Enhancing Employee Innovation Through Customer Engagement: The Role of Customer Interactivity,Employee Affect,and Motivations

Abstract

Customer engagement involves customers’ interactive experiences with a brand or service provider. Focusing on the hotel industry, this study investigates the role of customer interactivity, positive affect, and employee motivations in enhancing employees’ innovative behaviors under the S-O-R (stimulus–organism–response) framework. Using data collected via a mixed-mode quantitative survey of 830 Chinese hotel employees, the authors developed and tested a structural model. The findings suggest that customer interactivity, positive affect, and motivations as influential factors affect employee innovative behavior. Specifically, customer interactivity influences employee innovative behavior directly and indirectly through positive affect and intrinsic and extrinsic motivations. Theoretically, the study clarifies the mechanisms underpinning the effect of customer interactivity on employees’ innovative behaviors and extends the S-O-R model by applying it in the organizational behavior domain. Practically, the results highlight a need for reward systems to incorporate measures of employee performance in relation to fostering customer interactivity and engagement.

Keywords

customer interactivity employee innovative behavior positive affect intrinsic motivation extrinsic motivation hospitality

Introduction

Customer engagement (CE) “occurs by virtue of interactive, co-creative consumer experiences with a focal agent/object (i.e., a brand) in focal service relationship” (Brodie, Hollebeek, Jurić, & Ilić, 2011, p. 260). Therefore, customer–employee interactions and value co-creation are theoretically regarded as the root of CE (Brodie et al., 2011). Although much recent research has investigated CE in the online domain, the traditional off-line environment where customers interact directly with a brand through representations of the brand (e.g., service employee and product offerings) is also highly relevant (Alvarez-Milán, Felix, Rauschnabel, & Hinsch, 2018). However, this development occurs predominantly within the marketing domain, with little effort reflecting an organizational behavior perspective. In particular, few studies have linked CE to service innovation at an individual employee level. Focusing on the hotel industry, this study addresses this neglect by investigating the influence of the interactive aspect of CE on the innovative behavior of employees, who constitute an important group of internal customers.

Employee innovation in the workplace is of great importance to the effectiveness and survival of an organization (Bani-Melhem, Zeffane, & Albaity, 2018; Gu, Duverger, & Yu, 2017; Scott & Bruce, 1994). It represents a process in which individuals conceive novel ideas or create new problem-solving approaches in their work role and then seek to actualize those ideas (Amabile, 1988), relying on engaged employees and customers for input (Li & Hsu, 2016a). In the tourism sector, customer interactivity (CI) and the feedback it generates help firms create knowledge value through CE (Kumar et al., 2010).

Knowledge of employee innovative behavior (EIB) has been built largely through studies focusing on the manufacturing sector (Li & Hsu, 2016a), but employee innovation in service firms differs from that in manufacturing companies (Li & Hsu, 2016a; Sheehan, 2006). For instance, employee innovation in manufacturing centers on technology, patents, and transactions of property rights (Li & Hsu, 2016a), whereas innovations in the service industry focus on the importance of frontline employees (Cadwallader, Jarvis, Bitner, & Ostrom, 2010). Furthermore, organizational expectations of employees to innovate are more prominent in service-dominant firms, where employees often need to exceed job requirements to improve the overall service experience to promptly meet customers’ demands with more innovative conduct (Bani-Melhem et al., 2018; Ma & Qu, 2011), making employee–customer interaction a highly relevant aspect of innovative behavior.

Although employees’ innovation behavior is a complex concept (Schuhmacher & Kuester, 2012; Scott & Bruce, 1994), previous studies have primarily taken a within-organization perspective, attributing such behavior to internal factors such as organizational commitment (Gu et al., 2017), leadership (Gu et al., 2017; Pieterse, Van Knippenberg, Schippers, & Stam, 2010), individual differences (Chen, 2011), work requirements, and perceived equity of the effort–reward system (Janssen, 2001). Research has also found that EIB is closely related to job characteristics (Li & Hsu, 2016a), such as skill variety, job significance, and task autonomy, which can affect employee innovation by influencing their psychological states of work motivation and perceived work meaningfulness (Li & Hsu, 2016a) and subsequently making employees better at new-service development and problem solving (Sahu & Srivastava, 2017). This psychological perspective is another differentiating dimension between service and manufacturing, as the direct impact of customers on employee emotions and behaviors is to a much lesser extent in the latter. This difference in impact is due largely to the simultaneous production and consumption of services and the highly interactive nature of the service experience, which expose employees directly to the language and behavior of customers, creating more emotional interactions in the service industry than in the manufacturing sector (Ryu & Lee, 2017).

As service innovations will ultimately be judged by customers (Slåtten & Mehmetoglu, 2011), collaboration with customers can be a key source of innovative concepts and ideas (Kumar et al., 2010; Li & Hsu, 2018) as well as a value co-creator (Li & Hsu, 2018). In the general context of new product development and innovation, studies investigating the mechanisms underlying customer interaction’s influence have often taken the perspectives of resource dependence (Gruner & Homburg, 2000) and knowledge integration (Foss, Laursen, & Pedersen, 2011; Schaarschmidt, Walsh, & Evanschitzky, 2018). These studies have generated useful insights into product co-creation but have not sufficiently explained the relationships between employee–customer interaction and service innovation (Li & Hsu, 2016a; Ma & Qu, 2011). Earlier studies have also attributed the effect of employee–customer interaction to its ability to engage individuals in a cognitive process (e.g., Y. Liu & Shrum, 2002). More recently, scholars have investigated the relevance of other factors such as psychological states (Li & Hsu, 2016a; Ohly & Fritz, 2010) and motivational factors such as the effort–reward system (Janssen, 2001). Despite evidence that psychological states may affect employee motivation (Li & Hsu, 2016a; Ohly & Fritz, 2010), the roles of these factors have been investigated in separate studies, and thus the interplay of these factors to jointly influence EIB remains unclear.

In the field of tourism and hospitality, recent studies have examined employees’ innovative behaviors. Li and Hsu (2016a) present a comprehensive review of the literature on EIB in the service industry, summarizing its conceptualization and operationalization and suggesting the clarification of the roles of customer participation and CE as a worthwhile research direction. Li and Hsu (2018) focus on customer participation (a more one-directional, from-customer-to-employee construct) and suggest that affective trust mediates the effect of customer participation on EIB, highlighting affect’s relevance to employee innovation research. In contrast, Li and Hsu (2016b) investigate the role of customer–employee exchange. Although interactivity emphasizes the bidirectional nature of interaction (Bonner, 2010), CE emphasizes customers’ consideration of costs and benefits (Li & Hsu, 2016b), implying the different motivational emphases of the two constructs. Their study reveals that the effect of customer–employee exchange was partially mediated through the social-psychological climate for innovation—that is, employees’ perception of the support and resources provided by customers rather than those provided by organizations. The study does not consider the desire of individual employees for innovation.

Despite these efforts, empirical academic research on employee–customer interactivity has been limited (Bonner, 2010; Schaarschmidt et al., 2018). Previous research has focused on either firm-level parameters (Pieterse et al., 2010) or employee aspects such as job stress and workplace happiness (Li & Hsu, 2018) rather than on interactions between customers and employees, especially in a hospitality and tourism context. This study differs from previous studies by emphasizing CI as two-way interaction instead of one-way participation and by focusing on the innovators (i.e., employees) and their innate needs, motivations, and feelings. By highlighting service interaction as a psychological phenomenon, this research provides more depth and breadth in the understanding of the customer–employee relationship. Drawing on the stimulus–organism–response (S-O-R) framework (Mehrabian & Russell, 1974), this study proposes a conceptual model of the CI–employee behavior relationship and tests the model on a sample of 830 customer contact hotel employees in China.

Literature Review and Hypotheses Development

Theoretical Framework: The Stimulus–Organism–Response Framework

The S-O-R framework posits that the external environment contains material and social stimuli (S) that can affect an individual’s internal state or organismic response (O), such as emotion, motivation, attitude, and reasoning, subsequently leading to behavioral responses (R; Mehrabian & Russell, 1974). In the hospitality context, the S-O-R framework has been considered useful for exploring the relationship between service provision and guests’ behavior (Jang & Namkung, 2009), focusing predominantly on customer response to the service environment (e.g., ambience and atmosphere) and service mode (e.g., self-service; Ahn & Seo, 2018; Jani & Han, 2015).

In the present study, we utilize the S-O-R framework to investigate how customer–employee interactivity affects EIB. Customer–employee interactivity (S) acts as an external factor in the service environment that stimulates an employee’s innovative reaction (R; Li & Hsu, 2018) through the mediators of positive affect (PA) and motivations (O). This conceptualization aligns with previous suggestions of interactivity as a social stimulus in the service environment (Choi & Kandampully, 2019; Jani & Han, 2015; Hew, Leong, Tan, Lee, & Ooi, 2018) and the research framework proposed by Li and Hsu (2016a), which summarizes the relationship between job characteristics and EIB, asserting that employees’ motivational and emotional states are important mediators of this relationship. However, the mediation role of emotion has not been empirically verified (Li & Hsu, 2016a). Similarly, there has been a lack of investigation into the role of motivation, with a few exceptions (e.g., Cadwallader et al., 2010; Coelho, Augusto, & Lages, 2011; Gumusluoglu & Ilsev, 2009; Hon, 2012), which focus on only intrinsic motivation (IM).

Customer Interactivity and Employee Innovative Behavior

In a hotel setting, customer–employee interactivity can act as a stimulus (S in the S-O-R framework) that elicits the employee’s reactions. In a broad sense, customer interaction refers to all forms of contact, involvement, and engagement between customers and organizations in the process of value creation. It also includes customers’ interaction with the organization and its other customers outside of purchase (Kim & Baker, 2019; Y. Liu & Shrum, 2002; So, King, & Sparks, 2014). As a multilevel concept, interaction can take place between a customer and an enterprise (Foss et al., 2011), a project team within the enterprise (Bonner, 2005, 2010), or an individual employee of the enterprise (Fowler & Bridges, 2012).

Although there are a variety of definitions of CI, they generally describe interactivity as the degree to which interactions occur in communication between two parties and tend to focus on three distinct aspects of interactivity: bidirectionality (reciprocal communication), participation, and joint problem solving. Within the service encounter, communication between employees and customers is reciprocal rather than linear (Solomon, Surprenant, Czepiel, & Gutman, 1985), where information is communicated and analyzed and feedback is provided (Y. Liu, 2003), a feature labeled bidirectionality (Bonner, 2010; Mohr & Nevin, 1990). Another feature of CI, participation, refers to customers’ active and direct participation in service development (Bonner, 2010). For example, through complaints and feedback, customers inform hotel employees of problems so corrective actions can be taken (Namkung, Jang, & Choi, 2011). Customer participation also yields significant innovative ideas and suggests innovative solutions to service providers. Joint problem solving is another feature of CI (Bonner, 2010), as when dissatisfied customers become engaged in addressing the issues of customer retention and product design. In this study, we adopt this three-dimensional conceptualization of CI to explore its effect on EIB.

Studies of web interactivity (Fortin & Dholakia, 2005; Sheng & Joginapelly, 2012) have conceptualized interactivity as a stimulus. It is an important social stimulus in web-based advertising (Fortin & Dholakia, 2005) that influences attitudes and purchase intention by stimulating physiological arousal. In online stores, interactivity is an important atmospheric cue that stimulates consumers’ cognitive and emotional states and subsequently their behavioral response (Sheng & Joginapelly, 2012). In addition, interactivity also acts as a stimulus in an off-line environment, that is, a social impact that affects behavioral responses through psychological influences (Attiq, 2015).

EIB represents the behavioral response component (R) in the S-O-R model. Innovative behavior of frontline staff provides novel solutions for customers during the service process, which is to a large extent linked to the customization of services (Slåtten & Mehmetoglu, 2011). As employees can hardly achieve personalized service without a full appreciation of customer needs and demands, much innovative behavior consists of joint activity involving customers (Ma & Qu, 2011). Interactions between frontline employees and customers is a rich source of innovative thinking (Lai, Hon, & Lui, 2014; Schaarschmidt et al., 2018), as it promotes information and knowledge sharing between the two parties, enabling greater understanding of customer needs and preferences and fostering more inspired and innovative solutions (Cui & Wu, 2016). In particular, the bidirectional nature of interactivity allows for a process of modifying and improving ideas and solutions on the basis of feedback from both sides (Bonner, 2010). Customers’ active participation in the problem-solving process can also bring knowledge beyond organizational boundaries and may result in solutions that had not occurred to the employee (Nieves, Quintana, & Osorio, 2014). Moreover, as the nature of two-way interactivity is purposive as well as task and goal oriented, the accuracy and smoothness of communication improve (Bonner, 2010). Therefore, we hypothesize the following:

Hypothesis 1₀: Customer interactivity is not related to employee innovative behavior.

Hypothesis 1_a: Customer interactivity is positively related to employee innovative behavior.

Customer Interactivity and Positive Affect

PA reflects the “extent to which a person feels enthusiastic, active, and alert” (Watson, Clark, & Tellegen, 1988, p. 1063), and it can be measured as either a trait or a state. As a trait, PA “predisposes people to experience positive emotions and moods as well as to have a positive outlook and orientation” (George & Brief, 1992, p. 318). As a state, PA is more transient and captures how one feels at a particular time or in a certain situation (George & Brief, 1992). We regard PA as an induced feeling state that can facilitate flexible thinking, enable effective problem solving, and enhance performance (Aspinwall, 1998; Isen & Reeve, 2005). In the S-O-R paradigm, PA is considered an organism component (O), explaining the mechanism by which CI evokes an employee’s behavioral response.

Most studies on affective states focus on negative emotions and affect (e.g., depression, boredom, emotional exhaustion) resulting from interactions (Lee & Ok, 2012). However, interactions also generate positive emotions because employees have the opportunity to gain professional recognition, experience career development, and even find fun and excitement in interacting with customers (Slåtten & Mehmetoglu, 2011). Studies have suggested that social contact, such as a pleasant interaction with a customer, would likely generate feelings such as encouragement, happiness, and delight, contributing positively to employees’ psychological well-being (Barnes, Ponder, & Hopkins, 2015). From the perspective of social interactivity, when employees perceive that customers are actively involved in problem solving and innovation, they expend effort in adjusting their own emotional state and behavior to cope with the situation (Kiffin-Petersen, Murphy, & Soutar, 2012). In addition, active communication results in effective feedback, brings forth new ideas, and makes it easier for employees to perform tasks (Li & Hsu, 2018). Therefore, cooperation from customers may relieve employees’ role pressure, producing a positive impact on their emotional states. On the basis of the above discussion, we propose the following:

Hypothesis 2₀: Customer interactivity is not related to employee positive affect.

Hypothesis 2_a: Customer interactivity is positively related to employee positive affect.

Customer Interactivity, Positive Affect, and Motivations

In the S-O-R framework, organism, as mental states and mental processes, may refer not only to PA but also to motivations (Buxbaum, 2016), which can be classified into two categories: intrinsic and extrinsic. IM is the impetus to engage in a task for its own sake—out of interest and/or enjoyment—and not merely as a means to another reward, whereas extrinsic motivation (EM) impels individuals to engage in the work for an independent result (Deci & Ryan, 1985). Intrinsically motivated people look for the pleasure and inherent satisfaction derived from completing a task (Deci, 1975), but extrinsically motivated people focus on goal-driven reasons, such as financial rewards or other benefits earned from performing an activity (Deci & Ryan, 1985). Together, EM and IM influence individuals’ intentions regarding an activity as well as their actual behavior (Deci, 1975).

IM and EM are inseparable, and for a given task, the two can coexist in an individual (Kuvaas, Buch, Weibel, Dysvik, & Nerstad, 2017). Individuals will be extrinsically motivated to make efforts if they predict that their efforts will lead to good outcomes (Vroom, 1964). CI can be seen as a value co-creation strategy aimed at ensuring access to key information, resources, and opportunities related to innovation (Cui & Wu, 2016; Gruner & Homburg, 2000) and leading to good performance, recognition, and rewards. Employees are likely to become more extrinsically motivated at work when they judge that the activity will generate valued rewards (Cadwallader et al., 2010). In contrast to EM, IM is related primarily to the pleasure and satisfaction inherent in an activity or task (Deci, Connell, & Ryan, 1989). The social environment and social support can have a significant impact on people’s IM level (Deci & Ryan, 2000). For hotel employees, such social support comes not only from leaders and colleagues but also from customers (Teng & Barrows, 2009). Interacting with customers offers opportunities to explore and understand new things (Cadwallader et al., 2010), evoking employees’ sense of happiness, satisfaction, and delight (Barnes et al., 2015). Interaction also satisfies employees’ desire for problem solving (Kiffin-Petersen et al., 2012) and gaining trust and recognition from others (Li & Hsu, 2018), which strengthens employees’ motivation for work.

The literature posits that work motivation is a psychological process that is predicted by emotion and affect when an individual is in a social relationship (Sahu & Srivastava, 2017). PA, as a subjective psychological phenomenon, magnifies current experience and perception and accelerates the level of motivation to trigger action, such as increasing the valence of moderately desirable rewards or interest in and enjoyment of tasks (Isen & Reeve, 2005). Employees with PA are also more likely to be motivated to provide enjoyable products and services (Kahn & Isen, 1993). Therefore, PA plays a central role in enhancing employees’ IM (Barnes et al., 2015; Isen & Reeve, 2005) and EM (Isen & Reeve, 2005). On this basis, we hypothesize as follows:

Hypothesis 3₀: Customer interactivity is not related to (a) intrinsic motivation and (b) extrinsic motivation.

Hypothesis 3_a: Customer interactivity is positively related to (a) intrinsic motivation and (b) extrinsic motivation.

Hypothesis 4₀: Positive affect is not related to (a) intrinsic motivation and (b) extrinsic motivation.

Hypothesis 4_a: Positive affect is positively related to (a) intrinsic motivation and (b) extrinsic motivation.

Motivations and Employee Innovative Behaviors

Research suggests that IM has a positive effect on individuals’ innovative behavior. Amabile’s (1993) model of motivational synergy suggests a connection between IM and individual creativity. In the context of hospitality, intrinsic motivators such as an employee’s interest in tasks, feelings of accomplishment, and personal development may closely relate to the employee’s innovative behavior (Gumusluoglu & Ilsev, 2009). Hotel employees who regard their job as meaningful and anticipate accomplishment through good performance tend to take more responsibility and make full use of their abilities (Chiang & Jang, 2008). Thus, these employees are more likely to innovate and to solve problems effectively (Bani-Melhem et al., 2018). Researchers also suggest that IM enables employees to be more attentive and dedicated to creative tasks, facilitates their exploration of new pathways, and encourages risk taking (Coelho et al., 2011).

EMs are also related to employee creativity (Amabile, Hill, Hennessey & Tighe, 1994) and facilitate innovative behavior (Chiang & Jang, 2008), and employees will be motivated to work harder at new service development behavior if the perceived benefits equal or exceed the costs in terms of effort (Kelley & Thibaut, 1978). External factors (e.g., money, recognition, competition) constrain what people think and do (Deci et al., 1989), and external regulation can be transformed into EM by identifying a behavior’s underlying value (Deci & Ryan, 2000). Furthermore, through internalization and integration, EM can result in more autonomous motivation or regulatory orientations (Deci & Ryan, 2000). For example, individuals who appreciate the importance of regular exercise to health will exercise more willingly. Hotel employees’ needs for the extrinsic aspects of life, such as rewards, feedback, and recognition, are more prominent in a work condition of intensive labor, low pay, and low social status (Putra, Cho, & Liu, 2017). Under these circumstances, employees with a high level of EM are more likely to work hard, engage in their work, develop new skills, and seek novel solutions when customer needs arise (Amabile, 1988; Wong & Ladkin, 2008). Empirical research also suggests that extrinsic motivators such as opportunities for development, good wages, and job security are positively related to job creativity among hotel employees (Wong & Ladkin, 2008). Hence, the following hypotheses are proposed:

Hypothesis 5₀: Intrinsic motivation is not related to employee innovative behavior.

Hypothesis 5_a: Intrinsic motivation is positively related to employee innovative behavior.

Hypothesis 6₀: Extrinsic motivation is not related to employee innovative behavior.

Hypothesis 6_a: Extrinsic motivation is positively related to employee innovative behavior.

Figure 1 gives an illustration of the conceptual model based on the S-O-R framework.

Figure 1

Conceptual Model

Research Methods

Survey Instrument Development

We tested the proposed research hypotheses using structural equation modeling of data collected through a mixed-mode survey using a questionnaire. The questionnaire included measures of customer interactivity (CI), positive affect (PA), intrinsic motivation (IM), extrinsic motivation (EM), and employee innovative behavior (EIB), as well as questions about respondents’ social demographics (e.g., gender, age, education, position, and length of experience in the industry).

Customer Interactivity

The measurement scale for CI was developed from Bonner’s (2010) 10-item scale, which we adapted to suit the study context. One original item that focused on corporate customers (“Customers often participated in working meetings with project members”) was adapted to reflect an individual customer focus: “Customers had many opportunities to evaluate services and products at the hotel.”

Positive Affect

A three-item PA scale from Pugh (2001), George and Brief (1992), and Barnes et al. (2015) was used. An example item was “When I remember delightful service encounters, I feel enthusiastic about my work.”

Employee Motivations

IM for work was measured by four items from Low, Cravens, Grant, and Moncrief (2001). An example item was “I feel a great sense of personal satisfaction when I do my job well.” EM for work was assessed with a six-item scale developed by Amabile et al. (1994) and Sung and Choi (2009). An example item was “I am strongly motivated by the recognition I can earn from other people.”

Employee Innovative Behavior

We measured EIB with a six-item scale, following Scott and Bruce (1994) and Hu, Horng, and Sun (2009). A sample item was “At work, I come up with innovative and creative notions.”

All items in the questionnaire were measured on a 7-point Likert-type scale (1 = strongly disagree to 7 = strongly agree). The questionnaire was developed in English; it was translated into Chinese by a bilingual hospitality management professor working in a Chinese university. To ensure equivalence of meaning (Brislin, 1980), the translated questionnaire was then verified and cross-checked by two other bilingual tourism and hospitality scholars, who suggested minor revisions to the questionnaire. Following Chang, Van Witteloostuijn, and Eden (2010), we randomly ordered the questions and mixed items from different constructs to reduce potential common-method bias.

A pilot study of a sample of 196 employees of a four-star hotel in a northern city in China resulted in minor adjustments to the questionnaire to improve clarity. For instance, participants felt that several items contained awkward expressions that are inconsistent with common expressions in Mandarin, and these were adjusted for greater readability. Results of an exploratory factor analysis for the main constructs indicated a two-factor structure for CI, with the second factor containing only one item. After deletion of the item, the factor analysis on the remaining nine items suggested one factor explaining 62.42% of the total variance with a Cronbach’s alpha of .92 (Hair, Black, Babin, Anderson, & Tatham, 2006). This one-factor structure of CI was later confirmed in the main study (Cronbach’s alpha = .92, total variance explained = 59.97%).

Sample and Data Collection Procedure

A mixed-mode survey was carried out in China from January to April 2018 using a paper-based survey supplemented by an online survey. Methodologically, mixed-mode designs offer an opportunity to compensate for the weaknesses of a single mode and reduce coverage bias and nonresponse bias (Leeuw, 2005). Research has confirmed that the quality and reliability of data collected by paper-and-pencil and by Internet-based methods are generally equivalent (Denscombe, 2006). Practically, a mixed-mode design was deemed necessary because some hotels agreed to on-site data collection whereas others stated that an online survey was the only acceptable option for their hotels. Therefore, both distribution methods were used to better ensure the size and representativeness of the sample.

Hotel ratings signify quality of service, customer relationship, and value creation opportunities (Park & Allen, 2013). Therefore, four- and five-star hotels would offer a work environment more conducive to EIB (Tajeddini, 2011). We accessed potential participating hotels through the research team’s personal connections and their referrals. In total, six four-star and eight five-star hotels agreed to participate in the study. These hotels are located in 11 cities (Beijing, Guangzhou, Hangzhou, Ningbo, Chengdu, Jinan, Qingdao, Rizhao, Zibo, Zhengzhou, and Baishan) in seven provinces (Beijing, Guangdong, Zhejiang, Sichuan, Shandong, Henan, and Jilin) and represent 13 chain and independent brands. These cities have vigorous economic, cultural, and internalization programs as well as vibrant tourism and hospitality markets. Employees of the participating hotels come from diverse regions of China. The sampling framework was limited to full-time employees in the hotels’ front office and housekeeping, food and beverage, marketing, and recreation departments. These employees included frontline employees, supervisors, and managers who routinely interact with customers, who were either traveling personally or as representatives of corporate customers.

In seven hotels and with permission and full support from senior executives of the hotels, paper-based questionnaires were distributed to employees face-to-face on site during work hours. To avoid social desirability issues, the researchers excused themselves from the data collection sites while the respondents completed the questionnaires, and they returned later to collect the completed questionnaires in sealed envelopes. Of the 800 paper questionnaires distributed, 763 were returned, of which 683 were usable for analysis.

For the online survey, Sojump (or Wen Juan Xing in Chinese), a reputable professional survey platform in China, was chosen to host the questionnaire. Seven of the 14 hotels preferred participation via the web-based survey. An invitation e-mail with the questionnaire link embedded was sent to senior executives of these hotels, who then forwarded the e-mail to potential participants. Of the 280 invitations distributed, 157 returned as usable completions.

In total, 840 valid questionnaires were collected via online and face-to-face surveys. As data were collected using two different modes, the two sets of data were compared to check whether a mode effect existed (Denscombe, 2006). Chi-square (χ²) test results indicated no statistically significant difference (p >.05) in the distribution of 26 quantitative items, with the exception of 1 item in the EIB scale. Thus, the data collected by the two distribution modes were generally equivalent and could be combined for data analysis.

Normality tests for the variables returned maximum absolute values of skewness and kurtosis of all variables of 1.97 and 6.15, respectively (most values were lower than 1), which are within acceptable limits (skewness <2, kurtosis <7; Curran, West, & Finch, 1996). The chi-square test result (p < .001) suggests that the sample does not satisfy the multivariate normality assumption. However, our sample size was larger than 600, meeting the sample size requirement for a sample with multivariate nonnormal distribution (Gao, Mokhtarian, & Johnston, 2008). We used the Mahalanobis squared distance (p₁ < .001, p₂ < .001) to identify multivariate outliers. After the elimination of 10 outliers, the final sample for model testing included 830 respondents.

Because all data were collected through employees’ self-reports, Harman’s one-factor test was conducted to check for common-method bias (Podsakoff & Organ, 1986). The result did not reveal a single-factor structure, and the first factor accounted for 39.87% of the variance explained, which is less than the critical value of 40% (S. S. Liu, Luo, & Shi, 2002). Hence, common method bias is not a concern in our data.

Results

Sample Characteristics

As shown in Table 1, more than half of the respondents were female (62.0%), reflecting the reality that the majority of frontline employees in Chinese hotels are female (Chen, Chang, & Wang, 2018; Li & Hsu, 2016a). More than two thirds were aged 35 years or below. Most of the respondents had attained a college diploma or below, and 7.9% had a bachelor’s degree or higher. Three quarters of the respondents were common staff, with the rest working in a supervisory (19.0%) or a managerial (5.8 %) role; 27.6% of the respondents had worked for less than 1 year in a hotel.

Table 1

Sample Profile

Sociodemographic Variable	Frequency (n)	Percentage (%)
Gender (n = 830)
Male	315	38.0
Female	515	62.0
Age (years; n = 830)
≤22	243	29.3
23–27	132	15.9
28–35	186	22.4
36–45	182	21.9
46–55	74	8.9
>55	13	1.6
Education (n = 830)
Senior high school or below	397	47.8
College	367	44.2
Undergraduate	60	7.2
Postgraduate	6	0.7
Position (n = 830)
Common staff	624	75.2
Foreman/supervisor	158	19.0
Manager	48	5.8
Length of experience in the industry (n = 830)
<1	229	27.6
1–2	153	18.4
3–5	215	25.9
6–10	140	16.9
>10	93	11.2

Measurement Model Results

We conducted a confirmatory factor analysis on the overall sample data (n = 830) using AMOS 17.0, with all constructs modeled simultaneously as correlated factors using the maximum likelihood estimation method. Two items (one from the IM scale and the other from the EM scale) were eliminated owing to a factor loading value lower than .5 (Hair et al., 2006). Overall, the results indicated acceptable psychometric properties (Table 2). Each construct exhibited a composite reliability exceeding the recommended threshold value of .7, suggesting reliability of the measures. The results of the analysis indicated a good fit (Byrne, 2001), with χ² = 1000.35, df (degrees of freedom) = 277, p < .01, χ²/df = 3.61, GFI (goodness-of-fit index) = .91, CFI (comparative fit index) = .95, NFI (normed fit index) = .93, TLI (Tucker–Lewis index) = .94, RMSEA (root mean square error of approximation) = .06, and SRMR (standardized root mean square residual) = .05.

Table 2

Estimated Measurement Model

Construct and Item	SFL	t	CR	AVE
Customer interactivity (M = 5.41, SD = 1.08)			.91	.53
Communication between customers and service employees flowed frequently in both directions.	.63	19.49
Information was exchanged openly between customers and service employees.	.74	23.94
Service employees had extensive face-to-face interactions with customers.	.79	26.45
Customers interacted with service employees often in group discussions.	.70	22.46
Customers were highly involved in activities at the hotel.	.63	19.46
Customers and service employees worked together on problems.	.67	20.98
Issues were critically discussed between customers and service employees.	.82	28.191
Solutions were jointly developed between customers and service employees.	.82	27.61
Customers had many opportunities to evaluate services and products at the hotel.	.74	24.04
Positive affect (M = 6.28, SD = 0.86)			.90	.75
When I remember delightful service encounters, I feel enthusiastic about my work.	.85	29.14
When I remember delightful service encounters, I feel delighted about my work.	.93	33.34
When I remember delightful service encounters, I feel excited about my work.	.81	27.44
Intrinsic motivation (M = 6.27, SD = 0.77)			.76	.52
When I do work well, it gives me a feeling of accomplishment.	.67	20.21
I feel a great sense of personal satisfaction when I do my job well.	.72	21.95
When I perform my job well, it contributes to my personal growth and development.	.76	23.37
Extrinsic motivation (M = 5.75, SD = 0.82)			.87	.57
I am keenly aware of the goals I have for myself.	.80	26.57
I am strongly motivated by the recognition I can earn from other people.	.73	23.16
I want other people to find out how good I really can be at my work.	.75	24.43
I am keenly aware of the income goals I have for myself.	.75	24.57
To me, success means doing better than other people.	.73	23.39
Employee innovative behavior (M = 5.69, SD = 0.93)			.90	.60
At work, I come up with innovative and creative notions.	.73	23.90
At work, I try to propose my own creative ideas and convince others.	.70	22.50
At work, I seek new service techniques or methods.	.84	28.72
At work, I provide a suitable plan for developing new ideas.	.86	30.57
At work, I try to secure the funding and resources needed to implement innovations.	.78	25.78
Overall, I consider myself a creative member of my team.	.73	23.98

Note: SFL = standardized factor loading; CR = construct reliability; AVE = average variance extracted; M = mean; SD = standard deviation.

Convergent validity was evidenced by statistically significant item factor loadings (Anderson & Gerbing, 1988). As indicated in Table 2, the standardized factor loadings of measurement items were significant, ranging from .63 to .93, or moderate to strong (>.5), with t values greater than the recommended threshold value of 1.96 (Netemeyer, Bearden, & Sharma, 2003), suggesting that all items were significant indicators of their respective constructs (p < .01) and providing support for convergent validity. In addition, the average variance extracted (AVE) of all constructs ranged from .52 to .75 (see Table 2), which was greater than the .50 threshold value (Bagozzi & Yi, 1988). Thus, convergent validity for the measurement scale items was achieved. Discriminant validity was evaluated by comparing the square root of the AVE values and the correlation coefficient between constructs (Fornell & Larcker, 1981). As reported in Table 3, each construct’s square root of AVE was higher than the construct correlations, confirming discriminant validity.

Table 3

Latent Variable Correlation Matrix

Construct	1	2	3	4	5
1. Customer interactivity	.73
2. Positive affect	.26*	.87
3. Intrinsic motivation	.31**	.44**	.72
4. Extrinsic motivation	.47**	.43**	.69**	.76
5. Employee innovative behavior	.58**	.39**	.50**	.64**	.78

Note: Boldfaced diagonal elements are the square root of average variance extracted values. Off-diagonal elements are the correlations between constructs.

p < .05. **p < .01.

Structural Model Results

A structural equation model was estimated to empirically validate the conceptual model; it included employees’ gender, age, education level, position, and length of experience in the industry as control variables, as these factors may influence EIB (Scott & Bruce, 1994). The estimated structural model showed satisfactory model fit (χ² =1544.32, df = 408, χ²/df = 3.79, p < .01, GFI = .89, CFI = .92, NFI = .90, TLI = .91, RMSEA = .06, and SRMR = .05). Table 4 displays the estimates for the overall structural model and the hypothesized paths. The effects of CI on EIB (β = .27, p < .001) and PA (β = .20, p < .001) were significant. Thus, Hypotheses 1₀ and 2₀ were rejected, and Hypotheses 1_a and 2_a were accepted. CI was positively related to IM (β = .17, p < .001) and EM (β = .34, p < .001), thus rejecting Hypothesis 3₀ and accepting the alternative Hypothesis 3_a.

Table 4

Estimated Structural Model

Hypothesized Paths	Standardized Estimates	t Values	Hypotheses Testing Results
EIB←CI	.27***	8.40	H1_a: supported
PA←CI	.20***	6.65	H2_a: supported
IM←CI	.17***	7.26	H3(a)_a: supported
EM←CI	.34***	11.79	H3(b)_a: supported
IM←PA	.38***	11.08	H4(a)_a: supported
EM←PA	.41***	11.24	H4(b)_a: supported
EIB←IM	.22***	4.16	H5_a: supported
EIB←EM	.47***	11.14	H6_a: supported
Indirect Effects	Standardized Estimates	SE	95% BCa Confidence Interval
IM←PA←CI	.13***	.03	[.08, .18]
EM←PA←CI	.10***	.02	[.06, .15]
EIB←IM&EM←PA←CI	.30***	.03	[.24, .37]

Note: CI = customer interactivity; PA= positive affect; IM = intrinsic motivation; EM = extrinsic motivation; EIB = employee innovative behavior; SE = standard error; BCa = bias corrected and accelerated. Goodness-of-fit statistics: χ² = 1544.32,df = 408, χ²/df = 3.79, p < .01, GFI (goodness-of-fit index) = .89, CFI (comparative fit index) = .92, NFI (normed fit index) = .90, TLI (Tucker–Lewis index) = .91, RMSEA (root mean square error of approximation) = .06, SRMR (standardized root mean square residual) = .05. EIB←PA←IM&EM←CI means the mediating effect of PA, IM, and EM between CI and EIB. The other two relationships (IM←PA←CI and EM←PA←CI) also represent mediating effects. Results were based on a 1,000-bootstrap sample; a mediation path is significant if the BCa confidence interval does not contain the value of 0 (Hayes, 2013).

p < .05. **p < .01. ***p < .001.

Results also revealed that PA was positively related to IM (β = .38, p < .001) and EM (β =.41, p < .001), thus rejecting null Hypothesis 4₀ and accepting alternative Hypothesis 4_a. In addition, as IM (β = .22, p < .001) and EM (β = .47, p < .001) were positively and significantly related to EIB, Hypotheses H5₀ and H6₀ were rejected and the alternative Hypotheses H5_a and H6_a were accepted.

We further assessed the indirect effects of CI through PA, IM, and EM by performing bias-corrected percentile bootstrapping at a 95% confidence interval with 1,000 bootstrap samples (Taylor, MacKinnon, & Tein, 2008). The confidence interval of the lower and upper bounds was used to determine whether the indirect effects were significant (Hayes, 2013). As shown in Table 4, the test results confirmed the existence of a positive and significant indirect effect of interactivity through PA on IM (β = .13, p <.001, 95% BCa confidence interval [.08, .18]) and EM (β = .10, p < .001, 95% BCa confidence interval [.06, .15]). Similarly, positive and significant indirect effects through PA, IM, and EM were found between CI and EIB (β = .30, p < .001, 95% BCa confidence interval [.24, .37]).

Discussion and Implications

Drawing on the S-O-R framework, this study conceptualized an EIB model that investigates the role of CI, PA, and IM and EM in enhancing EIB in hotels. The model was tested on a sample of 830 hotel employees of four- and five-star hotels in China, and all the hypotheses were supported. The results of the study support previous arguments that link CI to employee psychology (Barnes et al., 2015; Hartline & Ferrell, 1996), demonstrating that effective employee–customer interaction and exchange of ideas can affect hotel employees’ motivation and innovation not only directly but also indirectly through influencing employees’ emotional states.

Theoretical Implications

The results of this study contribute to the literature by exploring the mechanisms underlying the relationship between customer–employee interactivity (as an important manifestation of CE) and EIB. First, by focusing on customer–employee interactivity, this study extends the investigation of CE into the off-line service delivery and innovation domain and addresses the call to strengthen research on the antecedents and outcomes of CE (So, King, Sparks, & Wang, 2016). The study conceptualizes CE’s interactive dimension as a stimulus to elicit hotel employees’ innovative behaviors at an individual level, thereby differentiating our study from previous studies, which view CE as psychological engagement or behavioral participation with a brand or a firm.

Second, our study responds to the call for expansion of the scope of the S-O-R model in hospitality settings (Ahn & Seo, 2018). Prior studies have adopted the S-O-R model to examine from a marketing perspective how the customer’s emotions and behaviors are influenced by a hotel’s environmental attributes, services, or staff behavior (e.g., Choi & Kandampully, 2019; Jani & Han, 2015). In contrast, our study expanded the S-O-R model by considering the complexity within the “O” element and simultaneously testing the effects of multiple organismic responses. The study revealed that customer–employee interactivity can induce hotel employees’ organismic responses of PA and motivations, which in turn lead to employees’ innovative behaviors. Our findings highlight the dynamic nature of the organismic component of the S-O-R model and suggest that multiple factors are at play to collectively lead to a behavioral response. Our conceptualization of CI as a stimulus and incorporation of multiple organismic responses in the model are novel. Furthermore, the study extends the application of the S-O-R framework to an organizational behavior context in hotels.

Third, with respect to interactivity at the individual employee and customer levels, the study also differentiates itself from previous studies with a predominant focus on customer–firm interactivity (Foss et al., 2011) and customer–technology interactivity (e.g., Barreda, Bilgihan, Nusair, & Okumus, 2016). As the study’s results reveal, direct interactions with customers during service delivery can, directly and indirectly, influence employees’ innovative behaviors.

Another theoretical contribution relates to the study’s consideration of the psychological mechanisms underlying employees’ innovative behaviors and PA in the workplace, in contrast to previous conceptualizations that interactivity exercises its influence by engaging employees in a cognitive process (Y. Liu & Shrum, 2002). As one of the few attempts to clarify the role of employees’ emotional state in influencing their performance, this study reveals how CI influences EIB through the psychological mechanisms of PA and IM and EM. This finding suggests that researchers should consider emotional constructs such as the emotional intelligence of employees when studying organizational and employee performance.

Practical Implications

This study also has several practical implications for enhancing employee-based innovativeness. First, the findings highlight the significance of CI for innovations in hotels. To maximize opportunities for two-way customer–employee communication on a comprehensive range of service attributes, hotels should establish a facilitating mechanism for customer–employee interactivity throughout the service process (i.e., before, during, and after customer stay). Hotels should formulate internal policies and operational procedures to ensure that employees initiate two-way communication with customers before their arrival at the hotel. Such actions can include direct contact with customers to inform about available services and matters needing attention (e.g., customer service contact details and weather forecasts), soliciting information from customers about their travel arrangements, and confirmation of special requests for the elderly and children. Hotels should also identify critical service touch points for customer–employee interactivity (e.g., check-in, end of a meal, and check-out) and establish and communicate service protocols to employees to ensure regular two-way interaction throughout customers’ duration of stay. After check-out, hotels should maintain interaction with customers via social media–based brand communities and actions such as customer satisfaction survey, solicitation of customer feedback, holiday greetings, and birthday wishes. Employees may be encouraged to keep in touch with customers if positive rapport has been established between the two parties. An effective facilitating mechanism for customer–employee interactivity should ensure timely handling of customer feedback and include continuous skill development for employees so they can effectively deliver interactive and personalized services to customers. For instance, hotels should reward employees who regularly solicit and respond to customer feedback, and they should provide effective channels for employees to report customer feedback in a timely manner. In addition, as employees inevitably encounter problems and customer complaints during service delivery, hotels should investigate ways to encourage and empower on-site joint problem solving by employees and customers. This collaboration may be achieved by (1) incorporating joint problem solving into performance evaluation; (2) improving employees’ problem-solving, communication, and service skills through training and development programs; (3) instilling a sense of duty in employees through better internal engagement strategies; and (4) adjusting/clarifying individuals’ responsibilities. Hotels should incentivize on-site problem solving through both financial and nonfinancial rewards.

Second, the significant effect of motivations identified in this study points to the need for hotels to establish an environment that satisfies both IM and EM of their employees. With a higher standardized regression weight, EMs proved to be a stronger predictor of customer innovative behavior than IM. This result may be related to the relatively low pay and low social status of hotel staff (McGinley, Hanks, & Line, 2017) and may also indicate that the hotel job is less inherently satisfying as its incentives are directly related to performance or results, making EM the main driving force for employees to do the work (Kuvaas et al., 2017). Indeed, contrary to public perceptions in the 1990s that hotels in China offered a good work environment and higher than average salaries, hotels are no longer able to provide good packages that meet employee expectations (Zhang & Wu, 2004). The stronger influence of EM found in this study also aligns with evidence obtained outside the hospitality industry that financial incentives relate positively to performance and that EM asserts a stronger influence than IM (Jenkins, Mitra, Gupta, & Shaw, 1998).

EMs are more goal driven and can be encouraged by a well-established benefit and reward system (Lin, 2007), such as improvements in salary level, vacation discounts, and free employee meals (McGinley et al., 2017). Therefore, as mentioned earlier, the hotel incentive system can incorporate measures and rewards related to customer interaction and engagement. Furthermore, hotels should intrinsically motivate employees, possibly by providing organizational support. For instance, adopting developmental rather than evaluative evaluation systems to provide feedback on specific innovative behaviors can alleviate the fear of the uncertainty caused by innovation. Hotels must create an atmosphere and culture of emphasizing CI, encouraging employee feedback, and empowering EIBs.

Third, PA plays an indirect role between CI and EIB. It is thus also worthwhile for hotels to invest in practices that facilitate the elicitation of PA of employees, such as providing more assistance for employees. In particular, supervisor or coworker support is important for improving employee satisfaction and positive emotion (Susskind, Kacmar, & Borchgrevink, 2018). In addition, the human resource department can consider recruiting employees who possess positive affectivity and extraversion traits, which may help build a more positive and innovative workforce at the hotel.

Limitations and Research Directions

Our study has several limitations. First, the study’s focus on four- and five-star hotels limits the generalizability of the findings. Further investigation is required to validate our model in hotels of lower star ratings, especially given that the expected level of customer relationship and value creation is lower in these hotels (Park & Allen, 2013). Second, while our study focuses on PA, in reality, both positive and negative emotions may arise from customer–employee interactivity. Evidence outside the tourism and hospitality literature reveals no facilitating role of negative affect on EIB (Choi, Sung, Lee, & Cho, 2011). Future research is required for insights into the role of negative affect in shaping employee innovation and performance in hotels. Third, this study viewed interactivity as a social stimulus in a service environment without investigating the antecedents of interactivity, such as organizational process as well as compensation and benefits schemes. Future research should explore the causes of and barriers to interactivity and how these factors interact with employee motivations to influence their innovative behaviors. In addition, our data did not support the three-dimensional structure of interactivity, which raises a need for further investigation into the nature of this construct in a hospitality context and for a service-specific measurement scale for interactivity.

Concluding Summary

This research contributes to the tourism and hospitality literature by conceptualizing and testing the roles of CI, PA, and employee motivations in enhancing EIB in hotels. Based on structural equation modeling of data collected from 830 hotel employees in China using a mixed-mode quantitative survey, the study demonstrates the theoretical and practical importance of focusing on employee–customer interactions in hotels as such interactions can affect employees’ motivation and innovation not only directly but also indirectly through influencing employees’ emotional states.

Footnotes

Authors’ Note:

Shandong Natural Science Foundation of China (ZR2019MG014) provided support for this research.

ORCID iD

Feng Zeng Xu

Feng Zeng Xu, PhD (e-mail: xfz@sdu.edu.cn), is a professor in the School of Management at Shandong University, Jinan, Shandong, People’s Republic of China. Ying Wang, PhD (e-mail: ying.wang@griffith.edu.au), is a senior lecturer at the Department of Tourism, Sport and Hotel Management, Griffith Institute for Tourism, Griffith University, Brisbane, Queensland, Australia.

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