Perceptions of Organizational Tightness–Looseness Moderate Associations Between Perceived Unfair Discrimination and Employees’ Job Attitudes

Abstract

Fusing social psychological theory on the BIAS map and attributions with cross-cultural theory on organizational tightness–looseness, we examine the interactive effects of active/passive facilitation/harm by organizational members and perceptions of organizational tightness on employee job attitudes. Study hypotheses were tested using a sample of bank employees located across 26 branches of a large bank in Addis Ababa, Ethiopia (N = 324). Using a norm–behavior alignment perspective, we hypothesized that (supportive) active facilitation behaviors would be more strongly related to employee attitudes in tight versus loose perceived organizational cultures, whereas (negative) passive facilitation, active harm, and passive harm behaviors would be less strongly related to employee attitudes in tight versus loose perceived organizational cultures. Results provided overall support for these expectations. The present findings have implications for the mitigation of the effects of unfair discrimination on employee attitudes in organizational contexts, theorized associations between cultural T–L and unfair discrimination, and the generalizability of cultural T–L theory to developing country contexts that are typified by collectivistic and tight societal cultures.

Keywords

organizational culture tightness–looseness job attitudes unfair discrimination prejudice Ethiopia

As a result of burgeoning social, cultural, and economic modernization and globalization, workforces the world over have become increasingly diverse, with women and racial, ethnic, and linguistic minorities now constituting a significant share of labor forces the world over (Mor-Barak, 2017). Yet, this increased diversity has not come about without problems. Demography-based unfair discrimination, whether unfairly positive (i.e., overly favorable attitudes and reactions) or unfairly negative (i.e., overly negative attitudes and reactions) is an albatross that impoverishes countries and decreases organizational competitiveness (Mor-Barak, 2017). For example, analysts estimate that negative discrimination against African-Americans has cost the US $16,000,000,000,000 ($16 trillion) over the last 20 years (Peterson & Mann, 2020); increased turnover as a result of negative discrimination against racial and gender-based minorities has been estimated to cost US businesses over $64 billion dollars per year (The Level Playing Field Institute, 2007). The cost of discrimination for organizations is thus crystal clear—lost productivity, loss of talent, and negative employee job attitudes weaken both societies and businesses. Business leaders have responded accordingly, taking concrete steps to build organizational cultures that may help create best diversity management (Mor-Barak, 2017).

Given this focus on building organizational cultures that are receptive to diversity, cross-cultural scholars have sought to shed light on the cultural determinants of unfair discrimination. This stream of thought has identified two cultural dimensions of interest, including individualism–collectivism, and more recently, tightness–looseness (Fiske, 2000; Harrington & Gelfand, 2014; Marcus & Fritzsche, 2016). Of present interest, tightness–looseness (T–L) refers simultaneously to the strength of social norms and the tolerance for norm deviation within cultural groups (Gelfand et al., 2006). Because T–L concerns the relative impermissibility of social norms and the sanctioning of socially deviant phenomena, scholars have theorized an essential link between T–L and unfair discrimination, whereby tighter and less permissive societal cultures have been found to be characterized by less tolerance for diversity and more societal inequality (Gelfand et al., 2011; Harrington & Gelfand, 2014; Uz, 2015). However, the extant research has been limited by a focus solely on associations between societal T–L and social outcomes such as economic freedom and religious diversity (Gelfand et al., 2011; Harrington & Gelfand, 2014; Uz, 2015). Although theorists have posited that T–L may surface at the organizational and individual levels, giving rise to group and individual level differences in T–L that may predict individual attitudes and behaviors related to unfair discrimination in much the same way as when societal cultural differences are considered (Gelfand et al., 2006; Marcus & Fritzsche, 2016), a lack of empirical research on the operationalization and study of T–L at lower levels of analysis hinders insight into the cultural bases of prejudice and discrimination. Illustratively, a recent review of the literature on organizational T–L by Gelfand (2018) provided only anecdotal evidence for the existence of T–L as an organizational-level phenomenon.

Broadly speaking, a lack of research on cultural values in organizational contexts has impeded understanding of how individuals with diverse values can work together and navigate their cultural differences (Kirkman et al., 2017). This is particularly problematic because culture at the group and individual levels of analysis has been found to be much better associated with work outcomes than societal culture (Marcus & Le, 2013), associations between societal culture and individual attitudes and behaviors are overall weak (Taras et al., 2010), and societal culture explains only approximately 1% of the variance in individual level outcomes (Field et al., 2021).

Furthermore, learning about associations between societal T–L and unfair discrimination does little to help business leaders understand how best to shape their own organizational cultures. Although information about societal differences in prejudice and discrimination may help organizational decision makers at the margins, by helping them figure out the particular geographies where unfair discrimination may be most likely to arise, it does not provide insight on concrete steps that organizations may take to mitigate discrimination. Societal cultural values are persistent and difficult to change; on the other hand, organizational cultures are more easily shaped by key decision makers (Aycan et al., 2014). Studying links between T–L and unfair discrimination at the group and individual levels of analysis is thus far more relevant to tackling the essential issue of lost productivity and wealth as a function of unfair discrimination in organizational contexts because organizations are much better positioned to take actionable steps to change their own organizational cultures, as opposed to creating broad societal change.

To the extent that cross-cultural scholars have studied associations between organizational cultures and individual level outcomes, that research has mostly considered only individualistic and loose societal cultures (Kirkman et al., 2017), and that are typified by WEIRD (Western Educated Industrialized Rich Democratic; Henrich et al., 2010) country contexts. Studying phenomena such as T–L in individualistic and loose societal contexts is particularly an issue when the expression of individual values and perceptions are considered—while loose societies encourage the expression of individual values differences, tight societies suppress them (Gelfand, 2018). That is, the study of group and individual level T–L in only “Western” and individualistic–loose country contexts may exaggerate differences found at the individual level because such cultures allow more leeway for individual expression, which would in turn lead to a larger range of responses in the endorsement of key individual values variables. Conversely, the study of these phenomena in collectivistic–tight cultures, where individuals may be expected to adjust their self-perceptions to match the prevailing societal norms, would likely lead to range restriction in such perceptual variables (see Savani et al., 2015, for more on links between individual values expression and societal culture). The study of organizational T–L in collectivistic–tight countries would thus represent a stronger test of cultural T–L theory because it would be more difficult for individual values differences to emerge in such contexts.

The Present Study

Accordingly, the present study responds to the concerns outlined above by studying the confluence between organizational T–L, unfair discrimination, and job attitudes, and as these phenomena coincide within one such non-Western and tight societal cultural context, Ethiopia, involving a sample of employees located across 26 branches of a large state-run bank in Addis Ababa. Based upon research and theory on cultural norm conformity (Gelfand, 2018; Gelfand et al., 2006; Savani et al., 2015), we test the moderating effect of organizational T–L on relations between unfair discrimination and job attitudes, and with the expectation that predictions based upon cultural norm conformity will be upheld in this strong test of T–L theory involving a tight societal culture—where the expression of values differences may be expected to be suppressed.

Because most empirical research on T–L has focused solely on the societal level (see Gelfand, 2018, for a review), the present study also contributes to the literature by being among the first to study the phenomenon as it occurs across small groups. Specifically, the organizational level of culture may include macro contexts involving comparisons across institutions or meso contexts involving comparisons across groups within a single organization; both types of contexts are broadly considered to be representations of organizational culture (Robert & Wasti, 2002). Thereby, we focus on comparisons involving the meso context by comparing across bank branches of a single state-run bank. We do so by adapting Gelfand et al.’s (2011) cultural T–L scale, replacing the word “society” with “organization”; however, because our comparisons involve individual level attitudinal variables such as job attitudes and unfair discrimination, we use the phrase “perceptions of organizational tightness–looseness” to underscore the fact that comparisons occur across individuals and not institutions (see Schwartz, 2011, for a distinction between perceptual and individual vs. concrete and societal operationalizations of culture). To be most precise, because individual employees are nested within bank branches, we utilize the Population Averaged Method (PAM; McNeish et al., 2017), to account for the fact that while the perceptual data are analyzed at the individual level, they are nevertheless embedded within groups. Our investigation of the individual and perceptual aspects of organizational culture thus adds to a nascent but growing body of literature that defines the culture construct as a dynamic, endogenously derived, and bottom-up phenomenon reflecting the beliefs of mutual actors and interactors, as opposed to a relatively immutable and top-down phenomenon existing exogenously of the immediate social context (Venaik & Midgley, 2015).

Following Cuddy et al. (2007, 2008), perceived unfair discrimination is operationalized as the extent to which organizational members are perceived to either behave in supportive ways (active facilitation; i.e., positive discrimination), or in socially distant (passive facilitation), neglectful (passive harm), or hostile ways (active harm; i.e., negative discrimination). We thus operationalize “unfair discrimination” as being either overly positive (i.e., positive discrimination) or overly negative (i.e., negative discrimination). Through the lens of social exchange theory (Blau, 1964), we examine the extent to which both overly positive and overly negative treatment by organizational others may be expected to lead to more or less favorable job attitudes. More precisely, we focus on the two most widely studied job attitudes in the work psychology literature, including organizational commitment, defined as “the relative strength of an individual’s identification with and involvement in a particular organization” (Mowday et al., 1982), and job satisfaction, defined as “a pleasurable or positive emotional state resulting from the appraisal of one’s job or job experiences” (Cranny et al., 1992; see Crede, 2018, for a recent review on job attitudes). The present study thus also contributes to the literature on unfair discrimination by examining the extent to which both positive and negative discrimination may be expected to impact upon employee job satisfaction and organizational commitment.

Last but not least, the present study contributes to theory on cultural T–L by examining the phenomenon as a regulatory mechanism impacting the strength of attitudinal responses to social bias. In this way, we further advance conceptual and empirical understanding of T–L than previous studies that have only studied the extent to which cultural T–L is associated with more or less unfair discrimination (i.e., directional differences; Gelfand et al., 2011; Harrington & Gelfand, 2014; Uz, 2015). We posit that perceived organizational T–L moderates the strength of associations between unfair discrimination and individual attitudes. To investigate this regulatory effect, we utilize attribution theory (Kelley & Michela, 1980; Martinko et al., 2004) to help explain the interplay between individuals’ perceptions of unfair discrimination, groups’ cultural T–L, and job attitudes. Via attribution theory (Kelley & Michela, 1980), we argue that when employees perceive a tight culture, they are more likely to assume that others’ positive behaviors are caused by external contextual pressures, rather than individual agency and benevolence, and thus show a generally weaker attitudinal response to reciprocate these behaviors compared to loose contexts (a weaker association between positive discrimination and job attitudes). Conversely, in tight contexts where behaviors that go against norms of reciprocity and social exchange are more likely to be perceived as a violation of group norms and attributed to individual agency or choice, a greater negative response should be elicited compared to loose contexts for others’ negative behaviors (a stronger association between negative discrimination and job attitudes).

The Ethiopian Sociocultural Context

To the best of our knowledge, there is no published research on cultural dimensions from Ethiopia—it is one of the few countries that has not been included in any of the major worldwide studies of societal cultural differences in recent decades (e.g., Gelfand et al., 2011; Hofstede, 1980; House et al., 2004; Inglehart, 1997; Uz, 2015). Nevertheless, given demographic, geographic, and historical similarities between them, Ethiopia may be considered to be culturally similar to other sub-Saharan East African nations such as Tanzania and Zimbabwe, all of which have been found to be highly collectivistic and tight (Hofstede, 1980; House et al., 2004; Uz, 2015). In terms of social demography, Ethiopia is highly diverse. Ethiopia’s population of approximately 110 million people is very young (median age = 19.5), is majority Christian with a substantial Muslim minority (≈35%), and encompasses over 80 different ethnic groups speaking 90 languages. The four largest ethnolinguistic groups include the Oromo (34.4%), Amhara (27%), Somali (6.2%), and Tigrayan (6.1%). Although peacefully coexisting for much of Ethiopia’s history, tensions have flared amongst these groups in recent decades, with widespread persecution and even genocide against the Tigrayan minority perpetrated by the plurality Oromo group currently making the world news (e.g., Muhumuza, 2021).

To provide more nuance, although the federal constitution of Ethiopia (Federal Constitution Part I, Article 25) clearly stipulates that citizens are equal and free to move and produce wealth anywhere in the country, regional political administration is explicitly awarded on the basis of ethnic group (Aalen, 2006). As a result of such institutional policy devolving sociopolitical power on the basis of ethnicity and along ethno-regional lines, and despite the fact that the Ethiopian constitution grants equal rights to ethnic minorities, ethnic minorities in Ethiopia have become socially marginalized and deprived of access to economic and political resources (Yoshida, 2009). These institutionally sanctioned divisions based on ethnicity have led to intolerance and animosity among the myriad ethnic groups living in Ethiopia. For instance, in the Kafa region of southwest Ethiopia, the Gomoro (Kafa) people distinguish themselves from the Manjo, not including the latter in their own category of “asho” (“people”), and sometimes regarding the Manjo as sub-human (Mengistu, 2003). Ethnic federalism has thus created contentious ethnic conflict in modern Ethiopia, with language policy a highly contentious matter, both historically and under the present political arrangement (Smith, 2013). The risk of discrimination against ethnolinguistic minorities by the dominant majority in Ethiopia is hence a real threat, leading to a social ranking system based on ethnicity (Abbink, 2006; Smith, 2013). Jobs are seen as a key source of such interethnic conflict, with stiff competition among ethnic groups for coveted white-collar jobs (e.g., as bank workers) running rampant (Abbink, 2006).

Summarily, these facts, including a highly diverse population beset by endemic ethnolinguistic conflict, make Ethiopia an ideal country for the study of unfair discrimination in organizations. Following, we delineate theory on social exchange and attributions to gain insight into the confluence of T–L, unfair discrimination, and job attitudes within the Ethiopian context.

The BIAS Map and Expected Associations With Job Attitudes

Behavior Integrated Affect Stereotype (BIAS) map theory posits that discrimination is experienced as a combination of social signals manifested as four distinct behavioral patterns including active facilitation, passive facilitation, active harm, and passive harm (Cuddy et al., 2007, 2008). Active facilitation involves “directly supportive behaviors such as helping, hiring, promoting, befriending, and providing assistance”; passive facilitation entails more aloof support including “tolerating obligatory associations in social or professional settings with people one would not otherwise choose as associates”; active harm involves “intentionally negative and hurtful behaviors such as bullying and harassment”; passive harm involves “neglectful but non-aggressive hurtful behaviors such as being dismissive, disregardful, or limiting access to necessary resources” (Cuddy et al., 2008). Support for the BIAS map’s four dimensions of discrimination has been established across societal cultures including both in relatively tighter (China, Germany, Japan, Portugal, South Korea, and the UK) and looser cultures (Belgium, France, Hong Kong, Netherlands, Spain, and the US; Cuddy et al., 2009; Guan et al., 2010).

From the above, it may be seen that only active facilitation involves overtly supportive behaviors; the other three types of BIAS map behaviors are either hostile (active harm), neglectful (passive harm), or socially distant (passive facilitation). Hence, only the former may be expected to generate positive affective responses and cognitive evaluations of the job situation by employees (job attitudes; Crede, 2018). From an employee perspective, active facilitation by fellow organizational members, such as help and friendship, may thus be expected to elicit positive identification and involvement with the organization and an overall more positive appraisal of the work context because organizational support engenders positive job attitudes (Riggle et al., 2009). In contrast, more distant (passive facilitation), neglectful (passive harm), or even overtly hostile behaviors (active harm) by other organizational members may be expected to generate negative organizational identification and a more negative appraisal of the work context because being treated poorly at work leads to negative job attitudes (Riggle et al., 2009).

That is, social interactions are universally founded on a norm of reciprocity (Blau, 1964). This means that all employees normatively expect to be treated favorably and fairly by their superiors and coworkers in kind (Cropanzano & Mitchell, 2005). Hence, employees expect to be provided support by others and not to be treated in socially distant, neglectful, or hostile ways. The degree to which workplace interactions are in line with these expectations and obligations is critical to the formation of employees’ positive workplace attitudes—including job satisfaction and organizational commitment—toward other individuals and groups in their workplaces as well as their employers (Cropanzano & Mitchell, 2005).

Therefore, given positive treatment by organizational others where active facilitation is concerned, employees may be expected to socially reciprocate by holding more positive job attitudes toward the organization; vice versa for negative and distant (passive facilitation), neglectful (passive harm), or overtly hostile (active harm) treatment by organizational others.

Hypothesis 1a: Active facilitation will be positively associated with organizational commitment and job satisfaction.

Hypothesis 1b: Passive facilitation will be negatively associated with organizational commitment and job satisfaction.

Hypothesis 1c: Active harm will be negatively associated with organizational commitment and job satisfaction.

Hypothesis 1d: Passive harm will be negatively associated with organizational commitment and job satisfaction.

The Moderating Role of Perceived Organizational T–L

Central to the construct of cultural T–L is the notion of norm conformity, whereby tighter cultures dictate more norm conformity and norm adherence (Gelfand et al., 2006). Individuals in tight cultures show less deviation in beliefs (Uz, 2015), and normatively oppositional attitudes and behaviors are more likely to be sanctioned in tight cultures (Gelfand, 2018). Experimental evidence on the prevalence of norm conformity across cultures triangulate toward these findings. Holding norm content constant, Savani et al. (2015) found that Indians (a tight societal culture) were more likely to shift their attitudes than Americans (a loose societal culture) when norms were made salient; Indians also overall displayed more susceptibility to specific norms than Americans. That is, individuals’ attitudes are more susceptible to normative influence in tighter cultures—normatively appropriate behaviors elicit more universal endorsement and normatively inappropriate behaviors elicit more universal backlash in tighter cultural contexts. Relative to those in looser cultures, individuals in tighter cultures would thus have greater expectations that behaviors by relevant others be consistent with norms of social exchange (i.e., “do good,” “do not do bad”). This focus of T–L on norm conformity in the service of socially acceptable norms has implications for individual attitudes as a function of social attributions because culture can also determine individuals’ causal attributions regarding others’ social behaviors.

According to attribution theory (Kelley & Michela, 1980), the salience of strong normative expectancies forms the basis of how individuals interpret the causality in a situation, which in turn largely determines their attitudes, and particularly so when judging the fairness of a situation (Martinko et al., 2004). Internal (person related) versus external (context related) attributions are formed based on the degree of consensus, consistency, and distinctiveness of the signals that are available in a context (Kelley & Michela, 1980). Per these covariation principles, if a given behavior is widespread across members (high consensus), occurs regularly over time (high consistency), and is common across a wide variety of situations (low distinctiveness), then the cause of that behavior is attributed to external factors (e.g., group norms) rather than internal factors (e.g., actor’s desire to help). When an external attribution is made, the praise or the blame is lifted from individual actors and hence its impact on attitudes is diminished. Conversely, internal attributions “heighten affective reactions such as pride for success and shame for failure” (p. 487, Kelley & Michela, 1980).

Viewed in this light, in tight cultures where norm conformity is expected and behaviors that go against social norms of exchange and reciprocity are discouraged or sanctioned, individuals are likely to attribute positive behaviors by organizational others to conformance to the prevalent social norms of the group rather than the agency or benevolence of individuals (Carpenter, 2000). Hence, the positive impact of helping and supportive behaviors (e.g., active facilitation) on job attitudes may likely be lower in tight contexts because such positive behaviors would more likely be attributed to social norms of exchange and reciprocity in organizations (external attributions, Cropanzano & Mitchell, 2005), as opposed to the agency of the individual that provides the help or support. In turn, this dynamic would likely lead to a weaker association between helping behaviors such as active facilitation and job attitudes in tight organizational contexts because employees would simply attribute said behaviors to prevailing social norms. In contrast, in a loose organizational context where norm conformity is not so dominant and individuals have greater leeway to act in ways that they would prefer, helping or supportive behaviors by organizational others may be more likely attributed to the agency of the organizational actor (internal attributions), shaping employee job attitudes about the organization. This line of thinking suggests that, when fellow organizational members behave in normatively expected patterns, employees who perceive their organizational environment as tight would be less likely to exhibit strong work attitudes compared to those who perceive their organizational culture as loose. Thus, we expect that helpful and supportive behaviors by group members (i.e., active facilitation) will be more weakly associated with employees’ job attitudes when the perceived culture is tight rather than loose.

Hypothesis 2a: Associations between active facilitation and job attitudes will be weaker for employees who perceive the organizational culture to be tight than for employees who perceive it to be loose.

Normatively deviant behaviors are those that are socially distant (passive facilitation), neglectful (passive harm), or overtly hostile (active harm, Cuddy et al., 2007, 2008). Such negative behaviors by organizational others go against norms of social exchange and reciprocity in organizations because employees expect to be treated well at work (Cropanzano & Mitchell, 2005). In tight cultures, “failures and negative behaviors might be attributed to individuals’ or subgroups’ choices to engage in non-normative behaviors” (Carpenter, 2000, p. 44), favoring internal rather than external attributions. As such, individuals in tight contexts are more likely to make internal attributions and perceive negative behaviors by others as stemming from their agency, rather than as a result of the context. Thus, behaviors that are deviant from social norms are likely to elicit greater individual backlash when individuals perceive tight organizational contexts. This would lead to a stronger association between deviant behaviors such as passive facilitation and active/passive harm with job attitudes when the organizational context is perceived as tight as compared to loose. In contrast, for individuals in looser organizational cultural contexts, where there is more latitude for behavioral expression and social norms of exchange and reciprocity are not so dominant (Carpenter, 2000), attributions can be equally internal or external, taking into account both the agency of the actors and the specifics of the situation. On average, this would lead to a more variable attitudinal response among individuals in looser cultures as they may be less likely to attribute said negative behaviors to malicious intent or agency on the part of the organizational actor (less likely to make internal attributions regarding negative behaviors).

Hypothesis 2b: Associations between passive facilitation and job attitudes will be stronger for employees who perceive the organizational culture to be tight than for employees who perceive it to be loose.

Hypothesis 2c: Associations between active harm and job attitudes will be stronger for employees who perceive the organizational culture to be tight than for employees who perceive it to be loose.

Hypothesis 2d: Associations between passive harm and job attitudes will be stronger for employees who perceive the organizational culture to be tight than for employees who perceive it to be loose.

Method

Participants and Procedures

Study participants were employees of a large Ethiopian bank. The sampling pool was defined to include bank branches located in the capital, Addis Ababa (290 bank branches), because this city represents the most cosmopolitan and politically stable region in the country. Every kth = 11th branch (with the first k selected using random lottery) of the 290 bank branches located in the capital was selected, for a total of 26 bank branches sampled. The third author visited the 26 branches and met with potential participants, explained the purpose of the research, and provided detailed survey completion instructions. All participants were provided with informed consent and assured anonymity in survey completion, with their completed surveys enclosed in sealed envelopes; a designated branch contact person then collected these envelopes for pick-up by the third author. In total, 450 surveys were distributed to participants at these 26 bank branches, with a 73% response rate obtained for completed surveys (N = 324), including between 8 and 23 responses collected per branch. All data were collected in April and May 2019.

Fifty seven percent of participants were male, 55.4% were single, and 34% were married. Reflecting the demographically young nature of Ethiopia (median age = 19.5), approximately 9% of participants were between 18 and 24 years old, 50% were between 25 and 29 years old, 25% were between 30 and 34 years old, 9% were between 35 and 39 years old, and 7% were over 40 years old. Approximately 97% of participants were employed full-time and 75% had worked at the bank for at least 3 years. 85.6% of participants identified as Christian (72.7% Orthodox; 12.2% Protestant; 0.6% Catholic), 5.3% identified as Muslim, and 5.3% identified as belonging to “Other” religious affiliations. The majority of participants identified as being from the Amharic ethnolinguistic group (65.5%), followed by Oromo (14.7%), Tigrayan (3.1%), and other minor ethnic groups (2.8%). Perhaps owing to the sensitivity of ethnolinguistic relations in Ethiopia, a substantial number of participants (13.8%) declined to report ethnicity.

Measures

The official language of education at both the secondary and tertiary levels in Ethiopia is English; knowledge of English is also a condition for hire at the bank. As such, all survey items were administered in English, using the original scales.

Perceived unfair discrimination

Perceived unfair discrimination was measured using the 31-item BIAS Treatment Scale by Sibley (2011), which consists of active facilitation, passive facilitation, active harm, and passive harm dimensions. A 7-point scale anchor (1 = “have never experienced this”; 7 = “have often experienced this”) was utilized. Participants were instructed to rate their subjective experiences of bias at their workplace based on “how often people in your organization act toward you in each of the following ways.” Sample items are “volunteer to help you out” (active facilitation); “treat you with respect but avoid socializing with you” (passive facilitation); “act in a threatening manner toward you” (active harm); “tell you what you should do despite what you might want” (passive harm).

Organizational commitment

Organizational commitment is a multidimensional construct that includes affective commitment (reflecting whether participants remain with the organization because they want to), normative commitment (reflecting whether participants remain with the organization because they feel they ought to), and continuance commitment (reflecting whether participants remain with the organization because they need to; Allen & Meyer, 1990). As such, 13 items from the organizational commitment scale by Meyer et al. (1993) were used, with scale points ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). Sample items are “I really feel as if this organization’s problems are my own” (affective commitment); “I would feel guilty if I left my organization now” (normative commitment); “It would be very hard for me to leave my organization right now, even if I wanted to” (continuance commitment).

Job Satisfaction

Job satisfaction was measured using the 3-item job satisfaction scale from the Michigan Organizational Assessment Questionnaire (Cammann et al., 1979), with scale points ranging from “strongly agree” (1) to “strongly disagree” (5). A sample item is as follows: “All in all, I feel satisfied with my job.”

Perceptions of organizational tightness–looseness

Five items¹ from the cultural tightness–looseness scale by Gelfand et al. (2011) were adapted to measure perceptions of organizational T–L by using the word “organization” in place of “society” (e.g., “In this organization, if someone acts in an inappropriate way, others will strongly disapprove”). Scale points ranged from “strongly disagree” (1) to “strongly agree” (6); higher scores indicate more perceived organizational tightness.

Results

We used structural equation modeling (SEM) to test the latent interaction effects of harm/facilitation behaviors and organizational tightness perceptions on employees’ work attitudes. This method affords more power in the analyses given the possibility to control for different kinds of random and nonrandom measurement error and produces more accurate parameter estimates (Bollen, 1989). Furthermore, to account for the clustering effects of branches, we utilized CR-SEs (cluster robust-standard errors) using the population-averaged method (PAM; McNeish et al., 2017), using the TYPE = COMPLEX option and MLR estimator (maximum likelihood estimation with robust standard errors) in Mplus 8.3. Because we utilize SEM methodology, latent correlation coefficients between all study variables are displayed in Table 1 (as recommended by Jackson et al., 2009), alongside descriptive statistics and alphas calculated using the observed variables. As shown, the correlations are generally in line with theory and all scales are internally reliable. Because the percentage of missing data was very low (0.60%), a full information maximum likelihood (FIML) method was used to handle missing data as this method is least prone to bias (Schafer & Graham, 2002).

Table 1.

Descriptive Statistics and Intercorrelations of Latent Variables.

Variable	M	SD	α	1	2	3	4	5	6	7	8
1. Organizational tightness	3.96	1.00	.75	–
2. Active harm	3.25	1.19	.81	−.07	–
3. Passive harm	3.86	0.98	.77	−.75^**	.22^**	–
4. Active facilitation	4.56	1.05	.84	−.10	.39^**	.43^**	–
5. Passive facilitation	4.00	1.00	.78	.63^**	.71^**	.17^**	.13	–
6. Affective commitment	4.24	1.11	.73	−.33^**	−.19^**	−.04	−.27^**	.05	–
7. Normative commitment	4.06	1.16	.72	.16^*	.35^**	.26^**	.15^*	.17^*	.28^**	–
8. Continuance commitment	4.02	1.03	.73	.10	.13	.19^**	.12	−.07	.42^**	.16^*	–
9. Job satisfaction	3.00	1.13	.79	−.11	.06	.25^**	−.01	.20^**	.51^**	.17^**	.25^**

Notes. N = 324. Means, standard deviations, and Cronbach’s α values are provided based on the observed variables.

p ≤ .05. ^**p ≤ .01.

Factor Structures and Model Fit

The hypothesized factor structures were tested by running a confirmatory factor analysis (CFA) model that included all study variables. According to conventionally accepted cutoff criteria, a model is considered to have an acceptable level of model fit if CFI is at or above 0.90, RMSEA is at or below 0.06, and SRMR is at or below 0.10 (Hu & Bentler, 1999). Results showed that the measurement model was within acceptable ranges for two of the three fit indices (χ²₍₁₁₉₃₎ = 1,969.61, p ≤ .01; CFI = 0.84; RMSEA = 0.05; SRMR = 0.08), excepting a low level of CFI, which is likely to be influenced by a relatively small sample size given model complexity (Hu & Bentler, 1999). A nested χ² difference test also indicated that decreasing the number of factors resulted in poorer fit. The standardized loadings of the items onto the hypothesized factors all exceeded 0.40 and were all statistically significant (p ≤ .01).

Analysis of Common Method Variance Effects

Because our study data are cross-sectional and single source, the potential influence of common method variance (CMV) on the findings were evaluated by conducting a marker variable CFA. We used participants’ responses about their religious affiliation as a marker because it is a variable that is not theoretically expected to be associated with relations between organizational T–L and job attitudes. The details of our analyses are provided in the Supplemental Materials. The results show that while there is some degree of CMV in the data where facilitation behaviors are concerned, it did not significantly bias factor correlation estimates in the hypothesized model.

Tests of Study Hypotheses

Study hypotheses were tested by specifying latent interaction models using TYPE = RANDOM COMPLEX and ALGORITHM = INTEGRATION in MPlus. Results are displayed in Table 2. Odd-numbered models in Table 2 provide estimates of model fit for the linear baseline structural equation models without the latent interaction terms, computed using the TYPE = COMPLEX specification.² As shown, fit statistics reported in the odd numbered models in Table 2 indicate generally acceptable levels of fit for the hypothesized models.

Table 2.

Structural Equation Model Results.

Independent variables and model statistics	Dependent variables
	Affective commitment				Normative commitment				Continuance commitment				Job satisfaction
	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12	M13	M14	M15	M16
Tightness	0.10	0.12	−0.02	−0.01	0.18†	0.15	0.15	0.15	0.09	0.08	0.17	0.15	0.17^**	0.18^**	0.17†	0.20†
AcFac	−0.04	−0.04			0.16	0.16			0.15^†	0.14			0.19^**	0.19^**
PasFac	−0.27^**	−0.25^**			0.10	0.08			0.19	0.08			−0.06	−0.06
AcFac × Tightness		0.12^*				−0.18^**				−0.04				0.04
PasFac × Tightness		−0.03				0.12				0.03				0.01
AcHa			−0.44^**	−0.41^**			−0.14	v0.13			0.10	0.14			−0.28^*	−0.26^*
PasHa			0.14	0.13			0.43^*	0.43^*			0.01	−0.01			0.22	0.21
AcHa × Tightness				−0.23^†				−0.10				0.05				−0.13
PasHa × Tightness				0.19				−0.03				−0.23^*				0.17^*
R-squared (DV)	0.08	0.09	0.12	0.13	0.10	0.13	0.16	0.18	0.05	0.05	0.04	0.08	0.09	0.10	0.09	0.10
Model fit
χ ²	359.68		323.50		463.97		452.17		431.48		403.60		351.52		322.48
df	245		222		292		267		292		267		223		201
CFI	0.93		0.93		0.91		0.89		0.92		0.92		0.92		0.92
RMSEA	0.04		0.04		0.04		0.05		0.04		0.04		0.04		0.04
SRMR	0.06		0.05		0.07		0.06		0.06		0.06		0.06		0.06

Notes. N = 324. All multi−indicator constructs were modeled as latent variables. Traditional fit indices are not available for models with latent product terms. AcHa = active harm; PasHa = passive harm; AcFac = active facilitation; PasFac = passive facilitation; CFI = comparative fit index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.

†

p ≤ .10. ^*p ≤ .05. ^**p ≤ .01.

Hypotheses 1a to 1d—main effects

Results indicating the coefficient estimates of the main effects (Hypotheses 1a–1d) are presented in the odd-numbered models in Table 2. As hypothesized, active facilitation (H1a) is positively associated with job satisfaction (M13; β = .19, p = .02), passive facilitation (H1b) is negatively associated with affective commitment (M1; β = −.27, p = .00), and active harm (H1c) is negatively associated with both affective commitment (M3; β = −.44, p = .01) and job satisfaction (M15; β = −.28, p = .05). The main effect of passive harm on normative commitment is significant but, in contradiction with our expectations, it is in the positive direction (M7; β = .43, p = .03). These results indicate partial support for Hypothesis 1a (active facilitation), Hypothesis 1b (passive facilitation), and Hypothesis 1c (active harm), but no support for Hypothesis 1d (passive harm). Overall, results support H1, upholding the theorized directions of effects between supportive (active facilitation) and negative behaviors (passive facilitation and active harm) with job attitudes.

Hypotheses 2a and 2b—facilitation interactions

Results indicating the coefficient estimates of the interactive effects (Hypotheses 2a–2d) are presented in the even-numbered models in Table 2. As shown, the active facilitation × tightness product terms are found to be significant for affective (M2; β = .12, p = .05) and normative commitment (M6; β = −.18, p = .01). Results for active facilitation are depicted using Johnson–Neyman plots³ in Figure 1a and b. As hypothesized, the conditional effects⁴ of active facilitation on affective (Figure 1a) and normative commitment (Figure 1b) are statistically significant (p ≤ .05) for employees who perceive loose cultures (for affective commitment: negative for β ≤ −1.0; for normative commitment: positive for β ≤ −.4), but not significant for those who perceive tight cultures. These results provide partial support for Hypothesis 2a, suggesting that the association between active facilitation and organizational commitment is weaker for employees who perceive the organizational culture to be tight than for employees who perceive it to be loose. However, none of the passive facilitation × tightness product terms are significant, indicating a lack of support for Hypothesis 2b.

Figure 1.

(a) Johnson−Neyman plot of active facilitation × organizational tightness interaction on affective commitment. Solid line indicates the conditional slope of affective commitment; dashed lines indicate upper and lower 95% confidence intervals. (b) Johnson−Neyman plot of active facilitation × organizational tightness interaction on normative commitment.

Hypotheses 2c and 2d—harm interactions

As shown in Table 2, the active harm × tightness coefficients fall short of the conventionally accepted α = .05 level of significance. A marginally significant interactive effect is found only for affective commitment (M4; β = −.23, p = .07), but not for other types of work attitudes. However, given statistical power issues associated with detecting interactive effects (and as recommended by Aguinis, 1995), we tentatively interpret this interaction. As shown in Figure 2, the association between active harm and affective commitment is negative and statistically significant (p ≤ .05) for employees who perceive tight cultures (for β ≥ −.2). Conversely, the association is not statistically significant for those who perceive loose cultures. These results provide partial support for Hypothesis 2c, suggesting that the association between active harm and organizational commitment is stronger for employees who perceive the organizational culture to be tight than for employees who perceive it to be loose.

Figure 2.

Johnson−Neyman plot of active harm × organizational tightness interaction on affective commitment.

The passive harm × tightness coefficients are significant for continuance commitment (M12; β = −.23, p = .05) and job satisfaction (M16; β = .17, p = .05). As shown in Figure 3a, the association between passive harm and continuance commitment is statistically significant and negative only for employees who perceive tight cultures (β ≥ .9). Similarly, as shown in Figure 3b, the conditional effect of passive harm on job satisfaction is statistically significant and positive for employees who perceive high levels of tightness (β ≥ .6) but not looseness. These results provide partial support for Hypothesis 2d, indicating that associations between passive harm and job attitudes are stronger for employees who perceive the organizational culture to be tight than for employees who perceive it to be loose.

Figure 3.

(a) Johnson−Neyman plot of passive harm × organizational tightness interaction on continuance commitment. (b) Johnson−Neyman plot of passive harm × organizational tightness interaction on job satisfaction.

Summary evaluation of hypothesis 2

Summarily, results indicate some support for Hypotheses 2a (active facilitation), 2c (active harm), and 2d (passive harm). As hypothesized, associations between active facilitation and job attitudes were overall weaker in tight compared to loose cultures, whereas associations between active and passive harm with job attitudes were overall stronger in tight compared to loose cultures. This pattern of associations broadly supports Hypothesis 2.

Discussion

Introducing the construct of cultural T–L as a phenomenon that occurs at both the societal and organizational levels of analysis, Gelfand et al. (2006) emphasized the role of organizational T–L as a representation of situational strength, with tighter cultures creating more normative constraints in social situations (i.e., typified by more rules and norms). Yet, and perhaps as a result of a focus solely on societal culture, research on discrimination and T–L has studied only directional differences, suggesting more/less prejudice and unfair discrimination in tight versus loose societal cultures (Gelfand et al., 2011; Harrington & Gelfand, 2014; Uz, 2015). The present study thus represents a refocusing of T–L more in keeping with the original theorization surrounding this organizational cultural dimension by Gelfand et al. (2006), as a contextually bound norm salience factor impacting the strength, as opposed to the directionality, of associations between unfair discrimination and individual attitudes.

Crucially, and drawing upon theory on culture and norm conformity (Gelfand et al., 2006; Savani et al., 2015), we posited and found support for the notion that norm-consistent and supportive behaviors such as active facilitation would be less strongly associated with individual job attitudes in organizationally tight cultures where normatively consistent behaviors may be an expectation; in contrast, norm-inconsistent and discriminatory behaviors such as active/passive harm were found to be more strongly associated with said attitudes in tight cultures. The present results thus suggest that, contrary to the conventional logic that prejudice may be more prevalent in tighter cultures (e.g., Gelfand et al., 2011; Harrington & Gelfand, 2014; Marcus & Fritzsche, 2016; Uz, 2015), it is in precisely such tighter cultures that the most backlash to prejudice may happen. That is, the present findings suggest it is possible that cultural T–L may in fact be a boundary condition mitigating the potential for unfair discrimination to occur, providing normatively defined sanctions (e.g., unfavorable job attitudes) for norm-inconsistent and (negative) unfairly discriminatory behaviors. Future research is needed in this regard.

The directionalities of associations between active/passive facilitation/harm and job attitudes also deserve comment. Theoretically consistent patterns of associations were found for the more overtly supportive/harmful behaviors of active facilitation/harm; in contrast, counterintuitive or even non-existent associations were found for passive forms of facilitation and harm. To the extent that more covert forms of discrimination such as passive facilitation and harm may be less subject to attitudinal backlash by their intended targets, it is possible that treatments to reduce prejudice and unfair discrimination (e.g., Gaertner & Dovidio, 2000) may prove less efficacious in such instances. If so, then covert and passive forms of discrimination such as microaggression and passive–aggressive behaviors may present bigger challenges for scientists studying unfair discrimination at work. Future research is needed here too.

The present findings also replicate previous research on unfair discrimination and T–L, based mostly in WEIRD countries, to a chronically understudied country, Ethiopia. To the best of our knowledge, there is no published research investigating either the BIAS map behaviors (Cuddy et al., 2008, 2009) or cultural T–L (Gelfand et al., 2011) in Ethiopia. Given that theoretically expected patterns of associations between these phenomena and job attitudes overall replicated, and given some support for the hypothesized interactions between organizational T–L, active/passive facilitation/harm, and job attitudes, the present results extend generalizability evidence on cultural T–L and the BIAS map to the Horn of Africa.

To the extent that Ethiopia’s population consists of many migratory ethnolinguistic communities that work in large cities, resembling those of many WEIRD countries such as France, the UK, and the US, the present results are able to help inform the science of unfair discrimination in such countries too. Notably, the present findings suggest that the effects of unfair discrimination in organizations within such countries may be mitigated by building looser organizational cultures that are better able to buffer the effects of negative behaviors by organizational actors on employee job attitudes. If so, then, a key take-home point for cross-cultural practitioners may be that one approach toward building organizational cultures that best enable effective diversity management may be to create cultures that are flexible, informal, and typified by less norm conformity (i.e., loose organizational cultures).

Limitations and Directions for Future Research

Although overall support was found for the posited moderating effects of organizational T–L on associations between perceived unfair discrimination and job attitudes, we only measured perceived T–L and not actual tight or loose organizational practices (see House et al., 2004, for a distinction between organizational cultural values vs. practices). This leaves open the possibility that different patterns of associations may be found if one were to use concrete and behaviorally-bound operationalizations of cultural T–L. Indeed, this values–practices distinction is an important schism within cross-cultural science, with actual cultural practices representing arguably more impactful operationalizations of culture. If so, then, given more psychologically meaningful behaviors, or cultural practices, it may logically be expected that the presently found effects may in fact be even more robust. That is, it is possible that the present findings represent a conservative estimate of the moderating effects of organizational T–L, with even more robust patterns of associations expected if objective organizational T–L practices were to be employed. Future research is needed to better evaluate these notions regarding organizational T–L practices.

Further, the present study only investigated generalized types of discriminatory behaviors such as active/passive facilitation/harm. Although useful, these results do not speak to more explicit forms of discrimination such as racial, ethnic, or religious prejudice. We had attempted to include this latter conceptualization of discrimination by asking participants to record their mother tongue, because unfair discrimination in Ethiopia has historically been directed toward members of the Oromo ethno-linguistic group by the more socioeconomically dominant Amhara and Tigrinya ethno-linguistic groups (Abbink, 2006). However, and perhaps as a result of the politically charged environment regarding ethnicity in Ethiopia (Muhumuza, 2021), a plurality of respondents either did not answer this question or were determined to have given potentially (false) and socially desirable information (e.g., by stating their mother tongue as “Amhara” even though it would likely not have been the case, given the location and overall employee composition of a given bank branch in Addis Ababa). Future research on culture and unfair discrimination in societal contexts where race, religion, or ethnicity may be politically volatile, such as Ethiopia, may perhaps thus benefit from the utilization of unobtrusive research methods that are less susceptible to social desirability bias.

Finally, although the theorized factor structures of all study constructs were borne out in our data and evidenced good fit, it is arguable that two BIAS map behaviors, including passive facilitation and passive harm, may not clearly represent unfair discrimination in some cultural contexts. For example, the passive facilitation item “treat you with respect but avoid socializing with you” could be construed in highly power distant cultural contexts as appropriate when applied to people in higher organizational positions, if only to avoid having social interactions with such “superior” others (i.e., to convey respect). Similarly, the passive harm item “tell you what you should do despite what you might want” can also be interpreted in a different manner—although this sort of behavior may be perceived as “harm” in cultures where personal desires and wants are prioritized (individualistic cultures), it is not necessarily harmful from the perspective of a larger collective for individuals to sacrifice personal interests for the group (collectivistic cultures). Indeed, given documented evidence that sub-Saharan African countries are overall more power distant and collectivistic (Hofstede, 1980; House et al., 2004), it is possible that our (sub-Saharan African) Ethiopian respondents may not have perceived passive facilitation or passive harm behaviors as unfairly discriminatory. Given this ambiguity of interpretation at the individual level for our sample of Ethiopian workers, we suggest that the present results involving passive facilitation and harm be treated with caution. Indeed, this limitation underscores the difficulty of adapting theory and concepts derived by scholars residing in WEIRD countries to understudied and culturally different contexts such as the Horn of Africa. More research is needed to understand how best to operationalize passive facilitation and harm in collectivistic and high power distance societies.

Conclusion

Via utilization of a sample of Ethiopian bank employees located across 26 branches of a large bank in the Ethiopian capital, Addis Ababa, our study is the first to investigate the joint effects of organizational tightness–looseness (T–L) and perceived unfair discrimination on job attitudes. Results largely evidenced support for study hypotheses on active/passive facilitation/harm on employees’ job attitudes, and on the moderating role of perceptions of organizational T–L on these associations. The present study thus offers new insights into the confluence of T–L, job attitudes, and unfair discrimination in organizational contexts, suggesting that loose as opposed to tight organizational cultures may be best positioned to mitigate the negative effects of unfair discrimination on employee job attitudes.

Supplemental Material

sj-docx-1-jcc-10.1177_00220221221077376 – Supplemental material for Perceptions of Organizational Tightness–Looseness Moderate Associations Between Perceived Unfair Discrimination and Employees’ Job Attitudes

Supplemental material, sj-docx-1-jcc-10.1177_00220221221077376 for Perceptions of Organizational Tightness–Looseness Moderate Associations Between Perceived Unfair Discrimination and Employees’ Job Attitudes by Justin Marcus, Eda Aksoy and Gashaw Tesfa Alemu in Journal of Cross-Cultural Psychology

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

Justin Marcus

Supplemental Material

Supplemental material for this article is available online.

Notes

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