Relational Balance in the Workplace: Exploring the Moderating Role of Organizational Commitment

Abstract

This paper uses a diverse organizational sample to test portions of Heider’s (1946, 1958) balance framework. First, a review of balance theory is provided, and then theoretical relationships between relational balance and organizational outcomes (i.e., job satisfaction, unproductivity, and depressive symptoms) are explicated. Following this, the role of organizational commitment is also considered as a moderator of the aforementioned associations. In the main, the results of this investigation indicate that balance has a substantial, positive effect on job satisfaction. The results also indicate that balance combines non-additively with organizational commitment to impact results, such that the negative effects of balance on unproductivity and depressive symptoms are stronger when members are committed to their jobs. These findings point to the importance of considering the impact of relational patterns within organizations, as opposed to considering the impact of any one relationship in particular.

Keywords

balance workplace relationships organizational commitment productivity

Introduction

To date, a substantial amount of research has shown that workplace relationships can have a profound impact on members’ organizational experiences (e.g., Sias, 2008). Moreover, this impact can range from positive to negative. For example, workplace relationships can provide social support (Warren & Johnson, 1995), help distribute critical work-related information (Miller & Jablin, 1991), and facilitate career development and advancement (Burt, 2001). Conversely, workplace relationships can be used to exert pressure on others to behave in an undesirable manner (e.g., Gibson & Papa, 2000), attenuate team cohesion (e.g., Manata, 2020), or deteriorate over time (e.g., Sias & Perry, 2004). As this description implies, the widespread and varied impact of workplace relationships are an important organizational dynamic for organizational communication scholars to examine. Indeed, the study of organizational relationships has received widespread attention from organizational communication scholars (e.g., Abu Bakar & Connaughton, 2019; Manata, 2020; Omilion-Hodges & Ptacek, 2021; Sias, 2008), which complements relational research in communication scholarship more generally (e.g., Altman & Taylor, 1973; Berger & Calabrese, 1974; Burgoon & Hale, 1988; Solomon & Knobloch, 2004).

One way for communication scholars to examine workplace relationships involves an application of Heider’s (1946, 1958) balance theory. In brief, Heider proposed that relational balance can occur when relationships between different entities (e.g., people) reach a state of structural harmony. If relationships between entities are imbalanced, however, then cognitive tension, discomfort, or strain is expected to follow. Notably, this framework is unique because it theorizes about the effect of certain group-level compositions. That is, Heider’s framework suggests that individual-level behavior and cognitions are impacted primarily by a specific pattern of relationships, and not by any one relationship in particular.

Although of decided value, the extent to which Heider’s logic is accurate when investigating actual relationships remains unclear (see also Chiang et al., 2020). Indeed, despite being proposed many decades ago, application and testing of Heider’s (1946, 1958) balance theory has stagnated considerably. Moreover, of the empirical investigations that have been conducted, many have focused exclusively on examining subject reactions to hypothetical relationships or situations in which relations are (im)balanced (e.g., Brickman & Horn, 1973; Chiang et al., 2020; Gerard & Fleischer, 1967; Harari, 1967; Miller & Geller, 1972). These points are also especially true when considering organizational relationships, as balance theory has rarely been applied to organizational settings or by organizational communication scholars (for recent exceptions, see Lan et al., 2020; Reid et al., 2017). Consequently, although Heider’s (1946, 1958) theoretical framework holds considerable promise for the study of real organizational behavior and organizational communication, additional work is required to determine both its theoretical scope and predictive accuracy (see also Miller, 2005).

This paper develops this line of inquiry by using an organizational sample to test portions of Heider’s (1946, 1958) balance framework. In achieving these goals, a review of balance theory is first provided, and then theoretical relationships between relational balance and organizational outcomes (i.e., job satisfaction, unproductivity, and depressive symptoms) are explicated. Following this, a study is reported that provides an explicit test and extension of Heider’s (1946; 1958) balance theory. Ultimately, this investigation makes contributions to different areas of scholarship. First, this work complements the numerous organizational communication studies that investigate the impact of different relational dynamics in the workplace (for a review, see Sias, 2008). Second, applying Heider’s (1946, 1958) theory to organizational contexts allows for an examination of relational balance in triads, which will contribute to our understanding of how people understand and perceive organizational communication networks more generally (e.g., Krackhardt, 1987; Monge & Contractor, 2003; Shumate et al., 2013). Lastly, this investigation provides an explicit test of Heider’s balance theory, which will help determine the extent to which Heider’s predictions are accurate and extend to organizational contexts.

Balance Theory

In his initial formulation, Heider (1946) posited that a set of relations between 3 entities (e.g., three persons) could be in a state of balance or imbalance. Moreover, Heider suggested that balance or imbalance could be deduced by considering the valence of the relationships connecting the three entities (see also Hunter et al., 1984). In the main, one primary way balance is achieved is when (dis)liked others hold (dis)similar attitudes. Conversely, when liked others hold dissimilar attitudes, or disliked others hold similar attitudes, imbalance will occur. For example, consider a scenario in which there are three persons (o, p, and q), where p and q are coworkers both supervised by o. If the coworkers like both one another and their supervisor (p likes o, q likes o, p likes q, and vice versa), all three relationships are positive, reflecting the fact that liked others hold similar attitudes. The triad is thus in a state of harmony or balance. Alternatively, if organizational member p likes both their supervisor (o) and their coworker (q), but the coworker does not like the supervisor, then two relationships are positive, but one is negative, suggesting that liked others hold dissimilar attitudes. The triad is thus in a state of imbalance.

One simple way to determine (im)balance is to treat the problem mathematically. In specific, (im)balance may be calculated for groups of three by taking the product term of the valence of all three relationships. In the first case, all three relationships are positive, so the resultant product term is positive, and the relational set is balanced. Alternatively, in the second case, there are two positive relationships and one negative, so the resultant product term is negative, and the relational set is imbalanced. In groups of three, eight states of (im)balance are possible (see Table 1 and Figure 1).

Table 1.

(Im)balance in Triadic Relationships.

P-O Relationship	P-Q Relationship	Q-O Relationship	Product	State
+1	+1	+1	+1	Balance
+1	+1	−1	−1	Imbalance
+1	−1	+1	−1	Imbalance
+1	−1	−1	+1	Balance
−1	+1	+1	−1	Imbalance
−1	+1	−1	+1	Balance
−1	−1	+1	+1	Balance
−1	−1	−1	−1	Imbalance

Note. P and Q = coworkers, O = a supervisor. P-O and Q-O relationships indicate relationships between a coworker and a supervisor, the P-Q relationship indicates a relationship between coworkers. +1 = a positive relationship (e.g., liking), −1 = a negative relationship (e.g., dislike). The product column indicates the product of the P-O, P-Q, and Q-O relationship. The state column indicates the interpretation of the product term for balance. See Figure 1 for a visualization of these relationships.

Figure 1.

(Im)balanced triangles.

Balance or a lack thereof is important to consider because states of imbalance are expected to result in cognitive tension, discomfort, or strain (e.g., Heider, 1946; 1958). Indeed, previous investigations have produced some evidence to suggest that imbalanced states are judged as unpleasant (e.g., Jordan, 1953). In addition, a recent fMRI investigation by Chiang et al. (2020) found that being nested within imbalanced triads activated regions of the brain known to process cognitive dissonance, thus implicating a direct causal mechanism responsible for the effects of imbalance. Stated differently, knowing that liked others hold dissimilar attitudes, or that disliked others hold similar views, is likely to produce a stressful, dissonant experience because it challenges one’s sense of self (e.g., the tendency to believe that we are rational beings; see Aronson, 1968, 1999; Festinger, 1957).

When individuals perceive such imbalanced states, balance theory predicts that individuals will strive to relieve the undesirable cognitive stress by restoring balance via cognitive reorganization or behavioral changes (Heider, 1946, 1958; Hunter et al., 1984). Alternatively, if relations are balanced, then individuals will strive to maintain the balanced state. In either case, balance is the desired outcome, and this can be procured or maintained in a variety of ways. For example, Davis and Rusbult (2001) found that close relational partners were more likely to align their attitudes (i.e., cognitive reorganization) when they realized that their attitudes were discrepant (i.e., when liked others held dissimilar attitudes). Hess (2000) found that when situated within negative, nonvoluntary relationships, individuals were more likely to distance themselves from the other member or engage in antagonistic behaviors. Additionally, Tong and Walther (2015) found that respondents were more likely to behave in ways that altered the negative disposition of others, presumably in the interest of making the relationship more enjoyable. Ultimately, such communication behaviors are expected to help restore relational balance, in that relationships characterized as unpleasant or lacking in utility are either terminated or altered in some manner (see also Newcomb, 1959). Alternatively, should individuals choose to maintain certain relationships, then they can choose to do so by engaging in the myriad communicative practices known to facilitate relational maintenance (e.g., self-disclosure, Mason & Carr, 2022; conflict management, Madlock & Booth-Butterfield, 2012; providing assurances, Ledbetter, 2009).

If cognitive reorganization or behavioral changes are not possible, however, then the lasting cognitive stress that stems from imbalance is likely to be problematic for the individual. Numerous researchers, for instance, have documented a strong connection between stressful life events and health impacts such as major depressive disorder (e.g., Hammen et al., 1992; Kendler et al., 1999; Melchior et al., 2007; see also Burke et al., 2005) and coronary heart disease (e.g., Chandola et al., 2008). Similar associations have also been documented in communication scholarship (e.g., Floyd et al., 2007; Pauley & Hesse, 2009; van Raalte & Floyd, 2022). Consequently, to the extent that cognitive imbalance results in cognitive stress, it may in turn be associated with depressive symptoms and other problematic health outcomes. Stated differently, extant theory suggests that the presumed effects of cognitive imbalance (e.g., depressive symptomology) are expected to occur because it induces a stressful cognitive experience.

Presuming such tension is due to imbalance in workplace relationships, then there may also be other negative workplace outcomes (e.g., Standen et al., 2014; Reid et al., 2017). Research shows that job-related stress is associated negatively with both job satisfaction (e.g., Hollon & Chesser, 1976; Klenke-Hamel & Mathieu, 1990; Reid et al., 2017) and productivity (e.g., Cocker et al., 2013; Lusch & Serpkenci, 1990). Moreover, these negative associations are maintained when examined in a longitudinal manner (e.g., Bateman & Strasser, 1983; Lamb & Kwok, 2016). As stated previously, such negative relationships are expected to manifest because job-related tension constitutes an unpleasant cognitive state that individuals seek to avoid or mitigate (Heider, 1946, 1958; Zivnuska et al., 2002). Thus, cognitive imbalance, as one antecedent of job-related tension, may be associated with decreased satisfaction and productivity, in addition to its negative health associations. Moreover, this is expected to occur because cognitive imbalance is expected to yield a stressful cognitive experience (e.g., Chiang et al., 2020).

Organizational Commitment

One variable that may qualify these otherwise intuitive associations is organizational commitment (e.g., Mowday et al., 1979). Indeed, the work of Schmidt (2007) indicates that there is an established precedent for treating organizational commitment as a moderating variable of stress-related variables. In the main, although conceptual definitions of commitment vary (for a review, see Cohen, 2007; cf. Cooper-Hakim & Viswesvaran, 2005), many scholars agree that a positive attitude constitutes one critical component of organizational commitment (e.g., Cohen, 2007). That is, those who are committed to an organization are disposed positively towards the organization, which means that they are also more likely to express a desire to remain in the organization and exert effort on its behalf (Manata et al., 2021). Thus, those that are committed to their organization are bonded to them, which implies that they will be highly attuned to stress-related matters that stem from this relationship (see Schmidt, 2007). In general, it is typical to find that organizational commitment is associated with numerous beneficial outcomes, some of which include increased job involvement and job satisfaction, as well as lower turnover intentions (see Cooper-Hakim & Viswesvaran, 2005; Meyer et al., 2002).

Committed members with balanced relationships are in an ideal position because they feel positively toward their jobs, and they are also expected to experience low relational tension. This creates a congruity between how these members feel about their workplace relationships and how they feel about the workplace itself—another form of balance (Heider, 1946; 1958).¹ Committed organizational members with imbalanced relationships, on the other hand, may experience a special sort of problem: they are committed, or disposed positively, towards a job that leaves them strained or in a state of discomfort. This creates an imbalance in that these members feel tension about their workplace relationships, even though they retain positive feelings about the workplace itself. Thus, in addition to the stress and tension that comes from having imbalanced workplace relationships, these members experience the additional tension of having conflicting feelings about their jobs (i.e., a double imbalance). Altogether, this logic suggests that strong levels of organizational commitment may make both the positive and negative effects of cognitive (im)balance more pronounced. To wit, although cognitive imbalance is expected to decrease job satisfaction and increase unproductivity and depressive symptoms on average, these associations are expected to be stronger for those with high commitment to their jobs.

To summarize, it was expected that organizational members would report better outcomes (higher satisfaction, lower unproductivity, and lower depressive symptoms) when their relationships were balanced and that these relationships would be stronger when they had high (vs. low) commitment to the organization. These predictions were investigated with a sample of workers from diverse organizations, using a cross-sectional survey design. The goals of this investigation were to contribute to the understanding of relational dynamics in the workplace, present a novel application of balance theory to organizational contexts, and speak to applied organizational issues such as unproductivity and mental health. In the interest of structuring this investigation, formal hypotheses are proposed:

H1: Relational balance will be associated positively with (a) job satisfaction, but negatively with (b) unproductivity and (c) depressive symptoms.

H2: Relational balance will combine non-additively (i.e., interact) with organizational commitment, such that the expected effects of relational balance on (a) job satisfaction, (b) unproductivity, and (c) depressive symptoms will become stronger as organizational commitment increases.

Method

Procedure

Participants were recruited via Qualtrics Services. In specific, participants were provided with an online link to complete the survey instrument, and participants who completed the survey were compensated by Qualtrics and their panel providers. Of note, services like Qualtrics have been shown to produce levels of data quality comparable to other traditional methods used in the behavioral sciences (e.g., see Buhrmester et al., 2011). It has also been noted that samples drawn from online panels are advantageous because they are generally more diverse than traditional samples (e.g., student samples; Landers & Behrend, 2015).

Sample

The majority of participants in the final sample (N = 818) self-identified as white (n = 661, 80.8%) women (n = 473, 58%), with an average age of 42.35 years (SD = 14.12). Participants reported a range of household incomes (from < $10,000, up to $150,000 or more) and working in organizations of various sizes (from 1–4 employees, up to 1000 or more). The effects of these variables were controlled for statistically when performing the main regression analyses.

Measures

Balance

Subjects were asked to report on their perceptions of three workplace relationships: (1) the extent to which they liked their supervisor, (2) the extent to which they liked their favorite coworker, and (3) the extent to which they believed their favorite coworker liked their supervisor. The supervisor and favorite coworker were chosen as points of reference because of Newcomb’s (1959) suggestion that balance-related tension is more pronounced when the objects in question are important (see also Davis & Rusbult, 2001; Reid et al., 2017).² Responses ranged from 0 to 100, where values ≤49 were coded as −1 (negative valence) and values ≥50 were coded as +1 (positive valence). Then, the balance variable was created by multiplying all three values (see Insko & Schopler, 1967). A triad was defined as balanced when the product was positive, and imbalanced when the product was negative (see also Table 1), which is consistent with previous work on cognitive social structures (Krackhardt, 1987). This operationalization yielded numerous balanced (n = 653, 82.8%) and imbalanced (n = 136, 17.2%) triads.

Commitment

Mowday et al.’s (1979) 15-item measure of organizational communication was used as self-report measure of organizational commitment. Participants rated items such as, “I feel very little loyalty to this organization,” and, “There is not much to be gained by sticking with this organization indefinitely.” When applicable, items were reverse coded so that higher values reflected stronger levels of organizational commitment. Item response options ranged from 1 (strongly disagree) to 7 (strongly agree).

Job Satisfaction

Babin and Boles’s (1996) 9-item measure of job satisfaction was used as a self-report measurement of job satisfaction. Participants rated items such as, “I am often bored with my job,” and “I definitely dislike my work.” When applicable, items were reverse coded so that higher values reflected higher job satisfaction. Item response options ranged from 1 (strongly disagree) to 7 (strongly agree).

Unproductivity

Endicott and Nee’s (1997) 21-item work productivity scale was used as a self-report measure of workplace unproductivity. Participants were asked to indicate how often they engage in behaviors such as “arrive at work late or leave early?” and “just do no work at times when you would be expected to be working?” Item response options ranged from 1 (never) to 5 (always).

Depressive Symptoms

Depressive symptoms were measured using eight items taken from Goldberg and Hillier (1979) general health questionnaire (GHQ). Participants rated the extent to which they had experienced certain symptoms in the past 2 weeks, such as “felt that life is entirely hopeless,” and “found yourself wishing you were dead and away from it all.” This measure was implemented because it covers a range of symptoms associated with major depressive disorder (e.g., suicidal ideation, low mood, inappropriate guilt; see American Psychiatric Association, 2013). All items were positioned on 4-point Likert-type scales, where higher values indicated more severe depressive symptomology.

Results

Measurement Validity

The validity of the four-factor measurement model was inspected using confirmatory factor analysis (CFA; Hunter & Gerbing, 1982).³ This model tested the fit of the four multi-item measures described previously (i.e., commitment, job satisfaction, unproductivity, depressive symptoms). These analyses were conducted using the lessR package in the R software environment (Gerbing, 2020; R Core Team, 2016). Centroid estimation methods were implemented to estimate factor loadings (see Gerbing & Hamilton, 1994), and internal consistency and parallelism theorems were used to calculate predicted correlation coefficients (Hunter & Gerbing, 1982); large discrepancies (i.e., residuals or errors) between the 2 types of coefficients were indicative of model misspecification. Moreover, items that evidenced large errors consistently were treated as invalid items and were thus removed from the measurement model (Anderson & Gerbing, 1988). That is, these items were removed because they lacked construct validity (Boster, 2012). Model fit was evaluated further with the comparative fit index (CFI) and standardized root mean residual (SRMR), both of which were attained in the lavaan package in the R software environment (R Core Team, 2016; Rosseel, 2012; see also Gerbing & Hamilton, 1994; Hair et al., 2007; Hu & Bentler, 1999).

Analyses of the four-factor measurement model produced poor fit, χ² (1319) = 6146.38, CFI = .83, SRMR = .14. Consequently, the residual matrix was inspected to specify the source of error. This inspection revealed that numerous items were creating large errors consistently (i.e., these items lacked validity). Thus, these invalid items were removed, and a subsequent CFA was performed. Analysis of this trimmed model produced better model fit, χ² (246) = 655.43, CFI = .97, SRMR = .03. That is, all three fit indices improved markedly and the CFI and SRMR were within range of recommended cutoff values (see Hair et al., 2007; Hu & Bentler, 1999). Consequently, the trimmed model was preferred moving forward because it contained valid measures of the constructs of interest. Correlations, descriptive statistics, and alphas are reported in Table 2.⁴

Table 2.

Descriptive Statistics and Correlations.

	1	2	3	4	5	M	SD
1. Cognitive balance	(1.00)					0.64	0.77
2. Org. commitment	.04	(.71)				3.95	1.55
3. Job satisfaction	.18	.45	(.77)			5.07	1.26
4. Unproductivity	−.05	−.40	−.45	(.96)		2.13	0.98
5. Depressive symptoms	−.06	−.33	−.34	.64	(.94)	1.61	0.80

Note. Coefficient alphas are included in the diagonals. Listwise N = 664.

Regression Analyses

Predictions were tested using ordinary least squares (OLS) regression. For each analysis, the independent and dependent variables were standardized, and then an interaction term was created by multiplying the values of the standardized independent variables. Analyses then proceeded by estimating the main effects of the independent variables, then estimating the effect of the interaction term while controlling for both main effects. For technical details regarding these procedures, see Cohen et al. (2014).⁵ Results are summarized in Table 3.

Table 3.

Ordinary Least Squares Regression Models.

	Model 1			Model 2
	B	S.E.	95% CI	B	S.E.	95% CI
Job satisfaction
Balance	0.17	0.03	[0.11, 0.24]	0.17	0.03	[0.11, 0.24]
Commitment	0.28	0.03	[0.21, 0.34]	0.28	0.04	[0.21, 0.35]
Balance x commitment	—	—	—	−0.01	0.04	[−0.09, 0.07]
Unproductivity
Balance	−0.01	0.03	[−0.07, 0.05]	−0.02	0.03	[−0.08, 0.04]
Commitment	−0.37	0.03	[−0.44, −0.31]	−0.36	0.03	[−0.42, −0.30]
Balance x commitment	—	—	—	−0.09	0.04	[−0.16, −0.02]
Depressive symptoms
Balance	−0.05	0.02	[−0.11, 0.02]	−0.05	0.04	[−0.12, 0.02]
Commitment	−0.28	0.04	[−0.35, −0.21]	−0.27	0.04	[−0.34, −0.19]
Balance x commitment	—	—	—	−0.08	0.04	[−0.16, 0.00]

Note. Demographic variables are included as control variables. Unstandardized coefficients are reported.

Main Effects

The first set of predictions posited that imbalance would be associated with higher levels of depressive symptoms and unproductivity, as well as lower levels of satisfaction. These predictions were only partially supported. Specifically, although cognitive balance had a substantial positive effect on job satisfaction, B = 0.17, 95% CI [0.11, 0.24], it had no statistically significant effect on either unproductivity, B = −0.01, 95% CI [−0.07, 0.05], or depressive symptoms, B = −0.05, 95% CI [−0.11, 0.02]. Consequently, balance was associated with positive workplace outcomes, though in a narrower range than expected. These results provide statistical support for H1a, but not H1b or H1c.

On the other hand, consistent with previous findings, organizational commitment was a positive predictor of job satisfaction, B = 0.28, 95% CI [0.21, 0.34], and a negative predictor of unproductivity, B = −0.37, 95% CI [−0.44, −0.31], and depressive symptoms, B = −0.28, 95% CI [−0.35, −0.21]. Thus, commitment was associated positively with beneficial health and workplace outcomes. Although not the purpose of this investigation, these findings are in line with previous meta-analyses that have summarized the effects of organizational commitment (e.g., see Cooper-Hakim & Viswesvaran, 2005; Meyer et al., 2002).

Interaction Effects

The next set of predictions posited that balance and commitment would combine non-additively to affect outcomes, such that balance had a stronger effect when commitment was high than when it was low. This general prediction also received partial support. Specifically, the interaction term had a noticeable effect on both unproductivity, B = −0.09, 95% CI [−0.16, −0.02], and depressive symptoms, B = −0.08, 95% CI [−0.16, 0.00]. Moreover, both coefficients were in the predicted direction, indicating that the effects of cognitive balance on unproductivity and depressive symptoms become increasingly negative as commitment increases. Conversely, the nonadditive effect on job satisfaction was trivial, B = −0.01, 95% CI [−0.09, 0.07]. These results provide statistical support for H2b and H2c, but not H2a.

To visualize these interaction effects, simple slopes were calculated for the effect of balance at different levels of organizational commitment (1 SD below the mean, at the mean, and 1 SD above the mean; see Cohen et al., 2014). The interaction effects are displayed in Figure 2 and Figure 3. As the figures show, the effect of cognitive balance on unproductivity becomes increasingly negative as organizational commitment increases (low: B = 0.07, p = .12; mean: B = −0.02, p = .54; high: B = −0.11, p = .04). Similarly, the effect of cognitive balance on depressive symptoms becomes increasingly negative as organizational commitment increases (low: B = 0.03, p = .51; mean: B = −0.05, p = .15; high: B = −0.13, p = .02). Overall, these results suggest that the effects of (im)balance are stronger among highly committed employees, though only for unproductivity and depressive symptoms.

Figure 2.

Balance x commitment interaction effect on unproductivity. Note. At low commitment, B = 0.07; at mean commitment, B = −0.02; at high commitment, B = −0.11.

Figure 3.

Balance x commitment interaction effect on depressive symptoms. Note. At low commitment, B = 0.03; at mean commitment, B = −0.05; at high commitment, B = −0.13.

Discussion

The results of this investigation indicate that cognitive balance is associated positively with job satisfaction. That is, as workplace relationships become increasingly balanced, organizational members are more likely to report greater job satisfaction. This finding provides some support for Heider’s (1946, 1958) theory, which proposed that cognitive imbalance would result in problematic outcomes for the individual (e.g., see also Reid et al., 2017). Heider (1946, 1958) argued that such relationships manifest because a lack of balance produces cognitive strain or stress, which impacts numerous outcomes negatively across many different situations (e.g., Cocker et al., 2013; Hammen et al., 1992; Hollon & Chesser, 1976; Kendler et al., 1999; Klenke-Hamel & Mathieu, 1990; Lusch & Serpkenci, 1990).

Alternatively, the effects of relational balance on unproductivity and depressive symptoms were more nuanced. In specific, the associations between cognitive balance and both variables were qualified by organizational commitment. As is displayed in Figure 1 and Figure 2, the harmful outcomes associated with imbalance (i.e., greater depressive symptoms and unproductivity) were most noticeable when organizational commitment was high. Alternatively, when commitment was low, the associations between imbalance and both unproductivity and depressive symptoms were more limited. As suggested at the outset of this paper, these relationships may have manifested because highly committed organizational members experience additional tension when there is an additional incongruity between their negative feelings about their workplace relationships and their positive feelings about their workplace in general. These broader feelings of tension combine with the feelings of relational tension that come from imbalance, heightening overall levels of stress, and thereby leading to greater undesirable outcomes (see also Chiang et al., 2020; Heider, 1946; 1958; Newcomb, 1959; Reid et al., 2017).

This study makes important theoretical and applied contributions to the study of balance and workplace relationships. For example, this study extends Heider’s (1946, 1958) theoretical framework by suggesting that commitment is an important moderator of some outcomes, including depressive symptoms and unproductivity. Accordingly, scholars interested in the effects of (im)balance should consider not only the extent to which a triad is balanced, but also the extent to which relational balance interacts with other dynamics. For example, and as is suggested herein, it may be worth considering whether different forms of balance are interdependent. Indeed, although how an organizational member feels about their relationships is one important source of (im)balance, these feelings are also embedded in broader networks that capture their feelings toward their workplace, job, and career (i.e., balance in one group likely impacts the effects of balance in another).

Regarding applied implications, these results suggest that managers would benefit from attempting to foster balanced sets of relationships within the workplace. Although this may be difficult to accomplish in practice, the simplest way to ensure balance is to facilitate positive relationships between all workgroup members (e.g., three positive relationships in a triad). In addition to procuring the positive outcomes suggested herein (e.g., job satisfaction), such conditions would presumably help facilitate a more cohesive workgroup environment. For example, Manata (2020) found that when supervisors treated their employees equally (e.g., everyone was treated positively and to a similar degree), greater cohesion between said members was manifest in the workplace (see also Baker & Omilion-Hodges, 2013; Yu et al., 2018). Importantly, as myriad meta-analyses have shown, cohesion is a consistent positive predictor of workplace performance (e.g., Grossman et al., 2022). As such, if certain forms of balance (e.g., everyone likes each other) are expected to yield numerous beneficial outcomes (e.g., satisfaction; cohesion), then expending energy on fostering such balanced relationships would constitute a worthwhile endeavor.

This study also makes two useful methodological contributions. First, the approach employed here offers a novel way to investigate the impact of workplace relationships on organizational outcomes. Most studies that focus exclusively on relationships require group- or network-level data (e.g., Manata, 2019), both of which can be challenging to procure. Alternatively, the method employed herein allows for the study of triadic relations in organizational contexts using individual survey responses, which require considerably less effort to collect (for a similar point, see Boster et al., 2011). Second, this approach also has the advantage of enabling the investigation of balance in real interpersonal or workplace relationships. Since its initial inception (Heider, 1946), the effects of balance have been assessed primarily by providing subjects with hypothetical scenarios in which relationships are balanced or imbalanced (e.g., Brickman & Horn, 1973; Chiang et al., 2020; Gerard & Fleischer, 1967; Harari, 1967; Miller & Geller, 1972). Consequently, this investigation provides a substantial step forward in the study of cognitive balance because it examines actual cases of cognitive (im)balance and its associations with important outcomes (i.e., job satisfaction, unproductivity, and depressive symptoms).

There are numerous ways in which this research can be extended. For one, additional research can assess the extent to which other important workplace outcomes are predicted by cognitive balance. For example, if cognitive imbalance were to promote interpersonal avoidance, then it may also produce a negative effect on intrinsic motivation (e.g., see Ryan & Deci, 2000). Relatedly, future research can also examine the extent to which the effects of relational balance on workplace outcomes are mediated by other variables. For example, although Heider’s (1946, 1958) original work and other research (e.g., Reid et al., 2017) suggests that imbalance results in negative outcomes because it causes tension or stress, this prediction was not tested explicitly. For example, although it was assumed that imbalance would increase depressive symptomology because it yielded a stressful cognitive experience, this theoretical assumption remains untested. Testing this theoretical explanation would help further specify the different processes and mechanisms (e.g., stress, cognitive dissonance; see Chiang et al., 2020) that are responsible for the effect of imbalance on relevant outcomes (e.g., depressive symptoms).

Second, it would also be beneficial to consider relationships that are different from the ones considered herein. That is, although supervisors and preferred coworkers were chosen as points of reference because of their relevance to balance theory (e.g., see Newcomb, 1959), it is possible that other relationship targets would also be of interest (e.g., see Sias, 2008) and thus applicable to the study of relational balance within organizations. For instance, asking supervisors about their subordinates (rather than the other way around) may offer a different perspective, as could examining less preferred coworkers or people external to the department or organization. Ultimately, these different perspectives may yield different results. For example, given that supervisors are tasked primarily with ensuring effective working relationships between their subordinates, they may be especially impacted by a lack of balance amongst their subordinates’ relationships. In addition, it may be useful to consider relationships other than affinity-type relationships (e.g., liking). Instrumental relationships, for instance, are task-related and based primarily on the exchange of advice (e.g., see Manata, 2019). It is not clear how imbalances in advice-seeking activity might impact individual- (e.g., job satisfaction) and group-level (e.g., cohesion) outcomes, if at all. Thus, examining both different types of relationships and different relationship targets may be useful in future research.

Third, scholars are also encouraged to examine whether different forms of (im)balance impact outcomes differently. A perusal of Table 1 suggests that relational balance can be achieved through myriad means. For example, balance can be achieved if all members like one another, but also if some members like one another and others do not (e.g., I dislike my supervisor and my coworker, both of whom like one another). Ultimately, such forms are likely to have different outcomes. For instance, when supervisor and member relations have equivalent valences (e.g., all positive, as in the first case), the group environment is likely to be more cohesive (e.g., see Manata, 2020). Alternatively, if some members feel isolated in their own groups (as in the second case; see also Klein et al., 1994), group cohesion or other individual-level outcomes (e.g., job satisfaction) may be attenuated. Thus, the specific form of balance may qualify the more general effects of balance. Future research that examines this possibility will likely help extend balance theory considerably.

Fourth, although the study of balance is important in its own right, very little is known about the different interpersonal behaviors that individuals might use to either maintain balanced relations or mitigate the negative impact of imbalanced relations. For example, as suggested previously, individuals are known to engage in myriad communication behaviors when they are nested within unpleasant interpersonal relationships (e.g., distancing; Hess, 2000). Alternatively, individuals are also known to engage in different communication behaviors when attempting to maintain a relationship (e.g., self-disclosure; Mason & Carr, 2022). The extent to which these behaviors are being enacted with the explicit intention to maintain or change the form of a set of relationships, however, remains unclear. As such, communication scholars in particular are encouraged to study this matter further.

Finally, despite the illuminating nature of these results, they cannot be generalized beyond the triadic case (i.e., groups of three). Consequently, additional work will be required to determine whether different sources and forms of balance combine to impact organizational outcomes in a meaningful manner. For example, future scholarship can examine balance from a multilevel perspective (e.g., Raudenbush & Bryk, 2002), whereby different sources of balance at different levels of analysis impact one another. For instance, the work of Khanafiah and Situngkir (2004) suggests that balance can occur at different levels of analysis, which implies that balance occurring at more macro-levels of analysis (e.g., organization) can impact the effects of balance at lower levels of analysis (e.g., group). Ultimately, approaching balance from this perspective may also help extend Heider’s (1946, 1958) theory considerably.

There are also limitations of the present study. First, although Goldberg and Hillier’s (1979) measure of depression captures many of the common symptoms of major depressive disorder (e.g., suicidal ideation, low mood, inappropriate guilt), it is also true that notable features are missing from this measure (e.g., fatigue; see American Psychiatric Association, 2013). Thus, the Goldberg and Hillier (1979) measure of depression may be criticized for lacking content validity (i.e., all relevant features of the depression construct are not captured by the measure of interest; see Cronbach & Meehl, 1955). One way to mitigate this limitation would be to use alternate measures that capture a broader range of depressive symptoms (e.g., the Quick Inventory of Depressive Symptomology [QIDS]; e.g., see Rush et al., 2003). Nevertheless, to the extent that alternate measures will yield alternate results remains an empirical question. That is, although the Goldberg and Hillier (1979) measure captures a narrower range of depressive symptoms, considerable overlap with other, broader measures of depression are to be expected. In practice, such circumstances would likely yield very strong correlations between Goldberg and Hillier’s (1979) measure and other measures (e.g., the QIDS), which suggests that similar results would be produced despite the use of alternate measures (e.g., see Cruz & Manata, 2020; Manata & Grubb, 2022; Manata & Spottswood, 2022).

Second, the sample included more balanced triads than imbalanced triads. This was likely due to two reasons. First, it has been well established that, because cognitive imbalance is uncomfortable, individuals are likely to engage in processes that promote balanced states (e.g., cognitive reorganization). Stated differently, imbalanced states may be difficult to find in practice because people are inclined to avoid them. Second, participants were asked to report on the relational valence of their relationship with their favorite coworker, so this relationship was obviously more likely to be positive than negative, which may have yielded more balanced relations. In either case, the general problem is one of restriction in range,⁶ which tends to attenuate effect sizes (see Schmidt & Hunter, 2015). In this respect, the smaller effect sizes produced in this investigation are unsurprising and may in fact have been stronger if a greater proportion of imbalanced states were introduced into the analysis. Consequently, future research can attempt to mitigate this limitation by a creating a balance variable using different points of reference. For example, scholars can ask participants to report the valence of their relationships with an important or even a randomly selected coworker (and thus avoid the favorite v. least favorite terminology). Presuming such a strategy allows for greater variance in the balance variable, stronger relationships would be expected (Schmidt & Hunter, 2015). However, it is also important to note that, despite this limitation, numerous significant relationships were produced. That is, if anything, the conditions created in this investigation made it more difficult to find evidence for the posited hypotheses, but support was nonetheless procured.

Third, although a certain causal ordering was assumed for the purposes of presenting theoretical arguments and conducting analyses, causality cannot be inferred using cross-sectional data. That is, although it is difficult to predict how the dependent variables (e.g., unproductivity) might engender cognitive (im)balance, in part because three different relationships would need to be impacted in such a way as to create (im)balance, these alternatives cannot be ruled out using these data. As such, scholars are encouraged to test the logic presented herein using longitudinal causal modeling (see Boster, 2012; Hunter & Gerbing, 1982; Manata & Bozeman, 2022). Ultimately, such investigations will help elucidate the causal ordering of the variables tested herein.

Lastly, it is important to note that these results may not generalize to other organizational samples or samples collected with different (non-Qualtrics) platforms. Moreover, although online samples have shown to produce adequate data quality (e.g., Buhrmester et al., 2011; see also Landers & Behrend, 2015), there are still those that question the reliability of such sampling sources (e.g., Chmielewski & Kucker, 2020). Such criticisms usually rest on the premise that online samples produce results that are nonsensical because they contradict commonly established relationships, but that did not occur in this investigation (i.e., results, even if they were insignificant, were generally in the anticipated direction). Nevertheless, scholars are encouraged to reproduce these results using alternate samples across different contexts. Doing so would increase the credibility of the findings produced herein, and it would also heed the advice of recent, general calls for more replication in communication science (e.g., McEwan et al., 2018; see also Schmidt & Oh, 2016).

Conclusion

In conclusion, the results of this investigation indicate that relational balance within organizations is associated with greater job satisfaction, consistent with Heider’s (1946, 1958) theoretical framework. Furthermore, the associations between relational balance and certain outcomes (i.e., unproductivity and depressive symptoms) are moderated by organizational commitment. Hence, these findings suggest the presence of a boundary condition of Heider’s (1946, 1958) initial theoretical framework, such that some effects of balance—at least in organizational contexts—are contingent on levels of organizational commitment. These findings point to the importance of considering the impact of relational patterns within organizations, in addition to considering the impact of any one relationship (e.g., supervisor-subordinate relationship; Graen & Uhl-Bien, 1995). Doing so emphasizes the importance of treating workplace dynamics as multilevel phenomena and provides a better understanding of members’ overall organizational experiences and general wellbeing.

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 iD

Brian Manata

Notes

Author Biography

Brian Manata (PhD Michigan State University, 2015) is an assistant professor in the Department of Communication Arts and Sciences at Pennsylvania State University, University Park, Pennsylvania, USA. His research focuses on organizational behavior, especially as it pertains to groups and teams. His work has appeared in Project Management Journal, International Journal of Project Management, and Human Communication Research.

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