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Abstract

  1. Top of page
  2. Abstract
  3. Theoretical Background
  4. Method
  5. Results
  6. Discussion
  7. Limitations, Future Research Directions, and Conclusions
  8. REFERENCES

This article considers the efficacy of matching the racioethnicity of employees and the customer base as a human resource strategy within service organizations. Despite being advocated widely, the literature on its effectiveness is scant and riddled with conflicting findings. We revisit the theoretical rationale underlying this strategy, formulate new theory, and introduce the demographic representativeness construct (i.e., the congruence between employee and customer base profiles) to the organizational literature to test our hypotheses. Using multisource data pertaining to 739 stores of a U.S. retailer, the results indicate a positive effect of racioethnic representativeness on productivity, which is accounted for by improved customer satisfaction. Moreover, additional analyses showed this indirect relationship to be more pronounced in stores with larger minority customer bases.

Scholars have devoted considerable attention to understanding the relationship between employee demographic diversity and organizational performance yet have detected no consistent pattern (Jackson, Joshi, & Erhardt, 2003; Kochan et al., 2003; van Knippenberg & Shippers, 2007). The failure to find a stable relationship has led many authors to consider moderators such as the context in which the diversity appears (e.g., Joshi & Roh, 2009; Richard, Murthi, & Ismail, 2007). This tact is quite reasonable, as inconsistent findings or failures to find empirical support for theory often indicate the existence of unidentified boundary conditions. Although unrecognized moderators are a potentially viable explanation, the equivocal findings regarding the diversity--performance linkage also call for reassessment of the underlying theoretical premise linking the two variables.

One reason diversity is believed to influence performance is the expectation that it will help organizations reach and serve a more diverse customer base (Cox & Blake, 1991). Thomas and Ely (1996; Ely & Thomas, 2001) termed this rationale the access-and-legitimacy perspective and described it in the following manner:

An access-and-legitimacy perspective on diversity is based in a recognition that the organization's markets and constituencies are culturally diverse. It therefore behooves the organization to match that diversity in parts of its own workforce as a way of gaining access to and legitimacy with those markets and constituent groups (Ely & Thomas, 2001: 243).

Essentially, this paradigm proposes that correspondence between the customer and organizational demographic profiles influences performance. It is important to acknowledge, however, that this correspondence (i.e., match) is not a measure of diversity per se, as higher correspondence actually entails less diversity when a customer base is homogeneous. Though matching is not directly about diversity, it does align nicely with several existing micro level theoretical frameworks (e.g., social identity theory, relational demography, similarity-attraction hypothesis). Nevertheless, empirical tests have proven unsupportive, with research at both the micro (e.g., Cunningham & Sagas, 2006; Jones, Moore, Stanaland, & Wyatt, 1998) and macro levels (e.g., Leonard, Levine, & Joshi, 2004; Sacco & Schmitt, 2005) failing to provide consistent support for the view that matching matters.

This calls into question the validity of the access-and-legitimacy paradigm and the logic used to support it. If employee--customer similarity supposedly enhances firm performance, why do studies fail to consistently support this relationship? Here, we seek to answer this question by providing an altogether different rationale for this linkage and empirically testing our theory. Specifically, we review the prior literature pertaining to demographic matching and consider the limitations of the theory used to support the anticipated relationships. To address these shortcomings, we (a) present a more macro framework explicating a higher-order effect of matching and (b) incorporate the demographic representativeness construct from the public administration literature (i.e., the demographic profile match of the unit's personnel and those of a referent population; Pitts, 2005, 2007) to help shed new light on the effects of matching. Using time-lagged, multisource data from more than 700 business units of a large national retailer, we examine the impact of the racioethnic representativeness of a unit's personnel (relative to the racioethnic profile of its customers) on unit productivity (i.e., total earnings before interest and taxes divided by the number of employees).

In the sections that follow, we articulate why matching should influence productivity, paying particular attention to prior studies that have proposed and tested similar relationships to explain why our results should differ. The goal is not to criticize or disparage those studies in any way, as they have contributed significantly to the current understanding of the effects of diversity. Rather, the contrasts are designed to clarify how representativeness influences organizational outcomes. Like Sacco and Schmitt (2005: 207) before us, we restrict our focus to racioethnicity in part “because race has received the most attention in this regard, and also because we think it is most salient.” Moreover, prior research supports this approach, as racioethnicity appears to be a more salient and powerful basis for categorization, segregation, and mistreatment than are other demographic categories (e.g., Ito & Urland, 2003; Levin, Sinclair, Veniegas, & Taylor, 2002; McCall & Simmons, 1978; McPherson, Smith-Lovin, & Cook, 2001).

Theoretical Background

  1. Top of page
  2. Abstract
  3. Theoretical Background
  4. Method
  5. Results
  6. Discussion
  7. Limitations, Future Research Directions, and Conclusions
  8. REFERENCES

Dyadic Approaches to Matching

People tend to categorize themselves and others using readily observable cues like racioethnicity (Turner, 1987). Doing so provides the basis for the formation of in- and outgroups according to similarity and dissimilarity along these dimensions, respectively. Because people are motivated to feel positively about themselves and the social groups of which they are members, they tend to view similar others more favorably than dissimilar others (Tajfel & Turner, 1986). Consequently, similarity facilitates attraction (Byrne, 1971), and individuals tend to prefer and be more comfortable in settings containing greater proportions of their in-group members (Tsui & Gutek, 1999). Extending this logic to the employee--customer interface leads to the prediction that customers and employees will be more attracted to one another when they are of the same racioethnic group. Given the importance of interpersonal attraction in sales settings (Reinhard, Messner, & Sporer, 2006), greater racioethnic similarity between salespeople and prospective buyers also should heighten employee productivity.

Despite the intuitive appeal of this logic, dyadic tests of it have not supported the notion that similarity promotes business success or its precursors. For example, field studies examining the efficacy of racioethnic matching have produced conflicting results (Juni, Brannon, & Roth, 1988; Martin & Adams, 1999; McCormick & Kinloch, 1986; Page, 1997). Observing customer checkout behavior in grocery stores, McCormick and Kinloch (1986) found that race had no significant effect on customers’ choice of clerk, though clerks were rated (by observers) as friendlier when interacting with customers from their racial group. To the contrary, there was a significant racial preference effect in fast-food restaurants and banks, with the authors reporting that “black customers preferred black cashiers while white customers preferred white cashiers” (Juni et al., 1988: 71). This finding was replicated subsequently among cafeteria patrons and medical patients, who were significantly more likely to select a racioethnically similar cashier or physician over a dissimilar one (Laveist & Nuru-Jeter, 2002; Malat & Hamilton, 2006; Page, 1997). By contrast, research indicated that employee propensity to thank customers was virtually unaffected by racioethnic similarity, and such similarity also had no influence on relationship strength between hairdressers and clients (Martin & Adams, 1999). However, as true field studies, none of these investigations provided the experimental control necessary to attribute their findings to racioethnic matching.

Subsequently, researchers have utilized more controlled designs to examine the efficacy of racioethnic matching on individual behavior, yet these studies have proven no more effective in providing clarity. In one study, 107 White professional buyers rated sketches of a hypothetical client, whose race and gender were manipulated, and indicated White male salespeople were most likeable (Henthorne, LaTour, & Williams, 1992). More recently, however, a similar study using an undergraduate sample found Black salespeople were rated as significantly more likeable, trustworthy, and higher in expertise than White salespeople even after controlling for the effects of social desirability (Jones et al., 1998). Further, they examined the interaction between seller race and buyer race and found that race matching did not produce greater perceptions of the salesperson's credibility.

Racioethnic Group-Level Approaches to Matching

The preceding findings suggest there might be no simple dyadic relationship between buyer--seller racioethnic similarity and performance. This could imply that similarity's impact on the bottom line is less direct than researchers previously thought. For instance, customers may be attracted to a store initially by racioethnically similar store personnel or decisions made by these employees (e.g., product offerings and placement) but could end up interacting with and purchasing from a dissimilar salesperson. In such a scenario, similarity would have influenced performance, but this relationship would not be reflected necessarily at the dyadic level. One way of assessing this possibility could be to look at the similarity between the level of a racioethnic group's representation within the store and that within the customer base.

A few recent studies have attempted such a strategy. Leonard, Levine, and their colleagues (Leonard, Levine, & Guiliano, 2010; Leonard et al., 2004) examined the interactive effects of racioethnic group proportions in retail stores and their surrounding communities on unit sales. This interactive approach is one means of testing the effects of similarity (Riordan, 2000), as the effect of having more Hispanic employees on performance should be greater when there are more Hispanic customers if, indeed, similarity sells. Their results, however, yielded little support for matching with one exception: Stores in predominately Asian areas exhibited higher sales when they employed a greater number of Asian employees. Sacco and Schmitt (2005) conducted a similar analysis using restaurants. Though they found two significant interactions, their interpretation of these effects led them to conclude that “the pattern of results does not support the notion that profitability is higher when a group has a high representation in both the restaurant's workforce and the zip code” (p. 217).

These demographic group-level, interactive approaches should be more likely than the dyadic analyses to reveal the effects of racioethnic similarity, but they also fail to consider the totality of why matching matters. For example, this approach considers the similarity of each group's proportions in isolation from one another. If one group is overrepresented in the store relative to its proportion in the customer base, customers from this group should be attracted. Simple math tells us, however, that for one group to be overrepresented, at least one other group must be underrepresented, which should repel customers belonging to that group. Depending on the relative group sizes, this could produce offsetting effects on overall sales, such that an increase in sales among one racioethnic group is masked by a decrease in sales among another. Moreover, examining interactions between store and customer base characteristics means including product terms of often highly correlated variables because the surrounding community typically is the source of both the customer base and labor for the organization (Fields, Goodman, & Blum, 2005; Holzer & Ihlanfeldt, 1998). Interpreting such product terms can lead to erroneous conclusions such as mistaking a curvilinear effect for an interaction or multicollinearity masking a significant interactive effect (Baron & Kenny, 1986; Cortina, 1993).

Beyond these statistical reasons for the failure of prior approaches, there are also theoretical explanations. First, both the dyadic and group-level approaches assume that similarity is the only salient racioethnic cue of importance to individual, prospective consumers. This ignores the possibility that a customer base (as a collective) takes note of the relative composition of employees of all racioethnic groups. Second, and perhaps more importantly, the prior approaches do not account for effects that employee heterogeneity could have on the level of service provided to a diverse client base. Research on the contact hypothesis illustrates that when individuals engage in structured contact with racioethnically dissimilar, but otherwise equal status others, their attitudes toward members of these groups tend to become more positive (Pettigrew & Tropp, 2006). Applied here, this suggests that when dissimilar employees work together, they may become more open to interactions with members of other racioethnic groups, which should prove beneficial when it comes to serving racioethnically dissimilar customers. Accordingly, we develop a more macro theoretical explanation for a racioethnic matching—productivity relationship by introducing demographic representativeness to the organizational sciences literature and logically linking it to the bottom line.

A Representativeness Approach to Racioethnic Matching

When there is a high degree of correspondence between the racioethnic distributions of employees and clientele, an organization's personnel could be said to be representative of its customer base (Pitts, 2005, 2007). Although Pitts (2005, 2007) introduced the representativeness construct in the public administration literature to help explain the effects of diversity in schools, the formula he created is ideal for our purposes because it compares the entire distribution of a sample to that of a target population simultaneously on a particular nominal demographic marker (with higher scores indicating greater congruence). It is this comparative property that differentiates representativeness (a measure of relative composition) from diversity (an absolute measure). Moreover, by concurrently comparing the relative proportions of all racioethnic groups inside and outside the organization, representativeness provides a comprehensive assessment comparing distributions as a whole that is not possible using interactive approaches like those discussed above or polynomial regression.

An example will help to clarify and differentiate the representativeness construct. Suppose that one retail store contains an employee demographic profile of 75% White, 12% Black, 11% Hispanic, and 2% Asian whereas a competitor located across the street employs personnel who are 90% White, 3% Black, 5% Hispanic, and 2% Asian. As direct competitors, the two stores share a customer base that is 66% White, 17% Black, 13% Hispanic, and 4% Asian. Applying typical approaches to examining the effects of matching on firm performance, a researcher would simply construct interaction terms comprised of pairs of the employee and customer racioethnic proportions per racioethnic group (i.e., White employee percentage × White customer percentage, Black employee percentage × Black customer percentage, etc.) for each store. Though such analyses would provide some measure of insight about the effects of matching employees and customers belonging to a single racioethnic group on firm organizational performance, they fail to consider the overall racioethnic profile of a business relative to its consumer base. In contrast, using the formula provided in the method section, we calculate the demographic representativeness of the store (which ranges from 0 =perfect misrepresentation to 1 =perfect representation) based on the entire employee and customer profiles presented earlier. The derived value of 0.91 indicates that the first store's racioethnic demographic profile nearly perfectly matches that of its customer base, whereas the second store's profile is less representative of its clientele (0.76). Consequently, this construct allows for testing matching at a more macro level than prior approaches.

In essence, representativeness involves the comparison of two collective constructs: store personnel and the store's customer base. According to Morgeson and Hofmann (1999), the theory we use to understand the behavior of these collectives should begin at the micro level because “it is not the collective construct, per se, that determines the behavior of individuals—rather, it is the individuals (or collective) who determine the collective construct, and, through their actions, influence the behavior of others in the collective” (p. 253). Accordingly, we build on micro-level theory regarding employee and consumer behavior to determine how representativeness is apt to influence organizational personnel and clientele collectively.

To begin, many types of organizational stakeholders appear to use company demographic profiles as an indication of whether or not a firm engages in discriminatory practices (e.g., Davidson, 2009; Kanter, 1977; Purdie-Vaughns, Steele, Davies, Ditlmann, & Crosby, 2008). For instance, a job seeker in one previous study looked at an ad and concluded: “okay, they have an Asian, African-American, looks like a Hispanic, and a woman on here, so it looks like …  they don't discriminate” (Cable & Graham, 2000: 935). Although it may appear that these individuals are looking for diversity when sizing up an organization, it is more likely that they are evaluating the company's representativeness, as a lack of employees belonging to a certain group should only cause concern if that group is represented in the larger setting in which the organization is embedded. Workers and shoppers may consider a number of prospective referents to use as a basis for comparison when formulating judgments of the organization's representativeness. In fact, reasonable arguments could be made for considering the demographic makeup of the surrounding neighborhood, city, or even county. Within the retail industry, however, one particularly relevant referent is the customer base, as many organizations often hire from their clientele (Patnaik, 2009; Williams & Connell, 2010), and some go so far as to make concerted efforts to maximize the correspondence between employee and customer demographics (Bendick, Egan, & Lanier, 2010; Borna, Stearns, Smith, & Emamalizadeh, 2008).

The perceptions of nondiscrimination or equity generated by an organization's employees being highly representative of their clientele are likely to enhance the company relationships with each of these key stakeholders. First, customers commonly notice the racioethnicity of employees (Hekman et al., 2010; Jones et al., 1998) and other customers (e.g., Baker, Meyer, & Johnson, 2008), meaning they cognitively possess the requisite information to formulate their own perceptions of the store's representativeness. Because these individuals are prone to attribute a lack of representativeness to discrimination (Cable & Graham, 2000; Davidson, 2009), lower levels of representativeness are apt to tarnish corporate images, repel customers, and precipitate financial losses (Borna et al., 2008; Sierra, Heiser, Williams, & Taute, 2010; Wright, Ferris, Hiller & Kroll, 1995). In short, representativeness is an organizational cue that should act as a signal to prospective customers that they interpret using a heuristic equating lower representativeness with discrimination (Highhouse & Hoffman, 2001; Spence, 1974).

Second, the perceptual link between representativeness and nondiscrimination should influence employee behavior. A number of recent studies (Brief, Dietz, Cohen, Pugh, & Vaslow, 2000; Petersen & Dietz, 2008; Umphress, Simmons, Boswell, & Triana, 2008; Zeigert & Hanges, 2005) have shown that workers tend to follow the lead of authority figures and the organization as a whole in determining whether or not discriminatory behavior is permissible. These findings suggest that employees who perceive their organization as engaging in discriminatory practices are more likely to discriminate against others themselves. Though it is true that this research focused on employee decision making in the context of personnel selection, there is reason to suspect that this will translate into the ways that employees treat customers as well (Bowen, Gilliland, & Folger, 1999; Masterson, 2001). In fact, employees perceiving higher levels of organizational justice (e.g., less discrimination) tend to engage in more customer-focused behaviors resulting in heightened customer satisfaction and, ultimately, better store performance (Maxham, Netemeyer, & Lichtenstein, 2008).

If higher representativeness conveys to stakeholders that the company does not discriminate, the resulting positive customer affect and employee customer-focused behavior should enhance retail store productivity. This leads us to propose the following:

  • Hypothesis 1: There will be a positive relationship between racioethnic representativeness and productivity.

The mediating role of customer satisfaction.  Several authors (e.g., Lawrence, 1997) have criticized researchers within the diversity literature for failing to consider the intervening mechanisms underlying compositional effects in organizations. We believe the theoretical mechanism linking representativeness and productivity is customer satisfaction. A more representative workforce is better suited to meet customer needs than a less representative one for three reasons. First, by signaling that the company does not discriminate, representativeness may lead many customers to identify with an organization, thereby enhancing affect toward the company, including customer satisfaction (Bhattacharya, Rao, & Glynn 1995; Bhattacharya & Sen, 2003; Luo & Bhattacharya, 2006).

Second, the signal of nondiscrimination sent to employees should reduce the likelihood of them mistreating customers. Dealing with personnel justly often induces a sense of employee obligation to repay the organization by committing to its objectives (e.g., customer service) and treating its customers well (Maxham et al., 2008). This implies that employees in representative workplaces will be more motivated to provide high quality customer service. Moreover, these individuals have the opportunity to interact with and learn from their dissimilar colleagues (Ely & Thomas, 2001; Estlund, 2003), thereby becoming more proficient at interactions across racioethnic lines. In fact, recent meta-analytic evidence indicates that employees working with dissimilar coworkers tend to become more accepting of and less biased against dissimilar others (Pettigrew & Tropp, 2006). If service employees become more comfortable interacting with their dissimilar colleagues, this should benefit them in their dealings with the similarly dissimilar customers they would likely encounter in highly representative settings. Thus, in addition to their higher motivation to provide higher service quality, they also should be more able to meet the needs of dissimilar clientele.

Third, though it does not ensure entirely within-group employee--customer interactions, high representativeness provides customers who prefer to work with similar salespeople the opportunity to do so. This is true even if there is only a single employee of a particular racioethnic group employed within a store. For instance, one recent examination of Hispanic shoppers in the U.S. indicated that many are intently attuned to the presence of Hispanic employees (particularly those who are bilingual) and may even schedule their visits to the store based on the known availability of a particular store associate (Fowler, Wesley, & Vasquez, 2007).

Collectively, these three reasons suggest greater racioethnic representativeness should correspond to heighted customer satisfaction. More satisfied customers, in turn, commonly facilitate greater financial performance (e.g., Gupta & Zeithaml, 2006; Schneider, Ehrhart, Mayer, Saltz, & Niles-Jolly, 2005) because satisfied customers tend to exhibit greater loyalty to the business in the form of greater patronage and word-of-mouth referrals. This leads us to anticipate that customer satisfaction mediates the effect of representativeness on productivity.

  • Hypothesis 2: Customer satisfaction will mediate the positive relationship between racioethnic representativeness and employee productivity such that greater representativeness will correspond with more satisfied customers and, thus, higher productivity.

The moderating role of minority customer base.  The preceding sections suggest racioethnic representativeness influences customer satisfaction and, indirectly, productivity because it signals to stakeholders that the company does not discriminate on the basis of racioethnicity. Although no one enjoys being a victim of discrimination, historical and continued societal realities of America invoke differential susceptibility to discriminatory treatment between various demographic groups. For instance, racioethnic minorities in the U.S. are disproportionally more likely than members of the White majority to report having experienced discrimination (Avery, McKay, & Wilson, 2008). Not surprisingly, other research shows that concerns about being discriminated against also tend to be greater among those belonging to minority groups (Levin, Sinclair, Viniegas, & Taylor, 2002). This suggests that the proportion of minorities in a store's customer base may be a prospective moderator of the effects of representativeness, as stores vary considerably in the racioethnic demographics of the clientele they attract and serve.

Beyond work on discrimination in general, recent evidence has demonstrated that the nature of the typical customer experience often differs considerably for White and minority customers in the United States (Gabiddon & Higgons, 2007, 2008; Harris, Henderson, & Williams, 2005; Jordan, Gabiddon, & Higgons, 2009; Schreer, Smith, & Thomas, 2009), forming the basis of the literature on what is known as consumer racial profiling. Broadly speaking, consumer racial profiling involves customers being discriminated against by store personnel on the basis of racioethnic group membership. Though there is variance in the extent that people believe profiling occurs and report having experienced it personally (Jordan et al., 2009), it appears fairly common. For instance, 43% of respondents in a Philadelphia telephone survey indicated they had been victims (Gabiddon & Higgons, 2007).

Although members of all racioethnic groups report being profiled (e.g., Gabiddon & Higgons, 2007, 2008; Harris et al., 2005), minorities are considerably more likely to report it and feel that it is problematic (Jordan et al., 2009). In fact, one author recently summed up this discrepancy in concluding, “unlike white shoppers, African Americans, Latinos, Asian Americans, and Native Americans cannot expect that store personnel will treat them with a minimal degree of respect” (Harris, 2006: 333). Due to their greater (a) concerns about discrimination and profiling and (b) relative difficulty in obtaining high-quality service (Pager & Shepherd, 2008), minority consumers should be more sensitive to representativeness and the signal it conveys regarding the likelihood of experiencing racioethnic mistreatment (Rosenbaum, 2005). For instance, it appears that minority students experience less discrimination as the proportion of minority teachers at their school increases toward the representativeness threshold, even if the teachers do not share the students’ racioethnicity (Rocha & Hawes, 2009). Consequently, the greater the proportion of minority customers, the more instrumental representativeness should be among the customer base in facilitating satisfaction and, in turn, productivity.

We also posited that representativeness acts as a signal to employees that the organization does not sanction discrimination. This, in turn, was proposed to diminish the likelihood of employees discriminating against customers (Brief et al., 2000; Ziegert & Hanges, 2005) and enhance perceptions of organizational justice, thereby generating employee goodwill toward customers. Given that minority customers are deprived of excellent or even equitable customer treatment relative to their White counterparts, they should exhibit stronger reactions (e.g., satisfaction) in response to the provision of good service (Crosby, 1984). Like the customer-centered argument above, this suggests that the effect of representativeness is apt to be stronger as the proportion of minorities within the customer base increases.

  • Hypothesis 3: Minority customer base will moderate the indirect, positive relationship between racioethnic representativeness and employee productivity through customer satisfaction, such that the representativeness–satisfaction linkage will be more strongly positive when the minority customer base is larger.

In sum, our hypotheses comprise a moderated mediation model (see Figure 1). The effect of racioethnic representativeness on store productivity is proposed to be indirect, as mediated by customer satisfaction. Essentially, more representative stores should do a better job meeting the needs of customers (i.e., higher satisfaction), consequently enhancing productivity. Moreover, this effect should be conditional, moderated by the minority presence within the customer base. The indirect effect will be stronger as the proportion of minorities within the customer base increases.

image

Figure 1. Hypothesized Research Model.

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Method

  1. Top of page
  2. Abstract
  3. Theoretical Background
  4. Method
  5. Results
  6. Discussion
  7. Limitations, Future Research Directions, and Conclusions
  8. REFERENCES

Data

Data were collected from the human resources department of a Fortune 500 department store retailer at two time points (i.e., 2005, 2006). The stores sell a wide variety of products ranging from home goods (e.g., furniture, window treatments), clothing (men's, women's, and children's), jewelry, and cosmetics. We elected to use only those stores with more than 50 employees to ensure that they were comparable to one another in terms of product and service offerings, and were typical of the organization as a whole (mean store N= 173 company wide). Only stores that were in existence when both the independent (2005) and dependent variables (2006) were collected were retained, yielding a final usable sample size of 739 stores. Although using data from store units within one company likely attenuates between-unit variance, it also controls for a number of between-company and industry confounds that could bias our results (Becker & Gerhart, 1996).

It is important to acknowledge that employee demographics were assessed at only one time point in 2005. Given that the organization experienced fairly high turnover that year (M= 91.69%, SD= 28.10), which is commonplace in the retail industry, it is possible that the demographics from one point in time would not be a very good indicator of the store composition throughout the year as a whole. To examine this possibility, we compared the demographic information we used from 2005 to that from the company records the following year. The mean correlation between racioethnic group proportions from one year to the next was .96, suggesting that there was very little change in the relative ordering of the group proportions across the stores. In addition, we computed the representativeness of the stores in 2005 (relative to the 2006 data) using the formula described in the following section and found a high mean (.95) with fairly little variance (SD= .06). This too suggests that the racioethnic group compositions remained fairly stable during the assessment of the store's productivity.

Measures

Productivity.  As in prior research (e.g., Huselid, 1995; Ployhart, Weekley, & Ramsey, 2009), we divided each store's key financial metric (i.e., total earnings before interest and taxes) by its number of employees. In essence, this variable represents the average earnings per associate employed by the store and provides a measure that is directly comparable across the stores in the sample. Stores with higher annual earnings per associate are more productive than those with lower earnings.

Racioethnic representativeness. Pitts (2005) introduced the following formula for representativeness to the literature:

Representativeness = 1 −✓((proportion in unit of group 1 – proportion in target population of group 1)2+ (proportion in unit of group 2 – proportion in target population of group 2)2+ (proportion in unit of group 3 – proportion in target population of group 3)2 … + (proportion in unit of group j– proportion in target population of group j)2)

where j= the number of group categories. Pitts’ measure, though useful, possesses a number of limitations that needed to be overcome. Whereas Pitts (2007) intended that “the values of the variable range from 0 (perfect misrepresentation) to 1 (perfect representation)” (p. 509), the formula as described above is not bounded by 0 and 1. This issue could be resolved by dividing the sum of the squared differences by j before taking the square root. Unfortunately, this introduces another problem whereby the lower bound of perfect misrepresentation is dependent on the number of groups represented. Thus, perfect mismatch with only two groups produces a different result from perfect mismatch with four groups are present1. To solve these problems, we modified the original formula to the following:

Representativeness = 1 – (|proportion in unit of group 1 – proportion in target population of group 1|+|proportion in unit of group 2 – proportion in target population of group 2|+|proportion in unit of group 3 – proportion in target population of group 3| … +|proportion in unit of group j – proportion in target population of group j|)/2

We used the percentages of White, Black, Hispanic, Asian, and other-racioethnicity associates in the store at the outset of 2005 (based on company HR information) and in the customer base. It is important to note that all store associates are visible to store patrons at multiple points during each workday. For instance, salespeople and cashiers work on the sales floor. Furthermore, the company's stockers frequently bring merchandise from the stockroom to the sales floor or to customers’ automobiles and are also readily identifiable by their uniforms when arriving at work, leaving work, or taking breaks in public areas. To create the percentages of Whites, Blacks, Hispanics, Asians, and other racioethnicities in the customer base, we relied on customers who made their purchases with credit cards and provided their residential zip codes. Prior authors (Leonard et al., 2004; Leonard et al., 2010; Sacco & Schmitt, 2005) have used the zip code of the establishment to define customer demographics. However, customers often travel beyond their residential zip code to shop for merchandise. In fact, a recent meta-analysis (Pan & Zinkhan, 2006) reported only a moderate corrected correlation between store location and customer patronage (r= .39, k= 12, N= 934). An alternative approach involves determining where customers actually reside and focusing on the demographic composition of those locales as opposed to the store itself. Matching data from the U.S. Census Bureau with the customer zip codes, we computed the proportion of residents belonging to each racioethnic group within each customer's zip code and then averaged these proportions across the entire customer base. If there were multiple customers from any particular zip code, that zip code received a proportionally greater weight in the average.

The introduction of a new measure to the diversity literature calls into question how it overlaps with and differs from existing measures. Harrison and Klein (2007) recently provided a conceptual typology to help organize the proliferation of measures within this domain. Broadly, they argued that measures tend to take one of three forms: separation, variety, and disparity. Separation captures the average demographic dissimilarity between one member and all others in the group along a particular dimension (e.g., racioethnicity). It is maximized when a distribution is bimodal, which means that everyone in a group is relatively highly dissimilar from the average group member. Variety encompasses the array of members from different categories of a particular demographic dimension within a group. Its zenith occurs when there are equal proportions of a high number of categories on a particular dimension (e.g., Blacks, Hispanics, Whites, Asians, and Native Americans). Finally, disparity pertains to the lopsidedness of a distribution. Its apex involves one individual (or a small subset of individuals) possessing the majority of the dimension in question (e.g., one worker with a PhD in a group of high school graduates). Because disparity does not translate well to data without ratio properties (e.g., racioethnicity), we exclude it from this discussion.

What differentiates representativeness from separation and variety is that it focuses on the relative makeup of a group compared to that of a referent group (e.g., a store's customer base) as opposed to being an absolute descriptive of the group's composition. As such, it is possible for a group with no variety or separation to be perfectly representative (i.e., the referent group also contains no diversity). Alternatively, a group could be high in either of these forms of diversity and also be representative. This indicates that representativeness is not actually a measure of diversity per se and should demonstrate a moderate degree of correspondence with diversity measures. In our data, racioethnic representativeness correlated negatively (rs =−.60, p < .01) with both variety (i.e., Blau's index) and separation (i.e., mean Euclidean distance), which correlated nearly perfectly with one another (r= .98, p < .01). This negative association makes sense in light of the fact that higher levels of racioethnic separation and variety are more likely within the stores in our sample than in their respective customer bases (i.e., the referent group), which would result in lower representativeness among stores scoring particularly high along these dimensions. This suggests sufficient theoretical and empirical convergent and discriminant validity exists to consider representativeness distinct from these existing measures.

Customer satisfaction.  When making purchases from the company's stores during 2006, patron receipts contained a link to an Internet survey that assessed their customer experience. Our measure was computed as the percentage of customers for a particular store during 2006 who endorsed the most favorable response (5 =strongly agree) on a five-point scale (1 =strongly disagree to 5 =strongly agree) to the item “I was satisfied with my overall experience at [The Company].” Though this metric sacrifices some degree of precision in measuring customer satisfaction, it correlated highly with scores for the overall customer satisfaction survey (r= .75), which were unavailable to the researchers. Indices of this nature are a common method of assessing customer satisfaction (Gupta & Zeithaml, 2006; Schmit & Allscheid, 1995), prior investigation supports their validity (Hurley & Estelami, 1998; Morgan & Rego, 2006), and customer purchase amounts and frequencies of return business are markedly higher when customers are “highly satisfied” versus merely “satisfied” (Jones & Sasser, 1995). Researchers commonly treat unit-level customer satisfaction as an additive composition variable (e.g., Hekman et al., 2010; Schneider et al., 2005), meaning that justification is unnecessary for aggregating data from the individual- to the unit-level of analysis (Chan, 1998).

Minority customer base.  We used the proportion of non-White (e.g., Black, Hispanic, Asian, or Native Americans) individuals in the customer base described above to represent the percentage of minority customers.

Controls.  Due to their prospective influence on both representativeness and productivity (Becker, 2005; Spector & Brannick, in press), we controlled for the effects of two variables in our analyses. First, we know that a store's geographic location within the United States can influence racioethnic hiring practices and productivity (Cohn & Fossett, 1995; Grissom, Nicholson-Crotty, & Nicholson-Crotty, 2009). Second, the level of racioethnic diversity within a store, which we operationalized as separation (i.e., average Euclidean distance), can relate to productivity (e.g., Herring, 2009) and representativeness.

Results

  1. Top of page
  2. Abstract
  3. Theoretical Background
  4. Method
  5. Results
  6. Discussion
  7. Limitations, Future Research Directions, and Conclusions
  8. REFERENCES

Means, standard deviations, and correlations for all variables are presented in Table 1. Through visual inspection of the univariate scatterplots, we identified one outlier on the dependent variable (more than five standard deviations above the mean) and removed it prior to conducting the analyses. We tested the study hypotheses using SPSS macros provided by (a) Preacher and Hayes (2008) to test the significance of the main and indirect effects proposed in the first two hypotheses and (b) Edwards and Lambert (2007) to test the moderated mediation outlined in the final hypothesis. These macros produce path coefficients between the independent variable, mediator, and dependent variable as well as bootstrapped confidence intervals for indirect effects. The bootstrapping procedure calculates total indirect effects across 1,000 resamplings from the data, thus ensuring the robustness of the obtained results.

Table 1.  Means, Standard Deviations, and Correlations
VariableMSD12345678
  1. Note. N= 739. *p < .05, **p < .01.

1 Northeast region.29.46       
2 South region.26.44      
3 Central region.22.41     
4 West region.23.42    
5 Racioethnic diversity.53.22−.24**18**−.28**35**   
6 % Minority customers.26.21−.25**.09*−.29**.47**.61**  
7 Representativeness.90.08.11**−.20**.23**−.13**−.61**−.41** 
8 Customer satisfaction44.534.49−.00.37**−.36**−.03−.06 .11**.07
9 Productivity13495.265465.73−.22**.21**−.15**.18**−.03 .17**.06.28**

Hypotheses 1 and 2 predicted direct and indirect effects, respectively, of racioethnic representativeness on unit employee productivity. After accounting for the controls, representativeness exhibited a significant, positive relationship with productivity (B= 6245.36, p= .04, ΔR2= .01), which provides support for Hypothesis 1. Though this effect is modest in absolute size, the coefficient indicates that a .1 increase in representativeness (e.g., moving from .5 to .6) corresponds in a productivity boost of $625/employee per year. Given the average store size in our sample (N= 150 employees), such a change amounts to roughly $94,000 per store, or a total of more than $69 million dollars for the parent company as a whole.

In addition, racioethnic representativeness significantly predicted customer satisfaction (B= 7.96, p < .001, ΔR2= .01), which in turn corresponded to greater productivity (B= 204.55, p < .001, ΔR2= .02). These relationships combined to produce a significant indirect effect of racioethnic representativeness on productivity (B= 1627.98, p < .01, 99% CI = 361.70 to 3745.27, R2med= .001, κ2= .02). After accounting for customer satisfaction, the racioethnic representativeness--productivity relationship was attenuated to nonsignificance (B= 4617.37, p= .13). The two effect sizes indicate that the mediating effect was relatively small in overall magnitude, as Preacher and Kelley (2011) suggested that κ2 sizes be interpreted similarly to those for R2. Nevertheless, the results indicate a fully mediated relationship wherein customers were more satisfied in highly representative stores, which related to greater productivity than observed in less representative stores. Thus, Hypothesis 2 also received support.

The third hypothesis predicted that the proportion of minority customers would moderate the indirect effect of racioethnic representativeness on productivity. The SPSS macro and accompanying Excel spreadsheet provided by Edwards and Lambert (2007) allows researchers to model moderation and mediation simultaneously. Using these tools, we estimated the direct and indirect effects of racioethnic representativeness on productivity at low (1 SD below the mean) and high (1 SD above the mean) levels of minority customer representation (see Tables 2–4). In the context of customer satisfaction (the proposed mediator), the direct effect was nonsignificant at both levels of minority customer representation, as was the difference between the two coefficients. Conversely, the indirect effect was significant when the percentage of minority customers was high (2092.29, p < .01) but not when it was low (−13.29, ns), and the difference between the two coefficients was significant (2105.58, p < .01). This reveals that the percentage of minority customers moderated the racioethnic representativeness—customer satisfaction—productivity relationship (see Figure 2 for an illustration), such that representativeness exhibited a significant, indirect relationship with productivity only for stores with a preponderance of minority clientele. These results strongly support Hypothesis 3.

Table 2.  Summary of Regression Analyses Predicting Employee Productivity
VariableB1B2B3B4
  1. Note. N= 739. *p < .05, **p < .01.

South region4366.67** (520.15)4399.06** (519.25)3708.87** (537.66)3705.91** (539.38)
Central region227.05 (522.21)150.99 (522.39)803.92 (538.11)798.67 (548.21)
West region4109.22** (591.89)3962.34** (594.92)3971.56** (587.94)3963.30** (589.07)
Racioethnic diversity−7641.85** (1103.28)−6373.68** (1262.39)−5384.99** (1268.56)−5629.95** (1436.46)
% Minority customers (MC)4587.38** (1178.59)4839.50** (1182.41)4002.02** (1184.63)4101.20** (1267.23)
Representativeness (R) 6245.36* (3042.60)4617.37 (3030.60)4026.35 (3336.92)
Customer satisfaction (CS)  204.55** (47.55)202.39** (48.37)
CS × MC   −12.58 (168.30)
R × MC   6196.69 (14575.11)
ΔR2.16**.01* .02**.00
R2.16    .17 .19   .19
Table 3.  Summary of Regression Analyses Predicting Customer Satisfaction
VariableB1B2B3
  1. Note. N= 739. *p < .05, **p < .01.

South region3.33** (.40)3.37** (.40)3.29** (.40)
Central region−3.10** (.40)−3.19** (.40)−3.22** (.40)
West region.14 (.46)−.05 (.46)−.10 (.45)
Racioethnic diversity−6.45** (.85)−4.83** (.97)−6.09** (1.03)
% Minority customers (MC)3.77** (.91)4.09** (.91)4.46** (.91)
Representativeness (R) 7.96** (2.34)4.31 (2.55)
R × MC  36.86** (10.58)
ΔR2.26**.01**.01**
R2.26   .27   .28   
Table 4.  Moderated Mediation Analysis of Simple Effects of Racioethnic Representativeness on Employee Productivity Through Customer Satisfaction
Moderator variableStageEffect
FirstSecondDirectIndirectTotal
  1. Note.  N= 739. Tests of differences for the indirect and total effect were based on bias-corrected confidence intervals derived from bootstrap estimates. Numbers in parentheses are standard deviations of bootstrapped estimates. *p < .05, **p < .01.

Minority customers     
 Fewer−.10 140.62*5299.46−13.29 5286.16
 (3.41)(61.84)(4136.92)(517.31)(4227.58)
 More12.47**167.81**5935.892092.29**8028.18*
 (2.61)(58.59)(3483.56)(769.44)(3426.82)
 Differences12.56**27.19 636.432105.58**2742.01
 (4.55)(78.18)(5741.54)(890.10)(5743.99)
image

Figure 2. The Interactive Effects of Racioethnic Representativeness and Minority Customer Base on Customer Satisfaction and Employee Productivity.

Download figure to PowerPoint

Supplemental Analyses

To further illustrate the importance of our conceptualization of representativeness, we compared our results to those obtained using the identity-group level of analysis approach, as employed by prior researchers (Leonard et al., 2004; Leonard et al., 2010; Sacco & Schmitt, 2005), and polynomial regression. This first analysis involved computing composite interaction terms between the proportion of an identity group in the business unit and in the customer base. In testing this possibility, none of the interactions were significant with the exception of that for the “other” racial-ethnic group (B=−39303.79, p= .04), but (a) it was in the direction opposite than would be expected, and (b) the tolerance level for this term was less than .20, indicating the presence of considerable multicollinearity. This lack of support is consistent with prior inquiries that have employed this analytic strategy (Leonard et al., 2004; Sacco & Schmitt, 2005). Likewise, polynomial regression yielded only one significant effect: the linear effect of the percentage of Hispanic employees. These nonsignificant findings suggest that, as we intended, our representativeness variable tests a qualitatively different question than these prior statistical approaches.

In addition, because our representativeness variable is a form of difference score, we deemed it important to show that incremental variance was accounted for beyond its component terms. Accordingly, we recomputed the indirect effect of racioethnic representativeness, controlling for the racioethnic proportions of employees in one set of analyses and the customer base in another (the average correlation between the two is .91, making it inappropriate to include both simultaneously). The indirect effect of racioethnic representativeness on productivity through customer satisfaction was significant in both analyses (results are available from the first author). We did not conduct this type of analysis for our moderated mediation model because our moderator (i.e., minority customer base) is the sum of the racioethnic minority customer base components.

Discussion

  1. Top of page
  2. Abstract
  3. Theoretical Background
  4. Method
  5. Results
  6. Discussion
  7. Limitations, Future Research Directions, and Conclusions
  8. REFERENCES

The results of our study make an important contribution to the diversity literature. Prior research produced little-to-no evidence linking employee--customer demographic matching to the bottom line at either the individual- or identity-group levels of analysis. Thus, it was unclear whether the theory underlying this premise was valid. We reformulated it, suggesting that the effects of similarity operate at a higher level of analysis and influence productivity through customer satisfaction. Accordingly, the present research introduced the representativeness construct (from the public administration literature) as a means of better operationalizing aggregate-level, employee--customer racioethnic similarity, and therefore, providing a more accurate assessment of the impact of matching. Using multisource data from an organization, its customers, and the U.S. Census Bureau, we found support for our reformulated theory concerning racioethnic representativeness. We now turn our attention to the implications of these findings.

Implications

Theoretical.  Many management personnel believe that employing more minorities in predominantly White workplaces will gain an organization access to and legitimacy with minority customers (cf. Leonard et al., 2004). Despite this popular sentiment and support from established theories such as the similarity--attraction hypothesis (Byrne, 1971) and social identity theory (Tajfel & Turner, 1986), prior empirical evidence has failed to validate this position consistently. We believed that the underlying premise (i.e., racioethnic similarity enhances success) was sound but that prior research and theory failed to conceptualize the relationship between employee--customer racioethnic similarity and performance properly.

Accordingly, we argued that previously reported null findings could be explained both theoretically and statistically by unrecognized offsetting effects. On the one hand, individual-level studies have failed to account for the possibility that customers may be attracted to stores initially by similar salespeople (or ideas for product placement and/or service provision that they have initiated) but end up being served by dissimilar salespeople after entering the store. On the other hand, identity-group level studies have ignored the fact that overrepresentation of one group must occur at the representational expense of at least one other group. In both cases, the underlying theory that similarity boosts performance could hold true, but past results would indicate otherwise.

Taking a representativeness perspective recognizes that employee--customer racioethnic similarity has both functional and symbolic value. The former builds on the contact hypothesis in suggesting that employees in representative workforces should be better suited to work with customers whose racioethnic profile matches that of their colleagues. The latter pertains to stakeholders interpreting racioethnic representativeness as a symbol of equity, prompting employees to provide better service and customers to patronize and identify with the organization. Collectively, this impact on employee and consumer behavior translates into representativeness enhancing customer satisfaction, which in turn, heightens store productivity.

Essentially, racioethnic representativeness appeared to serve as a service clue (Berry, Wall, & Carbone, 2006) or part of a “servicescape” (Rosenbaum, 2005), signaling to potential customers that the organization does not discriminate and, thus, is devoted to meeting or exceeding the service expectations of all prospective patrons. This appears to be particularly important to minority customers who often find it difficult to obtain service comparable to that received by their White counterparts (Pager & Shephard, 2008). Like Davidson (2009: 142), we believe that “the perception of discrimination is one example of a race-based perception that may influence minority consumers’ attitudes and shopping behavior,” and it appears that representativeness may be a tool these individuals use in formulating perceptions of discrimination.

Managerial.  The fact that racioethnic representativeness influenced customer satisfaction and, subsequently, employee productivity suggests that employers should be cognizant of the racioethnic congruence between their staff and customers. Our results support the commonly proposed notion that more closely mirroring the racioethnic composition of an organization's consumer market makes sound business sense. It appears that this type of employee--customer similarity holds promise for helping companies meet the needs of their clientele, resulting in greater customer satisfaction and higher organizational productivity.

We do, however, echo the concerns of prior authors (Bendick et al., 2010; Collins, 1997; Ely & Thomas, 2001) about the potential misuse of this strategy. Although Ely and Thomas did not explore variation in representativeness, they did compare and contrast the use of three diversity management perspectives (including access-and-legitimacy) in highly diverse settings. In doing so, they found some evidence of success associated with utilizing an access-and-legitimacy approach but also discovered associated drawbacks. First, companies tended to marginalize their minority employees, solely limiting their potential contributions to working with racioethnically similar customers. This robs minority employees of equal employment opportunity by perpetuating glass wall barriers (i.e., limited horizontal mobility between functions), thereby preventing them from fully utilizing their talents toward organizational performance (Bendick et al., 2010; Collins, 1997) and circumventing their career development (DiTomaso, Thompson, & Blake, 1988). Second, firms that endorse the access-and-legitimacy paradigm might become inefficient by creating homogeneous, duplicate departments to serve identical functions for different customer groups (Ely & Thomas, 2001). As noted diversity consultant Roosevelt Thomas said: “it's not a question of having Black people selling to Black people. You have to have people who understand those markets. Short run efforts may match people up Black to Black. In the long run, the best person to service a segment may not match ethnically” (quoted in Kelly, 1996: 34).

So, if matching appears to be profitable but is potentially perilous, what should organizations do? Our response to this question involves the following suggestions. First, companies should avoid the temptation to engage in concerted matching efforts. These would entail making racioethnicity a factor in staffing (i.e., selection and placement) decisions, which, in addition to the prospective problems cited above, is illegal (Borna et al., 2008; Brief et al., 1997). Second, companies should assess their representativeness regularly using several key referents, such as customers, their applicant pool, or the available labor market. The success of programs like affirmative action and diversity management are contingent upon organizations keeping track of levels of representation among their personnel (Jayne & Dipboye, 2004). A company that is oblivious to its level of representativeness may be unaware of potential problems or inadvertent messages being conveyed to their stakeholders.

Third, we encourage organizations to interpret their representativeness strategically. Specifically, those with lower values should examine why this may be the case. Although discrimination is one possible culprit, there are also legitimate reasons for a company being low in representativeness that should not precipitate changes in HR strategy. For instance, the available labor pool may not resemble the company's customer base demographically. In such cases, attempting to enhance representativeness would almost certainly prove discriminatory. Finally, we urge companies to pay greater attention to the signals they are sending to their key stakeholders. In the absence of representativeness, companies should make efforts to display and convey a commitment to equality. By making it clear to employees and customers that the organization does not engage in or tolerate discrimination, organizations may lessen the signaling capacity of representativeness and reclaim more direct control of their corporate image.

Given its linkage to such important business outcomes, many readers may wonder how corporate workforces may diverge from representativeness in the first place. This is especially true in the context of the present study, where all of the data involve units of a single conglomerate. There are a number of potential explanations, however, for how individual store units can become less representative over time, even in the face of a high degree of HR standardization employed by the parent company. For instance, competitors in a store's surrounding area could influence its representativeness if they exert a disproportionate appeal to a particular subgroup of the store's customer base (e.g., attract more job applications from Black than White or Hispanic job seekers). Alternatively, differences in representativeness could reflect differences in a particular store's corporate employment image (Highhouse, Zickar, Thorsteinson, Stierwalt, & Slaughter, 1999), indicating a need to take a more proactive approach to managing impressions held by external stakeholders (Avery & McKay, 2006). Finally, perhaps the most obvious deterrent of representativeness is a lack of employment equity, as biased HR decision making within a store undoubtedly would result in one group being overrepresented at the expense of another or others.

Limitations, Future Research Directions, and Conclusions

  1. Top of page
  2. Abstract
  3. Theoretical Background
  4. Method
  5. Results
  6. Discussion
  7. Limitations, Future Research Directions, and Conclusions
  8. REFERENCES

As with any study, there are limitations to acknowledge. First, our research design does not allow strong causal conclusions to be drawn. Though we collected data at two time points, our data are cross-lagged not longitudinal. A second limitation is that data were collected from a single organization, potentially limiting the generalizability of our findings. Third, our use of data from the retail industry, though potentially an ideal candidate for a test of racioethnic matching, may limit the generalizability of our results. Fourth, we recognize that our representativeness measure is a difference score and is subject to the shortcomings of these variables (Edwards, 1993). Nevertheless, we could find no other analytical method that permits a direct test of the more macro theoretical premise introduced here, as polynomial regression (i.e., the most commonly advocated alternative to difference scores) only allows one to statistically assess the racioethnic congruence of one group at a time as opposed to the entire racioethnic profile of a particular context.

Two further limitations arose from the way we operationalized the minority customer base. The first is that the averages contained information for some out-of-town customers. Though the exact proportion of out-of-town purchasers is unknown to us (unfortunately, we were not granted full access to customer data), we would expect the overwhelming majority of customers to reside within comfortable driving distance of the establishment (Rajagopal, 2011). The second is that the customer data involved only customers who used credit card transactions. Though this is somewhat limiting, the 2008 study of consumer payment preferences indicated that 80% of department store purchases involve credit or debit cards (BAI Research & Hitachi Consulting, 2008). Thus, our data represent the dominant form of customer transactions. Ideally, this variable would use only data from local customers and include all methods of payment.

To expand research in this area, future studies should attempt to replicate our findings to determine their generalizability across organizations, industries, and various customer--provider relationships. For example, customer-dependent, nonretail companies (e.g., restaurants, grocery stores, etc.) may experience similar or differing effects of racioethnic representativeness on employee productivity. Importantly, the present study provides a preliminary yet conservative test of racioethnic representativeness effects on productivity, considering our use of a single organization. Multifirm studies would be a useful extension of our work by determining if high representativeness is a source of competitive advantage. The resource-based view of the firm (Barney, 1991) indicates that valuable, rare, inimitable, and nonsubstitutable resources allow a firm to gain sustained competitive advantage. The theoretical rationale we developed and tested here suggests employee--customer racioethnic similarity diminishes perceived and actual discrimination, thereby enhancing the service provided to clientele. Consequently, high racioethnic representativeness appears to symbolize a key firm resource. Moreover, highly representative workforces are apt to be rare, inimitable, and nonsubstitutable as well, given the prospective benefits associated with these workforces, as well as the inherent difficulties associated with attracting and retaining them (McKay & Avery, 2005). Accordingly, the possibility exists that representativeness might be a useful means of distinguishing between-firm performance within industries, particularly in service contexts.

In addition, research is needed to identify and understand how other variables (e.g., service climate, service recovery efforts, organizational reputation, employee personality) that contribute to customer satisfaction, employee productivity, and the bottom line (e.g., Dietz, Pugh, & Wiley, 2004; Liao & Chuang, 2004; Roberson & Park, 2007) might pose boundary conditions for the effects of representativeness. Taking employee personality as an example, it would be interesting to examine if a firm's representativeness influences its modal personality (Schneider, Smith, Taylor, & Fleenor, 1998). According to Schneider's (1987) attraction--selection--attrition model, certain types of employees are attracted to particular organizations, are likely to be selected by them, and stay if their values align with those of the organization. Highly representative firms might be associated with a particular personality profile (e.g., individuals who are high in Conscientiousness and/or Openness to Experience) relative to those organizations with lower representativeness. Hence, multilevel research could be fashioned to examine potential individual differences in reactions to representativeness (e.g., work attitudes, job performance) as a function of the match between individual- and aggregate-level personality profiles. Furthermore, this matching process might have subsequent implications for service quality (Dietz et al., 2004), customer satisfaction (Liao & Chuang, 2004), and firm financial performance (Schneider et al., 2005).

Finally, though the indirect effect of representativeness on productivity through customer satisfaction is consistent with our theorizing, it is important that we explicitly acknowledge the existence of multiple, viable explanations for this process. For instance, we proposed that the effect is primarily symbolic, in that representativeness acts as a cue signaling an absence of discrimination thereby conveying to shoppers that higher quality service is likely and, ultimately, corresponding in heightened customer satisfaction (Berry et al., 2006). Beyond this signaling capacity, it is also probable that representativeness enhances organizational functionality by elevating a workforce's willingness and ability to anticipate and meet the needs of their clientele. In signaling to employees that the company does not sanction discrimination, representativeness encourages them to treat all customers with courtesy and respect, which should result in an enhanced overall customer experience. Moreover, by creating a more level playing field among personnel, an organizational atmosphere that is intolerant of discrimination should help to facilitate the development of positive attitudes toward dissimilar others with whom employees come into contact (Pettigrew & Tropp, 2006). This suggests a representative workforce may provide a competitive advantage in addressing customer needs. Unfortunately, however, our data do not allow us to empirically disentangle these symbolic and functional explanations. Consequently, we encourage future research to employ measures (e.g., customer perceptions of discrimination and employee insightfulness) that may shed further light on the specific causal mechanism underlying the representativeness--customer satisfaction relationship.

In spite of this study's limitations, there are several important conclusions to be drawn from this research. Our investigation makes an important contribution to theory and practice by more clearly specifying the rationale for why employee--customer racioethnic similarity should enhance organizational financial performance. Across 700 nationally located stores, racioethnic representativeness predicted employee productivity. Moreover, customer satisfaction and the size of a store's minority customer base served as a mediator and moderator, respectively, of the representativeness–productivity relationship. In sum, the results show that racioethnic representativeness has bottom-line implications for organizations.

Footnotes
  • 1

    We thank an anonymous reviewer for calling this to our attention.

REFERENCES

  1. Top of page
  2. Abstract
  3. Theoretical Background
  4. Method
  5. Results
  6. Discussion
  7. Limitations, Future Research Directions, and Conclusions
  8. REFERENCES
  • Avery DR, McKay PF. (2006). Target practice: An organizational impression management approach to attracting minority and female job applicants. Personnel Psychology , 59, 157187.
  • Avery DR, McKay PF, Wilson DC. (2008). What are the odds? How demographic similarity affects the prevalence of perceived employment discrimination. Journal of Applied Psychology, 93, 235249.
  • BAI Research & Hitachi Consulting. (2008). 2008 study of consumer payment preferences. Chicago, IL/Dallas , TX : Author.
  • Baker TL, Meyer T, Johnson JD. (2008). Individual differences in perceptions of service failure and recovery: The role of race and discriminatory bias. Journal of the Academy of Marketing Science, 36, 552564.
  • Barney JB. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99120.
  • Baron RM, Kenny DA. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 11731182.
  • Becker B, Gerhart B. (1996). The impact of human resource management on organizational performance: Progress and prospects. Academy of Management Journal, 39, 779801.
  • Becker TE. (2005). Potential problems in the statistical control of variables in organizational research: A qualitative analysis with recommendations. Organizational Research Methods, 8, 274289.
  • Bendick M, Egan ML, Lanier L. (2010). The business case for diversity and the perverse practice of matching employees to customers. Personnel Review, 39, 468486.
  • Berry LL, Wall EA, Carbone LP. (2006). Service clues and customer assessment of the service experience: Lessons from marketing. Academy of Management Perspectives, 20(2), 4357.
  • Bhattacharya CB, Rao H, Glynn MA. (1995). Understanding the bond of identification: An investigation of its correlates among art museum members. Journal of Marketing, 59(4), 4657.
  • Bhattacharya CB, Sen S. (2003). Consumer-company identification: A framework for understanding consumers’ relationships with companies. Journal of Marketing, 67(2), 7688.
  • Borna S, Stearns JM, Smith BN, Emamalizadeh K. (2008). Retail store image, bona fide occupational qualifications, and job discrimination: Establishing the essence of the business for retail organizations. Marketing Management Journal, 18(1), 5462.
  • Bowen DE, Gilliland SW, Folger, R. (1999). HRM and service fairness: How being fair with employees spills over to customers. Organizational Dynamics, 27(3), 723.
  • Brief AP, Buttram RT, Reizenstein RM, Pugh SD, Callahan JD, McCline RL, Vaslow JB. (1997). Beyond good intentions: The next steps toward racial equality in the American workplace. Academy of Management Executive, 11(4), 5972.
  • Brief AP, Dietz J, Cohen RR, Pugh SD, Vaslow JB. (2000). Just doing business: Modern racism and obedience to authority as explanations for employment discrimination. Organizational Behavior and Human Decision Processes, 81, 7296.
  • Byrne D. (1971). The attraction paradigm. New York , NY : Academic Press.
  • Cable DM, Graham ME. (2000). The determinants of job seekers’ reputation perceptions. Journal of Organizational Behavior, 21, 929947.
  • Chan D. (1998). Functional relations among constructs in the same content domain at different level of analysis: A typology of composition models. Journal of Applied Psychology, 83, 234246.
  • Cohn S, Fossett M. (1995). Why racial employment inequality is greater in northern labor markets: Regional differences in white-black employment differentials. Social Forces, 74, 511542.
  • Collins SM. (1997). Black mobility in White corporations: Up the corporate ladder but out on a limb. Social Problems, 44, 5567.
  • Cortina JM. (1993). Interaction, nonlinearity, and multicollinearity: Implications for multiple-regression. Journal of Management, 19, 915922.
  • Cox TH Jr., Blake, S. (1991). Managing cultural diversity: Implications for organizational competitiveness. Academy of Management Executive, 5(3), 4556.
  • Crosby F. (1984). Relative deprivation in organizational settings. Research in Organizational Behavior, 6, 5193.
  • Cunningham GB, Sagas M. (2006). The role of perceived demographic dissimilarity and interaction in customer-service satisfaction. Journal of Applied Social Psychology, 36, 16541673.
  • Davidson EF. (2009). Unintended consequences of race-based segmentation strategies. Journal of Consumer Marketing, 26, 141142.
  • Dietz J, Pugh SD, Wiley JW. (2004). Service climate effects on customer attitudes: An examination of boundary conditions. Academy of Management Journal, 47, 8192.
  • DiTomaso N, Thompson DE, Blake DH. (1988). Corporate perspectives on the advancement of minority managers. In N DiTomaso, and DE Thompson (Eds.), Ensuring minority success in corporate management (pp. 119136). New York , NY : Plenum.
  • Edwards JR. (1993). Problems with the use of profile similarity indices in the study of congruence in organizational research. Personnel Psychology, 46, 641665.
  • Edwards JR, Lambert LS. (2007). Methods for integrating moderation and mediation: A general analytic framework using moderated path analysis. Psychological Methods, 12, 122.
  • Ely RJ, Thomas DA. (2001). Cultural diversity at work: The effects of diversity perspectives on work group processes and outcomes. Administrative Science Quarterly, 46, 229273.
  • Estlund C. (2003). Working together: How workplace bonds strengthen a diverse democracy. New York , NY : Oxford University Press.
  • Fields DL, Goodman JS, Blum TC. (2005). Human resource dependence and organizational demography: A study of minority employment in private sector companies. Journal of Management, 31, 167185.
  • Fowler DC, Wesley SC, Vasquez ME. (2007). Simpatico in store retailing: How immigrant Hispanic emic interpret U.S. store atmospherics and interactions with sales associates. Journal of Business Research, 60, 5059.
  • Gabbidon SL, Higgins GE. (2007). Consumer racial profiling and perceived victimization: A phone survey of Philadelphia area residents. American Journal of Criminal Justice, 32, 111.
  • Gabbidon SL, Higgins GE. (2008). Profiling White Americans: Exploring “shopping while White.” In MJ Lynch, EB Patterson, K Childs (Eds.), Racial divide: Race, ethnicity and criminal justice, (pp. 197209). Monsey , NY : Criminal Justice Press.
  • Grissom JA, Nicholson-Crotty J, Nicholson-Crotty S. (2009). Race, region, and representative bureaucracy: Big questions facing public administration theory. Public Administration Review, 69, 911919.
  • Gupta S, Zeithaml V. (2006). Customer metrics and their impact on financial performance. Marketing Science, 25, 718739.
  • Harris AG. (2006). Survey of federal and state public accommodations statutes: Evaluating their effectiveness in cases of retail discrimination. Virginia Journal of Social Policy and the Law, 13, 331395.
  • Harris AG, Henderson GR, Williams JD. (2005). Courting customers: Assessing consumer racial profiling and other marketplace discrimination. Journal of Public Policy and Marketing, 24, 163171.
  • Harrison DA, Klein KJ. (2007). What's the difference? Diversity constructs as separation, variety, or disparity in organizations. Academy of Management Review, 32, 11991228.
  • Hekman DR, Aquino K, Owens BP, Mitchell TR, Schilpzand P, Leavitt K. (2010). An examination of whether and how racial and gender biases influence customer satisfaction. Academy of Management Journal, 53, 238264.
  • Henthorne TL, LaTour MS, Williams AJ. (1992). Initial impressions in the organizational buyer-seller dyad: Sales management implications. Journal of Personal Selling and Sales Management, 12, 5765.
  • Herring C. (2009). Does diversity pay?: Race, gender, and the business case for diversity. American Sociological Review, 74, 208224.
  • Highhouse S, Hoffman JR. (2001). Organizational attraction and job choice. In Cooper CL, Robertson IT (Eds.), International review of industrial and organizational psychology (pp. 3764). Chichester , UK : Wiley.
  • Highhouse S, Zickar MJ, Thorsteinson TJ, Stierwalt SL, Slaughter JE. (1999). Assessing company employment image: An example in the fast food industry. Personnel Psychology, 52, 151172.
  • Holzer HJ, Ihlanfeldt KR. (1998). Customer discrimination and employment outcomes for minority workers. The Quarterly Journal of Economics, 113, 835867.
  • Hurley RF, Estelami H. (1998). Alternative indexes for monitoring customer perceptions of service quality: A comparative evaluation in a retail context. Journal of the Academy of Marketing Science, 26, 209221.
  • Huselid MA. (1995). The impact of human resource management practices on turnover, productivity, and corporate financial performance. Academy of Management Journal, 38, 635672.
  • Ito TA, Urland GR. (2003). Race and gender on the brain: Electrocortical measures of attention to the race and gender of multiply categorizable individuals. Journal of Personality and Social Psychology, 85, 616626.
  • Jackson SE, Joshi A, Erhardt NL. (2003). Recent research on team and organizational diversity: SWOT analysis and implications. Journal of Management, 29, 801830.
  • Jayne MEA, Dipboye RL. (2004). Leveraging diversity to improve business performance: Research findings and recommendations for organizations. Human Resource Management, 43, 409424.
  • Jones E, Moore J, Stanaland A, Wyatt R. (1998). Salesperson race and gender and the access-and-legitimacy paradigm: Does difference make a difference? Journal of Personal Selling & Sales Management, 18(4), 7188.
  • Jones TO, Sasser Jr. WE. (1995, November/December). Why satisfied customers defect. Harvard Business Review, 73, 8899.
  • Jordan KL, Gabbidon SL, Higgons GE. (2009). Exploring the perceived extent of and citizens’ support for consumer racial profiling: Results from a national poll. Journal of Criminal Justice, 37, 353359.
  • Joshi A, Roh H. (2009). The role of context in work team diversity research: A meta-analytic review. Academy of Management Journal, 52, 59627.
  • Juni S, Brannon R, Roth MM. (1988). Sexual and racial discrimination in service-seeking interactions: A field study in fast food commercial establishments. Psychological Reports, 63, 7176.
  • Kanter RM. (1977). Men and women of the corporation. New York , NY : Basic Books.
  • Kelly RJ. (1996). Toward a more diverse salesforce. Sales and Marketing Management, 146(3), 3334.
  • Kochan T, Berzukova K, Ely R, Jackson S, Joshi A, Jehn K Thomas D. (2003). The effects of diversity on business performance: Report of the Diversity Research Network. Human Resource Management, 42, 321.
  • Laveist TA, Nuru-Jeter A. (2002). Is doctor-patient race concordance associated with greater satisfaction with care? Journal of Health and Social Behavior, 43, 296306.
  • Lawrence BS. (1997). The black box of organizational demography. Organization Science, 8, 122.
  • Leonard JS, Levine DI, Giuliano L. (2010). Customer discrimination. Review of Economics and Statistics, 92, 670678. doi:10.1162/REST_a_00018
  • Leonard JS, Levine DI, Joshi A. (2004). Do birds of a feather shop together? The effects on performance of employees’ similarity with one another and with customers. Journal of Organizational Behavior, 25, 731754.
  • Levin S, Sinclair S, Veniegas RC, Taylor PL. (2002). Perceived discrimination in the context of multiple group memberships. Psychological Science, 13, 557560.
    Direct Link:
  • Liao H, Chuang A. (2004). A multilevel investigation of factors influencing employee service performance and customer outcomes. Academy of Management Journal, 47, 4158.
  • Luo X, Bhattacharya CB. (2006). Corporate social responsibility, customer satisfaction, and market value. Journal of Marketing, 70(4), 118.
  • Malat J, Hamilton MA. (2006). Preference for same-race health care providers and perceptions of interpersonal discrimination in health care. Journal of Health and Social Behavior, 47, 173187.
  • Martin CL, Adams S. (1999). Thanking behavior in service provider-customer encounters: The effects of age, gender, and race. Journal of Social Psychology, 139, 665667.
  • Masterson SS. (2001). A trickle-down model of organizational justice: Relating employees’ and customers’ perceptions of and reactions to fairness. Journal of Applied Psychology, 86, 594604.
  • Maxham JG, Netemeyer RG, Lichtenstein DR. (2008). The retail value chain: Linking employee perceptions to employee performance, customer evaluations, and store performance. Marketing Science, 27, 147167.
  • McCall GJ, Simmons JL. (1978). Identities and interactions: An examination of human associations in everyday life (rev. ed.). New York , NY : Free Press.
  • McCormick AE, Kinloch GC. (1986). Interracial contact in the customer-clerk situation. Journal of Social Psychology, 126, 551553.
  • McKay PF, Avery DR. (2005). Warning! Diversity recruitment could backfire. Journal of Management Inquiry, 14, 330336.
  • McPherson JM, Smith-Lovin L, Cook JM. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415444.
  • Morgan NA, Rego LL. (2006). The value of different customer satisfaction and loyalty metrics in predicting business performance. Marketing Science, 25, 426439.
  • Morgeson FP, Hofmann DA. (1999). The structure and function of collective constructs: Implications for multilevel research and theory development. Academy of Management Review, 24, 249265.
  • Page S. (1997). An unobtrusive measure of racial behavior in a university cafeteria. Journal of Applied Social Psychology, 27, 21722176.
  • Pager D, Shephard H. (2008). The sociology of discrimination: Racial discrimination in employment, housing, credit, and consumer markets. Annual Review of Sociology, 34, 181209.
  • Pan Y, Zinkhan GM. (2006). Determinants of retail patronage: A meta-analytical perspective. Journal of Retailing, 82, 229243.
  • Patnaik D. (2009). Wired to care: How companies prosper when they create widespread empathy. Upper Saddle River , NJ : Financial Times Press.
  • Petersen L, Dietz J. (2008). Employment discrimination: Authority figures’ demographic preferences and followers’ affective organizational commitment. Journal of Applied Psychology, 93, 12871300.
  • Pettigrew TF, Tropp LR. (2006). A meta-analytic test of intergroup contact theory. Journal of Personality and Social Psychology, 90, 751783.
  • Pitts DW. (2005). Diversity, representation, and performance: Evidence about race and ethnicity in public organizations. Journal of Public Administration Research & Theory, 15, 615631.
  • Pitts DW. (2007). Representative bureaucracy, ethnicity, and public schools: Examining the link between representation and performance. Administration & Society, 39, 497526.
  • Ployhart RE, Weekley JA, Ramsey J. (2009). The consequences of human resource stocks and flows: A longitudinal examination of unit service orientation and unit effectiveness. Academy of Management Journal, 52, 9961015.
  • Preacher KJ, Hayes AF. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879891.
  • Preacher KJ, Kelley K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16, 93115. doi: 10.1037/a0022658.
  • Purdie-Vaughns V, Steele CM, Davies PG, Ditlmann R, Crosby JR. (2008). Social identity contingencies: How diversity cues signal threat or safety for African Americans in mainstream institutions. Journal of Personality and Social Psychology, 94, 615630.
  • Rajagopal. (2011). Determinants of shopping behavior of urban consumers. Journal of International Consumer Marketing, 23, 83104.
  • Reinhard M, Messner M, Sporer SL. (2006). Explicit persuasive intent and its impact on success at persuasion—the determining roles of attractiveness and likeableness. Journal of Consumer Psychology, 16, 249259.
  • Richard OC, Murthi BPS, Ismail K. (2007). The impact of racial diversity on intermediate and long-term performance: The moderating role of environmental context. Strategic Management Journal, 28, 12131233.
  • Riordan CM. (2000). Relational demography within groups: Past developments, contradictions, and new directions. Research in Personnel and Human Research Management, 19, 131173.
  • Roberson QM, Park HJ. (2007). Examining the link between diversity and firm performance: Effects of diversity reputation and leader racial diversity. Group and Organization Management, 32, 548568.
  • Rocha RR, Hawes DP. (2009). Racial diversity, representative bureaucracy, and equity in multiracial school districts. Social Science Quarterly, 90, 326344.
  • Rosenbaum MS. (2005). The symbolic servicscape: Your kind is welcomed here. Journal of Consumer Behavior, 4, 257267.
  • Sacco JM, Schmitt N. (2005). A dynamic multilevel model of demographic diversity and misfit effects. Journal of Applied Psychology, 90, 203231.
  • Schmit MJ, Allscheid SP. (1995). Employee attitudes and customer satisfaction: Making theoretical and empirical connections. Personnel Psychology, 48, 521536.
  • Schneider B. (1987). The people make the place. Personnel Psychology, 40, 437454.
  • Schneider B, Ehrhart MG, Mayer DM, Saltz JL, Niles-Jolly K. (2005). Understanding organization-customer links in service settings. Academy of Management Journal, 48, 10171032.
  • Schneider B, Smith DB, Taylor S, Fleenor J. (1998). Personality and organizations: A test of the homogeneity of personality hypothesis. Journal of Applied Psychology, 83, 462470.
  • Schreer GE, Smith S, Thomas K. (2009). “Shopping while Black”: Examining racial discrimination in a retail setting. Journal of Applied Social Psychology, 39, 14321444.
  • Sierra JJ, Heiser RS, Williams JD, Taute HA. (2010). Consumer racial profiling in retail environments: A longitudinal analysis of the impact on brand image. Journal of Brand Management, 18, 7996.
  • Spector PE, Brannick MT. (in press). Methodological urban legends: The misuse of statistical control variables. Organizational Research Methods.
  • Spence M. (1974). Market signaling. Cambridge , MA : Harvard University Press.
  • Tajfel H, Turner JC. (1986). The social identity theory of intergroup behavior. In Worchel S, Austin WG (Eds.). Psychology of intergroup relations (2nd ed., pp. 724). Chicago , IL : Nelson-Hall.
  • Thomas DA, Ely RJ. (1996). Making differences matter: A new paradigm for managing diversity. Harvard Business Review, 74(5), 7990.
  • Tsui AS, Gutek BA. (1999). Demographic differences in organizations: Current research and future directions. Lanham , MD : Lexington Books.
  • Turner JC. (1987). Rediscovering the social group: A self-categorization theory. Oxford , UK : Blackwell.
  • Umphress EE, Simmons AL, Boswell WR, Triana M. (2008). Managing discrimination in selection: The influence of directives from an authority and social dominance orientation. Journal of Applied Psychology, 93, 982993.
  • van Knippenberg D, Schippers M. (2007). Work group diversity. Annual Review of Psychology, 58, 515541.
  • Williams CL, Connell C. (2010). “Looking good and sounding right”: Aesthetic labor and social inequality in the retail industry. Work and Occupations, 37, 349377.
  • Wright P, Ferris SP, Hiller JS, Kroll M. (1995). Competitiveness through management of diversity: Effects on stock price valuation. Academy of Management Journal, 38, 272287.
  • Ziegert JC, Hanges PJ. (2005). Employment discrimination: The role of implicit attitudes, motivation, and a climate for racial bias. Journal of Applied Psychology, 90, 553562.