Meta-analysis of Outcomes of Leader–Member Exchange in Hospitality and Tourism: What Does the Past Say about the Future?

Purpose: Leader–member exchange (LMX) theory is particularly relevant to the hospitality and tourism industry due to its labor-intensive and service-focused nature. However, the hospitality literature regarding the impact of LMX on its various outcomes have inconsistent results. A holistic review of LMX studies is nonexistent in the current literature. Thus, the purpose of this study is to use a meta approach to quantitatively summarize and examine the relationship between LMX and its outcomes in the hospitality and tourism literature.

Design/methodology/approach: A total of 89 individual observations from 36 studies conducted between 1997 and 2018 were identified. A Bayesian random effect model was introduced into the hospitality and tourism literature for the first time to implement the meta-analysis.

Findings: Results suggest significant differences in the impact of LMX on various groups of outcomes. LMX has the strongest impact on firms’ practice-related outcomes, such as organizational justice and employee empowerment. Few moderators are identified on the impact of LMX, such as LMX measure, culture, industry sector, and statistical method.

Practical implications: Findings yielded several recommendations for both hospitality researchers and organizations in developing LMX related studies as well as managing employees.

Originality/value: This study is the first Bayesian meta-analysis in the hospitality and tourism literature; it complements LMX theory by linking it to cognitive appraisal theory. Specific characteristics of LMX in the hospitality and tourism industry, such as the measurement of LMX and the effect of industry sector, are also identified.

Key words: LMX, meta-analysis, Bayesian random effect model, moderator analysis

Introduction

Hospitality is a labor-intensive industry relying on employee performance. As an important linkage between organizations and employees (Northouse, 2018), leaders become crucial in managing employees in the industry. Supervisors and managers who are difficult to understand, disrespectful, and do not communicate will lose employees’ trust and cause a loss of employee morale, reduced performance and increased turnover (Rose, 2016). In contrast, effective leaders motivate employees by sharing their feelings, supporting their development, and caring about their concerns. Thus, various leadership theories articulate different mechanisms through which leaders influence a group of followers to achieve common goals (Northouse, 2018). For example, authentic leadership stresses leaders’ authenticity, transformational leadership focuses on follower development, and servant leadership emphasizes leaders’ attention to their followers’ concerns. Among these theories, leader–member exchange (LMX) theory is one of the most compelling.

LMX is favored among various leadership theories for two reasons. First, LMX is the only theory that focuses on the individual dyadic relationship between each leader and follower (Dansereau et al., 1975; Graen and Uhl-Bien, 1995). In examining the impact of leaders on followers, major leadership theories assume that leaders are homogeneous in terms of their treatment of different followers. Thus, average leadership, such as leaders’ general attitudes and behaviors, has become the focus of leadership studies instead of the diverse relationships between each leader and follower. Nevertheless, LMX suggests that the same leader can develop various types of relationship with different followers (Dansereau et al., 1975). Thus, using LMX is likely to depict the specific influence of leadership than using an average leadership perspective. Second, the focus of LMX on the linkage between leaders and followers allows LMX to provide a powerful and meaningful explanation of the hypothesized relationship between leadership antecedents and outcomes. Given these benefits, research on LMX has expanded in recent decades. Interest in investigating the explanatory power of LMX on employee performance has been growing. This trend led to several meta-analytic studies in the mainstream human resource literature on the relationship between LMX and organizational citizenship behaviors as well as employee performance (Ilies et al., 2007; Martin et al., 2016). However, similar effort is nonexistent in the hospitality and tourism literature.

This study’s purpose is to present a meta-analysis to depict the effects of LMX reported in the hospitality and tourism literature. A meta-analysis helps synthesize research findings of different studies on the same topic and quantifies the impact of various factors on the investigated relationships in the studies (Lipsey and Wilson, 2001). In this research, we applied a meta-analytical approach to answer the following questions: a) is the effect size different when LMX influences different types of outcome variables in the hospitality and tourism context; b) do influencing factors in the general management literature also apply in the hospitality and tourism context; and c) what is unique about LMX studies in the hospitality and tourism industry compared with the mainstream literature?

From the theoretical perspective, this research helps comprehend understanding of LMX by investigating its effects on outputs, which are unique in a labor-intensive and service-focused industry and its effects across different sectors within the hospitality and tourism industry. The results form a foundation for future research by providing an evaluation and synthesis of cumulative knowledge across previous studies. Conclusions and research opportunities are drawn for future theoretical development and empirical studies. From the practical perspective, given that industry practitioners and leaders may not read individual studies on this topic, this research can provide a synthesized conclusion on how they can better utilize the exchange relationship with their followers in their daily practices.

Theoretical Foundations and Hypotheses Development

Main impact of LMX

Early research on LMX theory was concerned with the vertical dyad linkage (VDL) a leader forms with each follower (Dansereau et al., 1975). Relying on role theory, the relationships between leaders and members were generally categorized into two types: in-group and out-group relationships. An in-group relationship is formed on the basis of leaders’ and members’ extra-role responsibilities and is characterized by mutual trust and respect. An out-group relationship is based on formal employment contracts. Leaders and the followers in out-group relationships perform the basic responsibilities required by their job descriptions (Dansereau et al., 1975). However, the theory fails to articulate how followers can form an in-group relationship with their leaders (Northouse, 2018).

The later evolvement of LMX theory transferred the focus from the dichotomous types of relationship to a relationship-based approach, which heavily relies on social exchange theory (Graen and Uhl-Bien, 1991; 1995). Social exchange theory inherited the core of VDL by admitting that leaders treat each follower differently and posited the quality of LMX as the core construct determining the outcomes of leaders, followers, groups, and organizations (Graen and Uhl-Bien, 1991). According to social exchange theory, leaders and members form work-related exchanges through a dyadic relationship. High-quality LMX involves greater relational exchanges than economic exchanges. The higher the quality of LMX, the stronger the reciprocity of these exchanges (Cropanzano and Mitchell, 2005). A partnership emerges between the dyad as leadership develops (Graen and Uhl-Bien, 1991; 1995). Leaders and followers are willing to provide additional effort to strengthen this relationship.

Previous meta-analyses mostly focused on the relationships between LMX and employee performance as well as citizenship behaviors (Martin et al., 2016; Hackett and Lapierre, 2004; Ilies et al., 2007; Jensen and Olberding, 1997). All of these studies suggested that LMX positively correlates with employee performance and citizenship behaviors. However, the relationships between LMX and other types of outcomes were largely overlooked. In hospitality and tourism organizations, employees have frequent interactions with guests; therefore, not only are their behaviors important but also their psychological states are critical. In addition, the estimates of the effects of LMX on its numerous outcomes vary greatly (Gerstner and Day, 1997). Trust motivation, empowerment, and job satisfaction are all mediators between LMX and employee performance (Martin et al., 2016).

After reviewing the LMX studies in the hospitality and tourism literature, three groups of outcomes emerged. The first group of variables concerns employees’ perception of firm practices (PFP) [footnoteRef:1]; the second group of outcomes covers employee psychological states and intentions (CET)[footnoteRef:2]; the last group of variables consists of employees’ behaviors (EMB)[footnoteRef:3]. [1: Variables in the PFP group include interactional justice, distributional justice, empowerment related constructs and psychological fulfilment by firm. ] [2: Variables in the CET group include different kinds of organizational commitment, group commitment, engagement and turnover intention. ] [3: Variables in the EMB group include different kinds of organizational citizenship behaviors toward both organizations and individuals, job performance, satisfaction, loyalty, creativity and innovation related behaviors. ]

Levinson (1965) noted that instead of viewing managers’ actions as their personal motives, employees tend to view those actions as indications of an organization’s intention. Thus, the quality of LMX is likely understood as a result of the directed or instructed behaviors and attitudes of organizations. Cognitive appraisal theory suggests that individuals create perceptions of stimuli, and then these perceptions are appraised to trigger human emotions, psychological changes, and actions (Arnold, 1960). Thus, according to cognitive appraisal theory, employees’ perceptions of LMX quality provide the basis for evaluating and coping with their employers. Better quality LMX will first be translated into an evaluation of firms’ actions, then into changes in employee psychological states and behaviors. Therefore, LMX is likely correlated more with employees’ perceptions of firm practices than with their psychological states and behaviors. Therefore, we hypothesize as follows.

H1: LMX has a significantly stronger impact on perceptions of firm practices than on the other two groups of outcome variables.

Moderators of LMX impact

Moderator analysis is a common practice used in meta-analysis to reveal boundary conditions and to better address theory issues (Martin et al., 2016). An extensive variance between LMX and its related constructs is unexplained (Dulebohn et al., 2012; Gerstner and Day, 1997). Inconsistent findings regarding the moderating role of LMX measure, work setting, nationality of participants, and data source were reported in previous meta-analyses (Dulebohn et al., 2012; Martin et al., 2016; Gerstner and Day, 1997). Thus, examining moderators’ effects between LMX and its outcomes provides additional understanding in the hospitality and tourism literature.

Measurements and scales. Two major measures have been commonly used in the previous hospitality and tourism literature. The first measure is the LMX-7 developed by Graen and Uhl-Bien (1995). It consists of seven items reflecting LMX as a unidimensional construct. The second measure is Liden and Maslyn’s (1998) multidimensional measure (LMX–MDM). This measure consists of 12 items reflecting four dimensions of LMX: contribution, loyalty, affect, and professional respect. Although the correlation between these two measures is high (Joseph et al., 2011), the LMX instruments have been criticized. Major criticisms include the questionable construct validity (Dienesch and Liden, 1986; Vecchio and Gobdel, 1984), the lack of scale development procedures (Liden and Maslyn, 1998; Schriesheim et al., 1999), and the random addition or deletion of items across studies (Keller and Dansereau, 2001). Previous studies had contradictory views regarding the moderating role of LMX instruments. Although Gerstner and Day (1997) supported the moderating role of measurements, Martin et al. (2016) and Dulebohn et al. (2012) found that the LMX measurements had no effect on the relationship between LMX and its correlates. Thus, to ascertain whether the LMX measurements used in hospitality and tourism LMX studies place a conditional effect in the current results is crucial because this effect would reflect the potential understanding of LMX among hospitality and tourism workers.

In addition to the types of measurement, the Likert scale levels used to measure LMX can also be an impact factor. The different points used in a Likert scale can affect a measure’s construct validity and reliability (Chomeya, 2010). Thus, the different Likert scale levels used to measure LMX may have different effects on the magnitude of the impact of LMX on its outcomes. Therefore, we hypothesize the following:

H2: The type of LMX instrument used significantly moderates the magnitude of the relationships between LMX and its outcomes.

H3: The Likert scale level used significantly moderates the magnitude of the relationships between LMX and its outcomes.

Type of raters. LMX describes the social exchange between a leader and a follower. Thus, the choice of raters determines if leaders’ or followers’ perceptions of this exchange relationship was considered in the studies. Previous studies in general management literature suggested a moderating effect of leader and follower perceptions between LMX and its correlates due to both conceptual and methodological reasons (Gerstner and Day, 1997). Conceptually, in communications between a leader and follower, a leader is the one who gives orders whereas a follower follows orders. Thus, the perception of the exchange relationship is different between the two parties. If data are collected from leaders, findings may be different from those produced from data collected from followers or from leaders and followers. It was found that when considering follower level outcomes such as employee wellbeing and performance, leader’s perception was more strongly correlated with the outcomes (Martin et al., 2016). However, since in the hospitality and tourism industry, leadership effort is considered a critical influencer to employees, follower’s rating might be more relevant in the hospitality and tourism industry. Therefore, we propose the following:

H4: When data are collected from employees only, the impact of LMX on its outcomes is significantly strong.

Culture and industry section. Hofstede (1980) categorized national culture into four dimensions, two of which—power distance and individualism–collectivism—are frequently discussed in the management literature. Power distance refers to “the extent to which a society accepts that power in institutions and organizations is distributed unequally” (Hofstede, 1980, p. 45). Individualism–collectivism refers to the extent of the tightness of the social framework regarding the relationship between people (Hofstede, 1980). Culture has an impact on the leadership effect. In a highly collectivist and high power distance culture, implicit and more directive forms of leader influence are more effective than they are in an individualistic and low power distance culture (Fikret Pasa, 2000). Culture also influences the relationship between LMX and some of its correlates, such as organizational citizenship behavior, justice perceptions, job satisfaction, turnover intentions, and leader trust (Rockstuhl et al., 2012).

Theoretically, Western culture is generally more individualistic with low power distance, whereas Asian culture is more collectivist with high power distance. Mainstream studies suggest that Asian and Western employees respond to the impact of LMX differently because Western employees are more individualistic and so are less influenced by collective interest than Asian employees; thus, Western employees are more influenced by individually perceived quality of LMX (Rockstuhl et al., 2012). However, given the labor-intensive and employee-focused nature of the hospitality industry, taking care of hospitality employees has become a key value in the industry (Dawson et al., 2011); thus, whether culture plays such a moderating role in the hospitality and tourism industry remains unknown. Thus, we hypothesize as follows:

H5: Compared with Western respondents, Asian respondents are significantly less impacted by LMX.

In addition to the influence of culture, we further investigate the moderating effect of the industry sector on the impact of LMX. Scholars have less explored the influence of the industry sector within hospitality and tourism. We commonly consider hotel and restaurant sectors to be the main sectors of the hospitality and tourism industry, but the work environment between a front-desk employee and a kitchen staff member can be dramatically different. Whether these differences influence the impact of LMX remains undetermined in the literature. We argue that employees from the restaurant sector are greatly sensitive to LMX because their work environment is extremely stressful. Conservation of resource theory suggests that when people experience resource loss, they have to invest more resources to prevent the loss (Hobfoll, 2011). Compared with hotel employees, restaurant employees tend to experience more stress at work because they are required to perform well in a relatively short period to possess a greater complex skill set or even to work creatively (Hcareers.com, 2017). Thus, they have to invest greater resources to distance themselves from negative work outcomes, such as exhaustion and burnout. Therefore, their leaders’ support and understanding become more salient. Therefore, we hypothesize the following:

H6: Restaurant employees are significantly more sensitive to LMX’s impact than hotel employees.

Statistical method. The last factor proposed to have a moderating role on LMX is the statistical method used in the studies. Previous meta-analyses overlooked the effect of statistical method on the impact of LMX. Given that LMX is commonly tested as a linkage between leaders’ behaviors and employees’ behaviors, the majority of studies use regression-based methods, such as structural equation modeling (SEM) and hierarchical regression to analyze its impact. No study has discussed whether the specific statistical method used in analyzing the proposed relationships of LMX has an impact on results. We propose that SEM may provide different results to simple or multiple regressions due to its nature of calculating multiple regression equations simultaneously and controlling for the measurement errors.

H7: The statistical method used significantly moderates the magnitude of the relationships between LMX and its outcomes.

Methodology and Data

Literature search and study selection

Following the suggestions of previous meta-analyses (Rockstuhl et al., 2012; Park and Gretzel, 2007), a systematic computer-based literature search of computer databases, including Google Scholar, Ebsco, and Emerald, was conducted. The keywords used were “LMX,” “leader–member exchange,” “vertical dyad,” and “hospitality/hotel/ restaurant/tourism.” The initial search yielded 63 results, including 56 studies in hospitality and tourism journals, and seven in non-tourism and hospitality journals. A manual review was then conducted to determine the eligibility of the 63 academic studies to be included in the following analysis. A study was included in the meta-analysis only if the following conditions were satisfied: (1) all of the items of LMX and the corresponding performance variables were measured on an individual level; (2) the standardized regression coefficients between any LMX measurement and any outcomes were reported; and (3) sample size was required to calculate the sampling error. Finally, 36 studies published between 1997 and 2018 were selected for further analysis (Figure 1). Among these 36 studies, more than half were published after 2016. Eighteen studies reported multiple relationships between LMX and employee performance or examined the relationships in different segments. These relationships were identified as independent observations in the sample. If LMX played a mediating role in the study, only the direct relationship between LMX and the dependent variable was included. Thus, 89 observations were retained for analysis.

Figure 1. Publication Trends of Studies Testing the Impact of LMX

Coding

To examine the proposed hypotheses, 13 dummy variables were selected as the moderators of the relationships between LMX and the corresponding outcomes. The 36 studies were randomly split into two subsets (18 studies in each). Two authors coded the studies in the two subsets independently, and the third author verified the combined coding thereafter. After discussions among the authors, discrepancies were resolved by re-examining the original articles. A non-author researcher who specializes in LMX was invited to randomly select 12 out of the 36 studies and verify the coding. No inconsistency was identified by the independent researcher, which meant that the coding was consistent and reliable. Table 1 shows the coding dictionary of the 13 dummy variables and their frequencies.

Within the 36 studies, LMX was regressed mainly on three types of employee performance: employees’ psychological states or intentions (CET), employee behavioral or innovative outcomes (EMB) and perceived firm practices (PFP). When the dependent variable in a study measured organizational commitment (Kim and Koo, 2017), job performance and engagement (Li et al., 2012), and turnover intention (Kim et al., 2017), the variable CET took the value of 1; otherwise, it took the value of zero. When a study investigated the relationship between LMX and employees’ behavioral or innovative outcomes, including in-role performance (Lee et al., 2015), extra-role behavior (Garg and Dhar, 2016), organizational citizenship behavior (Cha and Borchgrevink, 2018), creativity, and innovative behavior (Dhar, 2016), EMB took the value of 1; otherwise, it took the value of zero. If a study focused on justice (Kim et al., 2009), power (Borchgrevink and Boster, 1997), or empowerment (Kent, 2003), PFP took the value of 1; otherwise, it took the value of zero. Studies interested in other types of performance were considered the benchmark for comparison.

Table 1. Coding Dictionary of Selected Variables

Moderator

Code

Implication

Frequency

Outcome Variables

CET

If the dependent variable measures employee psychological states and intentions, it takes the value of 1; otherwise, zero.

21.35%

EMB

If the dependent variable measures employee behavioral or innovative outcomes, it takes the value of 1; otherwise, zero.

40.45%

PFP

If the dependent variable measures perceived firm practices, it takes the value of 1; otherwise, zero.

10.11%

Others

Other measurements are considered the benchmark.

28.09%

Measurement of LMX

LMX-7

If LMX-7 is used to measure LMX, it takes the value of 1; otherwise, zero.

26.97%

LMX–MDM

If the LMX–MDM is used to measure LMX, it takes the value of 1; otherwise, zero.

30.34%

Others

The application of other measurements is considered the benchmark.

42.69%

Likert Scale

5-point

If a 5-point Likert scale is used, it takes the value of 1; otherwise, zero.

55.06%

7-point

If a 7-point Likert scale is used, it takes the value of 1; otherwise, zero.

34.83%

Others

Other scales are considered the benchmark.

10.11%

Methodology

SEM

If SEM is used, it takes the value of 1; otherwise, zero.

50.56%

Type of Raters

EMD

Takes the value of 1 if all respondents are employees.

65.17%

BD

Takes the value of 1 if respondents are employees and managers.

29.21%

Others

If all respondents are managers, it is considered the benchmark.

5.62%

Industry Sector

Hotel

Takes the value of 1 if all respondents are from the hotel sector; otherwise, zero.

67.42%

Restaurant

Takes the value of 1 if all respondents are from the restaurant sector; otherwise, zero.

15.73%

Others

Other sectors are considered the benchmark.

16.85%

Nationality of Respondents

Asian

Takes the value of 1 if all respondents are Asian; otherwise, zero.

80.90%

When the measurement of LMX was coded, if the measurement was obtained from or rooted in the LMX-7 questionnaire developed by Graen and Ulh-Bien (1995), such as in Chiang and Lin (2016), the variable LMX-7 took the value of 1; otherwise, it took the value of zero. If the measurement was obtained or developed from the LMX–MDM by Lydan and Maslyn (1998), such as in Wang (2016), LMX–MDM took the value of 1; otherwise, it took the value of zero. Studies in which LMX was measured by other measurements were considered the benchmark for comparison.

Other factors, including types of raters, sector, nationality, Likert scale, and statistical method, were also considered possible moderators of the relationship between LMX and employee performance.

Meta-analytic Techniques

A random effect model was used in this study to address the heterogeneous errors across the selected studies:

(1)

where is the effect size of the coefficient between LMX and the dependent variable from the ith study (I = 1,2…K), is the between-study error, and is the within-study error, following and , respectively. Compared with the fixed effect model, which assumes the sampling error is completely from the within-study error (, the assumption of a random effect model is much closer to reality as it considers the heterogeneity () across different studies. Given that the scale of can be influenced by various factors, for comparison, it was transformed to a standard score as

.

(2)

The weight assigned to study i when calculating the combined effect size can be written as

(3)

In empirical studies, , where is the sample size of study i representing the within-study variance (Gao, Mattila, and Lee, 2016). The between-study variance can be calculated as

(4)

where , is the combined effect size in the fixed-effect model, is the degree of freedom, and . Thus, when the combined effect size is calculated in a random effect model, studies with high variance are assigned a small weight and the result would be greatly robust.

In a meta-analysis, if the effect size is compared between independent groups, the use of frequentist statistics is inappropriate, either because of the limited sample size for parametric tests or the weak power of non-parametric tests. To examine whether the difference of the effect size between groups is significant, this study introduced Bayesian meta-analysis into hospitality and tourism studies for the first time. In Bayesian meta-analysis, the posterior distribution is estimated by

,

(5)

which means the information of the posterior distribution is the integration of the likelihood and the prior distribution (Liu et al., 2018; Liu and Wu, 2019). Bayesian meta-analysis has been widely used in medical research (Sutton and Abrams, 2001; Leucht et al., 2017). In addition to its advances in computational methods and the inclusion of prior information, the main reason for using the Bayesian approach in this study was that by implementing a Markov chain Monte Carlo (MCMC) simulation, enough observations can be simulated to carry out the parametric statistic tests.

Results and Findings

Results of Descriptive Analysis

A forest chart in Figure 2 presents the distributions of the coefficient of LMX in the 36 studies. The blue dots in Figure 2 represent the coefficient of LMX in the selected studies, and the straight lines stand for the range of the 95% confidence interval of the coefficient, which was calculated under the assumption of the fixed-effect model. The red dot at the bottom is the combined effect size of the 36 studies. The blue dots in Figure 2 are the original values of the coefficients, which were not transformed to the standard scores. Most of the coefficients were positive and are located on the right half of the chart, with eight out of the 89 observations being exceptions. The second observation in Wang and Wong (2011) showed the largest confidence interval, ranging from −0.80–1.15 and indicating a much larger variance than other observations. The average impact of LMX (i.e., the red dot at the bottom) of the sample studies was 0.667, with a range of −0.96–0.91. Considering the wide range of the coefficients across different studies and the various length of the intervals, applying the random effect model for the following analysis was reasonable to address the heterogeneous issue among the studies.

Regarding the outcomes, 40.04% of the studies tested behavioral or innovative outcomes, 21.34% tested employees’ CET, and 10.11% tested perceived firm practices. The most popular measurement of LMX, LMX–MDM, accounted for 30.33% of the 89 observations, and LMX-7 accounted for 26.97%. The remaining 43% of the studies measured LMX by other items.

Regarding statistical method, SEM and linear regression were the main analytical tools used to investigate the relationship between LMX and its outcomes, each of them used by 50% of the selected studies. Five-point and 7-point Likert scales were used by 55.06% and 34.83% of the studies, respectively. Regarding the source of data, 65% was collected from employees (EMD), less than 30% was collected from employees and managers, and approximately 5% was collected from managers. In all of the selected samples, 19.10% utilized American respondents and 22.47% of the surveys were carried out in the United States. In terms of industry sector, 67.42% of respondents worked in the hotel sector and 15.73% worked in the restaurant sector. Among the 36 studies, only four were conducted in settings other than a hotel or restaurant; of these four studies, one was conducted in a casino, two in theme parks, and one in a travel agency.

Figure 2. Forest Figure of the Impact of LMX on Employees’ Performance

Results of Moderator Analysis

To address the heterogeneity across observations, a Bayesian random effect model was used to investigate the moderating effects of various factors. By applying the Bayesian approach, not only can the results be robust, but the sample size can also be expanded for inferred statistics facilitated by the MCMC simulation process. For example, for robustness, two MCMC chains, each with 10,000 simulated observations, were generated when investigating the moderating effect of the 14 dummy factors. The first 30% of the simulations were burnt and the remaining 7,000 observations were maintained in each chain to ensure the robustness of the results. Thus, each group of the inference test consisted of 14,000 observations (7,000 × 2).

Fourteen dummy variables were used to examine the moderating effects between LMX and its outcomes from eight perspectives, including the groups of outcomes, the measurement of LMX, statistical method, Likert scale, types of raters, nationality and sector of respondents, and investigated country (Table 2). Three heterogeneity test statistics were used to examine whether the application of the random model was appropriate. According to Higgens et al. (2003), when is significant and is larger than 50%, the random effect model is appropriate because the variance of observations is dominated by the heterogeneous errors and (≠ 0), the variance generated by the heterogeneity. The three statistics in Table 2 suggest that the random effect model should be used across all 14 dummy variables because all the s were significant at the 1% significance level and all the s were above 75% with s larger than 0.15. The case arm indicates the subgroup the observation of which was valued as 1 in the dummy variable, whereas the control arm stands for the zero group. The last column presents the t-statistics between the case and control arms.

The results of the outcome variable groups showed that when the outcome variables were EMB and CET, the impact of LMX was significantly smaller than that reported in the rest of the studies. If the dependent variable was PFP, then the influence of LMX was significantly larger than that found in other studies. An ANOVA test indicated that the impact of the outcome variable groups on the impact of LMX was significantly different at the 1% significance level (F = 7.79). After correcting the degree of freedom issue in multiple t-tests, the Bonferroni t-test suggested that LMX has the largest impact on PFP, followed by EMBs and CETs, indicating that LMX has the strongest association with perceived firm practices compared with employees’ psychological states, behaviors, and intentions. Thus, Hypothesis 1 and our argument that LMX is used as an indicator or organizational attitudes and behaviors were supported.

In terms of the measurement of LMX, when it was measured by LMX-7, its impact was significantly larger than that reported in the other studies; when the LMX–MDM scale was used, the impact of LMX was significantly smaller than that found in other studies. The impact of LMX measured by LMX-7 was significantly larger than that measured by LMX–MDM (t = 14.588), thus supporting Hypothesis 2.

Regarding the Likert scale levels, if LMX was measured by a 5-point or 7-point Likert scale, the impact was significantly different from that in the corresponding control groups. t-statistic showed that the impact of the 5-point scale was significantly larger (t = 13.668) than that of the 7-point scale. Thus, Hypothesis 3 was supported. The result indicated that in the selected 36 studies, the variance of LMX as measured by the 7-point scale was smaller, and thus the estimated coefficient of LMX was smaller than that produced by the 5-point scale. From a practical perspective, a 5-point scale is more appropriate than a 7-point scale for measuring LMX because it can capture the differences in perceived LMX across different respondents more effectively.

In terms of the type of raters, if raters were only employees or employees and managers, then the estimated impact of LMX was significantly larger than it was in the manager-only samples. The results showed that the impact of LMX estimated by employee-only samples was significantly larger than that estimated by samples collected from employees and managers (t = 15.55), indicating that employees are greatly sensitive to LMX quality and are greatly affected by LMX. Thus, Hypothesis 4 was supported.

The impacts of LMX based on Asian respondents were significantly smaller than those based on Western respondents, thus supporting Hypothesis 5. Consistent with previous findings in the mainstream literature indicating that LMX greatly impacts employees in Western culture (Rockstuhl et al., 2012), this finding suggests that hospitality and tourism employees in Asian countries are less impacted by the quality of LMX with their leaders. Besides the impact of LMX in the hotel sector being significantly smaller than that in the non-hotel sector, the results indicated that the influence of LMX in the restaurant sector is larger than that in the non-restaurant sector, particularly the hotel sector (t = 14.181), thus supporting Hypothesis 6.

All 36 studies examined the effect of LMX on its outcomes by using either the SEM technique or other non-path regression-based techniques, such as multiple regression and hierarchical regression. Compared with SEM, the estimated impact of LMX found in studies using regression was larger, thus supporting Hypothesis 7.

29

Table 2. Moderating Effects of Selected Variables

Moderator

Heterogeneity Test

Type

Case Arm

Control Arm

t-statistic

Q-statistic

I2

Mean

No. of Studies

Mean

No. of Studies

Outcome variables

402.02***

75.78%

0.156

EMB

0.179

36

0.228

53

−7.163***

397.55***

75.35%

0.153

PFP

0.411

9

0.108

80

21.782***

406.06***

76.02%

0.159

CET

0.210

19

0.340

70

−13.807***

Measurement of LMX

389.79***

74.86%

0.149

LMX-7

0.484

24

0.147

65

37.387***

405.77***

75.96%

0.158

LMX–MDM

0.187

27

0.313

62

−14.588***

Statistical method

407.05***

76.03%

0.159

SEM

0.236

45

0.272

44

−4.894***

Likert scale

405.61***

75.90%

0.158

5-point

0.279

49

0.228

40

6.770***

406.58***

75.98%

0.158

7-point

0.164

31

0.337

58

−21.333***

Types of raters

407.04***

76.04%

0.159

EMD

0.300

58

0.208

31

12.263***

407.07**

76.04%

0.159

BD

0.160

26

0.017

63

16.600***

Nationality of respondents

403.63***

75.85%

0.157

Asian

0.432

17

0.348

72

8.758***

Sector of respondents

404.95***

75.80%

0.157

Hotel

0.140

60

0.363

29

−24.600***

404.14***

75.69%

0.156

Restaurant

0.324

14

0.193

75

10.385***

*p < 0.05; **p < 0.01; ***p < 0.001; EMB = employee behavioral or innovative outcomes; PFP = perceived firm practices; CET = employee psychological states and intentions; EMD = data collected from employees only; BD = data collected from employees and managers.

Discussion and Conclusions

This study reports the results of a meta-analysis of 36 studies in the hospitality and tourism context to synthesize the findings on the impact of LMX on its outcomes. To address the heterogeneity across different studies and implement inference statistics, this study introduced a random effect model estimated by the Bayesian approach into the hospitality and tourism research for the first time. The results supported all of the proposed hypotheses. Among the outcome variable groups, the most affected group of variables was perceived firm practices (PFP). LMX measurement, Likert scale level, type of raters, region of respondents, industry sector, and statistical method all moderated impact of LMX on its outcomes.

Theoretical Contributions

This study unearthed a few unique findings in the hospitality and tourism industry that provide meaningful theoretical contributions to the literature. The findings complement previous research by echoing cognitive appraisal theory in recognizing perceived firm practices as an appraisal of LMX quality (Arnold, 1960). The meta-analysis identified the outcome most closely related to LMX in the hospitality and tourism industry as employees’ perception of firm practices, such as perceived organizational justice and empowerment. It links LMX with employees’ emotions, psychological states and behaviors by using cognitive appraisal theory. Employee emotions are significant factors in determining employee and customer satisfaction in the hospitality and tourism context (Ali et al., 2016; Lin and Mattila, 2010); thus, linking LMX with cognitive appraisal theory can be a specific new research direction for future studies on LMX in the hospitality and tourism context.

The impact of the measurement of LMX in the hospitality and tourism industry found in this study is different from that found in recent mainstream studies, such as those of Dulebohn et al. (2012) and Martin et al. (2016). It contributes to the hospitality and tourism literature by showing that in the hospitality and tourism literature, LMX-7 with a 5-point Likert scale has a stronger connection to the outcome variables. This finding may be due to the labor-intensive and service-focused nature of the industry. As employees are well treated and communication between leaders and followers is frequent in the hospitality industry (Dawson et al., 2011), LMX is a familiar concept to leaders and followers in this industry. Thus, compared with LMX–MDM and the 7-point Likert scale, a simpler measurement, LMX-7 with a 5-point Likert scale, is more appropriate.

Similar to the findings on the measurement of LMX, the type of raters was also found have a moderating effect on the relationship between LMX and its outcome variables. However, unlike the mainstream findings, in which leaders’ rating has a higher correlation with LMX’s impact, in the hospitality and tourism literature, followers’ rating was more correlated with the effect of LMX. It is revealed that when data was collected from employees, the impact of LMX on its outcomes were larger. This moderating effect can be explained by two reasons: conceptual and methodological. Conceptually, as discussed before, hospitality and tourism organizations tend to be more employee focused, thus leadership effort becomes a common support to employees (Wang, 2016). Consequently, their evaluation of the LMX may be more used to generate decisions and perceptions regarding the work. On the other hand, methodological issues need to be considered as well. The majority (65.17%) of studies on LMX in the hospitality and tourism literature rely on employees’ rating to access LMX, so bias may exist. Therefore, it is recommended that the readers need carefully read into these results.

Although the hospitality and tourism industry is unique, the sectors within the industry are not homogenous. LMX greatly affects employees in restaurant settings. Besides the potential spoiling impact of the grounded “treating employees well” culture of the hospitality and tourism industry, another possible explanation for this possibility may be the different work environments between hotel and restaurant settings. Compared with the hotel setting, the interactions between customers and employees are more intense in the restaurant setting. The work schedule of restaurant employees is shorter, but their stress level may be higher. Leaders are role models who give followers the most intuitive directions. Thus, a good LMX relationship is more desired by restaurant employees to help with daily services they provide. However, no research has empirically tested this logic. Future research can explore the different sectors of the hospitality and tourism industry to empirically reveal the reasons for this difference.

From the statistical perspective, the findings show that studies that used SEM generally found a small impact of LMX on its outcomes in the hospitality and tourism literature. One reason may be is that SEM is a systematic model controlling for measurement errors when all regression equations are estimated simultaneously. Thus, other equations in the model can offset the impact of LMX. However, this result does not necessarily support that regression is a better method to analyze the effect of LMX on its outcomes. Model selection should be determined by research objectives and sample size.

In summary, this study is the first to contribute to the literature by implementing a meta-analysis focusing on LMX in the hospitality and tourism industry. It also complements the methodology in hospitality and tourism literature by introducing Bayesian meta-analysis into the field for the first time. The originality of the research is as follows. First, it identifies the link between LMX and cognitive appraisal theory (Arnold, 1960). Given the intensive communication with guests in the hospitality and tourism industry, the relationship between the emotions and physiological states of employees and LMX is crucial. Second, it confirms a greatly appropriate measurement of LMX in the hospitality and tourism industry, which distinguishes this industry from other mainstream businesses. Lastly, the impact of LMX is also different across sectors, which deepens our understanding of LMX in this industry. Given the labor-intensive and service-focused nature of the hospitality and tourism industry, leader–member relationship plays an important role in the industry. This study is the first to explicitly support the crucial role of LMX by showing the aggregated knowledge from past research.

Practical Implications

This study provides valuable practical implications to the hospitality and tourism organizations as well. It identified that the high quality of LMX increases various positive outcomes for hospitality and tourism organizations, in which employees’ perception to an organization was affected the most. The quality of a relationship between a leader and a follow has a strong impact on employees’ perception regarding an organization. Thus, instead of considering leadership as a personal and individual effort, organizations must start to manage leadership from a strategic and organizational level. First, organizations must pay greater attention to leadership training. Various leadership skills can be trained to improve the quality of LMX (Graen, et al., 2006); thus, providing training and development opportunities to various levels of leaders can potentially help hospitality and tourism organizations maintain a good image among their employees. These trainings can include but are not limited to courses on communication and interpersonal skills, training and coaching skills, as well as emotional intelligence. Second, hospitality and tourism organizations can help employees form mentor relationships. Employees would seek for formal mentorship for the purpose of high LMX quality (Holt, et al., 2016); thus, providing support to employees on forming mentorship can assist in increasing the quality of LMX.

Limitations and Future Research

The main limitation of the study is the sample size. Given the small sample size, the outcomes of LMX were grouped into four. In future studies, expanding the primary study data would be greatly valuable in focusing on each type of the measurement and compare studies with great homogeneity. Moreover, various approaches were used in the 36 examined studies to estimate the model. However, limited by the sample size, methodologies were generally coded as SEM and regression. Therefore, an interesting and valuable research direction would be to further compare the influence of different methodologies on the impact of LMX. This study serves as the first attempt at a meta-analysis focusing on LMX in the hospitality literature. Future research with comprehensive and deeper analysis is warranted for the development of LMX research when more sample studies are available.

Over two decades ago, Graen and Uhl-Bien (1991; 1995) concluded that the evolution of LMX research should follow a four-stage model. The first stage concerns the validation of the different types of VDL (in-group vs. out-groups). The second stage focuses on the relationship and LMX outcomes. The third stage describes the dyadic relationship building (leadership making), and the last stage involves expanding the dyadic partnership to group and network levels. After reviewing existing studies in the hospitality and tourism literature, we found hysteresis in the LMX research in the hospitality and tourism industry. Out of the 36 studies, only five used multilevel (across group) techniques to estimate the impact of LMX (Chow et al., 2015; Dhar, 2016; Kent and Chelladurai, 2003; Li et al., 2012; Wu et al., 2013) and only Kent and Chelladurai (2003) considered network issues in LMX. Thus, this area is an opportunity for future research.

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