Moderating Avoidance Performance Goal Orientation Withdrawal Through Individuated Assessments and Common Affiliation

Abstract

Scholars have long assumed that members of small groups are more likely to help each other. We argue that, even in a small group with collective rewards, those with an avoidance performance goal orientation, a dispositional fear of inadequate performance, would view social interaction as more disadvantageous and, as a result, help their group less. Using random coefficient modeling, we also demonstrate that the strength of the individual-specific assessment of reliance on and a category-specific common affiliation with group members reduce this negative association. We discuss theoretical and practical implications and conclude by identifying limitations of our study and offer directions for future investigation.

Keywords

goal orientation helping social exchange social identity theory

When a person is unable to accomplish a task, one option is to seek assistance. Seeking assistance necessitates the revealing of an inadequacy and allows for the possibility that the potential helper’s response would be personally threatening to the receiver. Some people are more sensitive to this threat than others. One dispositional factor that seems relevant when facing such threat is an avoidance performance goal orientation (APGO). An APGO is a fear of receiving judgments from others that are derived from inadequate performance (VandeWalle, 1997). Unsurprisingly, past work has demonstrated that an APGO is negatively associated with seeking help (Roussel et al., 2011).

Contextual factors that have been shown to ease such threat and, thereby, facilitate such engagement include considerations like group size (Garmston & Wellman, 1992) and the sharing of objectives (Tjosvold, 1988). More specifically, members of small groups report more satisfaction (Steiner, 1972), fewer social motivational losses (e.g., Kerr, 1983; Kravitz & Martin, 1986; Latané et al., 1979), more cohesiveness (Seashore, 1977), and, most importantly, more cooperation (Komorita & Lapworth, 1982). Furthermore, when a helper and receiver endorse the same objective, at minimum, the objective of the receiver is not threatened. For example, when a group is evaluated on a collective product, their instrumental objective aligns and an incentive exists to help each other (e.g., McNeely & Meglino, 1994; Settoon & Mossholder, 2002; Van Scotter & Motowidlo, 1996). As such, small groups evaluated on a joint product should provide a context in which the potential threat for help-seeking from team members is greatly reduced.

However, would similar arguments hold if the helper (not the receiver) was sensitive to revealing a personal inadequacy? To investigate this topic, we focus on the “content” of instrumental help, as it directly ties to the reason why task groups (Sayles, 1957) form. Instrumental helping (henceforth helping), refers to acts of assistance with completing work-related problems (e.g., Anderson & Williams, 1996; Geller & Bamberger, 2009; Van Dyne & LePine, 1998). Helping has been found to improve the recipient’s performance (Podsakoff et al., 2009), increase the trust between partners (McAllister, 1995), and enhance a helper’s mood (Glomb et al., 2011). Though past work has investigated some antecedents to helping (Choi, 2009), including the poor performance of the recipient (Taggar & Neubert, 2004), the personal connections between the provider and the recipient (Stürmer et al., 2005), or even the norms within a group (Naumann & Ehrhart, 2011), little is known regarding the types of dispositions of the provider that reduce helping.

Are the sensitivities of those with an APGO potent enough to manifest in a collectively evaluated small group when they are providing (not seeking) instrumental help? That is, even when it is in their personal instrumental interest to do so, will those with an APGO withhold helping from their collectively evaluated small group? If so, how can this maladaptive outcome be reduced?

In building our conceptual model from literature relating to goal orientation (Dweck, 1986), social exchange (Blau, 1964), and social identity theory (SIT; for example, Tajfel & Turner, 1979), we attempt to make two major contributions. First, we argue that an APGO is sufficiently strong to manifest in a withdrawal from group helping, even in the low-threat context of a collectively evaluated small group. Though goal orientation and social exchange have been intensively studied independently, calls have been made to understand the effects of an APGO on within-group social interaction (Pieterse et al., 2013). Research has so far only focused on the effects of goal orientations averaged to the team level (Porter, 2005). Investigating individual differences in group members is important because it helps identify specific individuals who might “pull away” from activities that support group functioning.

Second, we move beyond examining the direct effect of a personal connection through individuated assessments of a partner (Fiske & Neuberg, 1990) and a common in-group affiliation (e.g., Choi, 2009; Dovidio et al., 1997; Liang et al., 2015) to examine how each may lessen the anticipated disadvantageous influence of an APGO. In line with SIT, our unique combination of within-group weighted social network ties and category proportions offers a novel way to simultaneously consider both.

We now review literature on goal orientation, highlighting an APGO. We then develop our arguments, describe our data, and outline our analysis. We conclude with implications for theory and practice as well as limitations and future research directions.

Theoretical Foundation and Hypotheses

Using social exchange theory (SET) to explain the social interaction that is helping behavior, we investigate the influence of an APGO on instrumental helping in small groups and reason how dispositional APGO disadvantageously influences helping one’s group. Furthermore, we employ SIT to isolate two possible means for reducing this disadvantageous effect. We now discuss our conceptual model in greater detail and present our hypotheses. The conceptual model is illustrated in Figure 1.

Figure 1.

Conceptual model.

APGO

Goal orientations are dispositions influencing how people allocate attention and effort when facing an achievement situation (Elliot & Church, 1997; Kozlowski & Bell, 2006). A learning goal orientation (LGO) predisposes a person to self-improve and master a task (Grant & Dweck, 2003), typically leading to advantageous individual outcomes, such as higher self-set goal levels, more successful learning strategies, and elevated levels of feedback-seeking (Payne et al., 2007). A performance goal orientation (PGO) was originally described as predisposing a person to gain favorable or avoid negative judgments from others through engaging in or pulling away from performance contexts (Dweck, 1986). Two subdimensions of PGO were eventually recognized (Button et al., 1996; Nicholls, 1984). The first, prove performance goal orientation (PPGO), is a dispositional prioritization of gaining favorable judgments about one’s competence through superior performance (Elliot & Church, 1997; VandeWalle, 1997). Past research suggests that, although an external standard of performance drives those with a PPGO, the type of performance sought generally determines whether this orientation is (mal)adaptive. Thus, PPGO’s association with many individual outcomes (advantageous or disadvantageous) is generally seen as context-specific (e.g., Chen & Mathieu, 2008; Elliot & Moller, 2003; Elliot & Thrash, 2002). The second, an APGO, is a dispositional fear of inadequate performance, leading to a prioritization of evading competence-based judgment. As an APGO leads people to pull away, it has been associated with a myriad of individual maladaptive outcomes such as lower self-set goal levels, lower feedback-seeking, higher state anxiety, inadequate learning, as well as substandard academic and task performance (Payne et al., 2007; Pintrich, 2000).

Most past work on APGO pertains to individual performance-related outcomes (e.g., Cellar et al., 2011). Although still examining individual performance, the study of Hirst and colleagues (2009) is notable because it considers elements of the social context in which one’s goal orientation manifests. We apply goal orientation theory to the social relations within small groups. The question remains, “Even in a small group with collectively evaluated rewards, when individual performance is not in contention and helping is in one’s own self-interest, will an APGO lead to maladaptive effects? If so, how can these disadvantageous effects on group functioning be reduced?”

SET

SET describes actors as “trading” social resources (Lawler & Thye, 1999) during interaction. Social resources are anything that can be transmitted between partners (Foa & Foa, 1976). SET suggests that actors maximize the accrual of personal social resources (e.g., Blau, 1964; Gouldner, 1960; Homans, 1958). As Perlow and Weeks (2002) suggest, helping may be best predicted if one comprehends how the net benefit of helping is understood from the point of view of the potential helper.

Certain enduring personal characteristics are known to influence an individual’s perceptions (Postman et al., 1948). We suggest that, in a small formal task group (Sayles, 1957), those with an APGO may perceive helping as creating the potential for personal threat. That is, though not directly linked to individual performance, the extent to which (instrumental) help satisfies the charge of a group likely sets a clear threshold upon which to judge the value of the help. For those with an APGO, despite the fact that personal self-interest is furthered through helping, a comparison with such a threshold may be sufficient to increase the psychological cost of helping.

Furthermore, DeWall and Baumeister (2006) find that rejection elicits an insensitivity to emotion. Although this insensitivity helps insulate against rejection, it also limits empathy (Twenge et al., 2007). Empathy is a critical antecedent to helping (Krebs, 1975; Stürmer et al., 2005). We suggest that for those with an APGO, the perceived risk of rejection associated with helping (inadequately) may be sufficient to reduce the level of empathy felt toward group members and, as a result, decrease the level of social-emotional benefit accrued from helping. Thus, those with an APGO should perceive an elevated cost and a reduced benefit of helping their group. In support of this reasoning, Chiaburu and colleagues (2007) found a negative correlation between an APGO and self-reported helping, a finding not necessarily generalizable to small groups that are collectively rewarded.

H1: One’s APGO is negatively related to helping one’s group.

Mitigating the Effects of APGO

SIT describes social interaction as both individual-specific and category-specific (Tajfel & Turner, 1979). We examine how each of these two extremes may reduce the abovementioned negative association between an APGO and helping one’s group.

Reliance and the individual-specific approach to interaction

The individual-specific approach describes interaction as based on individuated assessments (Fiske & Neuberg, 1990). Though SIT does not specify the “content” upon which such individuated assessments are formed, we focus on individuated assessments of reliance because they directly relate to the shared charge binding members of a formal task group (Sayles, 1957). Reliance, in a group context, is the confidence in, the skills, knowledge, judgments, or actions of one’s group members (Gillespie, 2003). Reliance is one of the two forms of behavioral trust and implies a decision to accept instrumental vulnerability. We focus on reliance over disclosure (the other form of behavioral trust) as disclosure includes information of a personal nature. We felt that such personal information may be very distant from or even unrelated to a group’s shared instrumental charge.¹

The willingness to be vulnerable includes an anticipation that one’s partner will do no harm (Rousseau et al., 1998), an assessment that should (partially) lessen any perceived threat in general and a perceived threat to one’s competence specifically. Furthermore, we reason that such a willingness may increase the self’s capacity to bear personal cost because the self may be willing to trade a greater load of psychological cost in return for the facilitation of a personal instrumental goal (the basis of reliance). Finally, helping those on whom one relies may enable monitoring of the extent to which one’s personal instrumental interests are furthered or even facilitate a greater felt obligation in the receiver to bolster the interests of the provider (Eisenberger et al., 2001). Thus, the personal benefit accrued from helping would increase. Applying this reasoning to a small group context,

H2: The strength of one’s reliance on group members moderates the negative association between that member’s APGO and helping one’s group. More specifically, the more one relies on group members, the less negative the association between that member’s APGO and that member’s helping of the group.

Common affiliation and the category-specific approach to interaction

SIT also suggests that a salient demarcation changes how actors perceive social interaction. First, the need for uncertainty reduction leads to the perception that members of the same group are more similar than members of different groups (Hogg et al., 2004; Hogg & Terry, 2000). Whether real or symbolic (Williams, 2001), the perception of a shared fate or common goals may change the interpretation of any feedback derived from helping, from critical to developmental. Second, identifying with a group may redirect (psychological) self-defense mechanisms to protect in-group members. More specifically, interaction with in-group members that lead to personal threat should be more easily forgiven or even deflected (Weber, 1994). Thus, the perceived benefit from helping members affiliated with the same in-group should be higher and any resulting cost lower (leading to a greater net benefit).

Applying this reasoning to a small group context, there should be a positive association between the proportion of group members who are categorized as “in-group” members and the perceived personal net benefit accrued from helping one’s group. Although SIT does not specify the content of what defines a salient in-group, follow-up work presents some clues. Taking a top-down perspective, three considerations have been proposed (Hogg & Terry, 2000; Mummendey & Otten, 2008). Structural fit occurs when a demarcation accounts for intragroup similarities and intergroup differences, normative fit occurs when a demarcation predicts behavior, and accessibility occurs when a demarcation is frequently used. Taking a bottom-up perspective, Lau and Murnighan (1998) theorize and demonstrate (Lau and Murnighan, 2005) that the salience of a subgroup may also depend on the alignment of personal attributes (aka fault line). That is, when one or more personal attributes align, the likelihood that a group would fracture into subgroups increases.

Among university students at a business school in the United States, we used a domestic/international student demarcation as a basis for determining a common affiliation. We now present several reasons why a domestic/international demarcation satisfies both the top-down and bottom-up perspectives. First, international group members may be easily recognized as different (Ying & Han, 2006). Since surface-level attributes quickly categorize people (Harrison et al., 1998; Stangor et al., 1992), stark differences in observable features may clearly differ between group members who are domestic and international. Even if some domestic group members share surface-level attributes with some international group members, even slight verbal differences have been shown to facilitate categorization (Abrams & Michael, 1987).

Nonetheless, over time, demarcations derived from surface-level attributes generally give way to demarcations derived from deep-level attributes (Harrison et al., 1998). Deep-level attributes are underlying properties of individuals that are not readily observable (Milliken & Martins, 1996). For example, within-culture members have relatively similar relationship patterns (Morris et al., 2008) and interpretations of social cues (Branzei et al., 2007). Therefore, the social expectations among domestic group members should be more similar than those across the domestic–international demarcation. Domestic students may also endorse the “myths of foreign students” (Arthur, 2004, p. 10). These myths include the assumptions that domestic students subsidize international students, international students prevent the attendance of domestic students, and that the families of international students are all wealthy.

Although the heritage of international students may be heterogeneous, their (varied) expectations/understandings are commonly subordinate to domestic students. In fact, this shared subordination likely begins prior to arrival to the host nation. For instance, international students must often demonstrate proficiency in the host language, have their profile vetted by the host national government, and endure predeparture immunizations. Upon arrival, international students may be managed by a distinct department and may even pay a distinct (elevated) level of tuition. In extreme cases, international students are separated from domestic students in foreign-only dormitories and welcome programs. A loss of the familiar (Romero, 1981) and culture shock (Oberg, 1960) are also shared by foreigners to varying degrees. We argue that a salient social category need not develop from an identical cultural heritage but simply a shared experience of being commonly segregated and/or subordinated. Thus, a pervasive common affiliation regarding a domestic/international demarcation accounts for intragroup similarities and intergroup differences can accurately predict (un)familiar behavior and is psychologically available for both domestic and international group members. The alignment of (surface-level and) deep-level attributes furthers this salience. Applying this reasoning to a small group context, we offer hypothesis three.

H3: The proportion of group members who share one’s affiliation moderates the negative association between that member’s APGO and helping one’s group. More specifically, the greater the proportion of other group members who share one’s affiliation, the less negative the association between that member’s APGO and that member’s helping of the group.

Method

Procedure and Participants

Third- and fourth-year undergraduate business school students registered in a strategic management course (81%) and an international business course (19%) attending a U.S. university participated. The sampling frame consisted of 32% accounting and finance majors, 20% marketing majors, 10% human resource majors, 9% corporate management majors, and 5% economics majors. The remainder (24%) were from other business and nonbusiness disciplines. In each course, a group assignment constituted 40% of their course grade. About 2% of the students declined to participate and were offered an alternative to earn extra credit associated with participation in the study.

These courses were taught by the same instructor from fall 2013 to spring 2016. The average class size was 38 (25–54). Course objectives and assessments were consistent in both courses. Students were randomly assigned to groups of four to six. As both courses focused on company-level phenomenon, group assignments were very similar. For example, the strategic management course required groups to research the role that a corporation played in a social problem (e.g., Nike and labor practices) while the international business course required groups to research the international distribution strategy of a corporation (e.g., Tesla batteries in South Africa).

Tasks and rewards

The task objective for each group was to produce a final research paper (12 pages) and presentation (15 min). Objectives were sufficiently vague to promote research, consensus building, and decision-making. Groups were instructed to choose a corporation and identify its challenges.

Groups organized the work in any way they deemed appropriate. Typically, groups brainstormed together early in the term. They then divided the work so that each member conducted research, analyzed data, and wrote a section of the report. Students worked on their assigned section independently and outside of class. Yet, each member was required to consider what the other members were doing. Group time in class was normally used to plan, delegate, coordinate, and integrate tasks. Social media and/or a learning management system were often used for intragroup communication.

By design, the nature and relationships among the tasks defy simple classification. For example, Thompson (1967) defines three types of task interdependence: pooled, sequential, and reciprocal. Some tasks in these group projects are pooled because they were not directly connected to each other but contributed to the whole project. Some tasks are sequential because they were performed in order. Yet, other tasks are reciprocal because they affect each other. Some tasks were additive and conjunctive in that members added on sections to complete the assignment. Other tasks were disjunctive and discretionary (English et al., 2004) in that only the efforts of one group member were used, whereas at still other times group members chose how to combine their efforts. Furthermore, even if there was an informal leader who delegated and/or coordinated, interdependence, complexity, and ambiguity arose because no single central authority existed (Walsh & Lee, 2015).

Students knew that their grades for the project depended on peer evaluations (50%) and the collective paper (50%). This ensured that free riders were punished and cooperation was encouraged.

Survey administration and group composition

Students completed a two-stage online survey. Stage 1, near the onset of the course, measured stable attributes (e.g., dispositional goal orientations and international student status). Stage 2, near the course’s completion, measured student views about the group project. Eight students were enrolled in both courses and were omitted (their groups were also omitted). After this omission, 90 groups remained although some respondents were missing values as they did not fully complete the survey questions. Of these responses, 85% reported that English was their best language, 48% were female, and 8% were international students. Students were randomly assigned to within-section groups. This resulted in groups each with a differing number of international students. In our sample of 90 groups, 30 (33%) had at least one international student. Even though there is a relatively small proportion of international students, their impact on common affiliation is substantial because even one international student in a group affects the common affiliation of all other (domestic) group members.

Measures

APGO

APGO was measured using a four-item Likert-type self-administered scale (VandeWalle, 1997) ranging from 1 (strongly disagree) to 6 (strongly agree). A sample item reads, “I am concerned about taking on a task in class if my performance would reveal that I had low ability.”

Strength of reliance on group members

Applying the formula of Barrat and colleagues (2004; that is, sum of outbound tie weights), we measured the reliance in group members using out-degree centrality strength of a within-group weighted social network (Opsahl et al., 2010). An average of the five-item subscale of the Behavioral Trust Inventory (Gillespie, 2003) measuring reliance was used as the content for this network. Anchors ranged from 1 (very rarely) to 6 (very often). Weighted outbound assessments were calculated (so each group member was assigned a single score by every other). A sample item reads, “I have voluntarily relied on the skills and abilities of [group member name].”

Common affiliation

This variable is the proportion of group members who shared a domestic or international affiliation with a focal group member. This metric began by asking whether a focal group member was an international student (yes/no). The number of group members who had the same domestic/international affiliation as a focal group member was summed and then divided by the total number of nonself group members. This process was conducted for each group member.

Helping one’s group

We adapted the scale by Settoon and Mossholder (2002) for this measure. Our adaptation was simply to have each group member evaluate each other. The anchors ranged from 1 (very rarely) to 6 (very often). Within evaluator averages were calculated (so each group member provided a single score for every other). Each group member was then assigned the mean of these averages (self-evaluations were omitted).

Controls

Cross-group variation was controlled within the random coefficient model (RCM). As it is likely more demanding to help a group if it has a greater number of members, we controlled group size. On the individual level, we identified several potentially relevant controls. First, to account for any variation due to the two courses from which the final sample was sourced, we controlled enrollment in international business (vs. strategic management). As those with a higher expected grade may have a greater perceived resource pool from which to support their group members, this was controlled. As women have been shown to help more than men (Eagly & Crowley, 1986), we controlled gender. It has also been shown that one’s interpersonal familiarity/relationship leads to greater helping behavior (Anderson & Williams, 1996). Therefore, we also controlled average number of past classes taken with group members. As we wanted to capture the effect of an APGO beyond that of LGO and PPGO, these two additional goal orientations were controlled. Finally, as we were interested in common affiliation and not international student status, international student status was controlled. A note to Table 2 details the coding.

Results

Table 1 includes descriptive statistics and the correlation matrix of the main variables. As we used data that were incomplete, we offer pairwise correlations (maximum sample N = 471 individuals).² We now highlight the bivariate correlations of the main variables in our model. APGO and helping the group were negative and significant (r = −.16, p < .001). In line with past research, both the strength of a person’s reliance on group members (r = .16, p < .001) and the proportion of group members who shared a common affiliation with a focal group member (r = .2, p < .001) were positive and significantly associated with helping the group. As a note, the direction and significance was paralleled when bivariate correlations were calculated using listwise deletion (n = 198).

Table 1.

Correlations and Descriptive Statistics.

Variables	M	SD	1	2	3	4	5	6	7	8	9	10	11
1. Group size	5.40	0.51
2. International business	0.18	0.39	.19***
3. Expected grade in course	4.60	0.55	−.02	−.02
4. Gender	0.48	0.50	.02	−.04	.06
5. Past classes with group members	1.90	0.24	.04	−.10*	−.10*	−.14**
6. Learning orientation	4.6	0.76	.02	−.08	.24***	.03	.08	.86
7. Prove performance goal orientation	3.4	1.10	.01	−.05	.07	−.06	.11*	.09*	.83
8. International student	0.08	0.27	−.03	.05	.01	.04	−.03	−.08	.12**
9. Avoidance performance goal orientation	3.10	1.00	.06	.06	−.03	−.02	.03	−.35***	.35***	.12**	.84
10. Reliance on group members	24.00	6.40	.50***	.10*	−.08	−.04	.12*	−.02	−.00	.01	−.002
11. Common affiliation	0.86	0.26	.03	−.09	−.02	−.02	.08	.04	−.09*	−.84***	−.07	.08
12. Helping the group	5.70	1.10	.06	.08	.06	.11*	.11*	−.02	−.19***	−.18***	−.16***	.16***	.20***

Note. Pairwise deletion with max individual level N = 471. Alpha reliability is on the diagonal when appropriate, comprising only fully completed scales.

p < .05. **p < .01. ***p < .001.

Table 2.

Pooled RCM Analysis of Helping One’s Group.

	Model 1		Model 2		Model 3		Model 4		Model 5		Model 6
Variables	Est.	SE	Est.	SE	Est.	SE	Est.	SE	Est.	SE	Est.	SE
Intercept	3.64***	1.06	4.23***	1.07	4.03***	1.12	5.22***	1.27	5.73***	1.26	6.94***	1.38
Level 1
International business	0.23	0.19	0.26	0.19	0.28	0.18	0.27	0.18	0.29	0.18	0.28	0.19
Expected grade in course	0.17	0.11	0.18^†	0.11	0.19^†	0.11	0.20^†	0.11	0.17	0.11	0.18^†	0.11
Gender	0.22^†	0.12	0.21^†	0.12	0.21^†	0.12	0.21^†	0.12	0.17	0.12	0.16	0.12
Past classes with group members	0.65*	0.28	0.64*	0.28	0.58*	0.27	0.60*	0.27	0.55*	0.27	0.57*	0.27
Learning orientation	−0.06	0.08	−0.14	0.09	−0.13	0.09	−0.13	0.09	−0.13	0.09	−0.13	0.09
Prove performance goal orientation	−0.17**	0.06	−0.12*	0.06	−0.12*	0.06	−0.12^†	0.06	−0.12^†	0.06	−0.11^†	0.06
International student	−0.69**	0.21	−0.67**	0.21	−0.37	0.43	−0.34	0.43	−0.18	0.43	−0.16	0.43
APGO			−0.14*	0.06	−0.15*	0.06	−0.55**	0.21	−0.67***	0.19	−1.09***	0.27
RG					0.01	0.01	−0.04	0.03	0.01	0.01	−0.04	0.03
Common affiliation					0.41	0.48	0.39	0.48	−1.45^†	0.80	−1.48^†	0.80
APGO × RG							0.02*	0.01			0.02*	0.01
APGO × Common affiliation									0.60**	0.21	0.60**	0.21
Level 2
Group size	0.15	0.14	0.15	0.15	0.07	0.15	0.07	0.15	0.10	0.15	0.10	0.15
Wald Test (D₁)			5.10*		1.10		4.20*^a		8.30**^a		6.50**^a
AIC	M	985	M	981	M	983	M	981	M	976	M	973
AIC	SD	4.20	SD	4.20	SD	4.20	SD	4.20	SD	3.40	SD	3.40
BIC	M	1028	M	1027	M	1037	M	1038	M	1033	M	1035
BIC	SD	4.20	SD	4.20	SD	4.10	SD	4.20	SD	3.40	SD	3.40
Deviance	M	963	M	957	M	955	M	951	M	946	M	941
Deviance	SD	4.20	SD	4.20	SD	4.10	SD	4.20	SD	3.40	SD	3.40

Note. International business: 1 = yes, 0 = no; expected grade in course: F = 1, D = 2, C = 3, B = 4, A = 5; gender: female = 1, male = 0; past classes with group members = average number of classes taken with group members; international student: 1 = yes, 0 = no: imputed data sets = 5; imputations method: two-level predictive mean-matching; iterations = 40. RCM = random coefficient modeling; APGO = Avoidance performance goal orientation; RG = Reliance on group members; AIC = Akaike information criterion; BIC = Bayesian information criterion.

Compared with Model 3.

†

p < .10. *p < .05. **p < .01. ***p < .001.

As we had greater than 10% of our data missing, at both the individual and group level (Newman, 2014), we assumed that the data was missing at random (Rubin, 1976) and applied imputation. We imputed missing data using multiple imputation by chained equations (MICE) combing Version 3.4 of the MICE package (van Buuren & Groothuis-Oudshoorn, 2011) with Version 3.2-48 of the MICEADDS package (Robitzsch et al., 2016) in R (R Core Team, 2013). However, as MICE cannot accurately model our reliance measure, the sum of peer-based within-team weighted ties, we removed any individual (and corresponding group) from the analysis if this measure was missing. Because it depended on connecting multiple within-team peer assessments, the largest source of missing data was from this measure. This omission dropped 155 individuals from the analysis and left us with 316 completed individual responses and 25 incomplete individual responses. The remaining individuals were clustered in 64 groups.

In brief, MICE imputes by predicting each variable by the others, starting with the variable that has the fewest missing values and moving to the variable with the greatest number of missing values. We used all variables from our final RCM analysis in the imputation process (more on RCM later). Due to its improved regression estimates, we applied an iterative version of transform-then-impute (von Hippel, 2009) also known as the just-another-variable approach (Seaman et al., 2012) entitled passive imputation (van Buuren, 2018). That is, when possible, we multiplied our independent variable with each moderator to create two new variables representing the interaction terms. However, when required, we imputed a missing variable in this multiplication and then calculated the product. After the omission of individuals who were missing reliance, only APGO needed imputation among the variables in the interactions. Passive imputation allowed us to ensure consistency between the factors and product in the multiplication.

We also used predictive mean-matching (PMM). PMM calculates values for missing entries that resemble the actual data more precisely than methods that assume a normal distribution (White & Royston, 2009). To simplify, PMM randomly borrows from a collection of similar individuals in choosing a value for a missing entry. We created five imputed data sets (Schafer, 1997) from 40 iterations each.

Prior to testing our hypotheses, we tested for nonindependence by calculating ICC(1) on our dependent variable (Bryk & Raudenbush, 1992). In other words, we verified that group membership mattered in predicting helping one’s group. As our dependent variable was not missing (after the omission of observations missing reliance), ICC(1) was consistently .14 across each of the five imputed data sets. This suggests that a maximum of 14% of the variability in the extent to which individuals help their group is related to group membership.

To test our hypotheses on the imputed data sets, we used Version 3.1 of the nonlinear and linear mixed effects (NLME) package for S-PLUS and R (Pinheiro & Bates, 2000). Again, due to the fact that our dependent variables were not missing (after the omission of observations and groups where reliance was missing), analysis of variance (ANOVA) contrasts consistently suggested that a model with (randomly) varying intercepts fit the data better than without (p > .001). As such, we included random intercepts. We applied a series of RCMs with maximum likelihood (ML) to the five imputed data sets and pooled the results for each, using Rubin’s (1987) Rules. We chose ML over restricted maximum likelihood (REML) because not only did our imputed data sets contain a greater number of Level-2 units than the threshold considered to be small (which causes bias in ML estimates) but also we wanted to be consistent with best practice in comparing models with different fixed effects (McNeish, 2017).

As we did not have a theory suggesting that the slope of APGO would differ across groups, we used a fixed effect to model APGO.³ As can be seen in Table 2, the controls were entered in Model 1. Model 2 confirmed H1 (β = −.14, p < .05). Support for H2 was found through an independent test (Model 4: β = .02, p < .05) and simultaneous test (Model 6: β = .02, p < .05). An independent test (Model 5: β = .60, p < .05) and simultaneous test (Model 6: β = .60, p < .05) also supported H3. As a note, with an average group size of 5.4, the results from the multiple imputated data sets parallels the results of RCM analysis conducted through listwise deletion on 198 individual responses clustered in 37 groups.

Discussion

To attain the improved potential of groups, group members need to learn to help each other (Schmuck & Schmuck, 1971; Slavin, 1985). However, we found that those with an APGO pulled away from instrumentally helping their small groups, even when these groups were assessed on a collective product. We also found that the strength of reliance in and a common affiliation with group members lessened this negative association. These findings offer contributions for both academics and practitioners.

Contributions for Academics

First, although group helping clearly satisfies self-interest and is distinct from individual performance, we found that APGO negatively relates to helping small groups. That is, despite the fact that small groups are generally found to be less threatening than larger groups, team members with a high APGO (as compared with those with a low APGO) seem to perceive group helping in a similar manner to individual performance and, therefore, seem to fear a risk to personal competence in so doing. This finding is important because it not only confirms that some group members may be “hardwired” to pull away from social activities supporting group functioning but also adds evidence suggesting that the effects of goal orientations may be found in social interactions that are not governed by objective performance (because nonperformance contexts seem to be perceptually transformed into performance-like contexts).

Second, we chose reliance for the content of the individual-specific approach because of its essential connection to a group’s charge. Our finding of moderation reinforces the idea that accepting vulnerability lessens the perception of threat. More interestingly, this finding also suggests that even group members sensitive to perceived threat seem to be swayed by monitoring or facilitating personal instrumental gain. That is, they seem to be willing to forgo the protection of one type of social resource (i.e., socioemotional) to accrue assurances of attaining another (e.g., instrumental).

Third, as it satisfies the bottom-up and top-down perspectives to social categorization, we chose a domestic/international affiliation as the “content” for the category-specific approach to interaction. Our findings suggest that a shared subordination may be sufficient to create a common affiliation. However, after controls, there was no significant direct effect of a common affiliation on group helping (Table 2, Model 3). Yet, our hypothesized moderating effect persisted (Table 2, Models 5 and 6). We interpret these results as suggesting that, in a small group context, a common affiliation (like a domestic/international demarcation) is not perceived as particularly relevant to group helping. However, such a common affiliation seems quite relevant for determining the level of perceived threat derived from within-group (social) interaction. As such, researchers should be mindful regarding the relationships between the different in/out-group memberships that are salient and on which social resources their effects manifest.

Our final contribution for academics is related to the measurement and method we employ. We used a full scale to measure the ties of a within-group weighted social network and combined it with category proportions. In doing so, we offer a novel way to simultaneously consider both individuated assessments of as well as a common affiliation with group members. Thus, we offer academics a means of moving away from a choice between either extreme on the identity continuum (Tajfel & Turner, 1979) to a method that simultaneously considers both.

Contributions for Practitioners

Our contribution for practitioners also pertains to the relevance of an APGO. We found that an APGO leads some group members to view social interaction supporting group functioning as more personally disadvantageous. Therefore, we reinforce past work suggesting that it may be valuable for group members and those responsible for group functioning (e.g., supervisor) to better understand the personal characteristics of group members. More importantly, once a personal characteristic like an APGO is identified, it should be noted that these individuals may view even social interaction within a small group as personally threat-provoking. As such, these group members may be hardwired to pull away from even (social) interaction that supports group functioning.

As with past work (e.g., Tajfel & Turner, 1979), our work also suggests that these same stakeholders should consider at least two simultaneous ways to lessen the abovementioned disengagement: individuated assessments and common social categorization. Given a shared instrumental charge, we argue that reliance may be a near-universal salient “content” on which to base individuated assessments in task groups (Sayles, 1957). Regardless, we demonstrate that ties based on instrumental dependence can lessen the tendency of those with an APGO to pull away. Therefore, it may be beneficial to purposefully design work so that these group members are dependent on other group members. This may intertwine personal instrumental objectives across group members and begin to shift the focus away from protecting against personal threat during interaction.

Our findings relating to a common affiliation also suggest that subgroup membership may develop in ways that might not seem obvious at first glance (e.g., national boundaries) but instead on a shared subordination. Furthermore, although it did not directly influence group helping (after controls), our finding of moderation suggests that a common affiliation seems to be very influential in reducing the negative association between a group member’s APGO and group helping. Therefore, in addition to the knowledge, skills, and abilities (KSAs) that have been identified as important for group members to learn (Chen et al., 2004), it may also be desirable that group members build a capacity to identify such demarcations and how to manage them to minimize group member withdrawal.

Limitations and Future Directions

Although our results provide both contributions to theory and practice, our work is not without limitation. First, we have examined business school students in the United States. As the goals of group work may vary across group contexts (e.g., be less instrumental), so too might group dynamics. Thus, future investigators might test our findings in other contexts.

Second, we used an outbound version of reliance strength as a moderator. Although we view reliance as a near-universal content upon which to base individuated assessments in a small work group, context-specific content for within-group individuated assessments may also be explored. Furthermore, our measure does not distinguish between a few ties of high intensity and many ties of low intensity.

Third, to create a measure for common affiliation, we used a domestic/international demarcation. Although only 8% of our respondents were international (prior to the omission of those missing reliance), these respondents were spread across various groups, also altering the proportion of common affiliation from the point of view of other group members (both international and domestic students). We believe that this low percentage adds support to our claim that a domestic/international demarcation is an appropriate basis for a common affiliation. Regardless, we encourage future researchers to investigate contexts with differing proportions of group members dispersed across a demarcation.

Fourth, our concept of common affiliation is theoretically different from social cohesion (Amir, 1969; Harrison et al., 1998) and social loafing (Latané et al., 1979) but we acknowledge the possibility that these concepts may also affect helping behavior.⁴ Social loafing involves one or more group members who disengage from activities that support group functioning (Comer, 1995), with the intention to take advantage of other group members who fill the gap (Jassawalla et al., 2009; Kerr, 1983). Group cohesion may also affect team helping (Amir, 1969; Ng & Van Dyne, 2005). In contrast, group withdrawal arising from an APGO is predominantly motivated by fear. As loafing and cohesion were not the focus of our study and were not measured, their effects could not be controlled. However, we believe the confounding effects of loafing to be minimal as the contribution of each member was assessed by their peer group members. Likewise, cohesion requires an active intervention or substantial personal contact, neither of which was present in the groups we studied. Regardless, we recommend further research to compare the relative strength of these alternative causes of group helping.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Justin Kraemer

Ken Chung

Notes

Author Biographies

Justin Kraemer received his PhD from Rutgers University, USA. He is currently employed in the School of Management at Mae Fah Luang University, Thailand. His interests include within- and between-group dynamics as well as advanced quantitative research methods. He is deeply involved in the research of .

Marjaana Gunkel received her PhD from Otto-von-Guericke University, Germany. She is professor of organization and human resource management (HRM) at the Free University of Bozen-Bolzano, Italy. She has held professorships in international human resource management (IHRM) and management, and management and organization. Her interests include IHRM and international organizational behavior.

Ken Chung earned his PhD at Rutgers University, USA. His research interests include how organizational institutions emerge and change. Past research efforts include studying environmental disasters, moral muteness, outsourcing, and temporal structures. He is retired.

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