The Elicitation of Frontline,Customer-Contact,Hotel Employee Innovative Behavior: Illuminating the Central Roles of Readiness for Change and Absorptive Capacity

Abstract

Although service innovations have been recognized to be important for the long-term strategic success of hospitality firms, to date, the elicitation of innovative behavior has received little attention in the extant hospitality research literature. In the current study, we used a matched set of responses from 294 frontline, customer-contact, hotel employees and their direct supervisors to address this lack. Consistent with extant human resource management (HRM) studies that have advocated the agent-centered perspective, this study’s results illuminate a causal chain through which employee self-reported (Time 1, Source 1) perceived high-investment human resource practices (HIHRP) augments individual frontline, customer-contact, hotel employee supervisor-rated (Time 2, Source 2) innovative behavior. This study contributes to the extant hospitality and HRM research literatures by elucidating individual hotel employee self-reported perceived HIHRP as a key proximal determinant and individual hotel employee supervisor-rated innovative behavior as a key proximal consequence of two positive organizationally relevant individual-level psychological outcomes: that is, frontline, customer-contact, hotel employee self-reported readiness for change and absorptive capacity. Findings, implications, and limitations as well as avenues for future research are discussed.

Keywords

innovative behavior readiness for change: absorptive capacity perceived high-investment human resource practices

Introduction

Scholars have underscored the crucial role that service innovations play in the competitiveness and long-term success of hospitality firms (Enz & Way, 2016). Service innovations in hospitality firms often involve considerable human activity in which frontline employees and customers are engaged simultaneously in the delivery process. For instance, in hotels, the execution or delivery of a new idea is often through service, because frontline employees are expected to use the innovation directly in relationships with customers or support the innovation’s use in unscripted and individual contact with customers (Enz & Way, 2016). The extant research literature (e.g., Cadwallader, Jarvis, Bitner, & Ostrom, 2010; Chang, Gong, & Shum, 2011) has underscored the key role that frontline, customer-contact employees play in the implementation of innovations and thus, the need for empirical inquiries to delve those frontline, customer-contact employee outcomes that advance service innovations and innovative behavior in hospitality firms.

Given that innovation always entails change (King, 1990), two potential key drivers of service innovations and innovative behavior in hospitality firms are frontline, customer-contact employees’ readiness for change (cf. Del Val & Fuentes, 2003; Hon & Lui, 2016) and absorptive capacity (cf. Chang, Gong, Way, & Jia, 2013; Cohen & Levinthal, 1990; Löwik, 2013; Löwik, Kraaijenbrink, & Groen, 2012). In this study, we build and extend on the extant hospitality and human resource management (HRM) research literatures and elucidate a positive psychological state (readiness for change) and a positive cognitive state (absorptive capacity) as proximal determinants of individual hospitality employee innovative behavior. The primary objective of this current study is to provide insight concerning the elicitation of frontline, customer-contact, hospitality employee innovative behavior. Considering the crucial role that frontline, customer-contact, hospitality employees play in implementing service innovations and that service innovations play in the competitiveness and success of hospitality firms, the current study affords an important contribution to the extant hospitality research literature.

In addition, consistent with extant HRM studies that have advocated the agent-centered perspective (e.g., Jiang, Hu, Liu, & Lepak, 2017; Liao, Toya, Lepak, & Hong, 2009; Nishii, Lepak, & Schneider, 2008; Nishii & Wright, 2008; Tummers, Kruyen, Vijverberg, & Voesenek, 2013, 2015), we posit that a frontline, customer-contact employee’s perception of high-investment human resource practices (perceived HIHRP) is a key proximal determinant of her or his readiness for change: that is, a positive individual-level psychological state that involves an individual’s beliefs, feelings, and intentions concerning novel ideas and changes (Kwahk & Kim, 2008) and her or his absorptive capacity, that is, a positive individual-level cognitive state that involves an individual’s ability to acquire, assimilate, and use knowledge (cf. Erhardt, Martin-Rios, & Way, 2009; Löwik, 2013; Löwik et al., 2012). Furthermore, consistent with a central dogma in the HRM research literature that psychological outcomes are precursors of human resource outcomes (see detailed descriptions in Note 1) we posit that an employee’s readiness for change and absorptive capacity (two positive individual-level psychological outcomes) are in turn key proximal determinants of her or his innovative behavior, that is, a positive individual-level human resource outcome that involves the individual generating novel ideas, promoting novel ideas to others, and implementing novel ideas (Janssen, 2000).

The current empirical inquiry rejoins Hon and Lui’s (2016) call for research to investigate whether employee readiness for change mediates the relationship between its precursors (e.g., employee-perceived HIHRP) and consequences (e.g., employee innovative behavior). Moreover, this empirical inquiry is one of the first to study absorptive capacity at the individual level of analysis and investigate employee-perceived HIHRP as a proximal determinant and employee innovative behavior as a proximal consequence of individual, frontline, customer-contact, hotel employee absorptive capacity. A schematic representation of this study is presented in Figure 1.

Figure 1.

Conceptual Model.

Published empirical investigations of the link between employees’ readiness for change and innovative behavior are rare; although, in an apposite study, Michaelis, Stegmaier, and Sonntag (2010) examined the link between employees’ commitment to change and innovation implementation behavior. Building on the Michaelis et al.’s (2010) study and the work of Hon and Lui’s (2016), we postulate that an employee’s readiness for change will be a proximal determinant of her or his propensity to resist or support innovative ideas and changes (cf. Armenakis, Harris, & Mossholder, 1993; Del Val & Fuentes, 2003) and her or his exhibited innovative behavior. Similarly, although Cohen and Levinthal (1990) and Löwik and colleagues (Löwik, 2013; Löwik et al., 2012) contended that the innovation performance of a firm is dependent on the absorptive capacities of its individual employees, to date, there are no published studies that have examined the link between employee absorptive capacity and innovative behavior at the individual level. In this study, congruent with the contentions of Cohen and Levinthal (1990), Löwik and colleagues, and Van Winkelen and McKenzie (2011), we postulate that an employee’s absorptive capacity will be a proximal determinant of her or his ability to adapt to change and of her or his exhibited innovative behavior. Thus, we hypothesize the following:

Hypothesis 1: Individual frontline, customer-contact employees’ self-reported readiness for change and supervisor-rated innovative behavior will be positively related.

Hypothesis 2: Individual frontline, customer-contact employees’ self-reported absorptive capacity and supervisor-rated innovative behavior will be positively related.

HRM scholars (e.g., Chadwick, Way, Kerr, & Thacker, 2013; Way, Lepak, Fay, & Thacker, 2010) have delineated a high-investment human resource system (HIHRS) as a set of distinct but interrelated high-investment human resource practices (HIHRP) that together engender positive organizationally relevant psychological and human resource outcomes.¹ Although the practices included in the conceptualization and measurement of an HIHRS have varied across studies, there appears to be some consensus that an HIHRS involves selective staffing, continuous training, developmental opportunities, developmental performance appraisals, employee involvement in decision making and voice, and performance- and group-based rewards. Published empirical HRM studies have highlighted the impact that HIHRP can have on organizationally relevant (a) psychological outcomes such as affective commitment (e.g., Gardner, Wright, & Moynihan, 2011), perceived organizational support (e.g., Alfes, Shantz, Truss, & Soane, 2013), and absorptive capacity (e.g., Chang et al., 2013); and (b) human resource outcomes such as employee attendance (e.g., Boon, Belschak, Den Hartog, & Pijnenburg, 2014), retention (e.g., Gardner et al., 2011; Way, 2002), and organizational citizenship behaviors (e.g., Messersmith, Patel, Lepak, & Gould-Williams, 2011).

Although the focus of many of these studies has been on HIHRP use as reported by managers (e.g., Chang et al., 2013; Gardner et al., 2011; Messersmith et al., 2011; Way, 2002), HRM scholars espousing an agent-centered perspective underscore (e.g., Jiang et al., 2017; Nishii & Wright, 2008), and the findings of extant empirical HRM studies indicate (see Kehoe & Wright, 2013; Liao et al., 2009; Nishii et al., 2008; Ployhart, Weekley, & Ramsey, 2009) that employees may not attribute or perceive HIHRP as reported by managers and that different employees within a firm may attribute or perceive the use of the same set of HIHRP in a variety of ways and, thus, have different perceptions of HIHRP from each other (Jiang et al., 2017). Moreover, Nishii and Wright (2008) proposed that variations in individual employees’ perceived HIHRP lead to variations in employees’ psychological outcomes, and that variations in individual employees’ psychological outcomes, in turn, lead to variations in employees’ human resource outcomes.

Published empirical studies (e.g., Jensen, Patel, & Messersmith, 2013; Liao et al., 2009) have presented convincing results, which support Nishii and Wright’s (2008) conception of the perceived HIHRP—human resource outcomes causal chain. For instance, Liao et al. (2009) found that employee self-reported perceived HIHRP were related to employee self-reported perceived organizational support (a positive individual-level psychological state), and that employee self-reported perceived organizational support fully mediated the positive relationship between employee self-reported perceived HIHRP and employee supervisor-rated general service performance (a positive individual-level human resource outcome). Moreover, congruent with Nishii and Wright’s (2008) conception of the perceived HIHRP—human resource outcomes causal chain, empirical studies have highlighted employees’ perceived HIHRP as a key proximal determinant of organizationally relevant psychological outcomes such as employees’ affective commitment (e.g., Kehoe & Wright, 2013), perceived organizational support (e.g., Liao et al., 2009), and anxiety (e.g., Jensen et al., 2013).

In this study, we build on the work of Nishii and Wright (2008) and Tummers et al. (2013, 2015) and posit that individual employee self-reported perceived (employee-experienced) HIHRP will be a key proximal precursor of individual employee self-reported readiness for change (a positive individual-level psychological outcome). To the best of our knowledge, to date, no published empirical studies have investigated this postulated positive link. For instance, although Tummers et al. (2013) conjectured that employee-perceived HIHRP was a key proximal determinant of employee proactivity, vitality, and readiness for change, they did not examine the posited positive relationship between individual employee-perceived HIHRP and readiness for change depicted in Figure 1. However, Tummers et al. (2013) did encourage future empirical studies to constructively replicate and extend on their findings; for example, to investigate whether variations in individual employees’ self-reported perceived HIHRP lead to variations in employees’ self-reported readiness for change.

Then again, although scholars (e.g., Cohen & Levinthal, 1990; Löwik et al., 2012) have underscored that firm innovativeness is dependent on the absorptive capacities of its individual employees, Chang et al. (2013) conjectured and presented empirical evidence, which indicated that firm-level flexibility-oriented human resource system use and firm absorptive capacity were positively related, and that firm absorptive capacity in turn mediated the positive relationship between firm-level flexibility-oriented human resource system use and firm innovativeness. In this study, we build on the work of Nishii and Wright (2008), Chang et al. (2013), and others (e.g., Liao et al., 2009), and posit that individual employee self-reported perceived HIHRP will be a key proximal precursor and that individual employee supervisor-rated innovative behavior will be a key proximal consequence of individual employee self-reported absorptive capacity. To the best of our knowledge, there are no published studies that have examined the posited causal chain between individual employee-perceived HIHRP, absorptive capacity, and innovative behavior depicted in Figure 1.

Furthermore, congruent with Lewin’s (1947) conception of unfreezing and Nishii and Wright’s (2008) conception of the perceived HIHRP—human resource outcomes causal chain, we postulate that an employee’s perception of HIHRP will indirectly shape her or his exhibited innovative behavior (a positive individual-level human resource outcome) via his or her readiness for change and absorptive capacity (two positive individual-level psychological outcomes). Therefore, we hypothesize the following:

Hypothesis 3: Individual frontline, customer-contact employees’ self-reported readiness for change and absorptive capacity will fully mediate the positive relationship between individual-level self-reported perceived HIHRP and supervisor-rated innovative behavior.

Method

Sample and Procedures

This study’s targeted respondents were all 102 and 196 frontline, customer-contact employees with three or more months tenure and their direct supervisors from a hotel property located in Hong Kong, China, and a hotel property located in Guangzhou, China, respectively. Each targeted respondent was instructed in a cover letter that her or his participation in this study was voluntary and confidential and to use the prepaid, return-addressed envelope provided to mail her or his completed survey directly to this article’s third author. In this way, respondents were assured of the confidentiality of their responses. To minimize common method variance concerns and better assess causality, the data used to generate the employee supervisor-rated (Time 2, Source 2) innovative behavior outcome (dependent) variable were obtained 3 months subsequent to when the employees’ self-reported (Time 1, Source 1) perceived HIHRP causal, readiness for change mediator, and absorptive capacity mediator variables’ data were obtained (cf. Way, Simons, Leroy, & Tuleja, in press). A matched set of responses were obtained from 294 frontline, customer-contact, hotel employees (Time 1, Source 1) and their supervisors (Time 2, Source 2), representing a 99% response rate. The sample’s frontline, customer-contact, hotel employee respondents (n = 294) were primarily men (60%) in their early thirties with less than 4 years tenure (60%) and from the hotel property located in Guangzhou, China (67%).

Measures

To help ensure the quality of the survey responses, the perceived HIHRP, readiness for change, absorptive capacity, and innovative behavior items used in this study were taken from extant multiitem scales. Response options ranged from 1 (strongly disagree) to 7 (strongly agree). In addition, each item included in our surveys was translated into Chinese by this article’s first and the third authors following the back-translation procedure (see Way et al., in press). In this iterative process, the two translators individually translated each item (from English to Chinese) and then compared notes with each other over multiple revisions. Once the most appropriate translation had been agreed upon by the two translators, the translated item was reviewed by four managers (two managers from each participating hotel property), and based on their feedback, the translated item was included or revised and included in this study’s surveys.

To obtain a more favorable indicator to sample size ratio (Little, Cunningham, Shahar, & Widaman, 2002) for the assessment of the measurement and hypothesized structural models (see Table 1), we followed the technique outlined by Way et al. (in press) and reduced the number of indicators to five for the study’s four single-factor measures with more than five items. Thus, the four latent variables included in this study’s measurement and hypothesized structural models were five-indicator employee self-reported perceived HIHRP, self-reported readiness for change, self-reported absorptive capacity, and supervisor-rated innovative behavior latent variables.

Table 1.

CFA of the Measurement and Hypothesized Structural Models.

Models	χ²	df	p	SRMR	CFI	TLI
1. Measurement model	444.11	164	.000	0.05	0.95	0.94
2. Hypothesized structural model	634.87	259	.000	0.06	0.93	0.93

Note. The table presents a summary of the CFA of the measurement (four latent variables) and hypothesized structural models (n = 294). Model fit was assessed by examining three conventional fit indices: SRMR values less than 0.08 indicate a good fit with the data. CFI and TLI values of 0.95 and higher are considered an excellent fit with the data, whereas CFI and TLI values between 0.90 and 0.95 are considered a good fit with the data. CFA = confirmatory factor analysis; SRMR = standardized root mean square residual; CFI = comparative fit index; TLI = Tucker–Lewis index.

Perceived HIHRP

Xiao and Tsui’s (2007) 15-item scale was used to assess employee self-reported (Time 1, Source 1) perceived HIHRP. A sample item was, “This company provides extensive training and socialization.” The composite reliability (CR) for this study’s five-indicator individual employee self-reported perceived HIHRP latent variable was 0.93 (n = 294; i.e., exceeded the 0.70 threshold) and the average variance extracted (AVE) by this five-indicator latent variable was 0.73 (i.e., exceeded the 0.50 threshold). The Cronbach’s alpha for the 15-item individual employee self-reported perceived HIHRP variable included in the auxiliary ordinary least squares (OLS) regression models presented below (Table 2, Models 2-4) was 0.81 (n = 294).

Table 2.

Auxiliary OLS Regression Results for Supervisor-Rated Innovative Behavior.

Variables	Innovative Behavior
	Model 1			Model 2			Model 3			Model 4
	β	SE	p	β	SE	p	β	SE	p	β	SE	p
Constant	4.24	(0.26)	***	2.07	(0.38)	***	4.05	(0.37)	***	3.98	(0.37)	***
Control variables
Gender	−0.02	(0.12)		−0.03	(0.11)		0.07	(0.09)		0.08	(0.09)
Age	−0.15	(0.07)	*	−0.14	(0.06)	*	−0.13	(0.05)	*	−0.14	(0.05)	**
Education	−0.03	(0.05)		0.02	(0.05)		−0.04	(0.04)		−0.03	(0.04)
Tenure	0.07	(0.03)	*	0.09	(0.03)	**	0.05	(0.02)	*	0.05	(0.02)	*
Hotel property	0.80	(0.13)	***	0.65	(0.12)	***	0.92	(0.10)	***	0.88	(0.10)	***
Causal and mediator variables
Perceived HIHRP				0.39	(0.05)	***	0.03	(0.06)		0.04	(0.05)
Readiness for change							0.38	(0.07)	***	0.43	(0.07)	***
Absorptive capacity							0.42	(0.08)	***	0.45	(0.08)	***
Interaction term
Readiness for change × Absorptive capacity										0.11	(0.04)	*
Model F	13.37		***	22.01		***	39.15		***	36.08		***
Model R²	0.19			0.32			0.52			0.53
ΔR²				0.13		***	0.20		***	0.01		*

Note. This table presents the OLS regression results for individual frontline, customer-contact, hotel employee supervisor-rated (Time 2, Source 2) innovative behavior (n = 294). β = unstandardized coefficient; ΔR² = the change in R² in comparison with R² for the preceding OLS regression model. HIHRP = high-investment human resource practices; OLS = ordinary least squares.

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

Readiness for change

Kwahk and Kim’s (2008) 13-item scale was used to assess individual employee self-reported (Time 1, Source 1) readiness for change. A sample item was, “I think change usually helps improve unsatisfactory situations at work.” The CR value for this study’s five-indicator individual employee self-reported readiness for change latent variable was 0.94 (n = 294); AVE = 0.76. The Cronbach’s alpha for the 13-item individual employee self-reported readiness for change mean-centered variable included in the auxiliary OLS regression models (Table 2, Models 3-4) was 0.94.

Absorptive capacity

Fifteen items taken from Löwik and colleagues were used to assess individual employee self-reported (Time 1, Source 1) absorptive capacity. A sample item was, “I often apply newly acquired knowledge to my work.” The CR value for this study’s five-indicator individual employee self-reported absorptive capacity latent variable was 0.95 (n = 294); AVE = 0.78. The Cronbach’s alpha for the 15-item individual employee self-reported absorptive capacity mean-centered variable included in the auxiliary OLS regression models (Table 2, Models 3-4) was 0.92.

Innovative behavior

Janssen’s (2000) nine-item scale was used to assess individual employee supervisor-rated (Time 2, Source 2) innovative behavior. A sample item was, “This employee transforms innovative ideas into useful applications.” The CR value for this study’s five-indicator individual employee supervisor-rated innovative behavior latent variable was 0.97 (n = 294); AVE = 0.86. The Cronbach’s alpha for the auxiliary OLS regression models’ (see Table 2, Models 1-4) nine-item individual employee supervisor-rated innovative behavior-dependent variable was 0.96 (n = 294).

Control variables

The auxiliary OLS regression models presented in Table 2, Models 1 to 4, included the following control variables (Time 1, Source 1; n = 294): (a) gender, 1 (female) and 0 (male); (b) age, response options ranged from 1 (less than 26 years of age) to 4 (greater than 45 years of age); (c) education, response options ranged from 1 (no secondary school diploma) to 6 (graduate degree); and (d) tenure, response options ranged from 1 (less than 1 year) to 11 (more than 10 years). To control for any unobserved heterogeneity between the two hotel properties, the auxiliary OLS regression models presented in Table 2, Models 1 to 4, included a hotel property dummy variable: 1 (Hong Kong hotel property) and 0 (Guangzhou hotel property).²

Analytic Approach

We used the bootstrapping function (Preacher & Hayes, 2008) in Mplus 7.4, a structural equation modeling (SEM) statistical software tool, to test the current study’s hypothesized structural model and to obtain standardized direct effect (SDE), two-tailed significance, standard error (SE), and 95% confidence interval (CI) estimates for the model’s four structural paths and standardized indirect effect (SIE), two-tailed significance, SE, and 95% CI estimates for the hypothesized positive, indirect effects of individual employee self-reported (Time 1, Source 1) perceived HIHRP on employee supervisor-rated (Time 2, Source 2) innovative behavior (Hypothesis 3). The data were analyzed in two steps: (a) measurement model assessment and (b) hypothesized structural model assessment (McDonald & Ho, 2002).

Results

First, as shown in Table 1, the confirmatory factor analysis (CFA) of this current study’s four-factor/latent variable measurement model demonstrated a good fit with the data (n = 294). Furthermore, as reported above, the CR values for the measurement model’s four five-indicator latent variables all exceeded the 0.70 threshold (CR values ranged from 0.93 to 0.97) and the AVE by each of the measurement model’s four five-indicator latent variables all exceeded the 0.50 threshold (AVE values ranged from 0.73 to 0.86). In addition to the current study’s four-factor (four five-indicator latent variables) measurement model demonstrating a good fit with the data (n = 294), it demonstrated a better fit with the data (n = 294) than three alternative (three-factor, two-factor, and one-factor) measurement models.³ Taken together, these results provide support for the convergent and discriminant validity of the current study’s measurement model and four (five-indicator) latent variables (Etemad-Sajadi, Way, & Bohrer, 2016).

Next, as shown in Table 1, the hypothesized structural model demonstrated a good fit with the data (n = 294). Figure 2 displays the hypothesized structural model with SDE and two-tailed significance estimates (solid-line arrows) for the hypothesized structural model’s four posited structural paths and SIE, and two-tailed significance estimates (dashed-line arrows) for the posited positive, indirect effects of individual employee self-reported (Time 1, Source 1) perceived HIHRP on supervisor-rated (Time 2, Source 2) innovative behavior (Hypothesis 3).

Figure 2.

Hypothesized Structural Model: SDE, SIE, and Two-Tailed Significance Estimates.

Individual employee self-reported (Time 1, Source 1) readiness for change and absorptive capacity (mediators) had positive, direct effects (SDE = 0.38, p < .001, SE = 0.09, 95% CI = [0.20, 0.56]; and SDE = 0.42, p < .001, SE = 0.11, 95% CI = [0.21, 0.63], respectively) on employee supervisor-rated (Time 2, Source 2) innovative behavior (outcome variable). Thus, Hypothesis 1 and Hypothesis 2 were supported (see Figure 2). In addition, individual employee self-reported (Time 1, Source 1) perceived HIHRP (causal variable) had a positive, direct effect (SDE = 0.41, p < .001, SE = 0.06, 95% CI = [0.30, 0.53]) on employee self-reported (Time 1, Source 1) readiness for change (mediator) and a positive, indirect effect on employee supervisor-rated (Time 2, Source 2) innovative behavior (outcome variable); that is, the SIE estimate for individual employee self-reported perceived HIHRP → self-reported readiness for change → supervisor-rated innovative behavior was 0.15, p < .001, SE = 0.04, 95% CI = [0.07, 0.24]. And, individual employee self-reported (Time 1, Source 1) perceived HIHRP (causal variable) had a positive, direct effect (SDE = 0.41, p < .001, SE = 0.05, 95% CI = [0.31, 0.51]) on employee self-reported (Time 1, Source 1) absorptive capacity (mediator) and a positive, indirect effect on employee supervisor-rated (Time 2, Source 2) innovative behavior (outcome variable); that is, the SIE estimate for individual employee self-reported perceived HIHRP → self-reported absorptive capacity → supervisor-rated innovative behavior was 0.17, p < .001, SE = 0.05, 95% CI = [0.07, 0.27]. Hence, in support of Hypothesis 3, these results (also see Figure 2) indicate that individual frontline, customer-contact employee self-reported (Time 1, Source 1) readiness for change and absorptive capacity fully mediate the relationship between individual employee self-reported (Time 1, Source 1) perceived HIHRP (causal variable) and employee supervisor-rated (Time 2, Source 2) innovative behavior (outcome variable).

Auxiliary OLS Regression Analyses and Results

An individual frontline, customer-contact, hotel employee’s supervisor-rated (Time 2, Source 2) innovative behavior was shown to be determined by both her or his self-reported (Time 1, Source 1) readiness for change and absorptive capacity (see Figure 2). We contend that employees’ supervisor-rated innovative behavior (a positive individual-level human resource outcome) is further strengthened by the interaction between their self-reported readiness for change (a positive individual-level psychological state) and absorptive capacity (a positive individual-level cognitive state). This current study’s individual employee self-reported (Time 1, Source 1) readiness for change and absorptive capacity mean-centered variables were used to generate the readiness for change—absorptive capacity interaction term (readiness for change × absorptive capacity; M = 0.44, SD = 1.15) included in the auxiliary OLS regression model used to test the above posited interaction (see Table 2, Model 4). The results of these auxiliary OLS regression analyses for employee supervisor-rated innovative behavior are presented in Table 2.

In support of the above posited interaction (see Table 2, Model 4), individual employee self-reported (Time 1, Source 1) readiness for change (Hypothesis 1; β = 0.43, p < .001), self-reported (Time 1, Source 1) absorptive capacity (Hypothesis 2; β = 0.45, p < .001), and the readiness for change—absorptive capacity interaction term (readiness for change × absorptive capacity; β = 0.11, p < .05) were all positively related to employee supervisor-rated (Time 2, Source 2) innovative behavior. In addition to demonstrating the positive interactive effect of individual frontline, customer-contact, hotel employee self-reported (Time 1, Source 1) readiness for change and absorptive capacity on employee supervisor-rated (Time 2, Source 2) innovative behavior, the auxiliary OLS regression results presented in Table 2 provide further support for Hypotheses 1 and 2. That is, these OLS regression results (see Table 2, Model 3) are complementary with the SEM results reported above and in Figure 2, which illustrated that employee self-reported (Time 1, Source 1) readiness for change (Hypothesis 1) and absorptive capacity (Hypothesis 2) have positive, direct effects on employee supervisor-rated (Time 2, Source 2) innovative behavior. Moreover, in support of Hypothesis 3 (see Baron & Kenny, 1986) and consistent with the SEM results reported above and in Figure 2, the positive relationship between individual employee self-reported (Time 1, Source 1) perceived HIHRP and employee supervisor-rated (Time 2, Source 2) innovative behavior (the positive relationship between the causal and outcome variables; β = 0.39, p < .001; see Table 2, Model 2) was no longer statistically significant when the individual employee self-reported (Time 1, Source 1) readiness for change and absorptive capacity variables (mediators) were included in the OLS regression model (β = 0.03, p > .05; see Table 2, Model 3).

Discussion

Advancing frontline, customer-contact employee innovative behavior is critical to the long-term survival and competitiveness of hospitality firms. The primary objective of the current study was to provide insight concerning the elicitation of frontline, customer-contact, hospitality employee innovative behavior. In this study, we elucidate two positive organizationally relevant individual-level psychological outcomes (readiness for change and absorptive capacity) as key proximal determinants of frontline, customer-contact, hotel employee innovative behavior.

Furthermore, consistent with the agent-centered perspective, this study’s findings indicate that a frontline, customer-contact employee’s self-reported perceived HIHRP (experienced HIHRP) is a key proximal determinant of her or his self-reported readiness for change (a positive individual-level psychological state) and absorptive capacity (a positive individual-level cognitive state), which in turn are key proximal determinants of her or his supervisor-rated innovative behavior (a positive individual-level human resource outcome). Therefore, in this current study, we extend on the prior work of Tummers et al. (2013, 2015) and postulate and show that individual employees’ self-reported perceived HIHRP lead to variations in employees’ self-reported readiness for change, as well as rejoin Hon and Lui’s (2016) call for research to investigate whether employee readiness for change mediates the relationship between its precursors and consequences. This empirical inquiry is one of the first to study absorptive capacity at the individual level of analysis. Finally, we illustrate that an employee’s perception of HIHRP will indirectly shape her or his exhibited innovative behavior via his or her readiness for change and absorptive capacity (two positive individual-level psychological outcomes). Overall, the current study builds and extends on the extant hospitality and HRM research literatures and affords novel insight about the elicitation of individual frontline, customer-contact, hospitality employees’ innovative behavior.

In addition, this study’s findings illuminate how hospitality firms can advance innovative behavior by implementing high-investment human resource practices (e.g., continuous training, developmental performance appraisals, and performance-based rewards) to manage (develop, motivate, etc.) their frontline, customer-contact employees. Moreover, given the importance of employee-experienced high-investment human resource practices (i.e., employees’ self-reported perception of HIHRP) in the elicitation of innovative behavior in hospitality firms, in addition to implementing/utilizing high-investment human resource practices to manage their frontline, customer-contact employees, a hospitality firm’s leaders may need to ensure that these employees are aware (realize) that the firm uses high-investment human resource practices to manage (develop, motivate, etc.) them.

Limitations and Avenues for Future Research

To minimize common method variance concerns, data obtained from two distinct sources with a 3-month time lag were used to generate the current study’s frontline, customer-contact, hotel employee (a) self-reported (Time 1, Source 1) perceived HIHRP causal, readiness for change mediator, and absorptive capacity mediator variables; and (b) supervisor-rated (Time 2, Source 2) innovative behavior outcome variable (cf. Way et al., in press). However, these data were obtained from two hotel properties located in China (one hotel property located in Hong Kong and one hotel property located in Guangzhou) and, thus, raise generalizability concerns.

Furthermore, although the data used to generate the current study’s causal variable and mediator variables were obtained 3 months before the data used to generate the outcome variable were obtained, the cross-sectional nature of the data we used to generate this study’s employee self-reported (Time 1, Source 1) perceived HIHRP causal, readiness for change mediator, and absorptive capacity mediator variables does not enable us to adequately assess the causal chain depicted in Figure 1. The assessment of the causal chain depicted in Figure 1 and demonstrating causality require longitudinal designs or at least the use of causal variable (e.g., HIHRP) data obtained before (e.g., at Time 1) the mediator variables (e.g., readiness for change and absorptive capacity) data are obtained (e.g., at Time 2) and the use of mediator variables data obtained before (e.g., at Time 2) the outcome variable (e.g., innovative behavior) data are obtained (e.g., at Time 3). Although this study’s results should be generalizable to frontline, customer-contact, hospitality employees from diverse contexts (firms, countries, etc.), we assert that a fruitful and important avenue for future research would be to use a more rigorous, longitudinal research design to constructively replicate this study’s results in other contexts and to ensure that they are generalizable to other contexts.

In the current study, we focused on individual frontline, customer-contact employees’ self-reported perceived high-investment human resource practices. And, consistent with published empirical HRM studies (e.g., Liao et al., 2009), we found (see Figure 2 and Table 2) that variations in individual frontline, customer-contact, hotel employee self-reported (Time 1, Source 1) perceived HIHRP resulted in significant variations in individual hotel employee supervisor-rated (Time 2, Source 2) innovative behavior (a positive individual-level human resource outcome) and self-reported (Time 1, Source 1) readiness for change and absorptive capacity (positive individual-level psychological outcomes). Nevertheless, we encourage future studies to examine the influence (include measures) of actual HIHRS use, which is likely to afford further insights about how hospitality firms can use high-investment human resource practices to advance their frontline, customer-contact employees’ innovative behavior and their successful implementation of service innovations.

Conclusion

This study provides novel insight about the roles and relevance of hotel employee-perceived (employee-experienced) high-investment human resource practices, readiness for change, and absorptive capacity in the advancement of innovative behavior in hospitality firms. We hope that our current study’s findings will help hospitality firms boost their frontline, customer-contact employees’ innovative behavior and will stimulate further empirical inquiries that afford more insight (clarity) concerning the roles and relevance of high-investment human resource practices and psychological outcomes (psychological states, cognitive states, etc.) in the advancement of frontline, customer-contact, hospitality employees’ innovative behavior and the competitiveness and strategic success (effectiveness) of hospitality firms.

Footnotes

Authors’ Note

Song Chang and Sean A. Way contributed equally to this research and are joint first authors.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, or publication of this article: This research was supported by Research Grants Council of Hong Kong (No. HKBU 490313).

Notes

Author Biographies

Song Chang is an Associate Professor at the Hong Kong Baptist University. He obtained his Doctor of Philosophy in Management from the Hong Kong University of Science and Technology. His recent research has focused on the direct and indirect effects of HRM systems, absorptive capacity, and organizational commitment on organization performance and innovativeness and employee creativity and extra-role and innovative behaviors.

Sean A. Way is an Associate Professor at Monash University. He obtained his Doctor of Philosophy in Industrial Relations and Human Resources from Rutgers University. His recent research has focused on the explication and illustration of the effects and contingent effects of HR flexibility, HRM systems, and focused organizational climates (e.g., internal marketing, support, and service climates) on the performance of organizations and their employees.

Derek H.K. Cheng obtained his Doctor of Business Administration (DBA) from the Hong Kong Baptist University. His DBA thesis research examined the direct and indirect effects of employee readiness for change, perceived HIHRP, and transformational leadership on employee innovative behavior.

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