This is an open access article distributed under the terms of the Creative Commons
Attribution License (As the economy increasingly becomes a "service economy", studying service providers' behaviors is essential. This has the potential to increase service effectiveness and the growth of service organizations (Hennig-Thurau, Groth, Paul, & Gremler, 2006). Inspired by the title of the book God of Small Things (Arundhati-Roy, 1997) we studied the components of service interactions, and focused on the "small things" composing them, and their motivational and emotional roots.
We link regulatory focus motivations (Higgins, 1997), positive and negative affective states (Watson, Clark, & Tellegen, 1988) and service interaction behaviors (Vilnai-Yavetz & Rafaeli, 2003) to suggest that the behavior of service providers in service encounters is determined in part by their motivation to focus on promotion or prevention, and whether their emotional tone in interactions is positive or negative. An important question that has yet to be answered is whether regulatory foci impact service behaviors through the mediating effect of positive and negative affective states. Although motivation and behavior have been studied a great deal in the context of service and marketing (e.g., Nysveen, Pedersen, & Thorbjørnsen, 2005), we found no study which addressed this issue. The current paper contributes to the literature by suggesting an answer to this question, and thereby offering a means to better understand service interactions and their antecedents.
Literature Review
Service Interactions
Several classifications suggest a dichotomy in service interactions. This dichotomy is captured by Clark and Mils' (1993) communal and exchange concepts. A communal interaction is governed by the service provider's desire for social belonging, while the exchange mode is more task-oriented. The dichotomy is echoed in Gutek, Bhappu, Liao-Troth, and Cherry’s (1999) “encounters” and “relationships,” Price, Arnould, and Tierney’s (1995) “transactional” and “interpersonal” relationships, and Lovelock and Wright's (1999) “continuous relationships” and “sporadic transactions.”
Vilnai-Yavetz and Rafaeli’s (2003) put forward a framework that identifies two conceptually distinct components of any interaction: the skeleton and the tissue. The skeleton refers to the fundamental content of exchange relations between partners to an interaction without which the interaction would not exist. The tissue comprises pro-social behaviors that cannot substitute for the skeletal behaviors but rather follow or accompany them. Behaviors comprising the tissue are informal and can be either positive (e.g., a smile) or negative (e.g., a frown), and thus enhance or detract from the experience of the interaction partners. What differentiates the skeleton and tissue idea from previous interaction concepts is that the skeleton and tissue are separate elements of the same interaction. The tissue elements can add an emotional tone to even very routine interactions; thus, learning to recognize the emotional tones associated with service interactions can be a critical step toward understanding and enhancing service delivery.
We will elaborate on the unique consequences of each of these two types of behaviors (i.e., tissue and skeleton), which impact the service process as a whole.
Regulatory Focus – Promotion and Prevention Motivational Systems
In the current study, we refer to motivation in terms of the regulatory focus model (Higgins, 1997). Higgins distinguished between two behavior regulation systems termed prevention and promotion, each of which serves a fundamental human need. The prevention system serves the need for security, or avoidance of loss; it favors strategies that are vigilant and conservative. The promotion system deals with the pursuit of gains (advancement, profit, or success); it favors strategies that involve risk.
The prevention and promotion systems are stable tendencies which develop during childhood, and can also be activated to high levels temporarily, in specific situations (Molden & Higgins, 2008). People driven by promotion goals tend to scrutinize their social world for information that bears on the pursuit of success (Higgins & Tykocinski, 1992). Studies have found that such individuals are especially well-attuned to emotions relating to the successful or unsuccessful pursuit of positive outcomes (Higgins, Shah, & Friedman, 1997), they focus on interpersonal strategies geared toward promoting desired outcomes (Higgins, Roney, Crowe, & Hymes, 1994), and they show high motivation and persistence on tasks that are framed in terms of promotion (Shah, Higgins, & Friedman, 1998). Motivations for promotion have been found to inspire more risky behavior, in which people prioritize goals and evaluate choices so as to maximize their potential for realizing gains, even at the possible cost of incurring significant losses (Förster, Higgins, & Bianco, 2003); therefore, we believe that skeleton behavior is not the typical behavior when it comes to promotion focus service employees.
In contrast, people with a prevention focus have been found to prioritize goals and evaluate choices so as to maximize their protection from losses even at the possible cost of foregoing significant gains. They tend to focus on information relevant to the avoidance of failure (Higgins, 1997), and are well-attuned to emotions relating to the successful or unsuccessful avoidance of negative outcomes (Higgins et al., 1997). People motivated toward prevention turn their thoughts to what must be done to avoid mistakes, and resolve their need to avoid loss by being extra-vigilant and cautious (Higgins, Roney, Crowe, & Hymes, 1994).
Given the above characterization we posit:
H1: A promotion focus will positively relate, and a prevention focus will negatively relate, to positive tissue behaviors.
H2: A prevention focus will be positively linked to increased performance of skeleton behaviors.
The relative dominance of either system (promotion and prevention) has been found to have a strong effect on emotion and behavior such that promotion focus was found to be positively related to positive affect (PA) and prevention focus relates positively to negative affect (NA) (e.g.,Van Dijk, Seger-Guttmann, & Heller, 2013). The second purpose of the present study was thus to address the question of whether positive and negative affect mediate the link between regulatory focus and service behaviors. We first define positive and negative affective states.
Positive and Negative Affective States
Barsade and Gibson (2007) proposed that affect encompasses the notion of feeling states, which are in-the-moment, short-term affective experiences, as opposed to feeling traits, which are more stable tendencies to feel and act in certain ways (Watson et al., 1988). Affect is frequently understood in terms of two dimensions, valence (positive or negative) and arousal (high or low) (Barsade & Gibson, 2007). In the present study we examined service providers’ positive and negative affective states.
Regulatory Focus and Affective States
In the theory of regulatory focus, emotions (or affective states) are determined by a personal focus on promotion or prevention (Brockner & Higgins, 2001). Higgins (1997) highlighted the relevance of regulatory focus motivation on an individual's affective state. Carver, Sutton, and Scheier (2000) demonstrated the positive relationship between an individual's promotion system and his or her positive affective state, and conversely the relationship between the prevention system and a negative affective state. Negative affect (NA) and positive affect (PA) have been found to be closely linked to both the prevention and promotion systems (Brockner & Higgins, 2001). When the prevention system is dominant, the failure to prevent loss leads to heightened agitation and anxiety. On the other hand, when the promotion system is dominant, achieving a gain leads to elation and cheerfulness (Brockner & Higgins, 2001). Thus, the NA system may monitor success or failure in the pursuit of prevention goals, and the PA system governs affective monitoring of success or failure in the pursuit of promotion goals (Carver et al., 2000). In keeping with the foregoing, we formulated hypothesis 3 as follows:
H3: A promotion focus will relate positively to PA and negatively to NA, while a prevention focus will relate positively to NA and negatively to PA.
The third and most critical objective of our study was to address the issue of whether PA and NA mediate the effect of regulatory foci on service behaviors. Although studies have documented the importance of affect for performance (Barsade & Gibson, 2007; Weiss & Brief, 2001), none of these studies has examined the specific relationship between employees’ affective states and their skeleton or tissue behaviors in the context of service interactions. Studies linking regulatory focus theory with service behaviors are also scarce. Therefore one of our aims was to fill this gap.
Positive and Negative Affective States and Pro-Social Behavior
The relationship between emotions and work performance has begun to attract scholarly attention (Dallimore, Sparks, & Butcher, 2007; Pugh, 2001; Weiss & Brief, 2001). Positive emotions have been identified as facilitating approach behavior (Fredrickson, 2004). Positive mood is also associated with positive outcomes, including helping behaviors at work (George, 1991) and improved performance (Cropanzano & Wright, 2001). Positive emotions evoke pro-social behavior (Barsade & Gibson, 2007). George (1991) reported that employees' positive emotions or affective states lead to increased pro-social behavior at work. In the context of marketing, Kelley and Hoffman (1997) found that PA in employees was positively related to the employees’ perceptions of altruistic organizational citizenship behavior and customer-oriented behavior, and negatively related to sales-oriented behavior. In the context of service, studies show the impact of employees' affect on performance and evaluations of service quality (e.g., Dallimore et al., 2007). As noted by Pugh (2001), consumers have expectations for affective input as part of the service provision. For example, empathy in the reaction of service providers is considered an important dimension of service quality (Parasuraman, Zeithaml, & Berry, 1988). Moreover, studies have shown that employees’ emotional expressions predict customers’ post-encounter moods (Tsai & Huang, 2002).
Since the display of positive emotion by a service provider can be viewed as a predictor of job performance (Kaplan, Bradley, Luchman, & Haynes, 2009) and of service quality (Hochschild, 1983), a display of negative emotion toward a customer may be considered a violation of customer expectations and can negatively affect the customer's perception of the relational aspect of service quality. Several theoretical explanations have been suggested for the positive relationship between PA and pro-social behavior. George (1991) claimed that employees whose affective state is positive are likely to perceive situations in a more positive way, and to respond accordingly. Isen and Baron (1991) suggested that positive affective states may lead to increased awareness of positive social indicators, making people more likely to display pro-social behaviors. Conclusions about the meaning and influence of NA on organizational life are far more complex (Barsade & Gibson, 2007). Negative emotions produce grave problems for individuals and society, including anxiety, stress, depression, and aggression (Fredrickson, 2004). Spector and Fox (2002) argued that such negative emotions increase the frequency of anti-social or deviant organizational behaviors. Hence our two final hypotheses:
H4: PA and NA mediate the positive relationship between a promotion focus and positive tissue behaviors.
H5: PA and NA mediate the negative relationship between a prevention focus and positive tissue behaviors.
Our predicted model for the relationships between regulatory focus, PA and NA, and service interactions is presented in Figure 1.
Figure 1
Predicted relationships between regulatory focus, affect, and service behaviors.
Method
Research Overview
This project is based on qualitative as well as quantitative data collection. The main study focuses on the quantitative data, as will be elaborated below, but it was based on an early stage of qualitative data collection and analysis.
Data Collection
In the qualitative stage of the study, we conducted in-depth interviews with apparel store salespeople. Based on the findings from these interviews, we constructed a questionnaire, which served as the foundation for the quantitative stage of the research: a survey of apparel store salespeople.
Participants
In-depth interviews and an in-shop survey were conducted during the months of April to May 2010. Apparel chain stores and private stores were chosen randomly from a list of apparel stores in the center and north of Israel. At all the chosen stores, employees were paid through a combination of salary and a commission system, where salespeople receive bonuses for any sales they contribute to, and objectives are set and achieved on a competitive basis.
Qualitative Stage
Seven salespeople, who participated in in-depth interviews on a volunteer basis, were asked to describe their work process in detail, including any relevant behavior as a salesperson during the working day. These interviews yielded a list of typical behaviors, which served as a basis for the survey questionnaire in the next stage of the research.
Quantitative Stage
Ninety Salespeople took part in the survey, of which 25 were men (28% of the sample) and 65 were women (72%). The respondents were aged 18 to 62, with an average age of 27.3 years (standard deviation 7.9). Most were single (71%) or married (24%). Most of the salespeople (83%) were employed by large, well-known Israeli apparel chains (e.g., FOX, Castro). The remainder (17%) worked for private independent apparel stores. The average tenure in the store was 21.1 months, with the newest salesperson having been on the job for only a month, and the most senior having worked for 84 months at the same store (standard deviation 18.1 months). Surveys were returned anonymously.
Research Variables
All the survey items which were translated from the original (English) into Hebrew were double checked using a back and forth approach. First a professional translator translated all the survey items from English to Hebrew. Then another professional translator translated those Hebrew items into English again. If discrepancies in wording were found, this back-translation process was repeated.
Regulatory focus was measured by two indices of 7 items each, adapted from Lockwood, Jordan, and Kunda (2002). Responses to the 7 items were indicated on a 9-point Likert scale, with 9 = "strongly agree about myself" and 1 = "strongly disagree about myself". The first index measured promotion focus (original reliability: Cronbach's alpha = .81) and the second index measured prevention focus (original reliability: alpha = .75). The reliability of the questionnaire was similar to the original, with an alpha of .89 for the promotion focus and .75 for the prevention focus items.
Positive and negative affectivity were measured by two indices of 10 items each (using a 5-point scale, with 5 = "certainly yes" and 1 = "certainly not") adapted from the PANAS scale developed by Watson et al. (1988). The first index measured positive affectivity (PA; original reliability: Cronbach's alpha = .88), and the second measured negative affectivity (NA; original reliability: alpha = .87). The reliability for the questionnaire was similar to the original, with an alpha of .79 for the PA items and .89 for the NA scale.
Service interactions. We created a list of 15 behaviors based on the qualitative stage of the research. Building on Vilnai-Yavetz and Rafaeli (2003), service behaviors were evaluated as core behaviors (skeleton) or accompanying behaviors (tissue). Participants responded to each item on a 5-point scale, with 5 = "certainly describing my behavior at work" and 1 = "certainly not describing it".
We applied exploratory factor analysis (EFA) with Varimax rotation to the 15 items. Thirteen of them were loaded on three factors, representing three different types of service behaviors: positive tissue – helping behaviors, positive tissue – impression management behaviors (e.g., displaying self-confidence), and skeleton – core sales activities. Two of the original 15 behaviors were dropped from the analysis because they did not converge into identifiable factors. A closer look at these items showed that they were ambiguous (e.g., “My appearance communicates fashion”). One additional item was dropped to increase reliability for the skeleton factor. Cronbach's alphas were .87 for the first positive tissue factor, .61 for the second positive tissue factor, and .65 for the skeleton factor. The final 12 items used in the analysis, and their division into the three factors, are presented in Table 1. The strong loadings of the items on their corresponding factors support the validity of this factor structure.
Although suggested by Vilnai-Yavetz and Rafaeli’s (2003) theoretical framework, no negative tissue was tested in the current study. We wished to avoid the social desirability bias (Randall & Fernandes, 1991), which argues that negative behaviors are unlikely to be reported by participants, even if actually occur, because they are neither desired nor rewarded.
Table 1
EFA Results for the Service Behaviors
| EFA loadingsa |
|||
|---|---|---|---|
| Survey items | 1. Positive tissue I – Helping behaviors | 2. Positive tissue II – Impression management behaviors | 3. Skeleton – Sales behaviors |
| I perform my work with the feeling that I work in a popular store. | .750 | .071 | .033 |
| I'm patient with the customers. | .790 | .051 | .183 |
| I show courtesy toward the customers. | .755 | .183 | .840 |
| I help other employees bring clothes from the storage area. | .676 | .276 | .120 |
| I give each customer personal attention. | .839 | .138 | .202 |
| I return from the storage area quickly to save the customer time. | .707 | .348 | .023 |
| I speak to the customers as equals. | .647 | .271 | .060 |
| I use my sense of humor when dealing with customers. | .099 | .798 | .015 |
| I communicate self-confidence. | .198 | .634 | .195 |
| I pay attention to details. | .166 | .727 | .132 |
| I can make customers buy items they didn't plan to buy. | .019 | .098 | .852 |
| I'm successful in selling accessories, in addition to clothing. | .195 | .087 | .835 |
Note. EFA: Exploratory factor analysis. Total variance extracted by the three factors = 59.3%. Rotation method: Varimax; Eigenvalues > 1.0.
aItems with high loading on factor are marked in bold.
Background data gathered included five items: gender, age, marital status, length of employment, and type of apparel store (chain/independent).
Findings
The analysis supported most of the research hypotheses, as elaborated below. Figure 2 presents the actual (confirmed) relationships between the research variables.
Figure 2
Actual relationships between regulatory focus, affect, and service behaviors.
Promotion Focus, Prevention Focus, and Positive Tissue Behaviors
Supporting Hypothesis 1, a promotion focus was positively related to positive tissue behaviors I and II (beta = .32, p < .01; beta = .35, p < .001, respectively). This hypothesis was only partially supported, as no relationship was found between a prevention focus and positive tissue behaviors. Multiple regression results are shown in Tables 2 and 3. As can be seen in these tables, power analyses using G*Power 3.1 software (Faul, Erdfelder, Buchner, & Lang, 2009) produced satisfactory levels of power for all statistical tests.
Table 2
Regression Analyses of PA and NA as a Mediator Between Promotion and Prevention Foci and Positive Tissue Behaviors I (N = 90)
| Variables entered | Beta
|
|||
|---|---|---|---|---|
| Stage 1: DV = PA | Stage 1: DV = NA | Stage 2: DV = Positive tissue behavior I | Stage 3: DV = Positive tissue behavior I | |
| Promotion focus | .40*** | -.21* | .32** | .08 |
| Prevention focus | -.18 | .55*** | -.19 | .07 |
| PA | .43*** | |||
| NA | -.34** | |||
| R2 = .16 | R2 = .30 | R2 = .11 | R2 = .38 | |
| Adjusted R2 = .14 | Adjusted R2 = .29 | Adjusted R2 = .10 | Adjusted R2 = .35 | |
| Power of test = 0.957 | Power of test = 0.997 | Power of test = 0.905 | Power of test = 0.999 | |
*p ≤ .05. **p ≤ .01. ***p ≤ .001.
Table 3
Regression Analyses of PA and NA as a Mediator Between Promotion and Prevention Foci and Positive Tissue Behaviors II (N = 90)
| Variables entered | Beta
|
|||
|---|---|---|---|---|
| Stage 1: DV = PA | Stage 1: DV = NA | Stage 2: DV = Positive tissue behavior II | Stage 3: DV = Positive tissue behavior II | |
| Promotion focus | .40*** | -.21* | .35*** | .23* |
| Prevention focus | -.18 | .55*** | -.06 | .16 |
| PA | .12 | |||
| NA | -.35** | |||
| R2 = .16 | R2 = .30 | R2 = .12 | R2 = .22 | |
| Adjusted R2 = .14 | Adjusted R2 = .29 | Adjusted R2 = .10 | Adjusted R2 = .19 | |
| Power of test = 0.957 | Power of test = 0.997 | Power of test = 0.919 | Power of test = 0.974 | |
*p ≤ .05. **p ≤ .01. ***p ≤ .001.
Prevention Focus and Skeleton Behaviors
In support of Hypothesis 2, a prevention focus was found to be positively correlated with skeleton behaviors (beta = .23, p < .05). R squared for the regression was .16 and adjusted R squared was .14.
Promotion Focus, Prevention Focus, and Negative and Positive Affectivity
Hypothesis 3 was almost fully supported. A promotion focus was found to be positively related to PA (beta = .40, p < .001) and negatively related to NA (beta = -.21, p < .05), while a prevention focus was positively related to NA (beta = .55, p < .001). No relationship was found between prevention focus and PA. See Table 2.
PA and NA as Mediators of the Relationship Between Regulatory Focus and Positive Tissue Behaviors
Hypotheses 4 and 5, as depicted in Figure 1, suggested PA and NA as mediators in the relationship between regulatory focus (promotion or prevention) and positive tissue behaviors. As summarized in Tables 2 and 3, we followed Baron and Kenny's (1986) recommendations to test the mediation predictions.
In testing Hypothesis 4, we first verified that the mediators (PA and NA) were predicted by the independent variable (promotion focus) – Stage 1 in Tables 2 and 3. In the second step, shown as – Stage 2 in Tables 2 and 3, we explored whether the dependent variables (positive tissue behaviors I and II) were predicted by the independent variable. In the third step, depicted as Stage 3 in Tables 2 and 3 we examined the outcome when the predicted mediators (PA and NA) were added to the independent variable (promotion focus) and both were entered as predictors of their respective dependent variables. These analyses confirmed that as predicted by Hypothesis 4, PA and NA mediated the relationship between promotion focus and positive tissue behaviors. A significant relationship was found between promotion focus and PA and NA (the mediators) in Stage 1 (beta = .40, p < .001; beta = -.21, p < .05 respectively). PA and NA fully mediated the relationship between promotion focus and positive tissue behaviors I. The relationship between promotion focus and positive tissue behaviors I was significant in Stage 2 (beta = .32, p < .01), but became insignificant in Stage 3 (when PA and NA were included in the regression), where the effect of PA and NA was significant (beta = .43, p < .001; beta = -.34, p < .01 respectively). The mediation is evident in Table 2.
Regarding positive tissue behaviors II, Stages 2 and 3 suggest partial mediation since, as is visible in Table 3, the relationship between promotion focus and positive tissue behaviors II was significant in Stage 2 (beta = .35, p < .001), and remained significant but weaker in Stage 3 (beta = .23, p < .05), where NA was highly related to positive tissue behaviors II (beta = -.35, p < .01). Thus, NA was shown to explain some but not all of the variance in positive tissue behaviors II explained by promotion focus. Since the relationship between PA and positive tissue behaviors II became insignificant in Stage 3, PA was not confirmed as a mediator in this relationship.
Hypothesis 5 which posited that PA and NA would mediate the negative relationship between prevention focus and positive tissue behaviors was not supported, as we found no significant relationship between prevention focus and positive tissue behaviors (Tables 2 and 3).
Discussion and Conclusions
Although regulatory focus theory has widely been used in the marketing literature (e.g., Louro, Pieters, & Zeelenberg, 2005), little is known about its implications in service settings. The primary purpose of this study was to ascertain the relationship between regulatory focus and the quality of service behaviors. Our results indicate that the strength of our participants’ promotion focus was positively associated with their positive tissue behaviors. Regulatory focus theory (Higgins, 1997) postulates that promotion- and prevention-focused individuals have different sensitivity toward positive or negative events. Promotion-focused people are concerned with the presence or absence of positive outcomes, whereas prevention-focused individuals are vigilant to the presence or absence of negative outcomes. Following this rationale, we suggest that prevention-focused service providers “do what they have to do” in order to prevent failure in interactions with customers, but do not initiate extra service to make the customer’s experience particularly pleasant. This fundamental difference has great significance in explaining how a promotion versus prevention orientation in service providers affects their service behaviors, especially when we distinguish skeleton from positive tissue behaviors.
As noted above, positive affect in employees has been shown to produce pro-social behaviors (Barsade & Gibson, 2007), whereas high negative affect is positively related to anti-social behaviors (Seger-Guttmann & Medler-Liraz, 2013; Spector & Fox, 2002). We therefore examined PA and NA as potential mediators in the relationship between regulatory focus and the quality of service behaviors. We found that a prevention focus in service providers was associated with increased NA, while a promotion focus was associated with higher PA and lower NA. This is in line with Brockner & Higgins (2001). However, our findings extend previous research by showing that a promotion focus also relates negatively to NA. Our findings regarding affect and regulatory focus are important especially in view of the need for service providers to regulate their feelings and expressions; that is, to avoid displays of negative emotions, a process known as emotional labor (Grandey, 2000).
Our results fully support the conjecture that a promotion focus influences service behaviors both directly and through PA and NA. It appears that a promotion focus strengthens PA and weakens NA, leading in turn to more positive tissue behaviors and to better service. Interestingly, the positive tissue behaviors that were most reinforced by affect overall were those in the first group; namely, behaviors that involve helping others. These are the behaviors which, it could be argued, are crucial to creating a positive service encounter from the customer’s point of view. A prevention focus positively impacted NA, but no significant relationship was found between prevention focus and positive tissue behaviors. These results may contribute to our understanding of why service providers with different regulatory foci tend to adopt different service behaviors.
Positive affective displays in service interactions have a positive effect on customer loyalty (Hochschild, 1983) and on perceived service quality (Pugh, 2001). The current findings contribute to this line of research. The relational aspect of service quality (Goodwin & Smith, 1990) seems especially relevant in the context of positive service tissue behaviors. As predicted, promotion-focused service providers express high PA and therefore exhibit high levels of positive tissue behaviors, and may thus lead to higher evaluations of service quality.
Managerial Implications
This study did not examine service interactions in terms of either a dichotomous structure or a bi-polar continuum as has been done in previous work (Clark & Mils, 1993; Gutek et al., 1999; Lovelock & Wright, 1999). Instead, we used Vilnai-Yavetz and Rafaeli’s (2003) conceptualization to suggest that positive tissue behaviors are distinguished from skeleton behaviors by the fact that they are separate elements of the same interaction. Managers might enhance consumers’ perceptions of service quality by training employees to recognize these different aspects of service interactions, and by encouraging them to exhibit positive tissue behaviors above and beyond their skeleton behaviors.
The findings of this research also offer insights that can be used in recruiting potential service providers. We recommend that managers look for specific characteristics in potential candidates for service positions, including the candidate’s regulatory orientation. Promotion-focused service providers are more likely to provide high-quality service by adopting positive tissue behaviors.
Research Limitations and Suggestions for Future Research
Negative tissue behaviors (Vilnai-Yavetz & Rafaeli, 2003) were not presented in the current study to avoid the social desirability bias (Randall & Fernandes, 1991) where only positive or acceptable behaviors are identified. In our study, all service behaviors were self-reported by research participants. The skeleton and positive tissue behaviors are those which service providers are expected to show and for which they are typically rewarded. Future research should focus on how to overcome this methodological limitation.
In addition, in order to deepen our understanding of the role of regulatory focus in service settings, future studies should examine the regulatory focus of service providers in the context of customers’ evaluations of service quality and satisfaction with the service. Finally, it is important to test the current model in different business sectors and service settings.