Psychological Capital in the Quick Service Restaurant Industry: A Study of Unit-Level Performance

Abstract

In this study, the construct of psychological capital (PsyCap) is explored within the quick service restaurant (QSR) industry. PsyCap, a second-order construct composed of hope, optimism, resilience, and self-efficacy, has received little attention in hospitality research despite its relationships with improving attitudes and behaviors. This study tested the relationships among PsyCap, service quality, customer satisfaction, and unit revenues through bivariate and mediational tests. Data were collected from a national chain of QSR employees, mystery shops, customer evaluations, and company records. The results indicate that collective PsyCap is positively related to all variables. Additionally, the results showed that service quality and customer satisfaction fully mediates the collective PsyCap to unit revenues relationship. Theoretical and practical implications are discussed.

Keywords

psychological capital revenues service quality customer satisfaction quick service restaurants

Introduction

In the workplace, managers, supervisors, and employees are considering more positive methods and resources to develop organizations and enhance performance (Bakker & Schaufeli, 2008). The concept of psychological capital (PsyCap) fits the call for a positive approach to understanding employee attitudes, as it is a second-order construct composed of the state-like, developable variables of hope, optimism, resilience, and self-efficacy (Luthans, Avolio, Avey, & Norman, 2007). However, PsyCap has been relatively unexplored in a group context and is relatively nonexistent in the hospitality field (Mathe & Scott-Halsell, 2012). Due to its reasonably new entrance into organizational literature, Ardichvili (2011) states that using collective constructs will increase understanding of employee development at the group and organizational levels, and employees will embrace the positive connotation of PsyCap.

In the quick service restaurant (QSR) context, developing employees and improving attitudes to fit the type of work is critical to deliver the highest quality product and service (Hancer & George, 2003). The QSR industry is strongly characterized by high turnover and an often monotonous, repetitive work environment requiring less technical skill, providing ample employment opportunity for the less experienced and often younger worker (DiPietro & Milman, 2004; DiPietro & Pizam, 2008; Mathe, 2011). Because of the type of work that occurs in the QSR environment, it is important to help combat turnover and low job satisfaction (Dienhart & Gregoire, 1993; Wildes, 2007) that characterizes the industry. One such method of improvement for QSR units is through positive means such as collective PsyCap. In the hospitality literature, one of the only published studies on PsyCap examined individual PsyCap and how perceived external prestige, or the belief about how others view one’s work, effects a QSR employee’s PsyCap and found that a positive relationship existed (Mathe & Scott-Halsell, 2012). That study and Wildes (2005, 2007) assert that the foodservice can carry a negative image due to its high turnover. Therefore, developing PsyCap through improved workplace perceptions is of critical importance to overcome QSR challenges, such as high turnover, to produce positive results (Mathe & Scott-Halsell, 2012).

Existing research has indicated that PsyCap has the ability to produce positive results in the areas of increased individual job satisfaction (Luthans et al., 2007), and commitment (Luthans, Norman, Avolio, & Avey, 2008), while decreasing individual stress and turnover (Avey, Luthans, & Jensen, 2009), among others (for a complete review, see Avey, Reichard, Luthans, & Mhatre, 2011). For hospitality businesses, and specifically restaurants, these individual variables are important as a lack of commitment, turnover, and stress are high (Kim & Gu, 2005). Examining methods to combat negative states and attitudes, as well as promote the positive effects, is therefore important.

Few studies have examined the effect of PsyCap on objective performance measures that were not rated by a supervisor or coworker. These studies have supported positive relationships with factors such as revenue on an individual basis (Peterson, Luthans, Avolio, Walumbwa, & Zhang, 2011). However, the studies examining performance have looked more so at individual factors, instead of groups of individuals working together, such as those found in restaurant or QSR units (see Table 1 for summary of studies between PsyCap and performance). Avey et al. (2011) state that these objective measures of performance will provide the most fruitful expansion of PsyCap, as a transformative construct for employees. Because PsyCap has a stronger impact on service workers, versus manufacturing workers (Avey et al., 2011), examining groups of QSR employees who engage in service interactions is important to the extension and development of the PsyCap research stream. By providing a quantifiable impact of PsyCap on service quality and customer satisfaction performance, QSR operators and executives can implement PsyCap development programs to aid in the increase of bottom line performance. Therefore, the present study examines three different types of objective performance indicators, each measured differently to explore collective PsyCap as a predictor of QSR unit performance. In other words, this study seeks to examine the bivariate and multiple mediated models of PsyCap and three objective performance measures including (a) service quality, (b) customer satisfaction, and (c) revenues in QSR units.

Table 1

Summary of PsyCap and Performance Studies Adapted From Newman, Ucbasaran, Zhu, and Hirst (2014)

Authors	Level	Finding	Measure	Industry
Luthans, Avolio, Avey, and Norman (2007)	Individual	PsyCap positively related to job performance	Self-reported	Students
Luthans, Avolio, Avey, and Norman (2007)	Individual	PsyCap positively related to job performance	Supervisor rated	Manufacturing
Avey, Nimnicht, and Pigeon (2010)	Individual	PsyCap positively related to supervisor rated performance and sales performance	Supervisor rated	Banking/Financial
Avey, Nimnicht, and Pigeon (2010)	Individual		Referrals
Peterson, Luthans, Avolio, Walumbwa, and Zhang (2011)	Individual	Increases (decreases) in PsyCap are positively related to increases (decreases) supervisor rated performance and sales performance, longitudinally	Supervisor rated	Banking/Financial
Peterson, Luthans, Avolio, Walumbwa, and Zhang (2011)	Individual		Individual sales performance
Paterson, Luthans, and Jeung (2014)	Individual	Increases in PsyCap are related to self-development and individual performance	Supervisor rated	Various (snowball sampling)
Walumbwa, Peterson, Avolio, and Hartnell (2010)	Team/group	Leader PsyCap positively related to follower PsyCap, which is positively related to follower performance	Supervisor rated follower performance	Law enforcement
Walumbwa, Luthans, Avey, and Oke (2011)	Team/group	Collective PsyCap positively related to team-level performance	Supervisor rated group performance	Banking/Financial
Clapp-Smith, Vogelgesang, and Avey (2009)	Team/group	PsyCap positively related to team performance, mediated by trust in management	Unit sales	Retail clothing
McKenny, Short, and Payne (2013)	Organizational	PsyCap positively related to financial performance	Text analysis of shareholder letters	Multiple

Literature Review

PsyCap is more specifically defined as an individual’s positive state of development, as characterized by the four resources of efficacy (confidence), hope (motivation), optimism (positive expectation), and resiliency (response to adversity; Luthans et al., 2007). Employees who have a sense of hope, optimism, resilience, and self-efficacy in their work are likely to provide superior performance for their organization; in large part through the outlook of the job and the resulting performance that occurs as a result of improved employee attitudes (Mathe & Slevitch, 2013).

The mechanisms in which performance can be generated in the context of QSR through PsyCap, are first understood by examining each construct that PsyCap encompasses individually (Avey, Nimnicht, et al., 2010). Self-efficacy is the confidence an individual has in one’s abilities (Stajkovic & Luthans, 1998). As Bandura (1997) posits, when self-efficacy is high, one is also more likely to put forth effort because he or she believes success is inevitable. In the service context, providing effort and believing one has the ability to satisfy customers should result in providing better quality service, leading to customer retention and increased financial performance (Zeithaml, 2000). Resilience, the second factor of PsyCap, describes the ability to recover after facing a problem or obstacle at work (Luthans et al., 2007). In a QSR setting, negative customer interactions are inevitable. Dealing with a difficult customer, then finding the resiliency to continue to satisfy customers in an effort to ensure return, can help to explain increased performance. Hope can be defined through two factors including the will to achieve an effect or goal, and the ways to reach the goal (Luthans et al., 2007; Snyder et al., 1991). In the service setting, hopeful managers tend to achieve higher financial performance for units they manage, as those managers have the willingness and pathways to achieve goals (Peterson & Luthans, 2003). Finally, optimism is a factor that uses positive attribution about success. Luthans, Avolio, Walumbwa, and Li (2005) showed that in Chinese factory workers, optimism was related to performance; in large part because successes were perceived as a result of internal contributions, whereas negative events were due to external forces.

Research supports that the four variables of PsyCap, together, predict greater positive employee attitudes and performance than each variable individually (Luthans et al., 2007). Avey et al. (2011) explain that this basis has its foundation in psychological resource theory which suggests that “some constructs are best understood as indicators of broader underlying factors” (p. 130). They state that while each variable alone is relevant to predicting employee attitudes and behaviors, together these four variables are a part of something larger and broader than itself. Examples of other second-order factors that show stronger performance as a collective of individual indicators, instead of each construct independently, include core self-evaluations (Judge & Bono, 2001), psychological empowerment (Spreitzer, 1995), and employee involvement (Lawler, 1996; Riordan, Vandenberg, & Richardson, 2005).

More recently, studies involving PsyCap have been focusing more so on PsyCap as a transformative variable, such that leadership styles are mediated by PsyCap to predict employees’ creativity (Rego, Sousa, Marques, & Cunha, 2012), or moderated by PsyCap to predict follower performance (Wang, Sui, Luthans, Wang, & Wu, 2014). Moreover, PsyCap has been shown to be a precursor of thriving, or the joint experience or learning and vitality, which is directly related to individual performance and personal development (Paterson, Luthans, & Jeung, 2014). And while it appears that the power of positivity, when present in a leader, becomes contagious to followers (Luthans, 2012), it is important to understand how a collective of individuals, who are conducting the work, are objectively performing regarding varying levels of PsyCap.

Collective PsyCap

PsyCap contributes to performance based on the “positive appraisal of circumstances and probability for success based on motivated effort and perseverance” (Luthans et al., 2005). Although PsyCap has been related to a variety of individual outcomes of performance (Avey et al., 2011), it is rarely used as a group-level construct. This is a fruitful area of extension since an individual’s positive psychological resources are convergent to groups and organizational effectiveness (Peterson & Zhang, 2011; Walumbwa, Luthans, Avey, & Oke, 2011), thereby enhancing their overall job performance (Walumbwa, Peterson, Avolio, & Hartnell, 2010). Consistent with this prediction, empirical evidence shows that PsyCap collectively facilitates greater organizational and group performance. Avey, Wernsing, and Luthans (2008) found that positive employees had an effect on organizational change given that their positivity was related to their positive emotions, their engagement, and organizational citizenship germane to the organizational change. In support, aggregating an individual’s PsyCap to groups, Walumbwa et al. (2011) found that collective PsyCap was positively related to group citizenship behavior and group performance as indicated by group direction, initiative, and innovation.

Extant literature is scant on front-line employees’ collective PsyCap in the employees-to-customer interaction quality as perceived by customers. This is surprising because the notion of service encounter (Surprenant & Solomon, 1987) suggests that customers often interact with different front-line employees each time (Gutek, Bhappu, Liao-Troth, & Cherry, 1999; Wallace, Johnson, Mathe, & Paul, 2011), and this service interaction quality is generally evaluated by the customers (Bitner, Booms, & Mohr, 1994; Wallace et al., 2011). However, it is prudent to note that employees’ collective PsyCap can affect employee–customer interaction quality via positive emotional contagion. In other words, employees’ positive emotions, spawned by collective PsyCap, can be contagious to customers in positive ways (Hatfield, Cacioppo, & Rapson, 1993). More specifically, employee positivity can create positive affective states that then may lead to positive emotional contagion to customers, which in turn may result in higher service quality as perceived by the customers. In support, Avey, Wernsing, et al. (2008) investigated and found that employees’ PsyCap was related to their positive emotions and their attitudes such as engagement and cynicism. In addition, Pugh (2001) suggests that positive emotions expressed by employees were positively associated with customers’ positive affect, which could lead to customers’ higher evaluations of service quality. In a similar vein, Liao and Chuang (2007) found that service employees in a positive service-oriented climate promote higher quality service to clients and customers. Walumbwa et al. (2010) also found that a positive service climate was the moderator of the relationship between PsyCap and their performance.

Building on the above discussion, based on theory and empirical evidence, it appears that collective PsyCap may influence QSR unit performance. Specifically, higher levels of collective PsyCap should foster a stronger confidence, motivation, positive expectation, and response to adversity aimed at higher desired performance outcomes, such as sales revenue and service quality. In addition, collective PsyCap may promote customers’ positive service quality evaluations of employee–customer relationships, which may also result in higher sales revenues (see Figure 1). As James et al. (2008) claim, a shared agreement at the individual level translates into the meaning of the construct at a higher level. Thus, the following hypotheses are put forward:

Hypothesis 1: Collective PsyCap is positively related to service quality.

Hypothesis 2: Collective PsyCap is positively related to customer satisfaction.

Hypothesis 3: Collective PsyCap is positively related to unit revenues.

Figure 1

Proposed Full Serial Mediation Model for Hypothesis 4

From a QSR perspective, Pettijohn, Pettijohn, and Luke (1997) suggested that quality, service and cleanliness were the most important factors at a QSR unit that create satisfaction. Yoon, Thompson, and Parsa (2009) concurred with this notion suggesting that quality, service, and restaurant ambience were precursors to a customer’s decision of selecting a restaurant; while Ryu, Lee, and Kim (2012) suggest that service quality is a predictor of restaurant image, which is positively related to value, satisfaction, and inevitably intent to return. Therefore, for the present study, we hypothesize that service quality will lead to greater customer satisfaction in a QSR unit.

To provide additional support for this logic, we examined DiPietro, Parsa, and Gregory (2011), who used third-party evaluations (mystery shops) of a QSR unit’s quality, service, and cleanliness in relation to revenues. While they failed to find significant support as predictors of financial performance, we believe that adding in the variable of customer satisfaction will help to support the relationship between service quality, customer satisfaction, and revenues. More specifically, we posit that with a multiple mediation model, PsyCap will lead to increased service quality at a QSR unit. An increase of service quality, should lead in turn to an increase of customer satisfaction as rated by customers, and predictably, revenues. As past studies have used customer satisfaction in relation to sales growth (Babakus, Bienstock & Van Scotter, 2004), we propose the following multiple mediator hypothesis:

Hypothesis 4: Service quality and customer satisfaction serially mediate the relationship between collective PsyCap and unit revenues.

Methodology

Sample and Procedure

The population of interest for this study was frontline workers in the QSR industry. The data were collected from a national chain of QSRs located throughout the United States. The sample was limited to employees who volunteered to participate after a notification and recruitment process. The surveys were administered via e-mail and survey links available on the parent company’s intranet site. Two regional vice presidents and directors within the organization, who agreed to assist in the data collection of the study, delivered the recruitment e-mail to their regions. A small monetary incentive was given to a randomly selected participating QSR unit, which qualified for entrance in a raffle. Qualification for the monetary incentive included receiving two separate responses from employees in the QSR unit. For every additional two responses, another entry was placed in the random drawing. Data collection took place over a 2-week period. The managers of the QSR units, who were contacted by the directors and regional vice presidents, invited the employees within each QSR unit to participate in the study. Since the exact number of employees within the QSR units who received the e-mail was not known, it was difficult to generate a response rate. It is estimated that approximately 100 QSR units received the e-mail; therefore, a 67% response rate from units was achieved. Of the 67 units, 328 responses were collected. However, of those 328, 124 were below the age of 18 years (these responses were unusable due to the protection of human subjects), leaving 204 qualified responses. Of the 204 responses, 168 were complete and usable. Participants’ responses from the sample were eliminated for two reasons (a) within the PsyCap measure, 20% of the items were not answered, or (b) central tendency, in which a respondent marked all answers in a particular column. The data was then aggregated to the unit level. In this case, every QSR unit that did not have at least two responses was eliminated leaving a total of 168 from 67 units. The number of participants per unit ranged from two to eight. The average group size was 2.46 (SD = .99). Wallace et al. (2011) in their estimation of empowering leadership climate used at least two QSR assistant unit managers per organizational unit for estimation; similarly Greenbaum, Mawritz, and Eissa (2012) used a focal employee and at least one coworker to operationalize the two together as “employee bottom line mentality,” providing support for the aggregation on group size.

Measurement

Psychological Capital

Luthans et al. (2007) constructed and validated the Psychological Capital Questionnaire, additionally used and validated in numerous other studies (Avey et al., 2009; Avey, Luthans, Smith, & Palmer 2010; Luthans et al., 2007; Norman et al., 2010). A revised 12-item measure was used to measure PsyCap, as it has also shown high validity (Avey, Luthans, & Mhatre, 2008). The scale uses a 6-point Likert-type format as has been used in the development and validation of the measure (Luthans, Avey, & Patera, 2008; Luthans et al., 2007). The Cronbach alphas for the PsyCap measure in the Luthans et al. (2007) study, which used four samples, were .88, .89, .89, and .89. Survey items were adapted to fit the QSR context. The full measure adapted to the QSR context is shown in Table 2. The present study Cronbach alpha was .90.

Table 2

PsyCap 12-Question Measure Adapted to QSR Context With Factor Loadings

Question	Factor	Standardized Factor Loading
I feel confident in representing my restaurant in meetings with management	Self-efficacy	.60
I feel confident contributing to discussions about my restaurant’s strategy	Self-efficacy	.74
I feel confident presenting information to a group of peers	Self-efficacy	.75
If I should find myself in a jam at work, I could think of many ways to get out of it	Hope	.49
Right now I can see myself as being pretty successful at work	Hope	.60
I can think of many ways to reach my current work goals	Hope	.52
I can be “on my own” if I have to	Resilience	.48
I usually handle/cope with stressful things at work well	Resilience	.56
I can get through difficult times at work because I’ve experienced difficulty before	Resilience	.50
I always look on the bright side of things regarding my job	Optimism	.86
I’m optimism about what will happen to me in the future as it relates to work.	Optimism	.66

Note: One item of hope “at this time, I am meeting the work goals that I have set for myself” was eliminated in a prior analysis due to low factor loading.

Service Quality

Service quality was measured by using data received by the participating QSR company. The data consisted of mystery shopping scores from activities conducted by a third-party organization, hired by the participating QSR company in the study. The use of mystery shopping is a cost–effective method of evaluation. Individuals who “shop” the QSR unit are trained to examine certain aspects of service during the evaluation and are able to provide the restaurant company unbiased, objective feedback appropriate to appraise service (Finn & Kayande, 1997).

Mathe and Slevitch (2013) and Wallace et al. (2011) both have used mystery shopping as a measure of service quality based on employee attitudes. Dimensions, on which service was rated, paralleled the Wallace et al. (2011) study including the friendliness, order accuracy, speed of food delivery, while responses to food orders evaluated the employee’s knowledge of the menu and upselling skills. The friendliness dimensions included customer greeting and salutation on food delivery. Accuracy items included repeating the order back to the customer and ensuring all items were delivered to the customer as ordered. Since the information is unique to one chain of QSR restaurants, the specific questions were not reported. Overall, 20 questions were asked and each item was evaluated on a 1 to 5 scale with 1 = poor to 5 = excellent. Scores were then scaled to a 100-point measure for ease of reporting to the restaurant company, as a gauge of performance.

Customer Satisfaction

Customer satisfaction was measured by a third-party organization that created a proprietary survey and database for the participating organization. Customers, after visiting the QSR unit, were provided with a URL to complete an online survey, or they could call a 1-800 number to complete the evaluation, on receiving the food and ending the transaction. Each QSR unit had 25 responses that were averaged for each unit. In return for participation, the participant was given a code for a select free menu item at their next visit. Consumers were first asked “Please rate your overall satisfaction with your experience today?” and were given a Likert-type scale of 1 = highly dissatisfied to 5 = highly satisfied for rating purposes. A total of 1,675 overall consumers responses, 25 for each QSR unit, were included and were then aggregated to the QSR unit level by the participating organization. For ease of use at the QSR unit level, the average score is transformed to a percentage. For example, if a unit had an average of 3.64 customer satisfaction score this translated to a 73% (3.64/5). The percentages were provided to the researchers by the QSR company. Demographic information of the consumers was not provided by the organization. Single items of satisfaction have provided as an acceptable measure in prior research (Drolet & Morrison, 2001; Wanous, Reichers, & Hudy, 1997). As Drolet and Morrison (2001) state:

An increase in the number of items encourages inappropriate response behavior and gives rise to positively correlated error terms across items within respondents. In short, multiple-item scales that produce high reliabilities (i.e., high alphas) may simultaneously reduce the quality of respondent responses and add very little information over a single- or, at most, two-item scale. (p. 201)

Because the data provided by the organization was part of a preconstructed, proprietary survey, we concur with Drolet and Morrison and find for the purpose of this study a single item indicator of customer satisfaction acceptable.

Revenues

Revenues were provided by the participating QSR company in total gross sales for the given month of data collection, for each individual unit. The average unit revenues was $104,212 (SD = $38,663).

Demographics

Questions regarding gender, age, and ethnicity were also asked in the employee survey. Respondents were 35% female and 65% male. Ethnicity yielded a majority of White/Caucasian respondents with 88% of valid responses. African American responses totaled 8% and Hispanic 2.5%. The average age of the participants was 28.33 years (SD = 10.77). Prior to analyzing the models, the group averages of demographics were analyzed. No demographic indicators were significantly bivariately correlated with PsyCap, service quality, customer satisfaction, or unit revenues.

Results

Wallace et al. (2011) serve as the foundation for aggregation of PsyCap. In order to establish the validity of aggregate variables from the individual level to the unit level, within group homogeneity and between-group heterogeneity must be achieved to ensure the group occurred naturally (Bliese, 2000; Wallace et al., 2011). The R_wg statistic compares the variance associated within a variable within a team to the expected variance within that team, assessing the agreement within a group (James, Demaree, & Wolf, 1984). Composite R_wg statistics for the PsyCap measure was .80, surpassing the threshold of .70 (Lance, Butts, & Michels, 2006). Intraclass correlation (ICC) offers an indication for the group-level variable’s reliability, and aggregation is justified with a significant F value (Klein & Kozlowski, 2000). For the PsyCap measure, ICC₁ = .515 and ICC₂ = .726 (F = 13.722, p < .01).

A confirmatory factor analysis (CFA) was conducted for the collective PsyCap measure to ensure that all items loaded properly onto the PsyCap measure. Dyer, Hanges, and Hall (2005) suggest to conduct a CFA to test the factor structure of constructs at the group level. PsyCap was measured as a second-order construct with the indicators loading onto their first-order factors (hope, optimism, resilience, self-efficacy). In the first run of the CFA, one item, “I can think of many ways to reach my current work goals,” an indicator of Hope, did not achieve the recommend .40 factor, loading at .38, so it was eliminated from the scale (Bernard, 1998). In the second analysis, all factor loadings were significant and above .40. The comparative fit index was .96 and nonnormed fit index of .95, both indicating good fit for the four-factor, second-order Model (Hu & Bentler, 1999; Rigdon, 1996). The χ² was 72.85 (df = 40), while the root mean square error of approximation (RMSEA) had a 90% confidence interval between .06 and .15. The four first-order constructs of hope, optimism, resilience, and self-efficacy all had high standardized loadings onto the PsyCap construct of .79, .87, .69, and .88. The total measure had a Cronbach alpha of .90. Considering each of the factors individually the Cronbach alphas were .90 for self-efficacy, .83 for hope, .72 for resilience, and .90 for optimism. To ensure that the four factor, second-order model, fit the data better than a single construct, all indicators were loaded onto one PsyCap variable. In the first evaluation of the single factor model, the hope item that failed to load on the four-factor model, also failed to load on the single factor model. After eliminating the item, the fit of the single factor model was poor with the comparative fit index = .88, and nonnormed fit index = .85. Additionally, χ² =146.23 (df = 44) and RMSEA had a 90% confidence interval between .15 and .22, outside of acceptable limits. Conducting a χ² difference test reveals the two models were significantly different, favoring the former, second-order construct model, and was therefore retained. Factor loadings and adapted items for the QSR unit context for the measurement model can be seen in Table 2.

First, the bivariate correlations and descriptive statistics among collective PsyCap and dependent variables were examined. The minimum total PsyCap score reported by a group was 35, and maximum 66, while the minimum service quality score was 69% and maximum 100%. Customer satisfaction minimum score was 36% and maximum was 92% (out of possible 100%), where revenues minimum was $45,249 and maximum was $267,730. Using histograms the data followed a normal curve. As seen in Table 3, the relationships among collective PsyCap and service quality, customer satisfaction, and unit revenues were all positive and significant. Because significant bivariate correlations existed among PsyCap and service quality, customer satisfaction, and revenues, the data is suitable for mediation as proposed in Hypothesis 4 (Baron & Kenny, 1986; Wallace et al., 2011). Hypotheses 1 to 3 were tested using multiple mediated regressions as seen in Table 4. As evidenced in Step 1, the relationship between PsyCap and unit revenues is positive and significant supporting Hypothesis 1 (β = .30, p < .05). Hypothesis 2, that PsyCap is positively related to service quality was also supported as seen in Step 2 of Table 4 (β = .29, p<.05). Hypothesis 3 that PsyCap is positively related to customer satisfaction can be seen in Step 3. The relationship between PsyCap and customer satisfaction failed to achieve significance directly. However, as described by the analysis next, service quality fully mediated the relationship between PsyCap and customer satisfaction (Table 5).

Table 3

Group-Level Means, Standard Deviations, and Correlations of Study Variables

Variable	Mean	SD	PsyCap	Service Quality	Customer Satisfaction	Unit Revenues
PsyCap	57.19	6.21
Service Quality	86.96%	7.24%	.37**	1
Customer Satisfaction	72.80%	9.65%	.30*	.46**	1
Unit Revenues	$104,212	$38,633	.28*	.29*	.42**	1

Note: Mean scores reported as average total scores (Possible max scores: PsyCap, 66; Service Quality, 100%; Customer Satisfaction, 100%).

p < .01. *p < .05.

Table 4

Multiple Regression Mediation Steps for Indirect Effects

	Step 1: Focal IV and Controls to DV	Step 2: IV and Controls to Mediator (Service Quality)	Step 3: IVs and Controls to Mediator (Customer Satisfaction)	Step 4: IVs and Controls to DV
Predictors:	DV = Sales	DV = Service Quality	DV = Customer Satisfaction	DV = Sales
Avg. Group Age	.06	−.24*	.03	.14
Avg. Group Gender	.04	.16	−.01	−.06
Collective PsyCap	.30*	.29*	.16	.17
Service Quality			.41*	.13
Customer Satisfaction			.02	.33*
F	1.976	5.332*	4.489*	3.545*
R ²	.09	.21	.23	.23

Note: IV = independent variable; DV = dependent variable. Effects reported are standardized Betas.

p < .05.

Table 5

Summary of Mediation Tests With Indirect Effects Using Bootstrapping Method

Mediation Relationships		Estimate	Boot SE	95% CI LL	95% CI UL	Supported
1	PsyCap → Service Quality → Customer Satisfaction	.0018	.0010	.0004	.2015	Yes
2	PsyCap → Service Quality → Revenues	226.9269	290.1381	−174.8376	1019.5414	No
3	PsyCap → Customer Satisfaction → Revenues	327.9369	289.6962	−65.2716	1035.3041	No
4	PsyCap → Service Quality → Customer Satisfaction → Revenues	245.8635	223.2808	27.6814	947.2188	Yes

Note: CI = confidence interval; LL = lower limit; UL = upper limit. Unstandardized regression coefficients are reported.

The bootstrapping method suggested by Shrout and Bolger (2002) using the PROCESS macro in SPSS is also recommended for multiple mediator models (Preacher & Hayes, 2008). Shrout and Bolger (2002) state that this method is ideal for small samples, where structural equation modeling is ideal for larger samples (Kim & Gu, 2005). Moreover, past studies have used aggregation of data like the present in the indirect effects mediation models (Mawritz, Mayer, Hoobler, Wayne, & Marinova, 2012). As Rucker, Preacher, Tormala, and Petty (2011) posit, examining the indirect effects of mediation analysis, like that of the present study, provides a greater emphasis on the relationship between independent and dependent variables than other methods. The results of the steps and series of mediation tests can be seen in Table 4. Hypothesis 4, which states that service quality and customer satisfaction fully mediate the relationship between collective PsyCap and unit revenues, was supported (p < .05; Table 5). It should be noted that two other models of mediation were examined, one that only included service quality as a mediator between PsyCap and revenues and one that only included customer service as a mediator between PsyCap and revenues, both were nonsignificant (i.e., crossed the 0 threshold into negative numbers) using the Preacher and Hayes (2008) method (Table 5).

To put into perspective the effects of the full mediation, the results from the indirect effects of mediation, without control variables, were constructed (not shown). The minimum PsyCap score recorded for a QSR unit was 35. Through the analysis, a collective PsyCap score of 35 would yield an estimated service quality score of 77.54%, a satisfaction score of 62%, and unit revenues of $65,516.11. A one unit increase of PsyCap would raise the unit revenues by $1,763.71. At the top end of the PsyCap measure, a score of 66 would yield a service quality score of 90.70%, satisfaction score of 77%, and average unit revenues of $120,191.30 (all results ±5%).

Discussion

In a service environment, such as the hospitality and specifically QSR industry, groups having strong positivity who work well together, are essential for success. In the QSR context as shown in this article, groups of individuals have the ability to directly and indirectly influence customer satisfaction, service quality, and unit revenues. The results of this study show that collective PsyCap has a direct influence on both revenues and service quality, and an indirect effect on customer satisfaction. The crux of the study showed that by having higher PsyCap, groups of employees have an impact on the total revenues through full mediation via service quality and customer satisfaction. For example, in a group with low collective PsyCap when a service interaction is poor and food is returned, a different type of reaction may occur. This back of house employee, whose responsibility is to remake the food, may get frustrated and disengage from their work. This could be because of the lack of combined elements of hope, optimism, self-efficacy, and resilience. This is not only going to directly impact the customer, through a slower delivery time and potentially decreased satisfaction of product quality, but also influence front of house employees who interact directly with the customer and the frustrated back of house employee.

Some researchers have suggested that employee attitudes, such as PsyCap, serve as the key explanatory mechanism between customer perceptions of service quality and satisfaction (Kim & Ok, 2010). It is believed that poor service, provided in a quick interaction in a restaurant, may result in the loss of a customer (Reich, McCleary, Tepanon, & Weaver, 2006) and poor restaurant image (Ryu et al., 2012). It should be noted in the results that the mediation relationship tested that the effect of customer satisfaction is critical in predicting revenues. DiPietro et al. (2011) found that no significant relationship existed between mystery shopping scores and revenues. However, with the addition of the triangulated data point of customer satisfaction, the relationship between service quality and revenues becomes null, implying the effect of service quality is fully mediated through customer satisfaction (Table 4, Step 3); a variable that was not present in the DiPietro et al. (2011) study.

The present study theoretically extends the PsyCap and hospitality literature by using a unique data collection and triangulation method that avoids common method variance. By having employee, customer, trained third-party evaluator (mystery shoppers), and raw sales data, this study provides evidence that, collectively, employees have a significant influence on the operations, customer evaluations, and bottom line of a QSR unit. By further understanding how QSR revenues can be influenced through employees, who in turn provide better service and satisfaction, organizational departments including marketing, customer insights, and human resources can use this information to effectively leverage resources.

Practical Implications

The findings from this research suggest that organizations should implement and develop training or intervention programs designed to enhance their employees’ overall levels of PsyCap, which should then enhance group-level performance. Organizations and supervisors have often recognized an employee’s job performance under the rubric of his/her relatively stable personality traits, employee-organization fit, employee-job fit, and core self-evaluation. However, employee training programs to increase PsyCap, conducive to performance impact, are thus far not widely recognized. Positive psychological interventions for public health purposes are effective in enhancing overall individual well-being and reducing depression symptoms (Bolier et al., 2013). These interventions include PsyCap and others that focus on positive elements such as hope therapy (Cheavens, Feldman, Gum, Michael, & Snyder, 2006), well-being therapy (Fava, 1999), and positive thinking therapy (Peters, Flink, Boersma, & Linton, 2010). Because past research has shown that well-being and happiness are precursors to improved job performance (Wright & Cropanzano, 2000), QSR companies should design programs specific to the type of work being conducted to reap the benefits that can occur from positive intervention.

PsyCap can be developed through a short training process that has a significant positive impact on the trainees’ work performance (Luthans, Avey, Avolio, & Peterson, 2010; Peterson et al., 2011). For example, Luthans et al. (2010) suggest face-to-face, short-term training guidelines of the PsyCap intervention (PCI) model: employees are to (a) set work-related valuable and challenging goals (hope), (b) plan multiple pathways to accomplish each goal and to share those pathways in group discussion (realistic optimism), and (c) evaluate and update their progress based on group feedback (efficacy and resilience).

For QSR employees, who tend to be younger and less experienced as discussed previously, managers may use one of the social network platforms (Twitter or Facebook, through a free private group account) embedded with these guidelines to develop unit-level PsyCap instead of face-to-face training. More specifically for each QSR unit, QSR companies can involve employees in developing and reaching QSR unit-level goals by asking for employee feedback. More specifically, how to improve performance when considering situations that require enhanced resilience, optimism, hope, or self-efficacy. For example, a service failure scenario may be presented to the employees, and with the feedback of the employees, managers can create scenarios that focus on resilience and how to recover from difficult situations. As the present study provided support for, a proper service recovery can increase perceptions of service quality (Magnini, Ford, Markowski, & Honeycutt, 2010), which then has a positive impact on both customer satisfaction and revenues. Luthans (2012) suggests that PsyCap training programs previously discussed are often 1-hour or 3-hour programs, and lead to PsyCap increases of between 2% and 5%. As shown in the results, a one-point increase of collective PsyCap is approximately a gain of $1,763.71 per month, making this positively oriented construct of value to the organization.

Through the growth of PsyCap by training and development, not only will employees become better individual performers (Paterson et al., 2014), create better group performance through improved service quality, customer satisfaction, and bottom line performance as the present study demonstrated, they will also likely be more intent to stay with the organization and develop as individuals (Avey et al., 2011). As Paterson et al. (2014) state, human resource departments, and in this context QSR companies and franchisees who control hiring decisions, should note the importance of PsyCap on thriving in the workplace, self-development, and performance such that “employees at all levels who are not just performing the work that needs to be done today, but are also developing into the employee they will need to be tomorrow” (p. 443).

Limitations and Future Studies

As with any study, several limitations exist. Although the number of usable responses was adequate (N = 168), aggregating to 67 groups served as a limitation. Less than 100 are considered “small” for CFA purposes. The small sample size means that power would also be limited (Kline, 2005). The CFA marginally met some standard thresholds of model fit (e.g., RMSEA), which is likely due to the smaller sample size, but the measurement model all achieved good fit on many key indices (Hu & Bentler, 1999). Because of the relatively small sample size of groups, the chi-square statistic and RMSEA can be an unreliable indicator of model fit (Kline, 2005). Kenny (2014) explicates that in adequate sized samples using CFA (n > 100), the RMSEA statistic is a stable predictor. The present study’s use of the RMSEA confidence intervals and can help understand the sampling error in the RMSEA. Because there is greater sampling error for small degrees of freedom and sample models (like the present study as it uses groups of individuals), Kenny, Kaniskan, and McCoach (2014) suggest to not even compute the exact RMSEA for low degree of freedom models.

Moreover, the primary data were collected across a 2-week period whereas the revenue, customer satisfaction, and service quality measurements as reported by the participating QSR company were for the entire month the data were collected in. The researchers requested the data for the 2-week time period only; however, reporting by QSR corporate office to the individual units is on a monthly basis. Additionally, customer satisfaction is measured using a single indicator. The use of multiple indicators is often used for the sake of increased reliability of the measure; however, some service researchers claim that increasing the number of items contributes very little to the information obtained from the original or first item (Drolet & Morrison, 2001).

Furthermore, the inability to control for leadership traits in the unit is a limitation and a fruitful area of further research since the supervisor or leader of a unit can have an influence through interpersonal interactions with employees (Mathe & Slevitch, 2011). Controlling for leadership traits such as transformational leadership or authentic leadership (Walumbwa et al., 2011) would be an important contribution to the literature.

Another limitation that presents rich opportunities for further research is that the present study is cross-sectional in nature. Measuring PsyCap attitudes over time in relation to objective performance measures will provide greater validity to the present results (Peterson et al., 2011). Future studies should also test pre- and post-effects of PsyCap training on groups as well as the resulting changes in performance. In particular, examining differences in back of house and front of house employees and the effects of interpersonal relationships would be salient research. Because QSR operations tend to be different than other industries, external generalizability is also limited.

Conclusion

This study was the first to examine PsyCap in the QSR context in the hospitality literature. The results of this study extend the limited research on collective PsyCap in predicting objective performance measures such as revenues, service quality, and customer satisfaction. The results supported positive relationships among all variables of interest, in that, direct relationships between PsyCap and customer satisfaction, service quality, and revenues, were supported. When considering PsyCap, service quality and customer satisfaction together, results suggest full mediation in that service quality and customer satisfaction carry the full effect of PsyCap in predicting revenues.

As mentioned previously, research on PsyCap has focused primarily on individual outcomes, whereas the present study focused on aggregated group-level outcomes. Because PsyCap has been shown to be more effective in the service industry compared with others such as manufacturing (Avey et al., 2011), this study extends this stream of research by showing how attitudes such as PsyCap can transform individual group members to create improved customer service and satisfaction as well as financial performance.

Footnotes

Kimberly Mathe, PhD (e-mail: kim.mathe@okstate.edu), is an assistant professor and Director of the Wayne Hirst Center for Beverage Education at the School of Hotel and Restaurant Administration at Oklahoma State University, Stillwater, Oklahoma. Sheila Scott-Halsell, PhD (e-mail: sheila.scott@ttu.edu), is an associate professor and associate chair in the department of Restaurant, Hotel and Institutional Management at Texas Tech University, Lubbock, Texas. Seontaik Kim, PhD (e-mail: seontaik.kim@morgan.edu) is an Assistant Professor in the Department of Business Administration at the Earl G. Graves School of Business and Management located at Morgan State University. Matthew Krawczyk (e-mail: Mattjk@vt.edu) is a PhD student in the Department of Hospitality and Tourism Management, Virginia Tech University, Blacksburg, Virginia.

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