Key Drivers of Frontline Employee Generation of Ideas for Customer Service Improvement

Abstract

Anchored in the service-dominant logic and service innovation literature, this study investigates the drivers of employee generation of ideas for service improvement (GISI). Employee GISI focuses on customer needs and providing the exact service wanted by customers. GISI should enhance competitive advantage and organizational success (cf. Berry et al. 2006; Wang and Netemeyer 2004). Despite its importance, there is little research on the idea generation stage of the service development process (Chai, Zhang, and Tan 2005). This study contributes to the service field by providing the first empirical evaluation of the drivers of GISI. It also investigates a new explanatory determinant of reading of customer needs, namely, perceived organizational support (POS), and an outcome of POS, in the form of emotional exhaustion. Results show that the major driver of GISI is reading of customer needs by employees followed by affective organizational commitment and job satisfaction. This research provides several new and important insights for service management practice by suggesting that special care should be put into selecting and recruiting employees who have the ability to read customer needs. Additionally, organizations should invest in creating work environments that encourage and reward the flow of ideas for service improvement.

Keywords

reading of customer needs perceived organizational support generation of ideas for service improvement frontline employees

Introduction

Frontline employees in service organizations are often the first and only point of contact between the firm and the customer (Hartline, Maxham, and McKee 2000). Their privileged position next to customers allows them to gather first-hand customer information (Coelho, Augusto, and Lages 2011) and, as such, they are the ones most familiar with customer needs within an organization (Bowers 1989). Since customer needs are often diverse, frontline service employees need to be creative to uncover and address the real customer needs (Wang and Netemeyer 2004). Thus, the generation of creative ideas and solutions becomes a requirement to tackle customer needs and provide the exact service customers want (Wang and Netemeyer 2004).

The link between frontline employee generation of ideas and successful innovation is exemplified in several service businesses. Increasingly, companies embark on new service development programs using service delivery teams to contribute new ideas (Heracleous, Wirtz, and Johnston 2004). For example, Singapore Airlines encourages feedback and new ideas from employees across departments such as in-flight and ground services. The airline is recognized for producing several service improvements such as Short Message Service (SMS) check-in and “Book the Cook” which allows passengers to order their food preference in advance. Its success relies on engaging “the people who are close to the actual processes” (Lovelock and Wirtz 2007, p. 345). In the food industry, Starbucks' Frappuccino, the first ice-cream coffee drink, originated as a frontline employee idea (Aufreiter, Lawver, and Lun 2000). Similarly, the JetBlue executive team “placed a high value on involving employees in all aspects of the business” (Zeithaml, Bitner, and Gremler 2009, p. 612). Other companies as diverse as Wal-Mart and Tata Global Beverages run in-house employee talent quests to encourage and reward employee ideas for business innovation (Smedley 2011).

Despite the promotion of employee idea generation in industry and the fact that nowadays “all businesses are service business” (Vargo and Lusch 2008, p. 4), it is surprising to note that scant research attention has been paid to the service development process (Bowers 1989; Edgett 1996), and in particular to the idea generation stage of this process (Chai, Zhang, and Tan 2005). This study intends to close this gap in the service literature by investigating the drivers of generation of ideas for service improvement (GISI) by frontline employees. Anchored in previous literature, which suggests that GISI is a function of both organizational environment and personal characteristics (cf. Bowers 1973; Donavan, Brown, and Mowen 2004; Wang and Netemeyer 2004), both organizational and individual factors are analyzed as antecedents. While perceived organizational support (POS) is investigated as an organizational driver, individual drivers include employee reading of customer needs, emotional exhaustion, job satisfaction, affective organizational commitment.

Potentially, our most important research contribution lies in the study of POS and reading of customer needs as antecedents to GISI. We focus on POS as a driver because when employees recognize that the organization is supportive of their well-being and values their contributions (cf. Eisenberger et al. 1986; Rhoades and Eisenberger 2002), they are likely to produce positive outcomes, namely, GISI. Idea generation can be promoted by an organizational culture characterized by free sharing of thoughts and suggestions among individuals within an organization, which gives rise to an exchange of new service ideas, suggestions, and solutions. Ultimately, it results in incremental improvements that constitute a competitive advantage for the firm (Berry et al. 2006).

Additionally, the wide recognition of the service-dominant (S-D) logic as a mind-set guiding marketing theory and practice emphasizes the importance of organizations being customer-centric and market driven as well as adaptive to customer needs (Day 1994; Sheth, Sisodia, and Sharma 2000; Vargo and Lusch 2004, 2008). This service-centered view of marketing and its inherent customer focus underlines the value of original research on employee reading of customer needs and the subsequent GISI (cf. Davis and Manrodt 1996; Vargo and Lusch 2004). In particular, accurately reading customer needs would allow frontline service employees to be better equipped to transmit relevant information to coworkers through sharing of ideas, suggestions, and solutions for service improvement (Aldrich and Herker 1977; Bettencourt and Brown 2003). Hence, reading of customer needs is included as a key driver in our study.

Anchored in the S-D logic and service innovation literature, this study addresses several major gaps in the service field. First, we propose a conceptual framework of organizational and individual factors, which impact the GISI. Additionally, this research contributes to understanding service employee reading of customer needs by proposing and empirically testing a new explanatory determinant of reading of customer needs, namely, POS and by investigating its unexplored impact on GISI. These are important links as the correct interpretation of customer needs promoted by organizational support may generate better ideas, suggestions, and solutions for service improvement, which ultimately may enhance organizational performance. Finally, this study contributes to the topic of organizational support by studying new outcomes of employee POS, namely, emotional exhaustion, and GISI.

The next section presents a conceptual framework and eight hypotheses. This is followed by a discussion of the research method and results from an empirical study of 740 frontline service employees. The study concludes with managerial and theoretical implications and directions for future research.

Model Development and Hypotheses

In this section, we develop a conceptual framework that includes the relationships among POS, reading of customer needs, emotional exhaustion, affective organizational commitment, job satisfaction, and generation of ideas¹ for service improvement. We develop eight hypotheses that are new to service marketing research. With regard to relationships that have been previously established in the literature, we briefly discuss the associated prior research. Figure 1 summarizes the research hypotheses.

Figure 1.

Conceptual framework- Service improvement model: Organizational and individual factors and generation of ideas for service improvement.

The service quality provided by companies has been the focus of marketing research since the mid-80s with the seminal work of Parasuraman, Zeithaml, and Berry (1985). Service excellence has been shown to drive customer satisfaction, customer retention, and ultimately the profitability of a company (Anderson, Fornell, and Lehmann 1994; Venetis and Ghauri 2004). The importance of service quality is widely acknowledged as a source of differentiation and competitive advantage (Venetis and Ghauri 2004). Thus, in today’s competitive and dynamic marketplace, it becomes imperative for firms to continuously strive to improve their service. The privileged role of frontline employees in interacting with customers (Hartline, Maxham, and McKee 2000) places them in a strong position to prevent potential service problems, provide creative solutions when problems occur, and to suggest ideas for service improvement (Bettencourt and Brown 2003). Ultimately, they are the ones that can contribute most to service excellence, in particular through the idea generation stage of the service development process (cf. Chai, Zhang, and Tan 2005).

GISI

The aim of this study is to investigate the factors that influence frontline employee GISI. GISI comprises an employee’s contribution and encouragement of coworkers to contribute ideas and suggestions for service improvement as well as the employee’s sharing of creative solutions for customer problems with coworkers (cf. Bettencourt and Brown 2003). We analyze how both organizational and individual factors promote employee GISI. With regard to organizational factors, we focus on POS.

POS

Employees tend to develop general beliefs regarding the degree to which their work organization values their contributions and cares about their welfare, that is, POS (Eisenberger et al. 1986; Rhoades and Eisenberger 2002; Shanock and Eisenberger 2006). Employees' perception of the way the organization treats them is enhanced by attributing “human” characteristics to the company (Eisenberger et al. 1986). Employees view the organization as an individual since it is “legally, morally, and financially responsible for the actions of its members as organizational agents” and implements policies and informal norms that guide agents' behavior (Levinson 1965, p. 378).

The norm of reciprocity, according to which one individual treats another favorably if that person has treated them well in the past (Gouldner 1960), underlines the importance of POS for the employee-employer relationship. If POS is high and the norm of reciprocity is active, benefits for both parties in the relationship will occur.

Frontline employees may perceive that the organization cares about their welfare in several ways (Eisenberger et al. 1986). First, organizational support, through the provision of job-related information and know-how, allows employees to gain new capabilities (cf. Janssen and VanYperen 2004; Lankau and Scandura 2002). Second, when the organization is supportive of employees' well-being and values their contributions (Eisenberger et al. 1986; Rhoades and Eisenberger 2002), employees tend to deal more effectively with service issues, problems and/or opportunities and think of better routes to service improvement (cf. Kram 1985). It is expected that the greater the POS, the more employees will be willing to make constructive suggestions and encourage coworkers to share creative ideas and solutions for service improvement (cf. Chen et al. 2009). Thus, it is hypothesized that:

Hypothesis 1: Employee POS is positively related to the GISI.

Reading of customer needs

Reading of customer needs is defined as an “employee’s desire to pick up on customer verbal and non-verbal communication” (Donavan, Brown, and Mowen 2004, p. 132), in order not only to satisfy but also to develop long-term relationships with the customer (cf. Dunlap, Dotson, and Chambers 1988; Kelley 1992; Saxe and Weitz 1982). Reading of customer needs reflects a set of employees' actions, which prioritize customer interests (cf. Rindfleisch and Moorman 2003). In service firms, reading of customer needs is particularly important, given the high level of employee-customer interaction that characterizes service delivery (Gremler, Gwinner, and Brown 2000), and the fact that frontline employees are pivotal in implementing the marketing concept (Saxe and Weitz 1982). By being customer focused, service firms are able to provide better customer service (Berthon, Hulbert, and Pitt 1999; Brady and Cronin 2001) and ultimately enhance the company’s financial performance (Theoharakis and Hooley 2008).

Service employees, who pay particular attention to reading the needs of the customers through their verbal and nonverbal communication, are particularly helpful for the organization and other employees (cf. Donavan, Brown, and Mowen 2004). They carry out constructive behaviors (directed toward the organization as a whole and its employees), so that customer needs are ultimately satisfied and customer satisfaction is achieved in the long run (cf. Bateman and Organ 1983; Donavan, Brown, and Mowen 2004; Podsakoff and MacKenzie 1994; Saxe and Weitz 1982). These behaviors may include giving suggestions for the improvement of services and sharing creative ideas with coworkers for solving customer problems (cf. Bettencourt and Brown 2003). The more the employee is prone to listening to customers and reading their needs, the more likely will be the exchange of ideas for service improvement with coworkers. Hence, the following hypothesis is formulated:

Hypothesis 2: Employee reading of customer needs is positively related to the GISI.

Job satisfaction

Job satisfaction reflects an individual’s psychological well-being on the job (Singh, Goolsby, and Rhoads 1994). Job satisfaction has received considerable research attention in the marketing field across different contexts, including salespeople (Brown and Peterson 1993), retail store managers (Lusch and Serpkenci 1990), and service providers (Boyt, Lusch, and Naylor 2001). It is usually either regarded as a general or as a multifaceted construct (Reichers 1986). While some studies investigate overall job satisfaction (e.g., Bagozzi 1980), others focus on the satisfaction with different job facets such as supervisor, pay, promotion opportunities, fellow workers, and customers, among others (e.g., Alexandrov, Babakus, and Yavas 2007; Churchill, Ford, and Walker 1974). Similar to previous research, which shows that the global assessment of job satisfaction is superior to that based on facets (e.g., Scarpello and Campbell 1983), we investigate overall job satisfaction.

In this study, we propose that job satisfaction positively impacts GISI for two reasons. First, there is a positive relationship between job satisfaction and customer-oriented behavior (Hoffman and Ingram 1992), as employees who are satisfied with their jobs tend to “engage in behaviors that assist customers” (Jones, Busch, and Dacin 2003, p. 329). Second, frontline employees relate their own feelings to particular job features. Acting in line with their feelings, if they are satisfied with the job, they will show gratitude to the company and reciprocate the feeling of satisfaction by performing better (cf. Bagozzi 1980; MacKenzie, Podsakoff, and Ahearne 1998). This performance may take the form of discretionary behaviors, such as sharing of suggestions for service improvement and creative solutions with coworkers for solving customer problems (Bettencourt and Brown 2003). Hence, it is suggested that:

Hypothesis 3: Employee job satisfaction is positively related to GISI.

Affective organizational commitment

Organizational commitment has been defined and measured in several ways (e.g., Bateman and Strasser 1984; O’Reilly and Caldwell 1980; Porter et al. 1974). These definitions and measures have a common ground: organizational commitment is a bond between the individual and his or her organization (Mathieu and Zajac 1990).

In this study, organizational commitment is viewed as an employee’s dynamic, positive attitude toward the organization (Buchanan 1974; Johnston et al. 1990), and as an employee’s emotional attachment to the organization (Jaros et al. 1993). Affective organizational commitment is therefore defined as the strength of an employee’s identification with, and contribution to, a particular organization (Porter et al. 1974). It is characterized by a belief in and acceptance of the goals and values of the organization and by a strong desire to continue as a member of the organization (Mowday, Porter, and Steers 1982; Mowday, Steers, and Porter 1979). Employees who are affectively committed to the organization want to continue working for the company; they do not feel the need or the obligation of continuing employment, rather the desire to do so (Meyer and Allen 1991).

Employees who are affectively committed to their organization have a tendency to perform better (Meyer et al. 1989). In particular, previous research has found organizational commitment to be positively related to service performance (e.g., Babakus et al. 2003; Boshoff and Allen 2000). This service performance may be in the form of behaviors that go beyond job specification requirements (cf. MacKenzie, Podsakoff, and Ahearne 1998), for example, by sharing solutions to problems with coworkers and encouraging them to contribute with suggestions and ideas for service improvement. Accordingly, we propose that the more affectively committed an individual is to the organization, the more he or she will provide and share ideas for service improvement. Thus,

Hypothesis 4: Employee affective organizational commitment is positively related to GISI.

Indirect Influences on GISI

Although reading of customer needs may be an intrinsic individual skill (cf. Donavan, Brown, and Mowen 2004; Wang and Netemeyer 2004), it can also be developed and enhanced by receiving organizational support. Usually employees view a supervisor’s positive or adverse treatment toward them as representative of organizational support (Rhoades and Eisenberger 2002). Their support allows employees to gain new capabilities by instructing them directly, by allocating them challenging tasks and providing them with the tools to deal with those, and by assisting employees in solving problems instead of providing them with answers (cf. Kram 1985; Lankau and Scandura 2002). In this way, the organization provides support to employees in order to facilitate the reading of customer needs. Hence, it is hypothesized that:

Hypothesis 5: Employee POS is positively related to reading of customer needs.

Emotional exhaustion

Emotional exhaustion reflects an individual’s feeling “of being emotionally overextended and drained by one’s contact with other people” (Leiter and Maslach 1988, p. 297). It arises mostly in intensive and people-oriented jobs characterized by emotional personal interactions (Singh, Goolsby, and Rhoads 1994). Importantly, frontline employees are particularly vulnerable to emotional exhaustion as they “are caught in a difficult position when they perceive that client demands cannot or will not be met by the organization” (Cordes and Dougherty 1993, p. 644). Despite its key importance in services provided by frontline employees, emotional exhaustion has been largely neglected in the service literature. Given that frontline employees are exposed to higher levels of emotional exhaustion than employees in nonboundary spanning positions, and emotional exhaustion is the first stage in a burnout experience (Cordes and Dougherty 1993), emotional exhaustion is included in this study.

Emotional exhaustion is an individual factor that may be affected by organizational factors such as POS. Individuals, who perceive organizational support to be high, tend to experience fewer strain symptoms and less burnout (Rhoades and Eisenberger 2002). This has been confirmed by research that has shown burnout to be negatively predicted by organizational support (Cropanzano et al. 1997), and emotional exhaustion to be positively predicted by unpleasant supervisor contact (an example of nonsupportive environment; Leiter and Maslach 1988). Since emotional exhaustion reflects a feeling of lack of energy and a sensation of being worn out due to extreme psychological demands (Babakus et al. 1999; Singh, Goolsby, and Rhoads 1994), we expect that individuals' beliefs that they are valued by the company and that the organization cares about their welfare (cf. Shanock and Eisenberger 2006) will contribute to reducing this feeling of exhaustion. Thus, this study proposes that:

Hypothesis 6: Employee POS is negatively related to emotional exhaustion.

In a dynamic marketplace, successful frontline employees need to identify and meet effectively the real needs of the customers (cf. Wang and Netemeyer 2004). In a high-contact service context, in which the key employee task is serving customer needs, employees who are predisposed to meet those needs tend to fit better in a service firm, because they are prone to take pleasure and satisfaction in the work of serving customers (Donavan, Brown, and Mowen 2004). As such, given that customer-oriented individuals fit better in a service job (Donavan, Brown, and Mowen 2004), understand their service role better (Franke and Park 2006), and find satisfying customer needs intrinsically pleasing (Brown et al. 2002; Frank and Park 2006), it is expected that their job satisfaction, that is, their psychological well-being on the job (Singh, Goolsby, and Rhoads 1994), is higher. Hence, the following hypothesis is proposed:

Hypothesis 7: Employee reading of customer needs is positively related to job satisfaction.

In addition to reading of customer needs, other antecedents of job satisfaction have been proposed. POS has been found to increase job satisfaction (e.g., Eisenberger et al. 1997; Randall et al. 1999; Riggle, Edmondson, and Hansen 2009; Shore and Tetrick 1991; Witt 1992), while emotional exhaustion decreases job satisfaction (e.g., Babakus et al. 1999; Karatepe 2006; Lee and Ashforth 1996).

In this study, frontline employee tendency to read customer needs is hypothesized to be positively related to affective organizational commitment. This occurs for three major reasons. First, in service companies, every employee is expected to implement the marketing concept. When individuals believe that not only the company as a whole but also each individual frontline employee implements the marketing concept, they tend to be more committed to the organization (Donavan, Brown, and Mowen 2004; Jaworski and Kohli 1993; Kohli and Jaworski 1990). Second, employees who are prone to meeting customer needs tend to fit better with a service company’s job specifications, given the high person-job fit (Donavan, Brown, and Mowen 2004). In other words, the match between the customer-related skills of these employees and the specific service job tasks is high. Third, in addition to person-job fit, an individual who tends to read customer needs will have a higher person-organization fit in service companies. The higher an individual’s compatibility with the organization for which he or she works, the higher his or her organizational commitment will be (Kristof 1996; Kristof-Brown, Zimmerman, and Johnson 2005). Together, the fact that the company as a whole and each employee implements the marketing concept and service employees have a natural predisposition to meet customer needs resulting in a higher person-job fit and person-organization fit leads to higher organizational commitment in service firms. Hence, it is proposed that:

Hypothesis 8: Employee reading of customer needs is positively related to affective organizational commitment.

In addition to employee reading of customer needs, other antecedents of affective organizational commitment have been studied. POS is positively related to affective organizational commitment (e.g., Eisenberger, Fasolo, and Davis-LaMastro 1990; Riggle, Edmondson, and Hansen 2009; Rhoades and Eisenberger 2002; Rhoades, Eisenberger, and Armeli 2001). Also job satisfaction positively drives affective organizational commitment (e.g., Alexandrov, Babakus, and Yavas 2007; Brashear et al. 2003; Brown and Peterson 1993; Singh, Verbeke, and Rhoads 1996), while emotional exhaustion has a negative impact (e.g., Babakus et al. 1999; Leiter and Maslach 1988). Thus, we include these relationships in the model without formal hypotheses.

Method

Sample and Data Collection

In line with recent research (cf. Ashill, Rod, and Carruthers 2008; Farrell and Oczkowski 2009; Harris and Ezeh, 2008; Harris and Ogbonna 2009), data were collected from frontline catering employees in a motorway service area operator company specializing in fast-food outlets and restaurants, located in the rest areas of major U.K. highways. Frontline employees were selected as the respondents in this study for three reasons. First, they are the ones most familiar with customer needs (Bowers 1989) and the ones in charge of implementing the marketing concept in service firms (Donavan, Brown, and Mowen 2004). Second, frontline employees are particularly vulnerable to emotional exhaustion since they have a one-to-one interface with clients (cf. Singh, Goolsby, and Rhoads 1994). Finally, given the amount of time that they spend in contact with the customer (Hartline, Maxham, and McKee 2000), they are the ones in the best position to provide ideas for service improvement.

All employees received an envelope with a cover letter and a questionnaire. To encourage honesty, employees were given a written guarantee that responses would be confidential. Respondents completed the questionnaire during working hours and returned it in a sealed prepaid envelope to the researchers. The survey questionnaire was distributed to 2,324 employees and 839 questionnaires were returned, resulting in a raw response rate of 36.1%. After removing 99 nonvalid questionnaires, the final response rate is 31.8%. The deletion of 99 questionnaires resulted from a thorough and demanding data cleaning process. In particular, cases were removed if they were not part of the population from which we intended to sample (i.e., frontline employees); the tenure of the respondent was less than 3 months; the respondent provided only neutral answers; or the cases had more than 30% missing data (Hair et al. 2006; Tabachnick and Fidell 2001). The usable response rate is comparable to that in past research involving frontline service employees (e.g., Harris and Ogbonna 2006; Hartline, Maxham, and McKee 2000; Schwepker and Hartline 2005).

The majority of respondents were female (61.9%), younger than 25 years old (52.5%), and worked on a full-time basis (60.5%). On average, frontline employees had been working in the catering industry for approximately 5 years and 3 months, for the company for 3 years and 5 months, and for the restaurant for 3 years and 4 months. Frontline service employees spent on average 76% of their time in contact with customers. The comparison of the sample profile with the total frontline catering employee profile is not possible due to the lack of availability of company data and U.K. legislation restricting personal information disclosure.

Assessment of Nonresponse Bias

Nonresponse bias was assessed by comparing early and late respondents (early respondents are the first 75% to send the questionnaire back and late respondents are the remaining 25%) with regard to all the items of interest (cf. Armstrong and Overton 1977), as well as other variables such as age, gender, industry tenure, company tenure, and proportion of time in contact with the customers. Results show nonsignificant differences between early and late respondents for 24 of the 25 variables suggesting that response bias is not a significant issue in this study (Diamantopoulos and Winklhofer 2001).

Assessment of Common Method Bias

Common method variance (CMV) requires consideration because it might inflate or deflate the relationships among constructs. Potential sources of common method bias include data on different variables being collected from the same respondent, using the same medium and collecting data at the same point in time (Podsakoff et al. 2003). In this research, we utilized both procedural methods and statistical tests to account for CMV (Smith, Fox, and Ramirez 2010).

With regard to the procedural methods, we followed a set of procedures suggested by Podsakoff et al. (2003) in order to control for method biases. First, care and time were taken in assuring that the indicators used were unambiguous, succinct, and exact. Second, the scale formats, anchors (strongly disagree to strongly agree vs. not at all characteristic of me to extremely characteristic of me), and values (1 to 5 vs. 1 to 7) were varied across the questionnaire. Third, the medium used to collect data was self-administered questionnaires rather than personal interview surveys. Fourth, to control for social desirability and obtain answers that reflected employees' true feelings, respondents were assured confidentiality. Fifth, respondents were assured that there were no right or wrong answers. Sixth, to overcome the potential impact implicit theories might have on respondents' answers, they were not informed about the conceptual model of the research. Additionally, the Harman's single-factor test was performed. The Harman’s single-factor test has some important limitations because it does not statistically control for CMV, no exact guidelines on variance for the first factor are provided for it to be considered a general factor, and the test is less conservative as the number of variables increases (Podsakoff et al. 2003). Despite these limitations, the Harman’s single-factor test is still the statistical remedy commonly used to address common method bias concerns (Podsakoff et al. 2003). Accordingly, all items were loaded into a unique exploratory factor analysis with a nonrotated solution. Since more than one factor emerged from the data and the first factor does not explain more than 50% of the variance (i.e., it accounts for 43.5% of the variance in the data), common method bias is judged not to be a major issue in this study.

In addition to the Harman’s single-factor test, we employed the marker variable technique (Lindell and Whitney 2001). A marker variable is theoretically unrelated with one or more variables of interest and the correlation between the marker variable and these theoretically unrelated variable/variables is an indicator of CMV (Malhotra, Kim, and Patil 2006). The marker variable included in this study is adaptability, which is represented by the following item “I have had to change my attitudes and values to be accepted in this unit” (cf. Ashforth and Saks 1996). First, adaptability has not been hypothesized to be theoretically related to our variables of interest. Second, results show that the marker variable is unrelated with all of our variables of interest (the correlation varies from −.04 to .02), with the exception of emotional exhaustion with which it has a low correlation (.18). The average correlation between the marker variable and the variables of interest is .047. It is important to note that the marker variable has a .00 correlation with our ultimate dependent variable “GISI” (r _Y.M = 0).

Finally, the marker variable was included in a structural equation model (SEM) and the structural parameters of both models—with and without the marker variable—were compared. The findings remain stable and thus CMV cannot account for our results. In sum, the use of procedural methods followed by the two statistical tests suggests that common method bias does not significantly impact on the parameters estimates of this study.

Measures

The questionnaire survey included measures of POS, reading of customer needs, emotional exhaustion, affective organizational commitment, job satisfaction, and GISI. All constructs were operationalized using measures drawn from extant literature. In order to ensure high-quality data, a balance between brevity of the survey instrument and the use of multi-item scales led to the use of short versions of the scales when available (cf. Singh, Goolsby, and Rhoads 1994). Hence, POS was measured using the short form of the Survey of POS (Eisenberger et al. 1986), which assesses the extent to which employees believe that the organization values their contributions and cares about their welfare. Reading of customer needs was measured using the scale developed by Donavan, Brown, and Mowen (2004) to assess the employee’s desire to interpret customer verbal and nonverbal communication. Emotional exhaustion was measured using Babakus et al.’s (1999) 8-item emotional exhaustion subscale of the Maslach burnout inventory (Maslach and Jackson 1981, 1986). Affective organizational commitment was measured using Hartline, Maxham, and McKee’s (2000) short version of the scale developed by Mowday, Steers, and Porter (1979). This measure assesses the degree to which employees feel affectively committed to the employing organization. Job satisfaction is measured using Judge and Colquitt’s (2004) version of the scale developed by Brayfield and Rothe (1951). It reflects an individual’s psychological well-being on the job (Singh, Goolsby, and Rhoads 1994). GISI was measured by adapting a scale developed by Bettencourt and Brown (2003), which assesses the extent to which the employee takes the initiative of communicating to the firm and other coworkers ways to improve service delivery by the firm as a whole, one’s coworkers and oneself. Table 1 displays the mean and standard deviation for each construct as well as the correlations among the constructs.

Table 1.

Matrix of Correlations Among Latent Constructs

Construct	M	SD	1	2	3	4	5	6
1. Perceived organizational support	4.43	1.55	.71
2. Reading of customer needs	5.00	1.14	.39^*	.57
3. Emotional exhaustion	2.80	1.03	−.57^*	−.17^*	.62
4. Affective organizational commitment	4.11	1.59	.82^*	.41^*	−.59^*	.76
5. Job satisfaction	3.33	1.01	.73^*	.36^*	−.60^*	.80^*	.69
6. Generation of ideas for service improvement	4.90	1.35	.39^*	.65^*	−.27^*	.48^*	.45^*	.69

Note: Average variance extracted values for each construct are on the diagonal.

*Correlation significant at p < .01 (two-tailed).

Data Analysis

To assess the validity of the measures, the items were subject to confirmatory factor analysis using maximum likelihood estimation procedures in LISREL 8.72 (Jöreskog and Sörbom 2001). Despite the χ² being significant (χ² = 595.51; df = 260; p-value < .001), the final measurement model fits the data well (non-normed fit index [NNFI] = .99; comparative fit index [CFI] = .99; root mean square error approximation [RMSEA] = .042).

Evidence of discriminant validity for each construct is shown in Table 1. The average variance extracted (AVE) for each construct is superior to the squared correlation coefficient between that construct and any other construct (Fornell and Larcker 1981). AVE ranges from .57 for reading of customer needs to .76 for affective organizational commitment. The highest shared variance occurs between affective organizational commitment and POS with a value of .67. Nevertheless, the AVE for affective organizational commitment (.76) and POS (.71) is superior to the value of .67. In order to assess convergent validity, the magnitude as well as the statistical significance (t-values) of the factor loading estimates is examined. All path coefficients are highly significant and the average loading size is .82 (cf. Hair et al. 2006). In addition, an AVE of .50 or higher is reported for each construct suggesting adequate convergence (Fornell and Larcker 1981). With regard to composite reliability (ρ_η), values of .70 or higher are indicative of good reliability (Hair et al. 2006). Composite reliability ranges from .84 for reading of customer needs to .95 for affective organizational commitment. Table 2 presents the results of the measurement model, namely, standardized loadings, t-values, composite reliability, and model fit.

Table 2.

Measurement Model

Latent Variable	Measures	Standardized loading	t-value
Perceived organizational support (ρ_η = .88)	[Company name] really cares about my well-being.	.84	a
	[Company name] cares about my general satisfaction at work.	.86	25.84
	[Company name] takes pride in my accomplishments at work.	.83	24.41
Reading of customer needs (ρ_η = .84)	I naturally read the customer to identify his or her needs.	.81	a
	I generally know what customers want before they ask.	.67	15.16
	I enjoy anticipating the needs of customers.	.85	23.61
	I am able to read the customer body language to determine how much interaction to give.	.67	15.61
Emotional exhaustion (ρ_η = .91)	I feel emotionally drained by my work.	.80	a
	I feel tired when I get up in the morning and have to face another day.	.77	24.51
	I feel burned out from my job.	.84	29.66
	I worry that this job is hardening me emotionally.	.69	19.33
	I feel frustrated by my job.	.82	27.14
	I feel like I am at the end of my tether.	.77	24.84
Affective organizational commitment (ρ_η = .95)	I talk up [company name] to my friends as a great organization to work for.	.85	a
	I find that my values and [company name]’s values are very similar.	.85	31.94
	I am proud to tell others that I am part of [company name].	.91	42.27
	[Company name] inspires the very best in me in the way of job performance.	.89	35.34
	I am extremely glad I chose [company name] to work for over other companies I was considering at the time.	.87	32.6
	For me, [company name] is the best of all possible organizations to work for.	.87	36.8
Job satisfaction (ρ_η = .87)	Most days I am enthusiastic about my work.	.83	a
	I feel fairly satisfied with my present job.	.83	22.65
	I feel enjoyment in my work.	.82	24.27
Generation of ideas for service improvement (ρ_η = .87)	I make constructive suggestions for service improvements.	.80	a
	I share creative solutions to customer problems with other unit members.	.86	22.24
	I encourage coworkers to contribute ideas and suggestions for service improvement.	.83	22.09

Note: Fit indices: non-normed fit index NNFI = .99; comparative fit index (CFI) = .99; root mean square error approximation [RMSEA] = .042; χ²(df) = 595.51 (260).

a. Item fixed to set scale; ρ_η = Composite reliability (Bagozzi 1980).

Results

SEM was employed to test the eight hypotheses using LISREL 8.72 (Jöreskog and Sörbom 2001). Although the χ² is significant (χ² = 600.40, df = 262, p-value < .05), all the indices suggest a good fit of the final model to the data: NNFI = .99, CFI = .99, and RMSEA = .042.

Direct Effects

When analyzing the drivers of GISI, Hypothesis 1 was not confirmed as findings reveal that employee POS does not have a direct impact on GISI (ns, p > .05). However, the findings show that GISI is driven by three factors, namely, reading of customer needs (.54, p < .01), job satisfaction (.16, p < .05), and affective organizational commitment (.26, p < .01), thus providing support for Hypotheses 2-4.

With regard to the impact of POS on both reading of customer needs and job attitudes, two Hypotheses (5 and 6) and two relationships are confirmed. The proposed Hypothesis 5 is supported, as POS is shown to positively influence reading of customer needs (.39, p < .01). Hypothesis 6 and two relationships are confirmed, as POS negatively impacts emotional exhaustion (−.57, p < .01), and positively influences job satisfaction (.53, p < .01) and affective organizational commitment (.47, p < .01).

Regarding the influence of reading of customer needs on job attitudes, both Hypotheses (7 and 8) are confirmed. Job satisfaction (.11, p < .01) and affective organizational commitment (.08, p < .01) are influenced positively by reading of customer needs.

Finally, with regard to the relationships within the job attitudes domain, emotional exhaustion influences negatively both job satisfaction (−.27, p < .01) and affective organizational commitment (−.09, p < .05). Additionally, job satisfaction has a positive effect on affective organizational commitment (.37, p < .01). Table 3 summarizes the results.

Table 3.

Standardized Estimates and Fit Indices

		Direct Effects		Indirect Effects		Total Effects
		Standardized Coefficient	t-value	Standardized Coefficient	t-value	Standardized Coefficient	t-value
Estimated path
POS → GISI	Hypothesis 1	ns	ns	.54^**	7.27	.39^**	8.23
RCN → GISI	Hypothesis 2	.54^**	10.14	.05^**	3.42	.59^**	11.07
JS → GISI	Hypothesis 3	.16^*	2.13	.09^**	2.87	.26^**	3.7
AOC → GISI	Hypothesis 4	.26^**	3.08			.26^**	3.08
POS → RCN	Hypothesis 5	.39^**	8.4			.39^**	8.4
POS → EE	Hypothesis 6	−.57^**	−13.63			−.57^**	−13.63
RCN → JS	Hypothesis 7	.11^**	2.62			.11^**	2.62
RCN → AOC	Hypothesis 8	.08^**	2.61	.04^*	2.36	.12^**	3.56
POS → JS		.53^**	9.98	.20^**	5.95	.73^**	16.88
EE → JS		−.27^**	−5.44			−.27^**	−5.44
POS → AOC		.47^**	8.95	.35^**	9.48	.82^**	22.33
EE → AOC		−.09^*	−2.38	−.10^**	−4.06	−.19^**	−4.67
JS → AOC		.37^**	6.69			.37^**	6.69
Squared multiple correlation
RCN	.15
EE	.33
JS	.66
AOC	.77
GISI	.49
Fit indices
NNFI	.99
CFI	.99
RMSEA	.042
χ²(df)	600.4 (262)

Note: AOC = affective organizational commitment; EE = emotional exhaustion; GISI = generation of ideas for service improvement; JS = job satisfaction; POS = perceived organizational support; RCN = reading of customer needs.

*p < .05.

**p < .01 (two-tailed test).

We considered the alternative option of adjusting the significance threshold in order to control for Type I errors through the use of Bonferroni-type procedures (cf. Cribbie 2000). Results show that 4 of the 13 direct effects become nonsignificant. Additionally, a revised SEM excluding the abovementioned four paths results in an higher χ², a deterioration of the model fit and a decrease in the R ² while increasing the possibility of Type II errors. Therefore, the significance threshold of 5% was ultimately retained.

We also tested the baseline model against three competing models: (1) in which the paths from reading of customer needs to both job satisfaction and affective organizational commitment were reversed, that is, job satisfaction and affective organizational commitment impact reading of customer needs; (2) in which the path from job satisfaction to reading of customer needs is proposed; and (3) in which the path from affective organizational commitment to reading of customer needs is proposed. Results show that all the competing models have a worse fit than the baseline model (the χ² and the χ²/df increases for each of the models).

Indirect and Mediating Effects

In addition to the direct effects, we estimated the indirect and mediating effects among latent variables. We followed Zhao, Lynch, and Chen’s (2010) procedure to establish mediation. The authors argue that the only requirement for setting up mediation is the significance of an indirect effect. Accordingly, we conducted bootstrap tests of the indirect effects and, subsequently, classified the types of mediation based on Preacher-Hayes script output. Results reveal that the seven indirect effects are significant and therefore mediation is established. Since the direct effects of the independent variable on the dependent variable are also significant and in the same direction as the indirect effects, we are in the presence of complementary mediation for six out of the seven relationships (Zhao, Lynch, and Chen 2010). As an example, the mean indirect effects from the bootstrap analysis of employee POS on job satisfaction through employee emotional exhaustion (.091) and reading of customer needs (.020) are positive and statistically significant. Since the direct effect is also positive and significant (.307), we classify the relationship between employee POS and job satisfaction as complementary mediation (cf. Zhao, Lynch, and Chen 2010). With regard to the relationship between POS and GISI, the indirect effects are significant while the direct effect is nonsignificant. Thus, this relationship is an indirect-only mediation (cf. Zhao, Lynch, and Chen 2010). For more details, see Table 4.

Table 4.

Estimation of Mediation Effects

	a	b	Mean Indirect Effect (a × b)	Direct Effect (c)	a × b × c
Estimated path
POS → GISI
JS	.418	.182	.076^*	ns
AOC	.778	.215	.167^*	ns
RCN	.225	.534	.120^*	ns
RCN → GISI
JS	.241	.161	.039^*	.529^*	.021^*
AOC	.463	.176	.081^*	.529^*	.043^*
JS → GISI
AOC	1.143	.282	.322^*	.204^*	.066^*
RCN → AOC
JS	.241	1.080	.261^*	.203^*	.053^*
POS → JS
EE	−.336	−.271	.091^*	.307^*	.028^*
RCN	.225	.089	.020^*	.307^*	.006^*
POS → AOC
JS	.418	.549	.229^*	.453^*	.104^*
EE	−.336	−.201	.067^*	.453^*	.030^*
RCN	.225	.124	.028^*	.453^*	.013^*
EE → AOC
JS	−.516	.954	−.492^*	−.353^*	.174^*

Note: IV = independent variable; DV = dependent variable; a = path from IV to mediators; b = direct effects of mediators on DV; c = direct effect of IV on DV. POS = perceived organizational support; RCN = reading of customer needs; EE = emotional exhaustion; JS = job satisfaction; AOC = affective organizational commitment; GISI = generation of ideas for service improvement.

*Significant.

Relative Effects

Given that in this study, we estimated the standardized coefficients of all variables, in addition to the analysis of the indirect and mediating effects, an analysis of the relative effects may be performed. This analysis reflects the relative importance of each variable in explaining the dependent variables of interest (Goldberger 1964; Schumacker and Lomax 2004). Understanding relative effects is important for service managers because this analysis provides them with information about which factors have a greater impact on the different dependent variables of interest. Consequently, managers are able to allocate resources accordingly.

We tested the relative effects directly by comparing our baseline model—unconstrained model (see Figure 2)—with a series of models with parameter constraints (two paths set to equality) and subsequently performed chi-square tests of differences. Results reveal that with regard to the drivers of GISI, the major driver is reading of customer needs by employees followed by affective organizational commitment and job satisfaction, which are equally important drivers of idea generation for service improvement.

Figure 2.

Results- Service improvement model: Organizational and individual factors and generation of ideas for service improvement.

With regard to the key drivers of job satisfaction, employee POS is the most important antecedent followed by emotional exhaustion and finally reading of customer needs. Finally, with regard to the key determinants of affective organizational commitment, POS and job satisfaction are the most important determinants followed by reading of customer needs and, finally, emotional exhaustion. These findings emphasize the key importance of promoting both POS and job satisfaction in order to enhance affective organizational commitment.

Discussion

This research provides an important contribution in demonstrating how individual and organizational factors impact the GISI by frontline employees. Individual factors such as reading of customer needs, job satisfaction, and affective organizational commitment directly influence the GISI. Interestingly, the research findings show that employee reading of customer needs is the major driver of the GISI. Furthermore, the relationship becomes stronger when we account for the indirect effects through job satisfaction and affective commitment to the organization. The reason being that employees, who are predisposed to read customer needs, gain satisfaction from the work of serving customers, and are more committed to the organization because they tend to fit well with the work setting of a service company (cf. Donavan, Brown, and Mowen 2004). Notwithstanding its importance, the relationship between reading of customer needs and GISI has been neglected in prior service research. One of this study’s most interesting contributions is to test this relationship.

Furthermore, while the study suggests that organizational factors such as POS has no direct impact on GISI, it does have a strong indirect impact, through reading of customer needs, affective organizational commitment, and job satisfaction, which ultimately results in a strong total effect. Our findings suggest that when employees believe that the organization as a whole values their contribution (Shanock and Eisenberger 2006), they are more willing to read the needs of the customers. The results also suggest that when employees believe that the organization values their contribution (Shanock and Eisenberger 2006), they tend to be more committed to the organization and are usually more satisfied with their jobs (Rhoades and Eisenberger 2002).

This study also contributes new insights into the key role of organizational support in the context of employee reading of customer needs and emotional exhaustion. The research findings reveal that employee POS plays a major role in influencing his or her focus on customer needs, but also that if organizational support is higher, individuals experience lower emotional exhaustion. Additionally, reading of customer needs has been found to be a new positive driver of both job satisfaction and affective organizational commitment.

Finally, with regard to job attitudes, the study results show that since emotionally exhausted employees are less willing to accept organizational goals (Leiter and Maslach 1988) and emotional exhaustion is the result of an assessment of job demands versus job resources (cf. Singh, Goolsby, and Rhoads 1994), the impact of the outcome of this evaluation on employee job satisfaction and affective commitment to the organization is negative. Our findings also confirm that an individual, who feels good on the job, tends to be more attached to the organization, reflecting the predominant effect of job satisfaction over organizational commitment (Brown and Peterson 1993).

These new insights have important managerial implications and suggest some interesting new research directions.

Managerial Implications

The capability of reading customer needs was identified as being critical for service improvement. This employee characteristic is particularly important in a service setting where very often the frontline employee is the only point of contact with the customer (Hartline, Maxham, and McKee 2000). If this key person in a service organization fails to read customer needs, the excellence of the service delivery process is at stake. As our findings reveal, if the capability of reading customer needs fails, the opportunity to provide solutions for customer problems, which underpins an improvement in service becomes weaker. In addition to continuous support from the organization, special care should be put into selecting and recruiting employees who have the ability to read customer needs. That assessment could be done, for example, through role-play service situations in which employees are evaluated, based on their customer skills. In addition to the intrinsic characteristic of the employee regarding being able to read customer needs, companies should maintain updated and easy to access database information about customers, through loyalty cards for instance. This database information would facilitate frontline employee reading of customer needs.

These results show that employees committed to reading the needs of the customers are particularly helpful for the organization in terms of generating ideas for service improvement (cf. Donavan, Brown, and Mowen 2004). Additionally, job satisfaction and affective organizational commitment promote the sharing of solutions with coworkers for solving customer problems (cf. Bettencourt and Brown 2003).

Several reasons justify the importance of employee POS in service companies. In addition to being a key driver of identification of customer needs and reduced emotional exhaustion, results show that organizational support influences both employee job satisfaction and affective organizational commitment. As a consequence, the firm as a whole, and service managers in particular, are encouraged to promote a set of practices aimed at transmitting organizational support to individuals. These practices may include the creation of opportunities for personal development, empowerment of employees, involvement of employees from different hierarchical levels in decision making, as well as fair treatment of employees and organizational rewards such as recognition, pay and promotions (Rhoades and Eisenberger 2002). Finally, affective organizational commitment has been found to directly affect GISI and strengthen the total effect of job satisfaction on the exchange and development of ideas among coworkers for service improvement. Therefore, service managers are recommended to promote affective organizational commitment within organizations.

Organizations are strongly recommended to invest in creating work environments that encourage and reward this flow of ideas and suggestions by for instance establishing a program to request new service ideas from frontline employees (cf. Bowers 1989). Additionally, in line with the current practice of several leading and innovative service companies (e.g., Google, Apple, IDEO), firms are recommended to create more relaxed work environments in which several opportunities for debate and discussion of ideas for customer problems and service improvement among employees might emerge. Overall, the results of this study show that service managers and the recruitment team have several tools at their disposal in order to promote the flow of ideas and GISI which ultimately will contribute to the delivery of an excellent service by the company.

Limitations and Future Research Directions

There are several limitations to the present study that suggest some promising research directions. The first limitation is related to data being collected at one specific point in time, using the same medium and from one type of respondents—frontline service employees (cf. Podsakoff et al. 2003). Future research is encouraged to apply a longitudinal research design, to use different media (e.g., computer-administered questionnaires), to approach different types of respondents to collect data (e.g., managers and customers), and to use company financial data. In particular, an interesting avenue for further research would be to link GISI to objective measures of financial performance and the consumer’s perception of service innovation. However, future studies are recommended to acknowledge that frontline employees are, in most cases, the first and only contact point between the firm and the customer (Hartline, Maxham, and McKee 2000) and, hence, are likely to be the key people who can provide useful suggestions for service improvement.

Second, this study was conducted in a single organization (with units spread across the country), which may limit the generalizability of the results to other organizations. Future research in other firms in the same industry and in other service industries (e.g., airlines, banking, hotel services) is therefore desirable. However, focusing on a single firm has the advantage of sample homogeneity, which reduces extraneous variance usually attributable to the heterogeneity among companies within the same industry (cf. Hess, Ganesan, and Klein 2003; Smith, Bolton, and Wagner 1999). Additionally, it helps to control for contextual effects, such as corporate culture and service delivery situation (Piercy et al. 2006). The underlying reason is that in a single company sample, all sampling units are exposed to the same environmental factors. It is also the case that our data were collected in a single country, exposing the results to bias from national cultural characteristics. It would be highly desirable for future research to explore international market differences in the relationships between these constructs in service businesses. Furthermore, future research could adopt samples of different types of service workers such as salespeople or professional service providers, which would enhance the generalizability of results.

Third, we consider both organizational and individual factors as drivers of GISI. While in this study, the organizational factor incorporated was POS, more comprehensively framed studies are recommended, which would include more organizational constructs.

Notwithstanding these limitations, we believe that this study provides a strong foundation for significant research endeavors to advance the field of service marketing. We have proposed several drivers of GISI, and results suggest that, in addition to believing that the company cares about their well-being (cf. Eisenberger et al. 1986), employees need to develop positive job attitudes and be able to read both the verbal and the nonverbal communication emanated by the customers in order to provide suggestions for service improvement (cf. Donavan, Brown, and Mowen 2004).

Finally, an interesting avenue for future research would be to study the importance of workgroup support in service companies. In spite of being distinct concepts (Self, Holt, and Schaninger 2005), future studies can draw on organizational support theory in order to understand workgroup support and its relationship with constructs such as reading of customer needs and GISI.

Conclusion

The study of GISI is important from both theoretical and managerial perspectives. Theoretically, despite the importance of the idea generation stage to the service development process (Chai, Zhang, and Tan 2005), empirical research to date has been scarce and, therefore, this study contributes to filling this gap in the service literature. From a manager’s perspective, frontline service employees contribute to the GISI, which creates a competitive advantage for the firm and translates into organizational success (cf. Berry et al. 2006; Wang and Netemeyer 2004). Therefore, managers are urged to recognize that GISI is a key requirement to remain competitive in today’s external dynamic environment, which can be determined within an organization (cf. Wang and Netemeyer 2004) by better understanding the organizational and individual factors that drive GISI.

Footnotes

Acknowledgments

The authors gratefully acknowledge the valuable comments on earlier versions of this article from Lloyd Harris, John Cadogan, Ray Fisk, and Peter Toh.

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Cristiana R. Lages is grateful to the Portuguese Science and Technology Foundation—FCT—(BD111502002) for financial support and the company's employees who provided invaluable assistance.

Notes

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