Decisive mechanism of organizational citizenship behavior in the hotel industry – An application of economic game theory

International Journal of Hospitality Management 31 (2012) 1244–1253

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International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman

Decisive mechanism of organizational citizenship behavior in the hotel industry – An application of economic game theory Xiang Wei a,d , Hailin Qu b,e,∗ , Emily Ma c a

Beijing International Studies University, No. 1 of Southern The Ding Zhuang Road, Chao Yang District of Beijing, China School of Hotel and Restaurant Administration, Oklahoma State University, Stillwater, OK 74078, United States Department of Tourism, Leisure, Hotel and Sport Management, Griffith University, Nathan Campus, Australia d National Academy of Economic Strategy, Chinese Academy of Social Science, 5, Jianguomennei Dajie, Beijing 100732, China e School of Business, Sun Yat-Sen University, Guangzhou, China b c

a r t i c l e

i n f o

a b s t r a c t

Keywords: OCB Game theory Decisive mechanism Equilibrium Hotel industry

This paper applies an economic game theory model to explain the decisive mechanism of organizational citizenship behavior (OCB). The Sub-game Perfect Nash Equilibrium (SPNE) indicates that an employee’s unique motivation of OCB is to maximize his or her own performance outcome. The findings also suggest that workload, importance of work to performance outcomes and cost of OCB jointly determine the amount of OCB that each individual employee exhibits. An empirical test utilizing frontline hotel employees was performed (N = 175). Using a partial related test and logistical model, the results supported three propositions, suggesting that the amount of OCB exhibited by each employee is jointly influenced by the workload, cost of OCBs and OCBs performed by coworkers. © 2012 Elsevier Ltd. All rights reserved.

1. Introduction

As suggested by Bergeron (2007), a zero-sum relationship exists when any change in time allocated to OCB is at the cost of task performance. This is also consistent with the rationale of Time Allocation Theory (Becher, 1965). However, most previous studies indicated that there is a strong positive correlation between OCB and task performance (William and Anderson, 1991; Conway, 1999; Johnson, 2001; Turnley et al., 2003). One possible explanation for the controversial results, as suggested by Bergeron (2007), is that research that found positive correlations between OCB and task performance did not control the amount of hours spent in working. In the present framework, OCB we considered as a substitution to in-role task performance based on the Time Allocation Theory, as well as extra-role performance, which directly contributes to the agent’s performance (an hotel employee in this case). In this way, it allows for both types of performance (in-role task performance and OCB) to make a positive contribution to job outcomes, as well as considering the cost of OCB.

1.1. Background of the study This paper focuses on the perfect information dynamics of an economic game theory model considering 2-stage in which organizational citizenship behavior (OCB) enters the employee’s performance outcome function. A game model is proposed to answer the questions of why and how OCB influences an individual’s performance outcome. Additionally, the question of what are the real determinants of the amount of OCB is asked. The theoretical model was tested in the context of China’s hotel industry. This paper proposes a new theoretical model of OCB based on classic game theory in modern microeconomics. Specifically, the model is used to characterize the real situation and interactive processes among employees, customers and the organization. A dynamic two-stage perfect information game was adopted to monitor how employees maximize their OCBs. The formal method used to solve this kind of game model was originated from Rubinsein (1982), who carried out a classical solution structure of perfect information dynamic game through a bargaining model. A unique contribution of the model is taking the cost of OCB (Bergeron, 2007; Vigoda-Gadot and Angert, 2007) into consideration.

∗ Corresponding author. Tel.: +1 405 744 6711; fax: +1 405 744 6299. E-mail addresses: [email protected] (X. Wei), [email protected] (H. Qu), emily.ma@griffith.edu.au (E. Ma). 0278-4319/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhm.2012.03.004

1.2. Suitability of using game theory in OCB studies The antecedents and consequences of OCB have been commonly researched for almost three decades (Bateman and Organ, 1983; Organ, 1988; Torlak and Koc, 2007). Researchers have attempted to explain the motivational mechanisms of OCB from various perspectives, such as social exchange (Organ, 1990; Zellars and Tepper, 2003; Ma and Qu, 2011), impression management (Bolino and Turnley, 1999; Rioux and Penner, 2001) and personality traits (Borman and Motowidlo, 1993; Morgeson et al., 2005). However,

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very few studies examined OCB motivation from the economic theory perspectives, for example the classic game theory perspective. Several important considerations warrant the analysis of OCB using game theory. First of all, game theory in modern economy is the best tool to tackle interactive processes with interdependent players (Nolan and Adam, 2007; Song, 2008). OCB is the type of behavior that falls into this category, as employees who perform OCB are interdependent and interactive with his or her co-workers, customers and the organization (Lambert, 2000; Bell and Mengüc¸, 2002). The dimensionality of OCB also indicates this feature. For example, according to Organ’s Five-dimensional Framework of OCB (1988), both courtesy and sportsmanship involve interactions between the employee and his or her co-workers (Morrison, 1994), while civic virtue represents the interaction between an individual employee and the organization (Podsakoff et al., 2000). Second, recent studies revealed that OCBs can be viewed as reflective behaviors. This is due to the fact that employees’ OCB performance reflects the degree of an employee’s Perceived Organizational Support (POS) as well as the OCBs that employee received from others (Organ and Ryan, 1995; Moorman et al., 1998; Lambert, 2000; Moorman and Harland, 2002; Vigoda-Gadot and Angert, 2007). The interactions and interrelationships involved in this process make the introduction of an interdependent game model necessary. Third, one basic premise of the OCB theory is that an employee will engage in OCB when he or she perceives that their employment relationship is based on social exchange (Organ, 1990; Moorman, 1991). Another premise of OCB theory is that OCB is a reciprocate behavior (Organ, 1988, 1990; Rousseau, 1990; Lambert, 2000). Both premises reveal that OCB is an interactive process. However, both of these two premises are unable to demonstrate the decisive mechanism of OCB mathematically, which clearly shows the interactive path from the determinants of OCB to actual OCB performance. Fortunately, game theory is a useful mathematic tool to determine the decisive path of interactive behaviors. Therefore, the main purpose of this paper is to highlight the major determinants of OCB. To deduce the decisive mechanism of OCB, an optimization solution of individual performance function was made with OCB to figure out how much OCB is decided by individual’s dynamic game interaction.

2. Literature review 2.1. Antecedents of OCBs-influencing factors or motivators? Although a few studies consider OCB as “discretionary” in nature (MacKenzie et al., 1993; Appelbaum and Roy-Girard, 2007), the benefits of OCBs to organizations inspired growing interest on OCB motivation studies. The most well cited conclusions include that there is a positive relationship between employees’ job satisfaction and OCB (Bateman and Organ, 1983; Puffer, 1987; Organ and Konovsky, 1989; Witt, 1991); OCB is motivated by the premise that an employee believes they are being treated fairly (Organ, 1990); OCB is induced through social exchange, reciprocity and equity (Moorman, 1991; Lambert, 2000). Therefore, OCB may be used as an employees’ currency of reciprocity (Huang et al., 2004). In addition, POS is considered as a strong antecedent of OCB (Rioux and Penner, 2001) although Moorman et al. (1998) believe that POS is a mediating variable. Researchers also suggest that psychological contract fulfillment plays a significant role in the elicitation and maintenance of OCB (Robinson and Morrison, 1995). Other variables found to predict OCB include leadership style (Podsakoff et al., 1990), employee commitment (O’Reilly and Chatman, 1986) and interdependence (Karambayya, 1990).

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Compared with the studies investigating why OCB occurs, recent research has paid more attention to exploring factors influencing OCB. For instance, some meta-analyses reveal that OCB is influenced by job satisfaction (Smith et al., 1983), perceived fairness (Moorman, 1993), organization commitment (Becher, 1991), leader supportiveness (Organ and Ryan, 1995) and perceived obligation (Eisenberger et al., 1999). In addition, researchers have also explored the effects of personality, attitudes, moods, and task characteristics on individuals’ OCB performance (Podsakoff et al., 2000). Further, culture and level of economic development have also been found to influence OCB. A comparative study performed in the USA and Romania suggested that national culture is a significant moderator of OCB (Turnipseed and Murkinson, 2000). This finding is also supported by Tang and Ibrahim (1998), who suggest that POS has a stronger influence on people’s OCB in Middle East than in the US. Huang et al. (2004) also state that the effect of POS on OCB would likely be stronger in Taiwan than that experienced by employees in North America and Western Europe. They further discovered that gender influences the amount of OCBs exhibited by employees specifically women are more likely to conduct OCBs than men. In addition, studies also found which factors influence OCB. While need for independence, task feedback, cohesive group dynamics, and intrinsically satisfying task positively influence OCBs, a reward outside leaders’ control negatively affects OCB (Torlak and Koc, 2007). Similarly, leadership behaviors such as, transformational leadership, contingent reward behavior, leader role classification, and supportive leader behaviors encourage OCBs, while non-contingent punishment behaviors discourage OCBs (Podsakoff et al., 2000). Many studies after the year 2000 have examined factors influencing OCB, leading to several new findings. Lambert (2000) found that work-life benefits have a significant positive effect on OCB. Bell and Mengüc¸ (2002) showed that organizational identification has a significant and positive influence OCB. Yoon and Suh (2003) suggested that OCB is based on trust. Turnley et al. (2003) pointed out that psychological contract fulfillment positively affects OCB performance. This finding is different from Robinson and Morrison (1995) who consider psychological contract fulfillment as a cause of OCB. Some researchers also report that contingent employees perform fewer OCBs than regular employees (Stamper and Van Dyne, 2001), which suggests that employment status affects OCB. Here unto, we have explored two main streams of OCB literature: one that focuses on OCB motivations, while the other focuses on influencing factors of OCBs. The former asks why OCBs occur and the latter asks what can change the amount of OCBs. However, an issue remains unsolved in the previous literature: how to identify whether an attribute is a motivator or an influencing factor. For instance, job satisfaction is considered as a motivator according to Bateman and Organ (1983), and is considered as an influencing factor by Smith et al. (1983). Rioux and Penner (2001) consider leadership support as a motivator. However Podsakoff et al. (2000) indicate that leadership support is an influencing factor. Psychological contract fulfillment had the same fate between Turnley et al. (2003) and Robinson and Morrison (1995). It is well understood that the existence of the paradox is problematic (Podsakoff et al., 1990; LePine et al., 2002), so it is necessary to clarify the paradox under clear mathematic economics framework. 2.2. Studying OCBs with game theory-conceptual rationale A mathematical economic framework/model is a theoretical construct that represents economic process using a set of variables and a set of quantitative relationships between those variables (Walsh, 1987). It has been widely used to explain the phenomenon and processes in the social and economic world. Using mathematical model has two major advantages. First of all, it can simplify

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Table 1 Summary of the literatures on OCB – antecedents and theories. Antecedents, author(s), year Contextual and external antecedents Perceived Organization Support (Rioux and Penner, 2001) Leadership Style (Podsakoff et al., 1995, 2000; Organ and Ryan, 1995) Fairness (Moorman, 1991) Cohesive Group Dynamics (Torlak and Koc, 2007) Contingent Reward (Podsakoff et al., 2000) Work-life Benefit/Balance (Lambert, 2000) Organizational Trust (Yoon and Suh, 2003) Individual Disposition, Attitude and Perception Job Satisfaction (Bateman and Organ, 1983; Smith et al., 1983; Puffer, 1987; Witt, 1991) Psychological Contract (Robinson and Morrison, 1995) Commitment (O’Reilly and Chatman, 1986; Becher, 1991) Obligation/Conscientiousness (Eisenberger et al., 1999; Illies and Reiter-Palmon, 2008) Moods (Podsakoff et al., 2000; Ma and Qu, 2011) Need for Independence (Torlak and Koc, 2007) Employee-organizational Identification (Bell and Mengüc¸, 2002) Theories on OCB Motivation The Social Exchange Theory (Organ, 1990; Zellars and Tepper, 2003; Ma and Qu, 2011) Impression Management Theory (Bolino and Turnley, 1999; Rioux and Penner, 2001) Personality Theories (Organ, 1994; Borman and Motowidlo, 1993; Morgeson et al., 2005; Illies and Reiter-Palmon, 2008) Game Theory (Nolan and Adam, 2007; Song, 2008; Lambert, 2000; Bell and Mengüc¸, 2002; Morrison, 1994; Organ, 1988; Podsakoff et al., 2000; Aghassi and Bertsimas, 2006; Andrianova et al., 2008)

and abstract the variables and relationships from observed data. Simplification is particularly important considering the enormous complexity of the processes and phenomenon. It can help us to study and analyze the process/object more clearly. Secondly, using mathematic model is a mean of selection of data based on a paradigm of econometric study. This is also important because we need to identify which are the important factors that we need to look into within each phenomenon and process (Holcombe, 1989). For example, the introduction of the economic game theory helped to analyze the process of conflict and cooperation among intelligent rational decision-makers from the mathematical perspective (Myerson, 1991). It has been widely applied in economics, political science and psychology to analyze interactive processes. Recently, the application of game theory has also appeared in the hospitality and tourism literature (e.g. Li et al., 2009; Bimonte et al., 2006). An economic model usually has the following components: endogenous variables which are selected by the players and decided by the model itself, and exogenous variables which are also selected by players and decided by conditions of the model. Parameters are constant and cannot be selected, but can be changed by environment factors out of the model. Finally, the objective function of optimization represents player’s motivation when the player selecting endogenous variables and exogenous variables (Chintagunta et al., 2006). The game theory model proposed by this study contains two representative players (Camerer, 1997): an employee and his or her co-worker in agent-normal form (Van Damme, 1989; Kaneko and Kline, 1994; Azhar et al., 2005). The

Summary of findings Perceived organizational support is a strong antecedent of OCB Supportive leadership style and transformational leadership encourages OCB Fairness of the organization is an important contextual factor that positively influences employees’ OCB Cohesive group dynamics also positive associated with employees’ OCB Contingent reward encourages employees’ OCB, while non-contingent punishment discourages OCB Work-life benefit has a significant positive effect on OCB OCB is based on trust Positive relationship has been observed between employees’ job satisfaction and OCB Psychological contract fulfillment plays significant role in eliciting and maintaining OCB Positive relationship has been found between employees’ commitment and OCB Conscientiousness is a strong indicator for both OCBs to both organization (OCB-O) and individual employees (OCB-I) Positive emotions generated from positive social exchange trigger employees’ OCB Need for independence is an important antecedent for OCB Employee-organizational identification has a significant and positive influence on OCB The Social Exchange Theory is can explain the motivational mechanism of OCB. Social exchange with leaders, coworkers and customers can influence OCB dimensions differently Impression management is an important motivation for employees’ OCB, especially when employees link OCB performance with certain benefits, such as promotion and monetary rewards Certain personality traits, such as the Big-five personality, empathy motivated certain dimensions of OCB Although no research has attempted to use the game theory to explain the motivational process of OCB, the suitability of using this theory has been suggested, because people performing OCB satisfies the assumptions of game theory: (1) OCB performance is an interactive process. Certain dimensions of OCB (such as courtesy, sportsmanship and civic virtue) are interactive in nature; (2) employees are rationale players and want to optimize performance outcomes; (3) Sender of OCB pose externality effects on receiver of OCB

player selects his or her level of OCB (endogenous variables) to optimize the performance outcome function. In other words, it means that the unique motivation of OCB is to optimize the outcome since a rational player’s motivation of behaviors is to pursue his or her utility or benefit at most (Aghassi and Bertsimas, 2006). Accordingly, all exogenous variables and parameters in the model are decisive factors of OCB. Therefore, motivation is consistent with an optimization process and typically should never be mixed up with multi-angular influencing factors structuring the mutative process (function). A summary of literatures on antecedents and motivational theories of OCB has been provided in Table 1. One key feature of OCB is that it goes beyond the receiver’s expectation as well as benefiting the receiver and overall organizational efficacy (Appelbaum and Roy-Girard, 2007). However, it is not directly or explicitly recognized by the formal reward system (Organ, 1988), so the sender of OCB would pose a kind of externality effect on the receiver of OCB. An externality exists when the actions of one individual affect the well-being of other individuals without any compensation or payment taking place (Andrianova et al., 2008). The presence of OCB introduces a potential source of positive externality in our optimization game problem since OCB enhances co-worker productivity (MacKenzie et al., 1991). As shown later, an employee’s OCB enters his or her co-worker’s performance-outcome function, vice versa. How to consider the specific effects of in-role task and extrarole OCB on individual’s job outcome will determine the structure

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of the performance outcome function. A large amount of literature supports that at the group level, OCB is positively related to both the quantity and quality product outcome (Podsakoff and MacKenzie, 1997; Koys, 2001). However, the relationship between OCB and outcomes at the group or unit level may not necessarily be the same as the relationship between OCB and outcomes at the individual level (Ostroff, 1992; Bergeron, 2007). At the individual level, individual performance outcomes consist of performance evaluations, reward and career advancement (Bergeron, 2007): first, with respect to performance evaluations, research has shown that in-role task and OCB both positively contribute to evaluations (Johnson, 2001; MacKenzie et al., 1993, 1999; Rodundo and Sackett, 2002); second, the performance of both in-role task and OCB often results in rewards (Allen and Rush, 1998; Van Scotter et al., 2000); third, past lab studies have indicated that both OCB and in-role task performance account for variance in career advancement (Van Scotter et al., 2000). Generally speaking, it could be concluded that in-role task and extra-role OCB are good for individual job performance. Certainly, the positive impact of in-role task on outcomes is reasonable, but at the same time some researchers doubt OCB’s positive influence on individual outcomes because OCB is just a by-product of in-role task (Allen and Rush, 1998). For example, Podsakoff and MacKenzie (1994) report OCB as a helping behavior having a negative effect on sales performance outcome in many insurance agencies. Bergeron (2007) proposes that OCBs may hurt individual performance as OCBs may come at the expense of task performance. Theoretically, we are unable to deny either side of the above literatures regarding OCBs’ influence on individual outcomes. In addition, in-role task and extra-role OCB enter the individual performance outcome function in the form of a power exponent. In this way, the exponent of OCB denotes its elasticity to the outcome and the symbol of elasticity (positive or negative) indicates whether OCB is positively or negatively related to the outcome. It should be stressed that we examine OCB in a two-stage dynamic game model while most studies examine OCBs at the cross-sectional level. In real life, people’s behavior and decisionmaking are a dynamic or sequential process in which one decides how to act or react after having observed others’ relevant action (Rasmusen, 2007; Webster, 2007). In the present framework, the dynamic history of players is standardized by a two-stage game. In other words, at stage one there exists a “first-mover” (Song, 2008) who conducts an OCB first. After that, at the second stage, a receiver namely “last-mover” (Song, 2008) has observed firstmover’s behavior and then decides how to conduct his or her own OCB. This assumption seems appropriate for microeconomic application and is consistent with real-life observations that OCB would be an interactive process (Vigoda, 2001; Connelly and Kelloway, 2003).

3. Theoretic model and propositions 3.1. The theoretical model In this section, a 2-stage perfect information dynamic game model was introduced with OCB. Perfect information is a term used in economic game theory to describe a state of complete knowledge about the actions of other players that is instantaneously updated as new information arises (Levinson, 2005). In the present setting, it is reasonable to assume that employee and co-worker completely observe and know their outcome and OCB of each other due to the fact that they are working together under the same evaluation system. Without loss of generality, in the game model we normalize players in the agent-normal form (Tirole, 1988) with player 1 and

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player 2: player 1 represents employee who is first-mover at the first stage and player 2 represents co-worker1 who is last-mover at the second stage. The whole game process of players is as follows. At stage 1: player 1 (the employee) moves first to select the amount of his or her OCB to optimize his or her performance outcome. At stage 2: player 2 (the co-worker) observed the above process and received OCB. Then he or she moves second to select the amount of his or her own OCB to optimize his or her performance outcome. In the long run, the optimal amount of the employee’s OCB, namely Sub-game Perfect Nash Equilibrium (SPNE) of our model, can be calculated through Backward Solution of game theory (see Appendix 1):

 1∗ =

u1 ˛u2 1 [˛(1 − ˛) + 1 2 ](1 − ˛)1 /˛ ˛(1 +˛)/˛

˛/(˛2 −1 2 ) −1

(1)

where, 1 represents the employee and 2 represents the employee’s co-worker; 1∗ , employee’s (player 1’s) optimal OCB sending to 2; u1 , employee’s (player 1’s) workload of in-role task; u2 , coworker’s (player 2’s) workload of in-role task;  1 , the importance of employee’s (player 1’s) OCB received from co-worker to employee’s performance outcome;  2 , the importance or contribution of coworker’s OCB received from employees to co-worker’s performance outcome; ˛, the importance or contribution of player 1’s OCB send to co-worker to employee’s performance outcome; , the cost of player 1’s OCB to send to player 2. Specifically, the time, energy and opportunity cost spent by 1 in sending OCB to his or her co-worker. Eq. (1) is the final theoretic model in which decisive mechanism of OCB is implied as follows. First of all, the employees’ unique motivation to conduct OCB is to maximize their performance outcome. Second, according to individual inherent economic motivation and its implication in the theoretic model, employees’ OCB is decided by only four determinants: employees’ workload (u1 , u2 ), the cost of OCB (), the importance degree of in-role task to performance outcome (˛), and the importance degree of OCB received from others ( 1 ,  2 ). Third, other factors such as psychological contract, perceived fairness and POS, are exact influencing factors instead of determinants of OCB, because these factors just influence OCB indirectly through the effect on the above 4 determinants of OCB. 3.2. Propositions The theoretic model indicates a very complicated first-order and second-order differentiating non-liner relationship between OCB and its determinants, so it is difficult to clearly illustrate a mechanism of OCB until some parameters are controlled for or a curve map used. Then, by proportion analysis, comparative static analysis, and map analysis (see Appendices 2 and 3 which prove all propositions mathematically), three propositions can be deduced as follows. P1: If other parameters are constant, the heavier an employee’s workload, the more likely he or she will send OCBs to others. P2: If other parameters constant, the less energy and time OCBs cost, the more likely OCBs are sent to others. P3: If an employee seldom or never benefits from others’ OCBs, and the cost of his or her giving OCBs to others is relatively low, OCBs

1

In the model, 2 may also represent 1’s customer.

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will have a negative relationship on the importance degree of his or her in-role task to his or her performance outcome. In fact, if the importance degree of in-role task to the performance outcome is relatively low, it implies that the reward system of an organization is a behavior-performance-based system instead of an outcome-based one. Some interesting studies found that OCB is restrained when the reward system is outcomebased (MacKenzie et al., 1991; Bergeron, 2007). These findings are consistent with Proposition 3. 4. Methods This study is a descriptive, cross-sectional research study and a questionnaire survey was conducted to survey employees’ perceptions on various aspects of OCBs in hotel settings in Mainland of China. 4.1. Sampling and procedure The target population of the study is all the employees who were currently working in three state-owned or state’s stake hotels in Mainland of China during the survey period from July 1st to July 31st, 2009. The three hotels are of 3-star, 4-star and 5-star, respectively. There are two major reasons why multiple hotels were included in the study. First of all, a satisfactory power of regression statistics relies on greater sample size (Milton, 1986), which one single hotel cannot provide the sufficient number of respondents. As a result, using respondents from multiple units has become a common practice, including OCB studies (e.g. Ehrhart et al., 2006; Ma and Qu, 2011). Consider the model itself, the interactive process of OCB performance is independent within each hotel. Therefore, the number of units included in the analysis would not influence the nature of the interactive process. A convenience sampling method was used, and a sample size of 150 was targeted for this study. Four hundred questionnaires were distributed, while 286 questionnaires were returned, representing a 71.5% response rate. After editing, 175 useable questionnaires were included for data analysis. The effective response rate was therefore 43.8%. The characteristics of this sample are as follows: 3-star hotel attendants comprise 39.5% of the sample, 4-star hotel employees, 35.5% and 5-star hotel employees, 25%. Gender composition was 82.6% female, with about 87.4% younger than thirty-five years of age. Less than half of respondents (43.4%) earned college/university degrees, and 75.2% were single. The work experience of the respondents was most commonly less than two years (58.3%) and 2–5 years (25.5%). Most respondents (87.3%) were in non-supervising positions, with only 12.7% holding a supervisor title. In order to test for non-response bias, the Armstrong and Overton’s method (1977) and Wong and Lin’s application (2007) for analyzing time responses across early and late respondents were employed. Early (n ¼ 129, 73.6%) and late respondents (n ¼ 46, 26.4%) were tested across a number of demographic variables using a w2 difference test. The respondents’ gender ðp ¼ 0:086Þ, age ðp ¼ 0:052Þ, and education level ðp ¼ 0:211Þ showed insignificant difference between early and late respondents. Results suggest that response bias was not a significant issue in the study. 4.2. Instrument and measures A self-administered questionnaire with ordered choices was used to survey a sample of employees of three hotels in China. The questionnaire is divided into five sections. Section I is composed of four questions on work related information and utilized a nominal scale. Section II asked employees to rated their in-role tasks.

Table 2 OCB measurements for hotel employees. Types of OCB for hotel employees 1. Help to finish the jobs of absent employees 2. Help to orient new employees 3. Help other employees when workload is heavy 4. Serve guests even it is beyond your job requirements 5. Provide customized services to guests 6. Serve guests proactively 7. Satisfy guests’ urgent needs 8. Always arrive at work on time in the morning, after lunch or breaks 9. Give advanced notice if cannot come to work 10. Give positive feedbacks for others’ good performance 11. Try to avoid creating problems to others 12. Consult with the person who might be affected inconveniently by your actions 13. Make efforts to build good relationships with co-workers 14. Accept temporary inconvenience for the betterment of the hotel 15. Work overtime when needed 16. Cherish and protect the hotel’s assets and belongings 17. Report potential safety concerns 18. Offer suggestions for ways to improve operations 19. Show proud of working for the hotel 20. Participate in social responsibility activities organized by the hotel Note: Measurement scale: 1 = Never, 2 = Rarely, 3 = Sometime, 4 = Often, 5 = Almost Everyday.

Two questions on overall “How important is the work?” and “How heavy the work load is?”, namely u and ˛, were asked on a fivepoint Likert-type scale. Section III asked respondents to rate the “importance” and the “cost”, namely  and , of each type of OCB in hotel setting. Section IV asked respondents to rate how often they “give” and “receive”, namely u and ˛, each type of OCB in hotel setting. Both sections III and IV used a five-point Likert-type scale and twenty statements adapted from previous literatures and modified to the specific situation of hotels in China were used (Houston et al., 2006; Morrison, 1994; see Table 2). Section IV and Section V collect respondents’ demographic information. 4.3. Econometric model The propositions deduced from the theoretic model need support from the empirical world, so econometric models here are developed to test the propositions in this study. The research framework could be illustrated in Fig. 1. For Propositions 1 and 2, a set of partial related test models, adjusted for all control variables, was used to test the association between cost of OCB, workload and the amount of OCB sent. Controlled variables derived from the theoretic model are  (the perceptive importance of received OCB to the outcome evaluation) and ˛ (the importance of in-role task to the outcome evaluation). Individual characteristics included age, gender, position and race. The test model for Propositions 1 and 2 is as follows. 1 = a0 + a1  + a2 ˛ + a3  + a4 u1 + 

(2)

where a0 and a1 –a4 are coefficients, and  represents statistic error. 1 , the employee’s OCB sending to others; u1 , the employee’s workload of in-role task; , the importance of received OCB to employee’s performance outcome; ˛, the importance of sending OCB to employee’s performance outcome; , the cost of the employee’s sending OCB, namely time, energy and opportunity cost spent in sending OCB to others. If the test is significant and the coefficient a3 is negative while a4 is positive, Proposition 1 and Proposition 2 will pass the empirical test. To test Proposition 3, the premises of the proposition had to be met. Therefore, the relationship between sending OCB ( 1 ) and the importance of workload (ˇ) was tested using a set of logistic regression models adjusted for two controlled dumb variables: 

X. Wei et al. / International Journal of Hospitality Management 31 (2012) 1244–1253

Independent variables

Coefficient

a1 Linear

τ1

a2

Regression

a3

a4

γ :importance of receiving OCB α :importance of sending

φ :cost of sending OCB

If a3 < 0 , Proposition 1 supported.

u :workload 1

If a4 > 0 , Proposition 2 supported.

Independent variables

Coefficient

b1 Logistic

τ1

Regression

b2 b3

b4 b5

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φ :cost of sending OCB

τ 2 :received OCB τ 2 =0 and φ =0,

β :importance of

If a3 < 0 when

φ×β

Proposition 3 supported.

τ2 × β

Note: τ1 means the employee’s OCB sending to others. Fig. 1. The framework of the research. Note:  1 means the employee’s OCB sending to others. Table 3 The logistical model: the relationship between sending OCB and the importance of workload. Dichotomized value

1

2



ˇ

0 1

≤2.5 >2.5

≤2 >2

≤2 >2

≤2 >2

(the cost of sending OCB), and  2 (the amount of received OCB). Here, dumb variable measures, , were dichotomized (0 = its mean value less than or equal to 2; 1 = mean value more than 2). The dumb variable  2 was dichotomized in the same way. Thus, we can meet the premise “the cost of his or her sending OCB to others is relatively low” by evaluating  = 0 in the regression equation while also meeting the premise “the cost of his or her doing OCB to others is relatively low” by evaluating  2 = 0 in the regression equation. To illustrate the effect of the importance of workload on sending OCB, the dependent variable  1 (sending OCB) measures were dichotomized at the median (0 = mean value less than or equal to 2.5; 1 = the other values) to reduce the influence of potential outliers. The logistical model with controlled dumb variables is presented as follows (see Table 3). The test model for Proposition 3 is as follows: 1 = a + b1  + b2 2 + b3 ˇ + b4 ( × ˇ) + b5 (2 × ˇ) + 

(3)

where a and b1 –b5 are coefficients;  represents statistic error;  2 is the employee’s OCB received from others;  is the cost of the employee’s sending OCB; ˇ is the importance of workload. This study defines the interaction of the importance of workload as cost of OCB, received OCB and importance of workload. Because the purpose of the study is to test the effect of workload importance on OCB, the selected interactions focused on importance of workload (ˇ), namely two types of two way interactions: cost of OCB × importance of workload ( × ˇ) and received OCB × importance of workload ( 2 × ˇ) were examined. If ˇ is negatively related to  1 , when  2 = 0 and  = 0, Proposition 3 is supported.

All tests were conducted using statistical software EVIEWS version 5.0. 4.4. Data analysis Data was coded and inputted into SPSS software. Several analyses were conducted. Descriptive statistics (frequency) were used to analyze Sections I and V of the data. The purpose was to identify the work-related and demographic characteristics of respondents. To find the effect of demographic variables on OCBs, related analysis between demographic variables and received OCB were conducted in this study. The level of significance used was 5% and the degree of freedom was 173. Results showed that sending OCBs was significantly related to gender (t = −3.055, sig = .003), marriage status (t = 3.055, sig = .003), age (t = −2.391, sig = .018) and education (t = −2.592, sig = .010) with t˛/2 = t0.025 = 1.97377 (see Table 4). 5. Results of model testing Based on the analysis, the means of the importance of work, the degree of workload, the OCB received and sent, and the importance as well as the cost of OCB were used to test the validity of the proposed model (namely u, ˛,  1 ,  2 ,  and ) through a partial related model and a logistical regression model. We tested the first proposition, the relationship between sending OCBs and workload, by examining the correlations among employee’s sending OCBs and workload. The second proposition, the relationship between cost of OCBs and sending OCBs, was tested by conducting a partial related regression model (see Eq. (4) and Tables 3 and 4). When controlling for the importance of received OCB () and the importance of sending OCB (˛), the first proposition was supported for the outcome: employees’ sending OCB was significantly and positively related to employees’ workload (t value of u1 is significantly 3.146 and the coefficient of u1 is positively .091). The second proposition examined the effect of the cost of OCB on OCB (see Table 5). The coefficient of  was negative (−.024),

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Table 4 The model of sending OCB and demographic variables. Model

1

Constant Gender Marriage Age Education

Unstandardized coefficients

Standardized coefficients

B

Std. error

Beta

4.122 −.226 .256 −.158 −.082

.262 .074 .084 .066 .032

−.216 .221 −.176 −.186

t

Sig.

15.752 −3.055 3.055 −2.391 −2.592

.000 .003 .003 .018 .010

Note: Dependent variable, sending OCBs.

Table 5 The model of the effect of the cost of OCB on OCB. Model

1

Constant  ˛  1

Unstandardized coefficients

Standardized coefficients

B

Std. error

Beta

.906 .588 .105 −.024 .091

.216 .065 .051 .031 .029

.599 .139 −.041 .164

R

R2

Adjusted R2

Std. error of the estimate

The goodness of fit .750a

.563

.552

.32637

t

Sig.

4.188 9.104 2.067 −.782 3.146

.000 .000 .040 .435 .002

Note: Dependent variable: sending OCBs ( 1 ). Sig. ≤ 0.05. Table 6 The relationship between sending OCBs and the importance degree of employees’ in-role task.

Step 1a ˇ ˇ 2ˇ Constant

B

S.E.

Wald

df

Sig.

Exp(B)

−1.567 −18.026 2.001 21.203

7.396E3 4.139E3 1.492 6.129E3

.000 .000 1.799 .000

1 1 1 1

1.000 .997 .180 .997

.209 .000 7.400 1.615E9

Sig. ≤ 0.05. a Variable(s) entered on step 1: ˇ,  × ˇ,  2 × ˇ.

which supported Proposition 2 that the costs of OCBs are negatively related to employees’ sending OCBs when other parameters are constant. The significance of the t-value of  was utilized to test whether the significance of relationship between cost of employees’ sending OCBs and sending OCBs. The t-value of  was not significant (t = −.782), meaning that the negative relationship between cost of OCBs and the OCBs was not statistically significant. Therefore, Proposition 2 was only partially supported.

Table 6 suggests that the cost of OCB (ˇ) has a negative and significant effect on z, and z is positively related to the value of expectation (please see Eq. (5)). According to Eq. (5), it can be concluded from the model that P has positive relationship with z. So it is natural that  1 is negatively related to ˇ, namely under the precondition of Proposition 3, sending OCBs have a negative relationship on the importance degree of employees’ in-role task.

1 = 0.906 + 0.588 + 0.105˛ − 0.024 + 0.091u1

z = 21.203 − 1.567ˇ − 18.026( × ˇ) + 2.001(2 × ˇ)

(4)

where  represents statistic error;  1 , the employee’s OCB sending to others; u1 , the employee’s workload of in-role task; , the importance of received OCB to employee’s performance outcome; ˛, the importance of sending OCB to employee’s performance outcome; , the cost of the employee’s sending OCB, namely time, energy and opportunity cost spent in sending OCB to others. In the logistic model (see Eq. (3)), to test Proposition 3,  1 ,  2 , ˇ and  were dichotomized to dumb variables according to the above rules (see Table 1). Let  2 =  = 0 to meet the precondition of Proposition 3, then the logistic model was as followed: P(1 = 1|(ˇ,  × ˇ, 2 × ˇ)) =

1 1 + e−z

(5)

where z = a + a1 ˇ + a2 ( × ˇ) + a3 (2 × ˇ);  is the cost of the employee’s sending OCB and ˇ is the importance of workload. The logistic estimate equation was conducted as shown in Eq. (6) and Table 6. The estimate of coefficient of ˇ (−1.567) shown in

(6)

6. Discussions, implications and future research The purpose of this study was to theoretically examine the mechanism of an employee’s sending and receiving OCBs, as well as to test the effect of determinants (workload, importance of workload to performance outcome, importance of workload to performance outcome and cost of OCB) on the amount of OCBs. The study extended past research on the decisive mechanism of OCB by: (1) using a new theoretical framework of dynamic game theory to explore generating mechanisms of OCB based on the maximization principle in behavioral economics; (2) determining that employees’ workload, importance of workload, and cost of OCB were the upmost factors to predicting OCB; and, (3) using a hotel sample in mainland of China other than vast of samples from the western world or developed countries in past research.

X. Wei et al. / International Journal of Hospitality Management 31 (2012) 1244–1253

6.1. Theoretical implications Perhaps most importantly, we found general support that OCB is in fact a kind of self-interested behavior in the form of altruistic behavior because the principal motivation of one’s OCB is to maximize his or her utility as a whole. An employee sending or conducting OCB purpose is to gain long term payback from either others’ similar OCBs or mutually beneficial atmosphere around him. Based on previous logic, the mechanism of OCB could be analyzed within framework of economics especially game theory.2 Secondly, the findings support the propositions in theory and in statistics that workload has a significant and positive effect on OCB, while cost of OCB and importance of in-role task has a significant and negative effect on OCB. Bergeron (2007) found that entry and junior level employees are more likely to conduct OCB compared with senior level employees. One possible explanation is that those employees’ job performance depends more on interpersonal interaction and helping each other than senior employees (Lin et al., 2008). This is especially true in the hotel industry, where creating of quality service relies greatly on teamwork (Debbie, 2007). As a result, helping coworkers with workloads inspire employees’ OCB because it creates supportive organizational environment (Farh et al., 2004). In the long run, conducting OCB can help each other to alleviate the heavier workload in the future. Another explanation could be entry and junior level employees have higher pressure and motivation in promotion. Engaging in OCBs can help to create positive image and good impressions to coworkers and more importantly to leaders, thus, help them to get better performance reviews for promotion (Bolino and Turnley, 1999).OCB is a specific type of individual performance involving behaviors that support organizations but that are not normally found in an individual’s job description (Organ, 1988). However, OCB is beneficial for an organization but may be costly to the individual (Bergeron, 2007). When the difficulty of conducting OCB (the cost of OCB) becomes higher, it is natural that one will decrease his OCBs so as not to lower his in-role job performance. In addition, according to most literature, there seems to be a pattern of results in which task performance is weighted somewhat more heavily than OCB (Podsakoff and MacKenzie, 1994, 1997). The high cost of OCB takes employees’ more time and energy but benefits employees less for performance evaluation, which makes employees conducting fewer OCBs when cost is high. As a result, Proposition 2 summarizes above analysis.As suggested by Proposition 3, when an employee’s in-role task is less important, then, he/she may have more time and opportunity to conduct OCBs and the OCBs are less obstruction at his/her in-role work. Alternatively, when the employee’s in-role task is very important, employees may have less opportunity to conduct OCB. This is an interesting finding and it is also consistent with previous research on task performance and OCB. For example, Eatough et al. (2011) found that when stressors of in-role tasks are strong and employees perceive the in-role task as important, it will negatively influence employees’ OCB performance. The theoretical implication for this finding is that although OCBs are discretionary in nature (Organ, 1988), organizations still need to create a supportive environment for employees to engage in OCB. To be specific, organizations need to create a working environment that is of less in-role task stressors (e.g. restrictions, overload, role ambiguity) in order to encourage employees’ engagement in OCB. When these necessary conditions are not met, even if employees would like to perform OCB they are not able to. Only when a certain level of flexibility exists in in-role task can employees spend part of their time and energy to going above and beyond.Workload,

2

“Long term payback” is suited to analyzed by dynamic game theory.

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cost of OCB, and importance of in-role task to performance, affect the amount of OCB as described as Propositions 1–3. In fact, the three factors are decisive variables in the generation mechanism of OCB. Considering the demographic variables, we found that gender and marriage have a significant effect on OCB – female and married employees are more likely to conduct OCBs than male and unmarried employees (see Table 4). It is possibly because they are more caring and socially oriented in nature than male employees (Iacobucci and Ostrom, 1993; Pulkkinen, 1996), therefore, they are more likely to engage in OCB. This finding is also consistent with previous researches (e.g. Khalid et al., 2009). When looking alone, the study found that the higher the education the fewer the OCBs. One possible explanation might be supervisory and management employees generally obtained higher levels of education than frontline employees. Previous research on role definitions and OCB showed that supervisors have broader role definitions than subordinates. Therefore, certain OCBs as perceived by subordinates may be considered as in-role tasks by supervisors (Lam and Law, 1999; Hsiung and Tsai, 2009). This may explain the finding why educational level is negatively associated with OCB. Another explanation is that the OCB measures are more task-specific while supervisors and managers’ job functions are mainly coordination, controlling and management. Therefore, they may not have as many chances as frontline employees to engage in various OCBs.The results of this study highlight the value of considering game theory in economics to deal with interactions that typically exist in conducting OCBs. One advantage of doing so is that it highlights the fact that OCB occurs within the context of a unique and reciprocal exchange relationship. Some research showed mutual interaction of OCB, such as theory of psychological contract (Morrison, 1994), but little research has considered using game theory to predict OCB. In fact, game theory is a typical method to examine reciprocal exchange relationships, which is a classical characteristic of OCB. Consequently, this perspective of game theory may lead to a better understanding of such behavior.Another advantage of considering game theory when studying OCB is that dynamic game analysis is based on player maximization of utility in determining the generating mechanism of OCB. In other words, what the essential determinants of OCB are. We proved with game theory that in an economic eye the employee’s workload, cost of OCB, and importance of in-role task are the most important factors in deciding the generation and its amount of OCB. The motivation and influencing factors have been studied thoroughly by past researchers (Robinson and Morrison, 1995; Torlak and Koc, 2007). However this paper analyzed what variables lead to the generation of OCB and decided OCB endogenously. This is an interesting, but thus far neglected topic for research. 6.2. Empirical implications The results of this study have important managerial implications for hotel management and operations. The three determinants of OCB induced from the theoretic model by game theory were used to interpret the generative and decisive mechanism of OCB. The results of this study indicate that employees’ extra-role task (such as OCB) largely depends on their in-role task (such as workload and importance of in-role task). In addition, certain demographic and job-related characteristics seem also influence employees’ OCB performance. The following practical implications are drawn based on the findings of the study.First of all, hotels need to provide a supportive environment in order to facilitate employees’ OCB. Although OCBs are discretionary in nature (Organ, 1988), the study showed that the performance of OCB is conditioned by a reasonable workload and importance of in-role tasks. When the workload is too heavy or when most of the in-role tasks are very important,

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employees would not be able to spare part of their energy to go above and beyond. However, hotel jobs are characterized by long working hours and heavy work load (Bohle et al., 2004), this may prevent employees from performing OCB. Therefore, hotel managers need to carefully design each hotel job and provide some flexibility in the job content to allow employees’ extra-role behaviors.Secondly, the study also reveals that frontline employees with lower education are more likely engaging in OCB than educated supervisory employees. This may due to two groups’ different definitions on in-role tasks and OCBs. According to Lam and Law (1999), supervisors generally have broader definition on in-role tasks than subordinates. Therefore, frontline employees have broader boundaries on OCB and consider more tasks as extra-role behaviors rather than in-role behaviors. This implies that managers need to provide a better job description, so that employees are clear about what behaviors are required by in-role tasks and what belong to OCBs. It should also be noted that employees of different position levels should have different in-role and extra-role job descriptions. The clear description will provide better guidance for employees to follow and help with performance appraisals, thus, benefiting the effective functioning of the hotels.Thirdly, the results also show that female employees are more likely in performing OCB. This may imply that female employees are better fit with certain hotel jobs. Therefore, managers should consider the nature of female and male employees and match them with the most suitable jobs. The study also shows that married employees are more willing to perform OCB, implicating that a good work-life balance will encourage employees to perform OCB. Therefore, managers should care about the well-being of employees and make sure they can balance their work and life well, so that they are capable and willing to go above and beyond and contribute to service excellence and the effective functioning of the organization. 6.3. Limitation and future research Despite its contributions, a few limitations of this study should be addressed. Because game theory is seldom used in hospitality literature and especially in OCB literature, we found little support for the rationale used in this study. In other words, how the literature justifies the use of game theory could be considered a limitation. Without such justification, it is very difficult to embark into using any given theory. Consequently, deductive reasoning has been used to make a case through an extension of the literature in this study. Also, although we assumed a causal direction (workload, cost of OCB, and importance of in-role task influence the generation and amount of OCB), our data did not allow us to test the causal directions. It is possible that the reverse may be true and future research should investigate the possibility of the reverse. The fact that our investigation only focused on hospitality might also be considered a limitation. An additional study of OCB within other service industries or occupational groups is needed to find out a more common mechanism of service employees’ OCB. In addition, the study collected data from three hotels with different star-ratings. Since different star-rating hotels may have different service requirements, which may influence employees’ OCB performance. Future studies may consider collecting data from hotels with the same star-rating and perform similar analysis, or comparing if different star-rating makes significant difference on hotels employees’ OCB performance. It was interested to find that education level and OCB have a negative relationship; however, the further study is needed to explore why such relationship existed. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.ijhm.2012.03.004.

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