Customer orientation: the interactive effect of role clarity and learning goal orientation

Customer-oriented service employees enjoy serving customers and strive to serve them well. Customer orientation is key to success in service industries. It leads to customer satisfaction and loyalty, sales growth and business performance (Dean, 2007; Jaramillo and Grisaffe, 2009; Susskind et al., 2007; Tajeddini, 2011; Yoon et al., 2007).

Customer orientation has been studied on the organizational level as well as on the individual level. The organization-level approach showed that organizations can foster employee customer orientation through their market orientation (Jones et al., 2003), culture (Tajeddini and Trueman, 2012) and human resource management processes (Strong and Harris, 2004; Widmier, 2002). Korunka et al. (2007) refer to this as institutional-related customer orientation and distinguish it from employee-related customer orientation, researched at the employee level. In our study, we take this individual-level, employee-related approach, as it allows for a greater understanding of how individual variations in employee customer orientation can arise, even when organizational variables, such as climate, pay practices and performance management, are consistent for all employees.

Earlier studies following the employee-related approach have found a multitude of factors that exert an influence on individual employees' customer orientation. Leadership style is one important factor (Lee et al., 2011). Other factors include employee responses to organizational and job characteristics, such as organizational identification (Wieseke et al., 2007), experienced meaningfulness of the job (Thakor and Joshi, 2005) and role clarity (e.g. Bennett et al., 1999). Moreover, relatively stable employee characteristics have been identified as antecedents to customer orientation, such as personality traits (e.g. Brown et al., 2002), emotional intelligence (Rozell et al., 2004), job resourcefulness (Licata et al., 2003) and learning goal orientation (Harris et al., 2005).

Identifying factors which affect employee customer orientation is important, but developing a more sophisticated understanding of how factors interact with each other is needed to advance theory and provide more concise and practically applicable recommendations. In this study, we draw from conservation of resources (COR) theory (Hobfoll, 1989) to clarify how role clarity, as an important employee response to the job, and learning goal orientation, as an influential motivational individual difference variable, interact to influence customer orientation.

In considering responses to the job and organizational context, we focus on role clarity, as research has demonstrated its high significance for important organizational outcomes. Grounded in organizational role theory (Katz and Kahn, 1978) and organizational stress theory (Kahn et al., 1964), role clarity is the extent to which employees have a clear understanding of what is expected from them at work. Meta-analyses provide clear evidence that a lack of role clarity, i.e. the work stressor of role ambiguity, is linked to tension, depersonalization, emotional exhaustion, intention to quit, reduced job satisfaction, lower organizational commitment and lower job performance (Jackson and Schuler, 1985; Gilboa et al., 2008; Örtqvist and Wincent, 2006).

Among the individual differences identified in prior research, learning goal orientation has so far received limited attention in customer service research. However, it has the potential to emerge as a powerful explanatory variable for employee customer orientation not only on its own but also as a moderating variable. Learning goal orientation is a growth mindset (Dweck, 2008). Individuals with this growth mindset strive to learn new skills and become more competent (Dweck and Leggett, 1988). Learning goal orientation has been related to innovative behaviors (Nguyen, 2018), positive affect, satisfaction, engagement, openness and adjustment to change, organizational citizenship behavior and performance (VandeWalle et al., 2019).

Given that low role clarity is a job stressor (Kahn et al., 1964) and that learning goal orientation exerts a proximal impact on the regulation of work behaviors (Cellar et al., 2011) and is able to mitigate negative effects of job stressors (Peng et al., 2019; Whinghter et al., 2008), it is intriguing that learning goal orientation has so far not yet been considered as a factor that interacts with role clarity to differentially affect the manifestation of customer orientation. This study attempts to close this research gap. Besides the theoretical importance, this is also of great practical importance. The knowledge of how role clarity interacts with learning goal orientation will lead to directly applicable recommendations for selecting and managing customer service employees.

Members of organizations are defined by their roles and the requirements, responsibilities and expectations that come with them (Katz and Kahn, 1978). Role theory states that unclear and ambiguously defined roles are a source of stress (Kahn et al., 1964). Low role clarity, also termed role ambiguity, is a hindrance stressor that causes negative job outcomes (Cavanaugh et al., 2000).

In developing a conceptual framework of customer orientation, Hennig–Thurau and Thurau (2003) argued that role clarity is an important determinant of customer orientation. Service employees who are clear about their role toward customers are more likely to believe that they will be able to behave according to expectations and will therefore be more motivated to display customer-oriented behaviors. Mirroring the negative side of this relationship, Bettencourt and Brown (2003) explained that role stress, such as the stress experienced when facing ambiguous role expectations, leads to psychological withdrawal from the organization. In their model, this psychological withdrawal leads in turn to behavioral withdrawal, which can manifest itself in reduced customer-oriented behaviors.

In empirical studies, role clarity has been shown to influence employee performance and perceptions of service quality in customer service roles (Flaherty et al., 1999; Hartline and Ferrell, 1996; Singh, 2000). A study investigating customer orientation in the banking sector found that role clarity was related to levels of employee customer orientation, with less clarity resulting in reduced customer orientation (Bennett et al., 1999). There have been similar findings in studies with hotel employees. Clark et al. (2009) found a positive relationship between role clarity and employees' commitment to service quality. Karatepe and Sokmen (2006) reported that role clarity of hotel employees was positively related to their perceived ability to handle customer problems and customer complaints.

Learning goal orientation is an achievement motivation. Individuals with high learning goal orientation exert effort to improve their competencies and find new strategies for solving problems (Dweck et al., 1995). Employees with high learning goal orientation respond to challenging tasks by engaging in “solution-oriented self-instruction” (VandeWalle, 1997, p. 998). They are proactive, take charge (Morrison and Phelps, 1999) and seek feedback (Anseel et al., 2015; VandeWalle et al., 2019). All these behaviors would be important for serving customers well.

Chien and Hung (2008) found a positive relationship between learning goal orientation and customer-oriented behaviors in two service-industry samples (investment firms and hospitals). Also for hotel employees, a positive effect of learning goal orientation on customer orientation has been demonstrated (Tajeddini, 2011).

Above, we have explained why role clarity and learning goal orientation are individually related to customer orientation, and we have shown the empirical support for these main effects. As the unique and novel contribution of this study, we now develop the hypothesis on the interactive effect of role clarity and learning goal orientation on customer orientation.

Conservation of resources (COR) theory is a motivational and stress model which posits that individuals are motivated to retain, protect and grow their resources (Hobfoll, 1989; Hobfoll et al., 2018). The threat of loss or actual loss of valued resources and the inability to meet demands and gain valued resources will cause stress and lead to undesired personal and organizational outcomes. Personal characteristics, e.g. hardiness (Kobasa et al., 1982), emotional intelligence, self-esteem, self-efficacy, locus of control, conscientiousness, and emotional stability (Halbesleben et al., 2014) have the potential to buffer the negative effects of hindrance stressors. A recent study showed that employees who face hindrance stressors experience reduced negative emotions when they deliberately learn something new at work, as this helps them obtain relevant resources, such as knowledge and skills (Zhang et al., 2018).

Research has emerged that suggests learning goal orientation can buffer the effects of work stressors, thereby functioning as a moderator in stressor–outcome relationships. Whinghter et al. (2008) found that individuals with high learning goal orientation are less affected by high quantitative workload and experience less frustration compared to those with low learning goal orientation. In another study, Peng et al. (2019) drew from COR theory to test the moderating effect of learning goal orientation on the relationship between hindrance stressors and employee innovativeness. Their measure of hindrance stressors included role ambiguity, which in terms of COR theory is a work demand that has the potential to deplete resources (Lee and Ashforth, 1996). They found that employees with low learning goal orientation were less innovative when they experienced a higher degree of hindrance stressors. On the other hand, high learning goal orientation buffered this negative effect. Employees with high learning goal orientation even showed a tendency to be more innovative when hindrance stressors were high.

For service employees, it is not always possible to clearly prescribe the activities required (Kelley, 1992). Customer service roles can be unstructured and dynamic. Strict reference to rules and regulations may not achieve customer satisfaction (Mukherjee and Malhotra, 2006). High learning goal orientation is likely to be associated with a willingness to learn from customer interactions and to be flexible in exploring new ways to solve novel problems (Harris et al., 2005). Learning goal orientation motivates employees to seek and appreciate negative feedback with the aim of improving (Janssen and Prins, 2007). Individuals with low learning goal orientation would interpret negative feedback as threat, while individuals with high learning goal orientation would respond with more effort and therewith are potentially able to protect themselves against resource loss and gain more resources.

We therefore hypothesize that even under conditions of low role clarity, service employees with high learning goal orientation will demonstrate high customer orientation, whereas employees with low learning goal orientation will not. Put differently, role clarity and learning goal orientation will not only exert main effects but also have an interactive effect on customer orientation.

We collected our data in 4- and 5-star hotels where service is paramount. This approach is supported by results from Grissemann et al. (2013) who found a strong relationship between customer orientation and financial performance in 4- to 5-star hotels but no relationship in 1- to 3-star hotels. After contacting twelve hotels in Singapore, the representatives of 8 hotels agreed to our data collection and helped disseminate the questionnaires to their employees. Respondents were assured that their participation was voluntary and anonymous. Based on a total of 600 questionnaires distributed and 323 questionnaires returned with complete data, the response rate was 54%.

The participants were on average 31.3 years old (SD = 10.4), and 52.2% were female. They were mainly in roles as front desk/concierge (43%), food and beverage (38%), sales and marketing (7%) and housekeeping (4%) employees and in non-managerial (64%), junior management (18%) and middle management (17%) positions.

Role clarity. We measured role clarity with four items from Rizzo et al. (1970). The respondents indicated the level of certainty at work with regard to their duties, authority, goals and job behaviors on a scale from 1 (strongly disagree) to 5 (strongly agree), e.g. “I know exactly what is expected of me at work”. Cronbach's alpha reliability was 0.80.

Learning goal orientation. Learning goal orientation was assessed with five items, using a scale from 1 (not at all) to 5 (to a very great extent), e.g. “I often look for opportunities to develop new skills and knowledge” (VandeWalle, 1997). Cronbach's alpha reliability was 0.85.

Customer orientation. We discussed existing measures of customer orientation with a senior hotel HR manager for feedback regarding item wording and relevance for the hotel industry. Our final measure comprised fourteen items, based on Brown et al. (2002), Donavan et al. (2004), and Saxe and Weitz (1982); e.g. “I keep the best interests of the customer in mind” and “I enjoy responding quickly to my customers' requests”. The response scale was from 1 (strongly disagree) to 7 (strongly agree). Cronbach's alpha reliability was 0.93.

Parallel analysis is a relatively objective means for determining the number of factors required to adequately explain correlations between items (Horn, 1965). Using Revelle's (2020) R package psych, we analyzed the 14 customer orientation items. Parallel analysis confirmed that the correlation matrix was best explained by a single underlying factor. Confirmatory factor analysis (CFA) also demonstrated acceptable fit for a single-factor model (⁠$χ2$ = 243.91, p < 0.001, df = 77; RMSEA = 0.08; SRMR = 0.04; CFI = 0.94). The values of CFI and SRMR indicate good fit, while RMSEA is consistent with adequate fit (Brown, 2006).

Control variables. Organizational tenure was included as a control variable as it may be related to role clarity, learning goal orientation and customer orientation.

Comparing one-factor and three-factor models. A CFA with all role clarity, learning goal orientation and customer orientation items, when loaded onto a single factor, resulted in unacceptable fit: $χ2$ = 1264.67, p < 0.001, df = 230; RMSEA = 0.12; SRMR = 0.11; CFI = 0.76. However, and as expected, loading the items onto three separate correlated factors (for role clarity, learning goal orientation and customer orientation) showed good model fit: $χ2$ = 465.01, p < 0.001, df = 227; RMSEA = 0.06; SRMR = 0.04; CFI = 0.94. This 3-factor solution was significantly better than the 1-factor solution (⁠$χdiff2$ = 799.67, df = 3, p < 0.001).

As the correlation table (Table 1) shows, both role clarity and learning goal orientation are related to customer orientation. Also, not surprising, those who strive to learn do report higher role clarity. For the control variable, employees with longer organizational tenure demonstrate lower learning goal orientation.

The statistical tests were conducted using Hayes' (2018) PROCESS program, specifically Model 1 (moderated regression analysis). Variables in the interaction term were mean-centered. Table 2 shows that role clarity and learning goal orientation exert significant effects on customer orientation. Confirming the hypothesis, the interactive effect is also significant.

The weakening of the relationship between role clarity and customer orientation at higher levels of learning goal orientation is illustrated in Table 3. This table presents the effects of role clarity on customer orientation at three levels of learning goal orientation (one standard deviation below the mean, the mean and one standard deviation above the mean). While the effect of role clarity is significant at all levels of learning goal orientation, it is very strong at a low level, but it significantly decreased at a high level.

Taking a different perspective on the same interaction, Table 4 depicts the effects of learning goal orientation on customer orientation at three levels of role clarity. While the effect of learning goal orientation on customer orientation is significant at average and high levels of role clarity, it is nonsignificant at a low level. Figure 1 visualizes these results, showing that employees with high role clarity do possess high customer orientation. Employees with low role clarity but high learning goal orientation do also show a considerably high customer orientation, which means that service employees with high learning goal orientation are to a large extent able to make up for low clarity. However, if role clarity and learning goal orientation are both low, employees do demonstrate a significantly lower degree of customer orientation.

This study makes an important contribution in explaining how employee-related characteristics may affect customer orientation. It not only replicates earlier studies that separately showed relationships of role clarity and learning goal with customer orientation but also demonstrates that role clarity and learning goal orientation exert an interactive effect on customer orientation. Role clarity is of a general advantage for customer orientation. However, some customer service roles can be unstructured and ambiguous (Mukherjee and Malhotra, 2006). Our study shows that if service employees have to deal with unclear, ambiguous role definitions, those with high learning goal orientation are able to maintain a high level of customer orientation.

COR theory is able to explain the underlying mechanism. Without clarity about the role, employees may be less confident that they are able to fulfill expectations (Hennig-Thurau and Thurau, 2003). In terms of COR theory, lowered confidence signifies a loss of resources. In line with the psychological withdrawal model, employees would subsequently be less motivated to serve customers well (Bettencourt and Brown, 2003). However, facing the challenge of role ambiguity, employees with high learning goal orientation will persist and use proactive approaches. They seek feedback from all possible sources. In interactions with customers, they learn from both positive and negative customer responses, and they do not perceive negative feedback as threat. As such, learning goal orientation presents itself as a psychological resource that is able to buffer against the threat of resource loss posed by role ambiguity.

Studies have also demonstrated that learning goal orientation is positively related to knowledge sharing among employees (Lu et al.,2012; Kim and Lee, 2013). This suggests that employees with high learning goal orientation tap on peers as a resource in order to learn from their experiences on how to fulfill customer demands. Even without clearly prescribed role behaviors, the mastery achievement motivation of learning goal orientation will result in higher customer orientation.

The use of self-report measures in this study may be perceived as a limitation. However, as Chan (2009) points out, these are the most appropriate means for assessing self-referential respondent perceptions. Understanding employees' perceptions of the clarity with which they understand their role, their learning goal orientation and customer orientation requires self-reports. We were interested in individual-level relationships between such variables, and these cannot be investigated with aggregate measures, such as those customer satisfaction surveys, which are commonly used in hotels. We sought to minimize possible common variance by using different anchors for our scales and by varying the number of scale points, as recommended by Podsakoff et al. (2003). Importantly, our key finding of an interaction effect cannot be attributed to common method variance because presence of common method variance actually reduces the likelihood of detecting interaction (Siemsen et al., 2010).

The cross-sectional nature of the data does not allow for clear causal interpretations. Based on other cross-sectional data, a reverse causal effect from customer orientation to role clarity has been suggested (Zablah et al., 2012). Possibly, there is also a bidirectional effect. Only a longitudinal study would be able to shed light on the causal relationship. However, in our study, we showed the interactive effect, thereby highlighting the important role of learning goal orientation in changing the relationship between role clarity and customer orientation.

The sole collection of data in one country and only from hotel employees could be considered a limitation for the generalizability of the results. Future research could test the relations in different countries for cross-cultural generalizability and in different service industries for generalizability across different service sectors. And as one reviewer pointed out, future research could also benefit from adding interviews, for a combined quantitative–qualitative research approach, in order to strengthen the interpretation of the results.

The results of our study lead to two very straightforward and practical implications. First, supervisors should, as far as possible, define the roles of their employees and reduce role ambiguity. Especially for employees with low levels of learning goal orientation, clear guidelines need to be established and proper procedures prescribed. Second, when hiring applicants for customer service roles that inherently entail high levels of ambiguity, organizations are strongly advised to select those with a growth mindset who demonstrate high learning goal orientation.

An earlier version of this paper was presented at the 2nd Asia Conference on Business and Economic Studies (2019, September) at the University of Economics Ho Chi Minh City (UEH), Vietnam.