The connector construct Recent literature has used the label of connector (or connectivity) to refer to individuals who are well-connected or the center of a social network (Boster et al., 2011; Gladwell, 2002). That is, they are the bridge or link between other individuals (Gladwell, 2002) and they are high in connectivity because they know many people, have a large social network, are casual friends with many, or are the central person who connects others (Boster et al., 2011). Pentland (2010) further reasons that connectors facilitate interaction among others and themselves by circulating within a group, listening intently, and urging conversation through questions. However, other than indicating connectors are skilled at connecting others or have many acquaintances, there is little description of who connectors are.1 One possibility is that connectors desire a large network for the social capital it can provide; that is, having a wider set of people from which to access help or resources, or transfer information among, if a future need arises (i.e., to gain

1

Pentland (2010) identified connectors based on what they did; he had participants wear digital sensors to measure nonverbal aspects of their social interactions such as vocal pitch and speed, and then used this data to infer who the connectors were. We characterize a connector more fundamentally based on a participant’s innate traits and skills.

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extrinsic benefits). A different possibility is that connectors are prosocial and genuinely enjoy cultivating relationships for more personal or altruistic reasons. To be prosocial means to act for the benefit of others even if it has costs to the self, and individuals who are prosocial have been described as good team players, agreeable (i.e., warm, friendly, and get along well with others), and possessing other-oriented empathy (Penner, Dovidio, Piliavin, & Schroeder, 2005; Keltner, Kogan, Piff, & Saturn, 2014).2 Further, an organization has more social capital when its members engage in prosocial behavior (Bolino, Turnley, & Bloodgood, 2002) or when its members are connected and have strong personal and cognitive connections (Nahapiet & Ghoshal, 1998).3 However, when prosocial behavior is perceived as proself, prosocial benefits fail to materialize (Treadway, Ferris, Duke, Adams, & Thatcher, 2007), suggesting that individuals must not only be prosocial but others must perceive their motives as sincere in order to positively cultivate relationships. In sum, those who are prosocial or other-oriented and are sincerely seen as such tend to relate and connect well with others at a personal level. Thus, we contend that connectors are skilled at linking with people or influencing others, but they also get joy or intrinsic reward from relating to and with others at a personal level, they sincerely care for others’ well-being (i.e., for the greater good), and they are seen as genuine. This combination should allow them not only to be the link among people but to truly bring them together as a group. Our connector construct includes three facets: being personable, having a desire to relate to others, and having an ability to influence others. Each facet is comprised of different traits or skills identified in prior research that in whole or in part predispose a connector to that facet. We discuss below and in Table 1 how each individual facet and its underlying traits and skills are necessary parts of our connector construct but individually are insufficient. The first connector facet, being personable, is comprised of the following traits that predispose a connector to be friendly and easy to talk to or approach: the Big Five (agreeableness, extraversion, openness to experience, conscientiousness, and emotional stability) and the net combination of positive and negative affect (Gosling, Rentfrow, & Swann, 2003; Watson, Clark, & Tellegen, 1988). By being personable, connectors draw others to seek relationships with them but they may not have the skills or desire to build such relationships or influence others; hence, the importance of the other two facets. The second facet, the desire to relate to others, is comprised of traits that predispose a connector to be genuinely curious about and sincerely care for others and to value personal relationships with others; these traits include interpersonal orientation and relational identity (Kashima & Hardie, 2000; Swap & Rubin, 1983). While connectors have a desire to build relationships with others, they also must have the ability to do so and have the personal appeal for others to want to build relationships with or through them; thus, there is a need for the first and third facet. The third facet, the ability to influence others, is comprised of skills that predispose a connector to be highly influential and effective at leading or reading social situations; namely, connectivity, political skill (e.g., networking ability), and self-monitoring (e.g., responsiveness to interpersonal or social cues) (Boster et al., 2011; Ferris et al., 2005; Snyder & Gangestad, 1986). Having these

2 A related construct, organizational citizenship behavior, refers to voluntary and helpful behavior in the workplace. 3 That is, organizations have greater social capital not simply when more employees know each other and can access each other at a later date but also when they trust and have deeper personal relationships with each other and better understand each other (Nahapiet & Ghoshal, 1998; Bolino et al., 2002).

Please cite this article as: Autrey, R. L et al., Deploying “connectors”: A control to manage employee turnover intentions?, Accounting, Organizations and Society, https://doi.org/10.1016/j.aos.2019.101059

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Table 1 Constructs and Scales used in Developing the Connector Construct and Survey. PRIOR RESEARCH

RELATING TO OUR CONNECTOR CONSTRUCT

Existing construct/scale

What the construct/scale captures

Relevant findings for people with this characteristic

Why it is included

Why it is not enough

BEING PERSONABLE Agreeableness

Extraversion

a

Warm, friendly, welcoming

a

Outgoing, energetic

Open To Experience Conscientiousness

a

Emotional Stability Positive Affect

a

b

a

a

a

Curious, broad-minded, unconventional a Hardworking, dependable

a

Steady, calm, not easily upset

a

Positive emotionality, enthusiastic b

Negative Affect

b

Negative emotionality, aversive

b

Perceived as helpful and unlikely to cause conflict; associated with prosocial behavior (and social capital) Perceived as sociable; more likely to approach others Perceived as open to meeting new and different people Perceived as trustworthy and unlikely to disappoint Perceived as comfortable, lowstress conversation partners Perceived as pleasant conversation partners Perceived as unpleasant conversation partners

All these characteristics (including the converse of negative affect) make one more approachable and/or less likely to be avoided by others; i.e., others are drawn to such individuals, providing increased opportunity to build relationships, particularly oneon-one

May lack skills or ability needed to influence others and/or build relationships, particularly among others May lack desire to influence others and/or build relationships, particularly among others

DESIRE TO RELATE TO OTHERS Relational Identity

Interpersonal Orientation

c

Self-view that emphasizes relationships with others c Empathetic, interested in others, does not see others as stereotypes d

d

Deeply value interpersonal relationships and interconnectedness Sincere care for others, associated with prosocial behavior (& social capital)

Intrinsic desire to devote time and energy building relationships with others Genuinely curious about others. Motivated to build relationships with and among others

May lack skills or ability and/or personal appeal needed to influence others or build relationships

ABLE TO INFLUENCE OTHERS0 RELATIONSHIPS Connectivity

e

Able (and strive) to bring people together e

Highly influential by virtue of being socially well-connected

Political Skill

f

Effective at understanding others and subtly convincing them to act in a manner that boosts personal gain f Able to respond to social and interpersonal signals, can control expressions, chameleon-like g

Perceived as strong leaders and as unselfish; use exchange and coalition tactics to resolve conflict

Self-Monitoring

a b c d e f g

g

Good self-control and impression management; perceived to be emotionally supportive of others

Able (and desire) to build connections with and among others Able to effectively influence others

Able to adapt to situations and send clear social signals of support to others

Desire may be self-serving not prosocial or for personal power rather than to enable others' connections May lack personal appeal needed to influence others May use skills or ability for reasons other than supporting others

Gosling et al. (2003): Ten-item Personality Inventory (i.e., Big Five Traits). Watson et al. (1988): Positive and Negative Affect Schedule (PANAS). Kashima and Hardie (2000): Relational, Individual, and Collective (RIC) Self-aspects Scale. Swap and Rubin (1983): Interpersonal Orientation Scale. Boster et al. (2011): Connectivity Scale. Ferris et al. (2005): Political Skill Inventory; this scale has four subscales: networking ability, apparent sincerity, social astuteness, and interpersonal influence. Snyder and Gangestad (1986): Self-monitoring Scale.

skills gives connectors the tools to build relationships, but without a prosocial motive (second facet) or personal appeal (first facet) their attempts may fail or be seen as self-serving or disingenuous (e.g., as strategic impression management). 2.2. Hypothesis development 2.2.1. Connector group members as a control to manage turnover intentions Within its MCS, an organization has many potential tools or mechanisms to utilize as controls to coerce, constrain, motivate, or inspire employees to align with and attain its goals (Birnberg & Snodgrass, 1988; Chenhall, 2003; Mundy, 2010). Such controls vary across several dimensions, ranging from formal to informal, coercive to enabling, and bureaucratic or rules-based to personnel or socialization-based (Cardinal et al., 2017). Organizations can hire and deploy connectors in work groups as one form of personnel control. Selection and placement personnel controls are meant to help organizations find employees with the right skills and values for the job and put them in an environment where they will be engaged and committed and perform the job welldultimately to

maximize revenues and minimize costs (Merchant & Van der Stede, 2017). By hiring connectors and placing them in work groups, organizations can select employees who, by virtue of their prosocial and relationship-enhancing nature, create a positive environment and inspire (rather than coerce) other employees to be engaged and committed. Thus, the purpose of utilizing a control to select and deploy connectors is less about finding the right environment for connectors and is more about creating the right environment for all other employees.4 In this study, we consider a setting in which connectors (1) work in groups with other members, and (2) work on more open-ended creative tasks as opposed to more rote mundane production tasks

4 Prior definitions of personnel controls included mechanisms now defined separately as cultural controls (Merchant, 1982). Cultural controls refer broadly to mechanisms that instill or shape desired organizational values and encourage employees to pressure or monitor each other to adhere to these values (Cardinal et al., 2017; Govindarajan & Fisher, 1990; Merchant & Van der Stede, 2017). Thus, connectors may also represent a cultural control to some extent; we propose their behavior instills a culture of group camaraderie and respect, but likely with less use of or need for coercion, pressure, or monitoring.

Please cite this article as: Autrey, R. L et al., Deploying “connectors”: A control to manage employee turnover intentions?, Accounting, Organizations and Society, https://doi.org/10.1016/j.aos.2019.101059

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that may be effectively completed without deliberation or drawing consensus. Tasks in our setting can be characterized as uncertain, non-routine, and complex (Brüggen, Feichter, & Williamson, 2018); more succinctly, we consider tasks that are more ambiguous. In these types of tasks, personnel controls such as placing a connector in a group are optimal because the informal socialization aspect of such controls offers flexibility in how group members respond to the uncertain task demands (Abernethy & Brownell, 1997; Govindarajan & Fisher, 1990). We expect connectors to create a more positive work group experience because they are predisposed to prosocial behavior and to deepening both personal relations and understanding among work group membersdfor example, by reaching out to, welcoming, and/or introducing others, taking turns speaking and listening attentively, or involving others in activities. Such relationshipbuilding behavior can improve group experiences by fostering a stronger work group identity or by facilitating greater interaction, trust, and cooperation among members (Dovidio, Gaertner, & Validzic, 1998). Indeed, connectors have been associated with more equal turn-taking, increased trust, and higher group performance in a variety of tasks and settings (Pentland, 2010). We use the term group experience to collectively refer to the extent to which group members share an identity, have fun, cooperate, and feel free to participate and express themselves.5 Enhancing group experience can improve a group’s functioning (i.e., interactions) and work outcomes, such as employee turnover (Guillaume, Brodbeck, & Riketta, 2012). In particular, a more positive work group experience can increase employee attachment to their work group, and because employees are often more committed to their work groups than to their “amorphous, distant” employing organizations (Feldman, 2000, p. 179; Dess & Shaw, 2001), higher work group attachment can reduce employee turnover (Ehrhardt & Ragins, 2019). Therefore, we predict that groups that include a connector member will express lower turnover intentions than groups with no connector, because the connector creates a more positive experience for fellow group members. H1. Relative to members of groups without a connector, members of groups that include a connector will have lower turnover intentions. H2. The lower turnover intentions of members of groups with versus without a connector will be mediated by member perceptions of a positive group experience. 2.2.2. Connector group members and demographically distinct group members According to Van Knippenberg and Mell (2016), two related trends have changed the face of organizational work over the past half-century: the proliferation of work performed in groups and the demographic diversity or variety of the workforce. Such diversity

5 Prior research suggests that another potential avenue for a connector to enhance the group experience is via other group members unconsciously reciprocating or mimicing the connector’s actions (Pentland, 2010); mutual mimicry is positively associated with rapport between individuals (Chartrand & Lakin, 2013). Since connectors merely behave according to their nature and reciprocal responses are likely unconscious, it may not be necessary for anyone in the group (including the connector) to be aware of the connector’s identity for a connector to have profound effects on group dynamics. As a practical matter, connectors may have historically been difficult to identify because their primary impact on groups is via their influence on others’ behaviors. Moreover, if others can mimic a connector then it may be possible to train others to act like a connector. However, the effectiveness of training a connector may be limited because connector traits and skills are not only associated with behaviors, but also with anticipating what behaviors are appropriate given a specific set of circumstances; such anticipation may be difficult to train.

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arises from differences in factors such as gender, age, work tenure, race or ethnicity, education or functional background (Riordan & Shore, 1997; Chattopadhyay, George, & Lawrence, 2004; Guillaume et al., 2012; Van Knippenberg & Mell, 2016). Workforce and work group diversity can provide organizations with a competitive advantage by combining the variety of experiences of their distinct members (Van Knippenberg et al., 2013), and organizations do not want to lose that advantage due to turnover. However, employees also prefer homogenous groups (Guillaume et al., 2012); this preference can cause demographically distinct work group members to feel less connected or have a diminished sense of shared identity with the rest of the group (Chattopadhyay et al., 2004). Feeling less connected can, in turn, lead distinct members to be less committed to their group and to their organization and more apt to leave (Riordan & Shore, 1997). There is scant research, however, on how and the extent to which demographic distinctiveness affects individual-level work outcomes, including turnover and turnover intentions (Guillaume et al., 2012). Within this limited research, gender and ethnicity are commonly-examined characteristics because they are “easily detected” (e.g., as similar or as distinct) and “are often the basis for [how] individuals spontaneously categorize each other” (Riordan & Shore, 1997, p. 344), and typically produce stronger results than other characteristics such as age or tenure (Guillaume et al., 2012). Accordingly, in our setting, we utilize gender and whether or not participants are natives of the US to represent our measure of distinctiveness. Group members who are, or feel they are, distinct tend to be less satisfied with their group experience, less well-integrated in group activity and communication, and less attached to or more motivated to leave the group (Guillaume et al., 2012; Jackson et al., 1991; Riordan & Shore, 1997; Zatzick, Elvira, & Cohen, 2003). But, as distinct members begin to feel more social support or a greater shared identity in their work groupdfor example, as the number of members with the same gender increasesdthey become less likely to leave their group (Zatzick et al., 2003). More generally, prior research suggests that social connections are fundamental to forming a shared identity (or bridging the gap) across demographic differences (Ren, Gray, & Harrison, 2015). We propose that a connector group member can reduce the more acute turnover intentions of distinct group members by fostering a shared group identity or group interactions. For example, as part of their interpersonal orientation, connectors are motivated to treat distinct work group members inclusively and fairly, rather than to view them as stereotypes, and they likely encourage fellow members to do the same (see Chartrand & Lakin, 2013; Pentland, 2010). As such, a connector’s behavior and influence can have a particularly profound effect on the group experience of distinct group members. Individuals who are demographically distinct, and who may feel more isolated in the absence of a connector (e.g., diminishing a shared group identity), are more likely to feel included and listened to (e.g., fostering a shared group identity) when the group includes a connector. Accordingly, we predict that the presence of a connector group member will lower the turnover intentions of distinct group members more than other group members. H3. Having a connector in the group will lower turnover intentions to a greater extent for group members who possess a demographic characteristic distinct to their group than for those who share demographic characteristics with one or more fellow group members.

Please cite this article as: Autrey, R. L et al., Deploying “connectors”: A control to manage employee turnover intentions?, Accounting, Organizations and Society, https://doi.org/10.1016/j.aos.2019.101059

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3. Experimental design and method We test our hypotheses using a three-step approach. In the first step, we create a survey designed to identify connectors (i.e., individuals who possess high levels of a connector’s traits or skills). In the second step, we administer the connector survey to undergraduate students (our participants) to identify and classify students as connectors or non-connectors. In the third step, we conduct an experiment whereby these same participants are placed in groups to perform a task, and we manipulate whether each group either includes or does not include a participant identified from the survey as being a connector. We elaborate on each step below.6 3.1. Step 1: Creating the connector survey Our survey is designed to detect the strength of individuals’ traits and skills associated with being a connector. Thus, we examined validated constituent scales from prior research in psychology to identify indicators of each of the three conjectured facets of our connector construct: (1) being personable; (2) having a desire to relate to others; and (3) having an ability to influence others’ relationships. Given our expectation that the connector construct is related to social capital, all scales capture constructs theoretically related to social capital. For facet (1), we included the Ten-Item Personality Inventory (Gosling et al., 2003) to capture the Big Five personality traits and the Positive Affectivity-Negative Affectivity Scale (Watson et al., 1988). For facet (2), we included the Relational, Individual, and Collective self-aspects Scale (Kashima & Hardie, 2000) to capture relational identity and the Interpersonal Orientation Scale (Swap & Rubin, 1983). For facet (3), we included the Connectivity Scale (Boster et al., 2011), the Political Skill Inventory (Ferris et al., 2005), and the Self-Monitoring Scale (Snyder & Gangestad, 1986). The resultant survey included 110 questions and was our starting point for detecting connector traits €uberer, 2011) and skills. We also included a social capital scale (Ha for validation purposes and scales to capture certain individual differences (e.g., collectivism; Wagner, 1995). Next, we administered the full survey to 299 Amazon Mechanical Turk respondents.7 Our participants were paid a flat $3 fee, ranged in age from 18 to 72 years old, with 38.5% female and 98.7% US natives.8 Using the survey responses, we conducted a factor analysis that produced 16 uncorrelated factors with eigenvalues greater than one, which collectively explained 59% of the variance among questions. We determined that the first factor (eigenvalue ¼ 18.49, explained 17% of the variance) best captured our construct of interest. It was the only factor that had high loadings

6 We obtained approval from the Institutional Review Board (IRB) for each step in our approach, and all participants consented to participate per IRB requirements. 7 Amazon Mechanical Turk (MTurk) is a survey platform associated with Amazon.com that matches firms requesting subjects to complete Human Intelligence Tasks with individuals who are intrinsically motivated to complete the tasks; compensation for completion is typically modest. Research has demonstrated that the MTurk subject pool is at least as representative of the US population as traditional (e.g., student) subject pools, the quality of data does not differ significantly between MTurk and traditional sources, and numerous traditional and accountingcentric JDM findings have been replicated using MTurk data (Farrell, Grenier, & Leiby, 2017; Paolacci, Chandler, & Ipeirotis, 2010). 8 We also administered the full survey and followed the same step 1 process using a student population similar to our experimental participants. Results for this process using the additional student sample are nearly identical to results using the MTurk sample, and details of the former can be found in the online supplement. Of note, the student sample (n ¼ 249) was comprised of more female (44.6%) and fewer US native (85.1%) participants than the MTurk sample, helping to validate our connector survey that was ultimately completed by student participants who were also comprised of more female (42.7%) and fewer US native (69.1%) participants.

on most of the expected determinants (56 questions had factor loadings greater than 0.30)dincluding the five items from the Boster et al. (2011) connectivity scale (which ranged from 0.77 to 0.85)dand was significantly positively correlated with the social capital measure (r ¼ 0.20, p < 0.001). The latter suggests good convergent validity. This factor’s Cronbach’s Alpha (a ¼ 0.95) also suggests good internal consistency and compares favorably to the original validated scales.9 Given the length of the initial survey and the number of survey items with negligible factor loadings, we decided to shorten the survey to a length that participants could complete in 15 min or less while ensuring we retained at least one item from each constituent scale. We retained all questions from the Connectivity and Big Five trait scales due to their strong fit with our connector construct and the brevity of the scales. For the other scales, we iteratively narrowed down the survey to retain items that had the highest factor loadings in the original studies (i.e., those that had validated the scales) while also removing repetitive items (e.g., from the Negative Affectivity scale, we retained items “scared” and “upset” but not items “afraid” and “distressed”). In addition, for the Political Skill scale with four explicit subfactors (per Ferris et al., 2005), we retained the item from each subfactor that had the highest loading, and then confirmed that these items were correlated to the full scale. After paring down the initial survey, the final survey (included as Appendix A) consisted of 31 items that yielded five factors with eigenvalues greater than one, which explained 52% of the variance among questions. Again, the first factor (eigenvalue ¼ 7.55, explained 24% of the variance) appeared to best capture the connector construct because it yielded high loadings on most of the expected determinants (21 factor loadings greater than 0.30), was significantly positively correlated with the social capital measure (r ¼ 0.22, p < 0.001), and had a high Cronbach’s Alpha (a ¼ 0.90). Further, the first factor from each of the final survey and the initial survey were highly correlated with each other (r ¼ 0.94, p < 0.001). Thus, we use the participants’ responses to the 31-item survey and the respective factor loading weights to calculate connector scores as described in Step 2 of our study. Additional details on the creation and validation of our connector survey can be found in an online supplement (see Appendix D). 3.2. Step 2: Administer connector survey to experiment participants Participants in our experiment are 140 undergraduate students at a large state university who received extra credit in an accounting course for their participation. We emailed participants a link to the connector survey, which also included measures of other individual differences and was deployed using the web-based Qualtrics platform. Participants completed the connector survey online, and Qualtrics accumulated these responses. After participants completed the surveys, we calculated weighted scores for each respondent by standardizing the responses for each question (with respect to all 140 respondents) and then multiplying these standardized survey responses by the weights obtained from the first factor in our factor analysis in Step 1. This weighted “connector score” represents the relative strength of that participant’s connector traits and skills. We then classified as connectors those respondents whose connector scores were in the top 10% of all participants; all remaining respondents were classified as nonconnectors.

9 The first factor also compared favorably to the second factor, which had a lower eigenvalue (8.10), percentage of variance explained (7%), Cronbach’s Alpha (a ¼ 0.70), and number of factor loadings greater than 0.30 (29).

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3.3. Step 3: The experiment To conduct our experiment, we needed to (1) form groups, some of which included a connector group member (i.e., “connector groups”) and some of which did not include a connector (i.e., “nonconnector groups”), (2) have all groups complete a cooperative task, and (3) obtain feedback from group members regarding their experience working in the group and their desire to leave or stay with the group. After calculating connector scores for each participant, we developed a preliminary list of groups using only non-connector participants, each group containing two members. We then randomly seeded 14 different groups with a connector participant, and randomly assigned the remaining non-connector participants to complete the groups. We formed a total of 43 groups with three to five members each.10 Participants did not know the purpose of the initial survey, were not aware of their performance on the survey, and thus could not know either their own “connector” status or that of their fellow group members. We then emailed the participants with their respective assigned dates, times, and locations for completing the experiment; the experiment was administered about one to two weeks after the survey. Of the original 140 respondents, 136 showed up for the experiment; therefore, we exclude the data relating to the four no-show respondents (all of whom were in non-connector groups) in all of our subsequent analyses.

3.3.1. Experimental procedure Once participants arrived at their assigned location, they were escorted into a small room with their fellow assigned group members where they read the instructions for the experimental task (provided in Appendix C). No information was provided to or collected from participants that linked them to their survey responses. As such, participants remained blind as to any participant’s connector classification; henceforth, investigators could only identify a connector’s group. Group members were instructed to introduce themselves to each other, collectively create captions one at a time for each of four cartoon images, and individually complete a post-experiment survey. Participants received no verbal instructions beyond the written instructions provided at the beginning of the session. Thus, participants received no guidance about how they were to derive the image caption, how they might interact with one another, or any goals beyond completing the captions. The intent was to let any social or “professional” interactions be initiated at the discretion of the group members. After completing the caption task, participants were asked to complete a post-experiment survey with questions about participants’ turnover intentions and group experience. Because the desire to leave a work group is a strong determinant of turnover intentions (e.g., Dess & Shaw, 2001), we measure turnover intentions by asking participants how much they would like to remain in the same group versus join a different group if given the opportunity to complete another task. Participants responded using an 11-point scale ranging from 11 ¼ Very much like to change groups, to 1 ¼ Very much like to remain in same group. To measure participants’ group experience, we used six questions asking them to assess how well they thought the group worked together

10 We intended for all groups to contain three members, but we created five (one) groups of four (five) members by forming groups “real-time” when not all participants arrived on time for their scheduled sessions. 11 We also asked participants one additional question, how much more fun they would have had if they had been in a different group. This question did not load in the group experience factor, and so we omit it from our analyses.

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(perceived functioning, cooperation, and participation of members) and how the experience made them feel (shared identity, how much fun they had, and their personal freedom to express themselves).11 Appendix B summarizes the questions used to measure turnover intentions and group experience. 3.3.2. Demographically distinct individuals In the post-experiment questionnaire, we captured two observable characteristics that are self-reported by all participants and that are commonly examined in prior research: gender (male or female) and ethnicity (whether or not a participant is a native of the US). In our sample, the participants who are not US natives are predominantly from China, have come to the US only upon entering college, and are thus culturally quite distinct from the participants who are US natives. We classified each participant as distinct (i.e., possessing a unique demographic characteristic in their group) if they are (1) the only female in their group, (2) the only male in their group, (3) the only non-US native in their group, or (4) the only US native in their group. All other participants share either gender or native country/speaker status with at least one fellow group member.12 4. Results and discussion 4.1. Tests of hypothesis 1 Our first hypothesis predicts that participants assigned to groups that include a connector will have lower turnover intentions than participants assigned to groups with no connector. We infer turnover intentions from participants’ preferences to change groups to complete a hypothetical additional task. For all directional predictions, tests are one-tailed. For our statistical tests, a multilevel analysis is performed. Such analysis addresses the potential for correlated error terms among members of the same group (i.e., observations within a group are nested and not independent) by adjusting otherwise underestimated standard errors. Statistics reported for the 95% confidence interval (two-tailed) of the random effects confirm that a multilevel analysis is appropriate (i.e., covariance estimates at the individual and group levels are significant). We include several variables, measured at the group-level, as covariates in our hypotheses tests. First, we include both the group’s connector score average and its standard deviation; this allows us to distinguish between the impact of having a connector in the group (our manipulation) and the impact of the overall level or heterogeneity of the group’s “connector” predisposition. We also include group size and the time it took groups to complete the task as each may influence the opportunity for a connector to influence the group environment and thus, influence other group members’ turnover intentions. Table 2 presents descriptive statistics for the four covariates (Panel B) and for connector scores at the individual level (Panel A). Table 3, Panel A presents descriptive statistics for the dependent variable of turnover intentions at the individual level. Table 3, Panel B presents the results of a Multilevel Analysis for our full sample, which support H1. Participants whose group includes a connector reported lower turnover intentions (M ¼ 3.22) than those whose group does not include a connector (M ¼ 4.51;

12 We did not seed members into groups to create groups with and without distinct members; rather, whether a group has a distinct member or not depends on random assignment.

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Table 2 Descriptive statistics. Panel A: Connector Score at the Individual Level Full Sample (n ¼ 136)

Connectors (n ¼ 14)

Non-Connectors (n ¼ 122)

Variable

M

SD

Mdn

Min

Max

M

SD

Mdn

Min

Max

M

SD

Mdn

Min

Max

Individual scores

0.03

0.83

0.05

3.12

2.11

1.37

0.32

1.29

0.99

2.11

0.12

0.72

0.06

3.12

0.98

Panel B: Connector Score and other Covariates at the Group Level Full Sample (n ¼ 43)

Connector in Group (n ¼ 14)

No Connector in Group (n ¼ 29)

Variable

M

SD

Mdn

M

SD

Mdn

M

SD

Mdn

Connector-Group Average Connector-Group Std. Dev. Group Size (# of members) Time to Complete (min)

0.03 0.75 3.16 10.40

0.44 0.41 0.43 3.75

0.10 0.69 3.00 10.00

0.41 0.92 3.14 10.41

0.23 0.37 0.53 4.66

0.45 0.97 3.00 9.50

0.15 0.67 3.17 10.39

0.39 0.41 0.38 3.33

0.07 0.67 3.00 10.50

Panel C: Mediating Variables at the Individual Level Full Sample (n ¼ 136)

Connector in Group (n ¼ 44)

No Connector in Group (n ¼ 92)

Variable

M

SD

Mdn

M

SD

Mdn

M

SD

Mdn

Factor-Group Experience How Well Function How Well Cooperate Participation Rate (%) Shared Identity How Much Fun Free Expression

0.00 8.78 9.55 0.95 5.04 7.24 9.41

1.00 1.60 1.37 0.12 1.22 2.26 1.73

0.17 9.00 10.00 1.00 5.00 7.00 10.00

0.30 9.22 9.73 0.97 5.32 7.55 9.68

0.79 1.34 1.06 0.08 1.05 2.08 1.62

0.24 9.00 10.00 1.00 5.00 8.00 10.00

0.14 8.57 9.47 0.94 4.91 7.09 9.28

1.06 1.67 1.49 0.13 1.27 2.34 1.77

0.07 9.00 10.00 1.00 5.00 7.00 9.00

Panel D: Counts and Percentages for Demographic Variables Full Sample (n ¼ 136)

Connector in Group (n ¼ 44)

No Connector in Group (n ¼ 92)

Variable

#

%

#

%

#

%

Distinct members (all categories) Distinct members (by category) Only Female & Non-US Native Only Female & US Native Only Male & Non-US Native Only Male & US Native Only Female Only Male Only Non-US Native Only US Native

49

36%

15

34%

34

37%

3 1 1 1 15 5 16 7

2% 1% 1% 1% 11% 4% 12% 5%

1 0 0 0 6 1 5 2

2% 0% 0% 0% 14% 2% 11% 5%

2 1 1 1 9 4 11 5

2% 1% 1% 1% 10% 4% 12% 5%

This table reports descriptive statistics for participants’ demographic information, covariates, and responses to the six questions (the last six rows of Panel C) used to measure group experience. The Connector Score is a weighted score that measures the relative strength of a participant’s blend of connector traits and skills and was captured prior to the experiment; statistics at the individual level are reported in Panel A. Connector groups include a participant whose Connector Score is in the top 10% of all Connector Scores; statistics for the mean and standard deviation, first aggregated at the group level, are reported in Panel B. Statistics for other covariates in Panel B are also aggregated at the group level; Group Size ranges from three to five members and Time to Complete captures the time, in minutes, it took groups to complete the group task in the experiment. Statistics for mediating variables are reported at the individual level in Panel C; Group Experience is the factor score determined from a factor analysis using the six component measures. Shared Identity is assessed on a seven-point scale, Participation Rate is expressed as a percentage, and the other four questions are based on an eleven-point scale. See Appendix B for the questions relating to Group Experience. Finally, Panel D reports counts and percentages of individuals who are demographically Distinct, overall and based on the dimensions (i.e., gender and ethnicity) that make them distinct. Participants are classified as Distinct if they are the only female in their group, the only male in their group, the only non-U.S. native in their group, or the only U.S. native in their group; all remaining participants are classified as demographically Similar and share both gender and ethnicity in common with at least one of their fellow group members.

Z ¼ 1.72, p ¼ 0.04).13 In addition to testing our primary hypothesis, we also conduct analyses to examine the possibility that connectors themselves might account for this result if their relationship-focused nature creates a high desire to remain in any group to which they belong. Ideally, we would simply omit the connectors from the analysis. However, to limit the risk of a demand effect associated with making the connector’s identity known to participants in the experiment, we made the design choice to keep both researchers and “connector group” members blind to which group members are connectors. Our own blindness to the connector participants means that we cannot match the post-survey responses (obtained

13 We also conduct a standard ANCOVA analysis (untabulated) and observe inferentially the same results.

in Step 3) to a specific participant’s connector score (obtained in Step 2). For robustness we perform two alternative analyses. First, we perform a Monte Carlo analysis, where we randomly omit one member from each connector group (in non-connector groups, all members are retained) and then perform our analyses in Table 3, repeating this process of random omission and subsequent analyses 10,000 times. Across these 10,000 replications, we find significantly lower mean turnover intentions reported by members of groups with a connector versus those without (mean difference ¼ 1.43; Z ¼ 1.75, p ¼ 0.04). Thus, our H1 result is robust across a large number of random samples, which may include the one sample of participants where all connectors, and only connectors, are excluded from analysis. Moreover, because any one of these random samples is just as likely as any other to be the connector-excluded sample and because our results hold, on average, across these random samples, our results are likely also

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Table 3 Test of H1: Impact of a connector group member on turnover intentions. Panel A: Estimated Marginal Means [Standard Error] for Turnover Intentions

a

Condition

n

Mean

SE

No Connector in Group Connector in Group

92 44

4.51 3.22

[0.34] [0.56]

Panel B: Multilevel Model Analysis of Turnover Intentions Fixed Effects

c

b

Coef

SE

Z

p-value

Connector in Group Connector Score, Group Average Connector Score, Group Std. Dev. Group Size Time to Complete Intercept

1.29 0.63 1.01 0.46 0.16 0.59

0.75 0.80 0.69 0.52 0.07 1.98

1.72 0.80 1.46 0.87 2.51 0.30

0.04 0.43 0.15 0.38 0.01 0.77

Random Effects

Coef

SE

d

Group Individual

1.05 4.43

0.56 0.65

95% C.I. 0.37 3.33

2.99 5.90

a As shown in Appendix B, turnover intentions represent participants’ responses to the question of how much they would like to remain in the same group if given the opportunity to complete another task, based on an eleven-point scale (reverse scored). Refer to Table 2 for description of the independent variable. b A multilevel analysis is performed to address the potential for correlated error terms among members of the same group. Statistics reported for the 95% confidence interval (two-tailed) of the random effects confirm that a multilevel analysis is appropriate (i.e., covariance estimates at the individual and group levels are significant). c Covariates in the model are evaluated at their full sample means as shown in Table 2. d Expectation is directional; thus, p-value is based on a one-tailed test.

robust for the unique connector-excluded sample. Second, we obtained a separate dataset of participants from a similar population as the participants from our experiment, for which the researchers could identify individual participants’ scores on both the connector survey and the group experience questionnaire.14 We analyze the data to examine whether connectors inherently have lower turnover intentions. Our analyses reveal no such evidence. Namely, when asked about their desire to leave their group, connectors’ mean response (5.58) is not lower than the mean response of non-connectors (4.64; t ¼ 1.36, p ¼ 0.91). Thus, connectors do not appear to inherently have lower turnover intentions. Taken together, these two analyses suggest that our results are not due to connectors reducing group turnover intentions by virtue of having low turnover intentions themselves.

4.2. Tests of hypothesis 2 Our second hypothesis predicts that having a positive group experience will mediate the lower turnover intentions of members of groups with, versus without, a connector. Specifically, H2 predicts that the presence of a connector will have a significant indirect effect on their fellow group members’ turnover intentions through a connector’s effect on perceived group experience. We construct our measure of participants’ perceived group experience by conducting a principal component analysis, with no rotation, on six group experience variables: perceived group functioning, perceived cooperation, perceived member participation rate, shared identity, extent of fun they had, and freedom of expression. This analysis extracts a single factor with an eigenvalue of 2.50 that explains 41.6% of the variance, and a Cronbach’s alpha of a ¼ 0.63. Table 2, Panel C presents descriptive statistics for the group experience factor variable and the six component variables,

14 The dataset, consisting of N ¼ 249 useable participants (in 60 groups), was collected in conjunction with a distinct research project and contained different elements. First, the groups were formed as part of an ongoing course rather than as a controlled experiment. Second, participants completed group projects as part of that course rather than completing the caption task we introduced in our experiment.

at the individual level. We test H2 with multiple regressions using the same covariates from the prior Multilevel Model Analysis, with one exception. To reduce multicollinearity, we substitute for the group’s average connector score by using a median-split indicator variable for members of groups having an average connector score above (¼1) or below (¼0) the median group average connector score. Following Hayes and Preacher (2014), we perform mediation analysis to analyze the respective direct and indirect effects of having a connector in the group on participants’ turnover intentions.15 Our primary mediation test employs 5000 bootstrap samples and computes 95 percentile bias-corrected bootstrap confidence intervals. Fig. 1 displays the resulting unstandardized regression coefficients (with standard errors in brackets) and significance. In particular, participants whose group includes a connector report a more positive group experience (B ¼ 0.44, SE ¼ 0.22; p ¼ 0.02) and lower turnover intentions (B ¼ 1.41, SE ¼ 0.66; p ¼ 0.02) than those whose group does not include a connector. Participants who report a more positive group experience also indicate lower turnover intentions (B ¼ 1.20, SE ¼ 0.25; p < 0.01). The indirect effect is significant at p < 0.05 since zero does not fall in the 95 percentile bias-corrected bootstrap confidence interval of [e0.98, 0.07]; the point estimate for the indirect effect is 0.52 (SE ¼ 0.28, bootstrapped). Lastly, having a connector in the group continues to have an incremental direct effect on turnover intentions (B ¼ 0.88, SE ¼ 0.57; p ¼ 0.06) after accounting for the participant’s group experience, supporting partial rather than full mediation.

15 With respect to mediation analysis, Hayes and Preacher (2014) strongly encourage reporting unstandardized regression coefficients as the preferred metric, particularly so when the independent variable is dichotomous (as is the case with our dummy variable indicating whether a group includes a connector or not). Accordingly, all mediation results report only unstandardized regression coefficients.

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Fig. 1. Test of H2: Mediation Test of the Effect of Having a Connector in the Group on Group Members’ Turnover Intentions Through Group Experience. The mediation model includes the same covariates as the models in Tables 3 and 4 and standard errors are robust clustered on group to address the potential for correlated error terms among members of the same group. The indirect effect has a point estimate of 0.52 [SE ¼ 0.28], with a 95% bias corrected confidence interval (one-tailed) of [e0.98, 0.07]; these results support H2’s hypothesized indirect effect. The dashed line represents the total effect of having a Connector in Group on the participants’ Turnover Intentions without the mediator Group Experience in the model. Please refer to Table 2 (Table 3) for descriptions of the independent and mediating (dependent) variables. *, **, *** significant at the .10, .05, and .01 levels, one-tailed, respectively.

turnover intentions in Panel A, results from a Multilevel Model Analysis in Panel B, and a series of simple effects tests in Panel C. Our H3 analyses employ the same covariates used to test H1 and H2. Although results in Panel B show no significant main effect of having a connector in the group (Z ¼ 0.67, p ¼ 0.50), there is a significant interaction between having a connector in the group and whether the group member is distinct (Z ¼ 2.42, p ¼ 0.02). More specifically, H3 predicts that turnover intentions will decrease when the group includes a connector, but to a greater

4.3. Tests of hypothesis 3 Our third hypothesis predicts that connectors will reduce group members’ turnover intentions more for members who are demographically distinct than for members who are more demographically similar. Table 2, Panel D presents the number of participants classified as distinct given each combination of the underlying demographic characteristics (gender and ethnicity) and overall. Table 4 presents descriptive statistics for the dependent variable of

Table 4 Test of H3: Impact of a Connector Group Member on Turnover Intentions for Participants who are Demographically Distinct. Panel A: Estimated Marginal Means [Standard Error] for Turnover Intentions

a

Condition

Demographic Type

n

Mean

SE

No Connector in Group

Similar (Not Distinct) Distinct Similar (Not Distinct) Distinct

58 34 29 15

4.42 4.63 3.87 2.07

[0.39] [0.45] [0.62] [0.72]

Connector in Group

Panel B: Multilevel Model Analysis of Turnover Intentions Fixed Effects

c

b

Coef

SE

Z

p-value

Connector in Group Distinct Connector in Group X Distinct Connector Score, Group Average Connector Score, Group Std. Dev. Group Size Time to Complete Intercept

0.55 0.21 2.00 0.60 0.91 0.40 0.17 0.72

0.82 0.46 0.83 0.81 0.70 0.54 0.07 2.04

0.67 0.44 2.42 0.74 1.29 0.74 2.52 0.35

0.50 0.66 0.02 0.46 0.20 0.46 0.01 0.73

Random Effects

Coef

SE

Group Individual

1.25 4.07

95% C.I.

0.58 0.60

0.50 3.05

3.09 5.42

Panel C: Simple Effects Tests for Turnover Intentions Source of variation d

Connector in Group vs. Not, given Similar Connector in Group vs. Not, given Distinct d Distinct vs. Not, given No Connector in Group Distinct vs. Not, given Connector in Group e

e

df

Z

p-value

1 1 1 1

0.67 2.76 0.44 2.61

0.25 <0.01 0.66 <0.01

a

Refer to Table 2 (Table 3) for descriptions of the independent variables (dependent variable). A multilevel analysis is performed to address the potential for correlated error terms among members of the same group. Statistics reported for the 95% confidence interval (two-tailed) of the random effects confirm that a multilevel analysis is appropriate (i.e., covariance estimates at the individual and group levels are significant). c Covariates appear in the model at their full sample means shown in Table 2. d Expectation is directional; thus, p-value is based on a one-tailed test. e Expectation is non-directional; thus, p-value is based on a two-tailed test. b

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influence on different components of group experience depend on the task itself or other features of the work group setting. 5. Conclusion

Fig. 2. Test of H3: Plot of the Estimated Marginal Means of Turnover Intentions Given the Presence of a Connector in the Group and a Demographically Distinct Group Member Refer to Table 2 (Table 3) for descriptions of the independent variables (dependent variable).

extent for distinct group members. We examine simple effects to further validate H3. Results, illustrated in Fig. 2, are mostly consistent with H3. Simple effects suggest that for distinct group members, turnover intentions are significantly lower when there is a connector in the group (M ¼ 2.07) versus not (M ¼ 4.63; Z ¼ 2.76, p < 0.01). However, the turnover intentions of more demographically similar group members, while directionally consistent with a connector effect, are not significantly different when there is a connector in the group (M ¼ 3.87) versus not (M ¼ 4.42; Z ¼ 0.67, p ¼ 0.25).16 4.4. Exploration of the components of group experience influenced by connectors We predict and find that connectors create a more positive work group experience and, while we expect group experience to comprise several components, we have no expectations about the extent to which connectors will influence them. In the following analyses, we explore connectors’ influence on each of the six components of group experience we measured: the extent to which group members share an identity, have fun, cooperate, function well together, and feel free to participate and express themselves. As noted earlier, descriptive statistics for these six components are presented in Table 2, Panel C, including by type of group. The means of all six components are higher in the groups with versus without a connector. However, these differences are significant for shared identity, group functioning, and participation rate (all p < 0.05) and are not significant for group fun, cooperation, and freedom to express oneself (all p > 0.10 and all p < 0.15). Thus, at least in the context of our task, the influence of connectors is most pronounced in group members’ perceptions that they are part of the same ingroup as other members, can participate in group discussions, or function well together as a group. For example, connectors may help every group member to have input or participate in group discussions and decisionsdsuch as proposing ideas about the work output, captionsdeven if members do not actively cooperate to generate those inputs. Or, for example, even if task demands limit the fun group members can have or limit the influence connectors can have on creating a fun experience, connectors can still enhance how well the group functions together overall. Future research can further examine whether connectors’

16 When we conduct our analyses using a conventional ANCOVA, our results (untabulated) yield a significant main effect for Connector (F ¼ 7.43, p < 0.01) in addition to a marginally significant interaction (F ¼ 2.92, p ¼ 0.09).

Preventing excessive voluntary employee turnover is critical to many organizations. In settings where employees work in groups, we examine whether individuals with innate traits and skills that predispose them to being “connectors” reduce group members’ turnover intentions, particularly those of demographically distinct members. Our experimental results demonstrate that participants in groups with a connector member have lower turnover intentions than participants in groups without a connector, although this result is driven by distinct group members. In addition, members of groups with versus without a connector report a more positive group experience, and this group experience mediates the effect of connectors on turnover intentions. Our results have important implications for the large and everincreasing number of organizations that are structured around work groups. In particular, while prior literature primarily focuses on formal, coercive controls within an organization’s MCS (Cardinal et al., 2017), we provide results that suggest connectors can be hired and deployed within work groups as a control to informally inspire members to achieve an organization’s objectives, such as managing voluntary turnover. Connectors appear to operate by enhancing the social climate of the group, creating a more positive group experience that increases members’ desire to remain with their group. Our findings indicate that strategically allocating connectors to certain groups could be advantageous. For example, in groups with members of diverse backgrounds, connectors could not only help members avoid feelings of isolation or division that threaten group cohesion, increase turnover, or disrupt group performance, but they could also encourage members to share the variety of ideas and solutions that make diverse groups valuable (cf. van Knippenberg et al., 2013). While we do not suggest using connectors is the only mechanism to manage voluntary turnover or turnover intentions, it can be an effective component of a more comprehensive retention strategy. These results further contribute to prior literature on organizational citizenship behavior (OCB), social capital, and work group diversity (i.e., relational demography). While Bolino et al. (2002) theorize that prosocial actions or OCB (e.g., encouraging other employees to speak up) lead to the development of personal social capital, we provide experimental evidence to suggest that connectors successfully perform OCB by enhancing the group experience of other group members, which in turn lowers turnover intentions, particularly those of demographically distinct group members. Further, the connector construct we develop is associated with measures of social capital, suggesting that individuals who possess the connectors’ underlying traits and skills (e.g., innate personal social capital or a desire to generate it) can deliver OCB. Limitations of our study provide opportunities for future research. For example, while we extend prior research by identifying connectors based on ex-ante characteristics that make them personable or willing to relate to others on a personal level, we do not know if others viewed them as such because we did not make connector identities known to any group members. We do have evidence that members felt their experience was more positive in groups with a connector, but future research can explore whether connectors themselves give off “good vibes” or more simply create “good vibes” within the group. Moreover, future research can examine which facets of being a connectordbeing personable, having a desire to relate to others, or having an ability to influence othersdare most important to being a connector or contribute most to their effects on their groups. We more simply claim that

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being a connector requires some holistic combination of these facets and their underlying traits and skills. In addition, our groups jointly completed a single task in groups of three to five members, and thus our results most directly generalize to work environments involving small groups who collaborate to produce a group output. Given that our results support theory that connectors enhance the group’s experience, our findings likely also generalize to settings in which members work on individual tasks to provide a series of inputs for a group output, as the group-level process of combining inputs provides an opportunity for a more positive work group experience to build and take effect. Future research can explore whether connectors have weaker effects as the size of the group increases or for settings where members of a group (e.g., an academic department, a call center) primarily complete tasks individually but interact intermittently (e.g., when providing help or advice, at group meetings, at break or social times). Another feature of our setting is that group members complete an open-ended creative task. Concurrent research indicates that connectors can improve the quality of creative outputs (Autrey, Jackson, Klevsky, & Drasgow, 2019), but future research can examine whether having a connector in the group can improve performance or lessen turnover intentions in tasks that are more routine, mundane, or production-based. Our findings should generalize to the former tasks to the extent that they, like creative tasks, are ambiguous or complex. Nevertheless, it is an empirical

question whether evaluating or rewarding connectors explicitly on group outcomes would weaken or intensify the effect we document, given that connectors are partly characterized as individuals who find intrinsic joy and reward in relating to and with others on a personal level. Moreover, our research does not speak to what incentives or enticements organizations could offer connectors that would address their turnover intentions. Connectors could be a challenge to retain given their propensity to make connections or the potential burnout they could feel in trying to enhance the group experience. That said, it is also possible that connectors, as “people” people, could find it satisfying and enjoyable if they are assigned to groups in a way that exploits their natural talents.

Appendix A. Connector Survey The leading letters in each variable name indicate the original scale from which the item was obtained (PA ¼ Positive Affect, NA ¼ Negative Affect, RIC ¼ Relational, Individual, Collective self-aspects, Ag ¼ Agreeableness, Ex ¼ Extraversion, Co ¼ Conscientiousness, OTE ¼ Openness to Experience, ES ¼ Emotional Stability, PS ¼ Political Skill, SM ¼ Self-monitoring, BC ¼ Boster (et al.) Connectivity, IO ¼ Interpersonal Orientation). A trailing R indicates that the item was reverse-scored.

Instructions: Please indicate to what extent you generally feel this way, that is, how you feel on the average. 1 Very slightly or not at all PA1 PA2 PA3 NA1R NA2R

2

3

4

5

A little

Moderately

Quite a bit

Extremely

interested enthusiastic determined upset [r] scared [r]

1 1 1 1 1

2 2 2 2 2

3 3 3 3 3

4 4 4 4 4

5 5 5 5 5

[r] ¼ reverse-scored.

Instructions: Please select the response that best completes the statement below. RIC1

RIC2

RIC3

RIC4

RIC5

I think it is most important in life to a. Have personal integrity/be true to myself. [0] b. Have good personal relationships with people who are important to me. [1] c. Work for causes to improve the well-being of my group. [1] The most satisfying activity for me is a. Doing something for myself. [0] b. Doing something for someone who is important to me. [1] c. Doing something for my group (e.g., my school, church, club, neighborhood, and community). [1] When faced with an important personal decision to make a. I ask myself what I really want to do most. [0] b. I talk with my partner or best friend. [1] c. I talk to my family and relatives. [1] When I attend a musical concert a. I feel that enjoying music is a very personal experience. [0] b. I feel enjoyment if my company (partner, friend, guest) also enjoys it. [1] c. I feel good to be part of the group. [1] I am most concerned about a. My relationship with myself. [0] b. My relationship with a specific person. [1] c. My relationship with my group. [1]

abc

abc

abc

abc

abc

[0], [1] ¼ score for these five dichotomous variables; “a” responses indicate “Individual” self-aspect, “b” responses indicate “Relational” self-aspect, and “c” responses indicate “Collective” self-aspect.

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1

2

3

4

5

6

7

Strongly disagree

Disagree

Somewhat disagree

Neither agree nor disagree

Somewhat agree

Agree

Strongly Agree

Instructions: Please indicate how much you agree with the following statement about yourself. I see myself as Ag1 Ag2R Ex1 Ex2R Co1 Co2R OTE1 OTE2R ES1 ES2R

Sympathetic, warm. Critical, quarrelsome. [r] Extraverted, enthusiastic. Reserved, quiet. [r] Dependable, self-disciplined. Disorganized, careless. [r] Open to new experiences, complex. Conventional, uncreative. [r] Calm, emotionally stable. Anxious, easily upset. [r]

1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2 2 2

3 3 3 3 3 3 3 3 3 3

4 4 4 4 4 4 4 4 4 4

5 5 5 5 5 5 5 5 5 5

6 6 6 6 6 6 6 6 6 6

7 7 7 7 7 7 7 7 7 7

[r] ¼ reverse-scored.

Instructions: Please indicate how much you agree with the following statement about yourself. PS1 PS2 PS3 PS4 SM1 BC1 BC2 BC3 BC4 BC5 IO1

I spend a lot of time and effort at school networking with others. I am able to make most people feel comfortable and at ease around me. It is important that people believe I am sincere in what I say and do. I always seem to instinctively know the right things to say or do to influence others. I would probably make a good actor. I’m often the link between friends in different groups. I often find myself introducing people to each other. I try to bring people I know together when I think they would find each other interesting. I frequently find that I am the connection between people who would not otherwise know one another. The people I know often know each other because of me. The more other people reveal about themselves, the more inclined I feel to reveal things about myself.

1 1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2 2 2 2

3 3 3 3 3 3 3 3 3 3 3

4 4 4 4 4 4 4 4 4 4 4

5 5 5 5 5 5 5 5 5 5 5

6 6 6 6 6 6 6 6 6 6 6

7 7 7 7 7 7 7 7 7 7 7

Appendix B. Questions Used to Measure Group Experience and Turnover Intentions

For convenience, scoring information is provided below in italics and category headers in bold. Question 1 responses consisted of two circles with progressively more overlap. Questions 2 through 6 and 9 are Likert-style responses from 1 to 11. Participants saw only the normal (non-italicized, non-bolded) text. Group Experience Q1 e Shared Identity: Select the pictures below that best describe how your personal attributes, qualities, and values align or overlap with the attributes, qualities, and values of your group. [Scored as 1 ¼ non-overlapping circles to 7 ¼ almost completely overlapping circles] Q2 e How Well Function: How well did your group perform in completing the assigned tasks? [Scored as 1 ¼ Performed Very Poorly to 11 ¼ Performed Very Well] Q3 e How Well Cooperate: How well did the group cooperate in completing the assigned tasks? [Scored as 1 ¼ Cooperated Very Poorly to 11 ¼ Cooperated Very Well] Q4 e How Much Fun: How much fun did you have working with your group in completing the assigned tasks? [Scored as 1 ¼ Not at all fun to 11 ¼ Very fun] Q5: How much more fun would you have had completing the assigned tasks if you had been part of a different group? [Scored as 1 ¼ Not at all more fun to 11 ¼ Very much more fun] Q6 e Free Expression: How free did you feel to offer your opinions or suggestions when participating in the group discussion? [Scored as 1 ¼ Not at all free to express myself to 11 ¼ Very much free to express myself] Q7: Please indicate the number of participants in your group (including yourself). Q8: Please indicate the number of group members who helped complete the tasks (including yourself). [Participation Rate scored as Q8 divided by Q7] Turnover Intentions Q9: If given the opportunity to complete an additional task, how much would you like to remain in the same group versus join a different group? [Reverse scored as 1 ¼ Very much like to remain in same group to 11 ¼ Very much like to change groups]

Appendix C. Experimental Instructions The following instructions were taped to the table for participants to read upon entering the room where they completed the task. No other written or verbal instructions were provided to participants. Instructions. 1. Preliminaries 2 min a. Sign in and sign the Informed Consent forms. b. Introduce yourselves. c. You have been provided a Cartoon, index cards, pencils & eraser, notepad, and white board markers & eraser. If you are missing any items, please notify the person administering the experiment (“administrator”).

2. Task: Caption Cartoons 15 min a. As a group, create a caption for the first cartoon. b. Write the caption on an index card. c. Indicate to the administrator that you are ready for the next cartoon. d. He or she will collect your index card and give you the next cartoon. e. Repeat steps a. through d. until the 15 min have elapsed. 3. Complete the Post-Task Questionnaire 8 min After the administrator collects your last caption, he or she will give you the post-task questionnaire to complete. You are free to leave once you have given your completed questionnaire to the administrator. If needed, please erase the white board for the next group. Thank you for participating in this research!

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