An investigation of green hotel customers’ decision formation: Developing an extended model of the theory of planned behavior

International Journal of Hospitality Management 29 (2010) 659–668

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

An investigation of green hotel customers’ decision formation: Developing an extended model of the theory of planned behavior Heesup Han a,*, Yunhi Kim b,1 a b

Department of Tourism Management, College of Business Administration, Dong-A University, Bumin-dong 2-ga, Seo-gu, Busan 602-760, Republic of Korea Division of Tourism Management, Kyungnam University, Woryeong-dong, Masan, Gyeongnam 631-701, Republic of Korea

A R T I C L E I N F O

A B S T R A C T

Keywords: Green hotel Extended theory of planned behavior Revisit intention Decision-making

The current study sought to extend the theory of planned behavior (TPB), which is rooted in the theory of reasoned action (TRA), to more comprehensively explain the formation of customers’ intention to revisit a green hotel. In particular, the extended TPB incorporates the critical constructs in the consumer behavior and marketing literature (i.e., service quality, customer satisfaction, overall image, and frequency of past behavior) into the original TPB model. Results of a structural analysis revealed that the new model provides a better fit with the data, and explains significantly greater amounts of variance in revisit intention in comparison to the TRA and TPB. Added constructs in the new model considerably contribute to improve our understanding of the complicated process of green hotel customers’ decisionmaking. In this study, all relationships appeared to be significant as conceptualized according to the theory. In addition, a mediating effect of satisfaction and attitude was found. The article includes discussions on theoretical and managerial implications. ß 2010 Elsevier Ltd. All rights reserved.

1. Introduction For the last few decades, the public has been recognizing the seriousness of environmental problems/disasters, causing their concerns for the environment to become broader (Kirk, 1995; Roberts, 1996). Such environmental concerns and awareness have led to great changes in consumer buying behaviors and attitudes toward eco-friendly business establishments (Environmentally Friendly Hotels, 2008; D’Souza and Taghian, 2005). More and more customers prefer green products/services and environmentally responsible companies that meet customers’ green needs, as exemplified, for example, in their willingness to pay for eco-friendly products/services (Roberts, 1996; Vandermerwe and Oliff, 1990). This green consumerism has brought about modifications in purchasing methods, manufacturing processes, and operation procedures, including ecologically conscious decisions in various business segments (D’Souza and Taghian, 2005; Wolfe and Shanklin, 2001). In particular, in the hotel industry, recognizing the green shifts in consumer behaviors and the importance of promoting environmentally responsible products/services, increasing numbers of hotel companies are adopting proactive environmental management, and implementing environmentally conscious, practices to improve their competitiveness (Claver-Cortes et al., 2007). Such

* Corresponding author. Tel.: +82 51 200 7427; fax: +82 51 201 4335. E-mail addresses: [email protected] (H. Han), [email protected] (Y. Kim). 1 Tel.: +82 55 249 2444; fax: +82 55 249 2444. 0278-4319/$ – see front matter ß 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhm.2010.01.001

greening efforts by eco-friendly hotels not only contribute to fulfill customers’ needs in the marketplace, but lower operational costs by reducing the significant extent of solid waste and energy/water consumption (Manaktola and Jauhari, 2007). Accordingly, in recent years many hotels have been proactive in advancing their environmental performance in order to position themselves distinctively in the increasingly competitive lodging market, searching for effective ways to become ‘‘green’’ (Chan and Wong, 2006; Manaktola and Jauhari, 2007; Wolfe and Shanklin, 2001). Specifically, marketers in the hotel industry are striving to increase their firms’ competitiveness (e.g., earning recognition and increasing customer retention) through the greening of their firms, thereby eventually enhancing their hotel firms’ profits (Chan and Wong, 2006; Department of Environmental Protection (DEP), 2001; Manaktola and Jauhari, 2007). Many hospitality and marketing researchers agree that increasing customers’ positive pre/post-purchasing decisions is the key to firms’ long-term success (e.g., Han and Back, 2008; Lewis and Chambers, 2000; Yesawich, 1997). An understanding of green hotel marketers’ decision-making process is essential in developing effective marketing and service strategies that induce positive purchasing decisions (Han et al., 2010). While customers’ decisionmaking process is very intricate, it is generally believed that their decision formation can be a clue in comprehending this process (Lam and Hsu, 2006; Han et al., 2010). Specifically, an investigation of the underlying volitional and non-volitional factors affecting customers’ decisions may provide important insights into their purchasing decision-making process (Ajzen, 1991; Lam and Hsu,

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2006; Han et al., 2010). Given this, the current study employed the theory of planned behavior (TPB), an extension of the theory of reasoned action (TRA) (Ajzen, 1991; Ajzen and Fishbein, 1980), in order to gain a better understanding of green hotel customers’ post-purchasing decision-making formation. According to Ajzen (1991) and Perugini and Bagozzi (2001), modifying the TPB model by altering paths and including additional critical constructs in a certain context often contribute to enhancing our understanding of the theoretical mechanism of the model and increasing the prediction power for individuals’ intention/behavior in that specific context. That is, the theory can be broadened and deepened through such a process (Ajzen, 1991; Perugini and Bagozzi, 2001). There is growing empirical evidence that service quality, customer satisfaction, overall image, and frequency of past behavior are critical to the decision-making process and powerful predictors of post-purchasing decisions (e.g., Barsky, 1992; Han and Ryu, 2006; Morgan and Hunt, 1994; Oh and Hsu, 2001). These studies stressed the significance of the variables in explaining customer post-purchase behaviors. Thus, the current study also attempted to extend the TPB model by including such constructs, which are important in a hospitality context, as service quality, customer satisfaction, overall image, and frequency of past behavior, and by altering the path in the model to improve our ability to predict intention and understanding of green hotel customers’ decision formation. 1.1. Purpose of the study The TPB has been applied in various contexts, but little research has employed the TPB to explain hotel customers’ decision-making process. In addition, no research has yet compared the predictive abilities of the TRA, TPB, and extended TPB, including additional constructs in a hotel setting, particularly in a green hotel context. Further, while the significance of service quality, customer satisfaction, overall image, and past behavior in explaining postpurchasing behaviors have been stressed in various contexts, to the best of our knowledge no research has integrated these variables into the TPB model to predict customer intentions/behaviors. Therefore, the purpose of this study was to develop an extended TPB model by taking such variables as service quality, customer satisfaction, overall image, and frequency of past behavior into account in order to better predict green hotel customers’ revisit intention. The specific objectives were to compare the predictive ability of the TRA, TPB, and modified TPB models, to investigate the relationships among the proposed study constructs, and to examine the mediating role of customer satisfaction, attitude, subjective norms, and perceived behavioral control. 1.2. Proposed research model The modified TPB model is presented in Fig. 1. The model includes the original variables in the TPB and new constructs (i.e., service quality, customer satisfaction, overall image, and frequency of past behavior). The bold lines indicate the newly added paths on the original TPB model. 2. Review of the literature 2.1. Green hotels Environmental protection continually attracts public attention (Chan and Wong, 2006). Individuals are becoming more and more aware of the environmental damage caused by various business activities (Manaktola and Jauhari, 2007). This increasing public concern is stimulating the implementation of environmentally responsible management in the hotel industry (Manaktola and

Fig. 1. Proposed extended TPB model for green hotel repurchasing behavior.

Jauhari, 2007; Wolfe and Shanklin, 2001). That is, a growing number of hotels are joining the green movement to reduce harmful impacts on the environment, and thereby in doing so eventually increasing their profitability (e.g., cost savings and customer attraction/retention) (Pizam, 2009; Wolfe and Shanklin, 2001). The term ‘‘green’’ is alternatively known as ‘‘eco-friendly’’, ‘‘environmentally friendly’’, or ‘‘sustainable’’ (Han et al., 2009; Pizam, 2009). Wolfe and Shanklin (2001) indicated that ‘‘green’’ refers to actions that decrease negative impacts on our environment (e.g., recycling, eco-purchasing). Similarly, according to the Green Hotel Associations (GHA) (2009) and DEP (2001), a green hotel is an eco-friendly lodging property that has implemented various green practices and institutes sound and environmentally friendly programs to protect the environment and reduce operational costs. In particular, in green hotels, the following are quite commonly used, practiced, and served: durable service items, cotton towels and linens for air quality, donations to charity, well-educated staff about green practices, energy conservation, environmental cleaning, eco-friendly/organic foods, fresh air, water recycling/conservation, recycling bins, towel re-use program, etc. (GHA, 2009; DEP, 2001). 2.2. Theory of planned behavior Ajzen and Fishbein (1980), across a number of publications, developed and explicated the TRA in order to account for mechanisms of human behaviors in decision processes (e.g., Ajzen, 1985; Ajzen and Fishbein, 1980; Fishbein and Ajzen, 1975). The TRA was specifically designed to predict human behaviors under complete volitional control. That is, this theory assumed that most of individuals’ decisions/behaviors are derived from the intensity of volitional efforts for the specific decisions/behaviors. According to this theory, individuals are rational and motivation-based in their decision-making processes, and make a reasoned choice among various alternatives (Fishbein and Ajzen, 1975). In their investigation of the effectiveness of TRA, which involved use of a meta-analysis, Sheppard et al. (1988) verified the predictive power of TRA. Their findings indicated that the model accurately predicted individuals’ decisions and behaviors when applied in any situation or to any activity. Because of its accuracy in predicting human behaviors and effectiveness in explaining psychological processes in decision-making, the TRA is widely utilized in comprehending customers’ decision-making processes in various contexts.

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The TPB is an extended version of the TRA. This theory considers not only volitional control but non-volitional control in explaining an individual’s behavior. A central factor of the TRA and TPB is an individual intention, which provides the most accurate prediction of particular behaviors (Ajzen and Fishbein, 1980; Fishbein and Ajzen, 1975). In this theory, intention, which is viewed as an immediate antecedent of behavior, indicates an individual’s readiness/willingness to engage in a particular behavior (Ajzen, 1985, 2009). In a similar manner, in this study, intention refers to a green hotel customer’s readiness/willingness to repurchase a green hotel product. The TPB postulates three conceptual determinants of intention. Specifically, intention is based on such variables as attitude toward the behavior, subjective norm, and perceived behavioral control. According to Ajzen and Fishbein (1980), attitude toward the behavior refers to the degree of an individual’s positive or negative evaluation/appraisal of behavior performance. This attitude is based on salient behavioral beliefs and outcome evaluations. Behavioral beliefs refer to one’s perceived probability of an expected outcome’s occurrence by engaging in a particular behavior, and outcome evaluations involve the assessment of the possible consequences of a specific behavior (Ajzen and Fishbein, 1980). The strength of each behavioral belief (BBi) is multiplied by the corresponding evaluation of the outcome (OEi), and the products are aggregated to estimate attitude (SBBiOEi) (Ajzen, 1991, 2009; Ajzen and Fishbein, 1980). The second predictor of intention is the subjective norm, which is believed to be a social factor in nature (Ajzen and Driver, 1992). The subjective norm refers to the social pressure exerted to engage in a particular behavior (Ajzen and Fishbein, 1980). The subjective norm is believed to be a function of normative beliefs and motivation to comply. Normative beliefs are the perceived behavioral expectations of one’s important referents (e.g., family, relatives, friends, neighbors, or co-workers), and motivation to comply involves a person’s desire to accommodate the opinions of his/her salient referents with regard to a behavior (Ajzen and Fishbein, 1980). The strength of each normative belief (NBj) is weighted by the corresponding motivation to comply (MCj), and the products are summed to determine the subjective norm (SNBjMCj) (Ajzen, 1991, 2009; Ajzen and Fishbein, 1980). The third determinant of intention is a non-volitional factor termed perceived behavioral control. This predictor, which is not included in the TRA, reflects an individual’s perception of the ease or difficulty in performing a specific behavior (Ajzen and Fishbein, 1980). A person’s perceived behavioral control in TPB should be greater when he/she has significant resources and opportunities (Madden et al., 1992). Perceived behavioral control is based on the function of control beliefs and perceived power. Control beliefs are the perceived presence (or absence) of resources and opportunities that facilitate (or impede) performance of a particular behavior, and the perceived power of each control factor refers to individual assessment of the importance of the resources and opportunities in achieving behavioral outcomes (Ajzen and Madden, 1986; Chang, 1998). Numerically, perceived behavioral control can be predicted by using the summed index generated by multiplying each control belief (CBk) to a corresponding perceived power (PPk) (SCBkPPk) (Ajzen, 1991, 2009; Ajzen and Fishbein, 1980). While the efficacy of TRA in explaining a variety of behaviors has been widely validated in various situations, it is believed that TRA only adequately predicts a person’s intention under conditions of volitional control (Lam and Hsu, 2004; Park, 2003). The inclusion of perceived behavioral control with control beliefs increases predictive power by accounting for intention/behavior that is not under complete volitional control or that stem from non-volitional factors (Ajzen, 1991; Lam and Hsu, 2004; Lee and Back, 2007). Fig. 2 presents the TPB model. The model in the dotted square is the TRA model.

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Fig. 2. TRA and TPB models.

2.3. Derivation of the extended TPB model According to Ajzen (1991), the TPB is, in principle, open to modification by altering the paths in the TPB and including additional predictors if it can be shown that they capture a greater proportion of the variance in intention/behavior after the original TPB constructs have been taken into account. This modification process is described as theory broadening and deepening (Perugini and Bagozzi, 2001). Indeed, based on the assumption that a theoretical mechanism of the TPB is better understood by altering the paths to be more adequate in a particular context and by including significant antecedents for a possible increase in the ability to predict an intention/behavior, the TPB has been revised in various contexts (e.g., Bansal and Taylor, 1999; Han et al., 2010; Oh and Hsu, 2001; Perugini and Bagozzi, 2001). Researchers’ efforts to expand the theory have considerably enhanced the ability to predict human behavior in a given context, capturing a greater proportion of the variance in intention/behavior not sufficiently accounted for by the original TPB. Findings from previous research suggest that service quality, satisfaction, image, and past behavior are significant constructs in pre/post-purchase decision-making processes (e.g., Ajzen, 1991; Bansal and Taylor, 1999; Lee and Back, 2009; Han et al., 2009; Han and Ryu, 2006; Oh and Hsu, 2001; Perugini and Bagozzi, 2001; Taylor and Baker, 1994). Such constructs could be added to the TPB; indeed, this has been done in previous research (e.g., Bansal and Taylor, 1999; Lee and Back, 2009; Han et al., 2010; Oh and Hsu, 2001; Perugini and Bagozzi, 2001). Accordingly, the present study model integrated service quality, customer satisfaction, overall image, and past behavior into the original TPB in order to better predict revisit intention. 2.3.1. Service quality and satisfaction Service quality and customer satisfaction are critical concepts in marketing and consumer behavior. Numerous firms are increasingly dedicating considerable energies to tracking service quality and satisfaction since these can be essential measures of the firms’ performance and ultimately drive future profitability (Anderson et al., 1994; Han and Ryu, 2006). While the conceptualizations of service quality and customer satisfaction are not identical in the literature, there are two fundamental aspects of these variables in the marketing and consumer behavior literature: (1) service quality from a general point-of-view involves a comparison of excellence in overall services over the long run (Bitner and Hubbert, 1994; Parasuraman et al., 1988; Taylor and Baker, 1994); and (2) customer satisfaction is the post-purchase, transaction-specific evaluation process for products/services based on expectations prior to purchase (Kotler, 1991; Oliver, 1997; Parasuraman et al., 1988). In a number of studies, service quality and satisfaction have been found to significantly explain behavioral intention. Cronin and Taylor (1992) studied the casual relationships among quality,

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satisfaction, and intention. They found that service quality is a significant predictor of customer satisfaction in forming behavioral intention. Ting (2004) examined customer behaviors in a service setting and found that service quality predicts customer satisfaction in the decision-making process. Consistent with these findings, in a restaurant setting, Han and Ryu’s (2006) findings indicated that service quality affects satisfaction, and satisfaction fully mediates the effect of quality on intention to revisit and be engaged in positive word-of-mouth behavior. In addition, in their integrative approach to understanding attitude-behavior relationships, Bansal and Taylor (1999), in developing a service provider switching model, found that service quality is an important influence on attitude toward switching and satisfaction, such that including service quality and satisfaction in their model was essential to the precise prediction of an individual’s intention. In their study, both quality and satisfaction (mediator) contributed to a decrease in intention to switch. Given this evidence, it seems appropriate to incorporate service quality and satisfaction into a model designed to explain the formation of repurchasing intention in order to ensure a sounder theoretical underpinning. 2.3.2. Overall image The definition of overall image varies in the marketing and consumer behavior literature. According to one of the clearest definitions, individuals’ total perceptions of a product (or service) and its salient attributes are generated by processing information from various sources (Assael, 1984; Bloemer and Ruyter, 1998). Researchers generally agree that the formation of an overall image about a product/service is based on a cognitive and perceptual process (Assael, 1984; Bloemer and Ruyter, 1998; Han et al., 2009). Consistent with these studies, the concept of overall image in the current research is lodging customers’ overall perceptions of a green hotel and its significant attributes as shaped by information from diverse sources and by prior or explicit knowledge about them (Han et al., 2009). Overall image has increasingly attracted the attention of academic and industry professionals and is regarded as playing an important role in predicting a consumer’s favorable/unfavorable decisions. Many researchers across various fields have identified the significance of overall image in customers’ decision-making processes (Han et al., 2009; Prendergast and Ho, 2002; Ryu et al., 2007). Prendergast and Ho (2002) investigated the impact of store image, and found that a firm’s image is a critical driving force in intention. In their empirical investigation of the role of overall image in a hospitality setting, Ryu et al. (2007) stated that a positive overall image of a hospitality firm increases customers’ willingness to revisit and spread favorable word-ofmouth. In addition, Han et al.’s (2009) recent research on hotel customers’ behaviors indicated that one’s overall perception of a green hotel (overall image) is positively associated with his/her intention to buy a green hotel product, to recommend it, and to pay for it. Considering these empirical studies, it seems likely that overall image may play an important role in predicting a green hotel customer’s intention to revisit the hotel when this construct is integrated into the TPB model. 2.3.3. Frequency of past behavior Frequency of past behavior is believed to be another significant predictor of intention/behavior (e.g., Lee and Back, 2009; Oh and Hsu, 2001; Quellette and Wood, 1998; Perugini and Bagozzi, 2001; Ryu and Jang, 2006). While past behavior was not originally included in the TRA/TPB, numerous researchers have identified the need to add this construct equivalently to other predictors of intention or future behavior (e.g., Lee and Back, 2009; Oh and Hsu, 2001; Quellette and Wood, 1998; Perugini and Bagozzi, 2001). In their meta-analysis, Quellette and Wood (1998) examined 64

studies across diverse fields, and found strong evidence for the critical role of frequency of past behavior in forming intention and behavior. In addition, in testing the competing models that use the TRA, TPB, and extended TPB, Lee and Back (2009) verified that past behavior is an important determinant of intention and behavior. Further, Perugini and Bagozzi (2001) developed a modified model of the TPB, a model of purposive behavior, and found that frequency of past behavior plays a significant role in decisionmaking processes along with other added constructs in the original TPB model. Specifically, their findings showed that frequency of past behavior contributes directly to desires and behavioral intention in both body weight regulation and studying effort settings. Ajzen (1991) insisted that frequency of past behavior can be utilized to assess the completeness/sufficiency of a model designed to predict decision/behavior. According to Ajzen, if frequency of past behavior explains a decision/behavior in a certain setting after the original model variables have been accounted for, this construct may be employed to better predict such a decision/behavior in a particular setting. Indeed, many researchers in various fields have attempted to include this variable as a determinant of intention and enhanced the predictive power of the TPB (e.g., Lee and Back, 2009; Oh and Hsu, 2001; Perugini and Bagozzi, 2001; Ryu and Jang, 2006). 2.3.4. Relationship between subjective norm and attitude A possible link between subjective norm and attitude toward the behavior was proposed and tested in previous studies (e.g., Chang, 1998; Han et al., 2010; Ryu and Jang, 2006). Chang (1998) developed a modified model of the TPB and found that adding the path from subjective norm to attitude was essential to improving the structural model fit. In a hospitality setting, Ryu and Jang (2006) found that perceived subjective norm is positively associated with one’s attitude toward a certain behavior. Similarly, in examining hotel customers’ eco-friendly behaviors, Han et al. (2010) verified that attitude toward a green hotel is a positive function of a subjective norm; this added path in the original TPB significantly improved the fit of the model. Given this evidence, the current study assumed that one’s perceived social pressure from salient referents to engage in a certain behavior induces his/her favorable/unfavorable evaluation of a behavior. Thus, the path from subjective norm to attitude was added to the proposed model. In summary, a modification of the TPB is associated with the introduction of additional variables explaining how they influence intention in relation to existing predictors and with the revision of any path in the original model. Accordingly, the proposed extended model completes the systematic integration of four key factors into the TPB, considering their possible relations with the existing TPB variables, and included a specific path in order to better comprehend green hotel customers’ complicated decision-making process. In the current study, the following 12 hypotheses were developed: H1. Behavioral beliefs have a positive influence on attitude. H2. Normative beliefs have a positive influence on subjective norms. H3. Control beliefs have a positive influence on perceived behavioral control. H4. Attitude has a positive influence on revisit intention. H5. Subjective norm has a positive influence on revisit intention. H6. Perceived behavioral control has a positive influence on revisit intention. H7. Service quality has a positive influence on customer satisfaction.

H. Han, Y. Kim / International Journal of Hospitality Management 29 (2010) 659–668

H8. Service quality has a positive influence on attitude. H9. Customer satisfaction has a positive influence on revisit intention. H10. Overall image has a positive influence on revisit intention. H11. Frequency of past behavior has a positive influence on revisit intention. H12. Subjective norm has a positive influence on attitude.

3. Methodology 3.1. Questionnaire development 3.1.1. Elicitation study According to Ajzen (2009) and Ajzen and Fishbein (1980), there is no standard questionnaire for TPB. With regard to belief constructs, they insisted that formative research (i.e., elicitation study) and validation of the theory’s belief constructs were needed prior to construction of the final questionnaire. They indicated that such an endeavor helps researchers construct a questionnaire that is adequate for a specific behavior and population of interest. Accordingly, an elicitation study and pilot-test were conducted in the present study. As an elicitation method, a focus group was employed (Cheng et al., 2006; Lam and Hsu, 2004; Lee and Back, 2007). The focus group participants were green hotel customers, hospitality academics, and hotel industry professionals. To obtain the new set of belief items, salient beliefs and referents were discussed in the focus group, and an open-ended eliciting questionnaire was completed. The initial questionnaire, which included 12 items for beliefs (6 items for behavioral beliefs; 3 items for normative beliefs; and 3 items for control beliefs) and 12 items for evaluative components, was generated through this process and a literature review. The generated questionnaire was refined through hospitality experts’ reviews. The salient referents identified were: family/relatives, friends, and colleagues/co-workers. As a next step, a pilot-test was performed with 62 hotel customers. The results indicated that the instrument had a sufficient level of reliability and validity. 3.1.2. Construct measures for beliefs A six-item scale was used with a 7-point Likert-type scale ranging from strongly disagree (1) to strongly agree (7) for measures of behavioral beliefs (e.g., ‘‘Staying at this green hotel when traveling to the same location next time would enable me to be more socially responsible.’’) and very unimportant (1) to very important (7) for measures of outcome evaluations (e.g., ‘‘Being more socially responsible is’’ 1 = very unimportant, 7 = very important). Three items with a 7-point Likert-type scale ranging from very false (1) to very true (7) for measures of normative beliefs (e.g., ‘‘My family (or relatives) thinks I should stay at this hotel when traveling to the same location next time’’) and extremely unlikely (1) to extremely likely (7) for measures of motivation to comply (e.g., ‘‘Generally speaking, how likely are you to do what your family (or relatives) thinks you should do?’’) were utilized. Finally, a three-item measurement with 7-point Likert-type scale was used to assess control beliefs (e.g., ‘‘Staying at this hotel is expensive’’) and each item’s control power (e.g., ‘‘The price/cost of staying at a green hotel would influence my decision for hotel selection.’’), ranging from strongly disagree (1) to strongly agree (7). In order to gain an overall level of three belief constructs, items for each belief construct were multiplicatively combined with their evaluative components (SBBiOEi, SNBjMCj, and SCBkPPk) (Ajzen, 1991, 2009; Ajzen and Fishbein, 1980).

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3.1.3. Construct measures for other study variables Items on attitude, subjective norm, perceived behavioral control, revisit intention, service quality, customer satisfaction, overall image, and frequency of past behavior were based on the existing validated measures from the previous literature (e.g., Ajzen, 1988, 1991; Ajzen and Fishbein, 1980; Baloglu and McCleary, 1999; Han and Ryu, 2006; Lam and Hsu, 2004, 2006; Lee and Back, 2007; Taylor and Baker, 1994). The measures were slightly modified for use in a questionnaire suitable for a green hotel setting. In particular, 6 items and a 7-point semantic differential scale were employed to assess attitude (e.g., ‘‘For me, revisiting this green hotel when traveling to the same location next time is’’ 1 = extremely bad, 7 = extremely good). The measure of frequency of past behavior was assessed using 5 frequency categories (e.g., ‘‘Which statement best describes how many times you have stayed at this hotel’’ 1 = 1 time, 2 = 2–3 times, 3 = 4–5 times, 4 = 6–10 times, 5 = more than 10 times). Three items and a 7-point Likert-type scale were used to measure overall image (e.g., ‘‘Overall image for staying at this green hotel is’’ 1 = very negative, 7 = very positive). Other study constructs were assessed using a 7-point Likert-type scale ranging from strongly disagree (1) to strongly agree (7). In addition, multi-item scales were used to assess subjective norm (3 items) (e.g., ‘‘Most people who are important to me think I should stay at this green hotel when traveling to the same location next time’’), perceived behavioral control (3 items) (e.g., ‘‘Whether or not I stay at this hotel when traveling to the same location next time is completely up to me’’), revisit intention (3 items) (e.g., ‘‘I am willing to revisit this hotel when traveling to the same location next time’’), service quality (2 items) (e.g., ‘‘I believe this green hotel offers excellent service’’), and customer satisfaction (3 items) (e.g., ‘‘I am very satisfied with the overall service at this green hotel’’). 3.2. Data collection An Internet-based survey was used to collect data. A questionnaire was sent to 4500 general hotel customers in the U.S. through a survey system available via an on-line market research company. In the opening instructions of the survey, a detailed description of a green hotel was provided along with a list of green hotels in each state certified according to well-recognized and -respected national/regional organizations (e.g., U.S. Green Building Council Leadership in Energy and Environmental Design (LEED) and the Green Seal). The hotels on the list are believed to actively follow green guidelines and ideas. In addition, before completing the questionnaire, two screening questions were given to the participants (i.e. (1) ‘‘Have you ever stayed at a green hotel?’’; (2) ‘‘If yes, when was the most recent stay at a green hotel?’’). Those respondents who had stayed at a green hotel within the last 6 months were asked to complete the questionnaire. According to Keaveney (1995), the time frame (6 months) is recent enough to ensure that customers’ will reliably recall their service experiences. The participants who had stayed at a green hotel not on the list were also free to fill out the questionnaire. The respondents were also asked to indicate the name of that green hotel. A total of 469 responses were received from the survey participants, for a response rate of 10.42%. An Internet-based survey is considered a more immediate and effective means of response (Kim, 2001). With the development of the Internet, increasing numbers of researchers have used web-based survey (e.g., Asperin, 2007; Han et al., 2009; Kim and Ok, 2009; Kim et al., 2009; Yang and Peterson, 2004). The response rates of these recent studies employing an online survey range from 8.1% to 17.1%. The response rate of the present study is reasonable given the rates predicted by these recent studies. A panel of experts from hotels and academic institutions reviewed the hotels named in the completed ques-

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664 Table 1 Construct summary statistics (N = 434).

Correlations among latent constructs (squared)a and reliabilities of constructs Constructs

SQ

SQ BB NB CB CS AT SN PBC OI RI

1.000 .634 .452 .084 .537 .627 .529 .438 .789 .685

Mean SD Composite reliability AVE Alpha

5.295 1.322 .900 .895 .912

BB (.402) (.204) (.007) (.288) (.393) (.280) (.192) (.623) (.469)

1.000 .387 .227 .366 .582 .443 .422 .715 .675 35.225 6.190 .842 .829 .967

NB

(.150) (.052) (.134) (.339) (.196) (.178) (.511) (.456)

1.000 .106 .442 .357 .738 .223 .394 .495 16.015 5.399 .837 .824 .933

CB

(.011) (.195) (.127) (.545) (.050) (.155) (.245)

1.000 .147 .129 .042 .186 .160 .147

CS

(.022) (.017) (.002) (.035) (.026) (.022)

32.679 5.003 .660 .601 .814

AT

1.000 .404 .509 .248 .508 .569

(.163) (.259) (.062) (.258) (.324)

4.045 1.222 .949 .947 .959

1.000 .498 .273 .683 .669

(.248) (.075) (.466) (.448)

5.543 1.231 .823 .779 .956

SN

PBC

OI

RI

1.000 .233 (.054) .498 (.248) .638 (.407)

1.000 .371 (.138) .465 (.216)

1.000 .783 (.613)

1.000

3.941 1.167 .913 .909 .967

5.545 1.196 .644 .584 .787

5.677 1.322 .937 .934 .974

5.303 1.327 .744 .712 .888

Note 1. SQ: service quality; BB: behavioral beliefs; NB: normative beliefs; CB: control beliefs; CS: customer satisfaction; AT: attitude; SN: subjective norm; PBC: perceived behavioral control; OI: overall image; RI: revisit intention. Note 2. Frequency of past behavior (FPB) was not included in the table in that FPB was measured by using a single item; the mean and standard deviation of FPB were 3.136 and .984, respectively. a Correlation coefficients are estimates from AMOS 5. Model measurement fit: x2 = 1228.954, df = 512, p < .001, x2/df = 2.400, RMSEA = 0.057; CFI = 0.959; NFI = 0.931.

tionnaires. Of the 464 responses, 30 were disregarded because the panel determined the hotels named in these questionnaires were not considered to be green. In addition, some extreme cases identified through a test for multivariate outliers were eliminated. Specifically, with the use of a p < .001 criterion for Mahalanobis’ distance, five extreme multivariate outliers among the cases were found (Mahalanobis’ D (42) > 76.054, p < .001) and removed from further analyses, retaining a final sample of 434 cases.

average variance extracted (AVE), and the correlation between constructs. As shown in Table 1, all AVE values were above .50, indicating adequate convergent validity. In addition, the AVE value for each factor exceeded the squared value of each correlation between constructs, providing evidence of discriminant validity (Fornell and Larcker, 1981).

4. Results

4.3.1. Modeling comparisons Before assessing the hypothesized paths, three models (i.e., TRA, TPB, and proposed models) were independently tested. The results of the SEM revealed that all three models were a good fit with the data (see Table 2). In particular, the TRA model provided a satisfactory fit (x2 = 1221.286, df = 246, p < .001, x2/df = 4.965, RMSEA = 0.076; CFI = .914; NFI = .896). All paths were positive and significant (p < .01). Two predictor variables (i.e., attitude and subjective norm) explained about 51.9% of the total variance in revisit intention (see Fig. 3). The TPB model also showed an excellent fit to the data (x2 = 1018.622, df = 311, p < .001, x2/ df = 3.275, RMSEA = 0.072; CFI = .942; NFI = .919). All linkages in the TPB model were significant (p < .01). Attitude, subjective norm, and perceived behavioral control jointly explained approximately

4.1. Respondents’ profile The sample contained almost equivalent numbers of men and women––51.4% of respondents were female and 48.6% were male. The survey participants’ mean age was 44.72 years. Most participants’ level of education was relatively high. About 80.6% had a higher education degree. Most reported household income of less than $69,000 (79.0%); 21.0% indicated that their household income was more than $70,000. Approximately one-half of the sample (51.4%) stayed at a hotel at least 2–5 times a year, and most participants stayed at a green hotel on one or fewer occasions each year (71.0%). 4.2. Data quality testing Before assessing the measurement model, data were screened to avoid any violation of the assumptions of the general linear model. Based on this evaluation of assumptions, several variables, which were not within acceptable limits, were transformed to improve normality and linearity. A Confirmatory Factor Analysis (CFA) was conducted using SPSS and AMOS5 to assess the measurement model. The results of the CFA are summarized in Table 1. The fit indices indicated that the model was relatively consistent with the data, with all of the fit indices better than the recommended values (x2 = 1228.954, df = 512, p < .001; RMSEA = 0.057; CFI = 0.959 NNFI = 0.931). In an assessment of the reliability of the measures, composite reliability for each construct was calculated. All reliability values were greater than the suggested threshold of .60, indicating high internal consistency (Bagozzi and Yi, 1988). As a next step, the validity of the measures was assessed using the factor loadings within the constructs,

4.3. Structural model

Table 2 Modeling comparison results—explanatory power and fit indices. Fit indices and R2

Suggested valuea

x2 df 2

x /df RMSEA CFI NFI Adjusted R2 AT SN PBC CS RI

2 to 5 .08 .90 .90

TRA

TPB

Extended TPB

1221.286 246 4.965 .076 .914 .896

1018.622 311 3.275 .072 .942 .919

1492.186 563 2.650 .062 .957 .938

.340 .553

.336 .553 .144

.519

.557

.470 .556 .145 .317 .720

Note. AT: attitude; SN: subjective norms; PBC: perceived behavioral control; CS: customer satisfaction; RI: revisit intention. a Suggested values were based on Hair et al. (1998).

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Fig. 3. TRA model results (N = 434).

55.7% of the variance in revisit intention (see Fig. 4). Finally, the extended TPB model exhibited a good fit to the data (x2 = 1492.186, df = 563, p < .001, x2/df = 2.650, RMSEA = 0.062; CFI = .957; NFI = .938), and six antecedent variables explained about 72.0% of the total variance in revisit intention (see Fig. 5). Overall, the results of model evaluations implied that the TRA, TPB, and proposed extended model well predicted hotel customers’ intention to revisit a green hotel. As a next step, the TRA, TPB, and extended models were compared to assess their ability to explain revisit intention and fit statistics. Findings indicated that the extended model had superior explanatory power to that found in the TRA and TPB model (adjusted R2 for intention in the extended TPB = .720 vs. adjusted R2 for intention in the TRA = .519 and adjusted R2 for intention in the TPB = .557). Specifically, the extended TPB model improved R2 from .519 to .720 and from .519 to .720 in comparison with the TRA and TPB models, respectively. In addition, the extended TPB model (x2/df = 2.650, RMSEA = 0.062, CFI = .957, NFI = .938) had relatively better fit statistics than the TRA (x2/df = 4.965, RMSEA = 0.076, CFI = .914, NFI = .896) and TPB models (x2/df = 3.275, RMSEA = 0.072, CFI = .942, NFI = .919). For decades, many researchers have attempted to refine and extend the TRA and TPB models by modifying the paths or by including new construct(s) to better understand a wider range of human behaviors in various settings (Bansal and Taylor, 1999; Chang, 1998; Han et al., 2010; Oliver and Bearden, 1985; Ryu and Jang, 2006; Taylor and Todd, 1995; Vallerand et al., 1992). Consistent with these studies, the current study findings revealed that the proposed extended model, which included significant constructs in the consumer decision-making process (service quality, customer satisfaction, overall image, and frequency of past behavior), increased our prediction of intention and appeared to be superior to the TRA and TPB models. Accordingly, this extended TPB model was used in hypotheses testing.

Fig. 4. TPB model results (N = 434).

Fig. 5. Extended TPB model results for green hotel repurchasing behavior (N = 434).

4.3.2. Hypotheses testing Hypotheses 1, 2, and 3 were tested. The regression paths from behavioral, normative, and control beliefs to attitude (b = .253, t = 4.917, p < .01), subjective norm (b = .745, t = 17.751, p < .01), and perceived behavioral control (b = .211, t = 3.640, p < .01) were significant. Thus, hypotheses 1, 2, and 3 were supported. These findings were consistent with expectancy-value theory and previous studies in various settings (e.g., Ajzen, 1991; Han et al., 2010; Lam and Hsu, 2004; Lee and Back, 2009). The findings imply that customers’ perceived probability of an expected outcome’s occurrence, perceived behavioral expectations of their important referents, and perceived presence of resources and opportunities contribute to building their favorable attitude toward revisiting a green hotel, inducing their perceived social pressure to perform the behavior, and stimulating their perception of the ease in engaging in the behavior, respectively. Hypotheses 4, 5, and 6 were tested. The findings indicated that attitude (b = .147, t = 3.480, p < .01), subjective norm (b = .263, t = 7.047, p < .01), and perceived behavioral control (b = .134, t = 3.901, p < .05) were positively and significantly associated with revisit intention, supporting hypotheses 4, 5, and 6. These findings aligned with previous studies (e.g., Ajzen, 1991; Lam and Hsu, 2004, 2006; Lee and Back, 2009) implying that an increase in favorable attitude, subjective norm, and perceived behavioral control will result in an increase in the likelihood of revisiting a green hotel. Subjective norm was found to have the greatest direct effect on intention among these three variables. That is, the intention to revisit a green hotel was associated with hotel customers’ perceived social pressure from their significant referents. Hypotheses 7 and 8 were tested. Results showed that service quality had a positive influence on satisfaction (b = .563, t = 12.140, p < .01) and attitude (b = .412, t = 4.360, p < .01). Thus, hypotheses 7 and 8 were supported. This result indicates that as hotel customers’ perceived service quality increases, so do their satisfaction levels, as well as attitudes toward revisiting a green hotel. The regression path from customer satisfaction and intention (b = .157, t = 4.347, p < .01) was significant. As expected, overall image was found to be a significant predictor of revisit intention (b = .285, t = 3.587, p < .01). Therefore, hypotheses 9 and 10 were supported. The findings imply that increasing customers’ satisfaction level and favorable overall image contribute to building strong intention to revisit a green hotel. This result was in line with previous studies

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Table 3 Standardized parameter estimates—extended TPB (N = 434). Hypothesized paths H1: BB ! AT H2: NB ! SN H3: CB ! PBC H4: AT ! RI H5: SN ! RI H6: PBC ! RI H7: SQ ! CS H8: SQ ! AT H9: CS ! RI H10: OI ! RI H11: FPB ! RI H12: SN ! AT R2 R2 R2 R2 R2

(CS) (AT) (SN) (PBC) (RI)

Standardized estimates .253 .745 .211 .147 .263 .134 .563 .412 .157 .285 .257 .171

5. Discussion

t-Values **

4.917 17.751** 3.640** 3.480** 7.047** 3.901** 12.140** 4.360** 4.347** 3.587** 2.930** 4.134**

Results Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported .317 .470 .556 .145 .720

Goodness-of-fit statistics: x2 = 1492.186, df = 563, p < .001 (x2/df = 2.650) RMSEA = .062 CFI = .957 NFI = .938 Indirect impact on RI SQ = .149** BB = .037 NB = .105 SN = .025 CB = .028 Note 1. SQ: service quality; BB: behavioral beliefs; NB: normative beliefs; CB: control beliefs; CS: customer satisfaction; AT: attitude; SN: subjective norm; PBC: perceived behavioral control; OI: overall image; FPB: frequency of past behavior; RI: revisit intention. ** p < .01.

in different contexts (e.g., Bansal and Taylor, 1999; Han et al., 2009; Han and Ryu, 2006; Taylor and Baker, 1994). In addition, frequency of past behavior was positively associated with revisit intention (b = .257, t = 2.930, p < .01), supporting hypothesis 11. This finding is also consistent with the results of previous studies (e.g., Lee and Back, 2009; Oh and Hsu, 2001; Quellette and Wood, 1998; Perugini and Bagozzi, 2001). Ajzen (1991) insisted that frequency of past behavior plays a critical role in decision-making processes, especially when individuals deliberately make a decision. Thus, in the present context, it would be true that customers consciously form an intention to revisit a green hotel. Finally, the relationship between subjective norm and attitude was tested. The hypothesized link was positive and significant (b = .171, t = 4.134, p < .01). Therefore, hypothesis 12 was supported. Based on this finding, it can be inferred that one’s social pressure from critical referents is important in explaining customers’ eco-friendly purchasing behaviors. This finding also supported empirical studies, which demonstrated that subjective norm has a direct impact on attitude toward a behavior (e.g., Chang, 1998; Han et al., 2010; Randall and Gibson, 1991; Ryu and Jang, 2006). Overall, all hypothesized paths were supported. The findings are summarized in Table 3. As a next step, the mediating roles of customer satisfaction, attitude, subjective norms, and perceived behavioral control were tested by examining the indirect effect of service quality, behavioral beliefs, normative beliefs, subjective norms, and control beliefs on revisit intention. The findings revealed that only service quality significantly affected revisit intention through customer satisfaction and attitude (bSQ-CS&AT-RI = .149, p < .01). This finding indicated that customer satisfaction and attitude have a significant mediating role in the relationship between service quality and revisit intention.

Little research has focused on green hotel customers’ decisionmaking process. The current study sought to provide a deeper understanding of hotel customers’ intention to revisit a green hotel by incorporating four critical constructs––service quality, satisfaction, overall image, and frequency of past behavior––into the TPB model. The extended model was tested using SEM, and there was strong support for the model. Specifically, the study results indicated that the proposed model had a satisfactory fit to the data and the inclusion of these variables significantly increased the predictive power of customers’ intention to revisit a green hotel. All twelve hypotheses in the study model were supported. The findings also indicated that satisfaction and attitude acted as mediators between service quality and intention. Overall, the current study achieved all study objectives. 5.1. Theoretical and practical implications Study findings hold both theoretical and practical implications. First, the prediction of green hotel customers’ revisit intention under the extended TPB model was well supported. The results of the modeling comparisons implied that both TRA and TPB are insufficient for comprehending intention formation in a green hotel context, and that the extended TPB model represents a substantial improvement over the TRA and TPB. Ajzen (1991, 2009) stated that the modification of the TPB should occur cautiously, and provided some criteria. The extended model generally met the criteria for revising the TPB: similar to existing predictors of the theory, the proposed variables are behavior-specific in compliance with the principle of compatibility; the factors are conceptually independent of existing constructs of the theory; the constructs are regarded as casual factors that determine decisions; and finally, the variables are potentially applicable to a broad range of behaviors in different settings (Ajzen, 1991, 2009). In particular, added constructs in the current research (i.e., service quality, satisfaction, overall image, and frequency of past behavior) apparently provide sufficient impetus for the post-purchase decision-making process, and a simultaneous analysis of these constructs and original determinants of the TPB considerably improved our understanding of the intricate process of green hotel customers’ revisit intention formation. Accordingly, beyond the simple application of the TPB to hospitality product purchasing activity, researchers should add variables as critical factors as they develop any theory that explains customers’ decision-making process in both green hotel and hospitality contexts. Second, this study found that service quality, satisfaction, and overall image have a significant positive association with revisit intention. From a practical standpoint, the findings suggested that green hotel marketers should pursue the excellent attributes and services that induce customers’ positive evaluations and stimulate the formation of favorable attitudes toward visits to green hotels. In addition, the findings suggested that marketers should develop efficient strategies for enhancing their image by advertising their environment-friendly practices (e.g., donation of usable furniture/ equipment to charitable organizations, recycling, conservation of water and energy) to visiting and potential customers using multiple information sources. These various efforts would contribute to building customers’ strong intention to repurchase a green hotel product. Third, frequency of past behavior was found to be a significant predictor of intention. That is, customers’ intention to revisit a green hotel becomes stronger with an increase in the number of prior visits to the hotel. Ajzen (1991) which indicated that frequency of past behavior should be utilized if the inclusion of this construct in the TPB contributes to better prediction of decision-making in a specific

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setting after the original TPB constructs have been accounted for. The current study result verified the critical role of this construct in explaining green hotel customers’ post-purchase decision-making process. From a practical point-of-view, customers who have had favorable experiences at a specific green hotel on a number of visits are more willing to repurchase the eco-friendly hotel product. Accordingly, for those marketing a green hotel, the development of more effective service strategies is essential to improving their customers’ favorable experiences while staying at a hotel. Fourth, as shown in Table 3, the present study result indicated that among the four mediator variables in the proposed model, only customer satisfaction and attitude significantly mediated the impact of service quality on revisit intention (bSQ-CS&AT-RI = .149, p < .01). This finding implied that service quality shapes satisfaction and attitude toward a behavior, and these variables enhance green hotel customers’ favorable decisions. Accordingly, green hotel operators should seek to increase the level of customers’ attitudes and satisfaction in order to take full advantage of the impact of service quality on decisions to repurchase a green hotel product. It should also be noted that green attributes in a hotel should not be seen as an alternative to quality of service. It may be true that green credentials can partially and occasionally compensate for a poor quality product or service performance. Indeed, empirical evidences indicated that environmentally responsible customers are willing to sacrifice a little convenience on occasion (e.g., Dalton et al., 2008; Diekmann and Preisendorfer, 2003). However, they are not prepared to experience consistent inconveniences, continuously accepting lower product and service performance levels (e.g., Dalton et al., 2008; Diekmann and Preisendorfer, 2003; Manaktola and Jauhari, 2007). Thus, for green hotel operators, maintaining a level of service quality in their hotels that is at least comparable to that in non-green hotels is critical for retaining customers. Lastly, as hypothesized, the subjective norm exerted a significant influence on attitude toward revisiting a green hotel. This finding indicated that attitudinal and normative factors of the TPB were not independent. Some researchers argued for a possible link between these two variables in the TPB, which should be considered when testing the theory in any context (e.g., Chang, 1998; Han et al., 2010; Randall and Gibson, 1991; Ryu and Jang, 2006). The current study result is consistent with these researchers’ claims. Thus, application of the theory in explaining customer decision-making in any context should occur cautiously by considering the relationship between attitudinal and normative constructs of the theory. From a practical point-of-view, the findings implied that hotel customers’ favorable perceptions of a green hotel led to their positive appraisal of the eco-friendly decision. In other words, green hotel customers’ favorable/ unfavorable attitudes toward repurchasing a green hotel product largely depend on the positive/negative way in which their salient referents (i.e., family/relatives, friends, and colleagues/co-workers) consider revisiting a green hotel. Thus, green hotel marketers should actively find ways to help such referents develop favorable perceptions of their hotel. Presenting the particular environmentally friendly attributes of their hotel (e.g., water- and energyefficient fixtures/devices, durable service items, environmental cleaning, fresh air exchange system, organic foods) to the public through various information sources may improve such referents’ favorable perceptions of a green hotel. 5.2. Limitations and avenues for future research This study’s several weaknesses should be pointed out. First, the extended TPB model was tested in a green hotel context. Since the measurement items for the study constructs were particularly designed to be adequate in a green hotel setting, generalizing the

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current study findings to other types of hotels (e.g., regular hotels) should occur cautiously. Future research should apply the extended model to other types of hotels. Second, this study used a Web-based survey method to reach and sample a broader range of hotel customers. Future studies should examine green hotel customers’ post-decision-making process in an actual green hotel purchase setting to reduce extraneous variance and increase internal validity. That is, a replication of the study in a field setting is required in future studies. The third limitation lies in the response rate. While it is frequent to have a relatively low response rate, such as 10%, when using a web-based survey (e.g., Asperin, 2007; Han et al., 2009; Kim and Ok, 2009; Kim et al., 2009; Yang and Peterson, 2004), increasing the response rate would enhance the generalizability of the findings of this study. Thus, future studies should include more effective ways to induce Internet users’ active responses. Finally, while the current study satisfactorily extended the original TPB model, a broader variety of variables (e.g., personal characteristics, value, self-concepts) should be included in this study model. Such efforts would ensure more comprehensive understanding of green hotel customers’ decisionmaking process. Acknowledgement This study was supported by research funds from Dong-A University (No. 20090270). References Ajzen, I., 1985. From intentions to actions: a theory of planned behavior. In: Kuhl, J., Beskmann, J. (Eds.), Action Control: From Cognition to Behavior. Springer, New York, pp. 11–39. Ajzen, I., 1988. Attitude, Personality, and Behavior. Dorsey Press, Chicago, IL. Ajzen, I., 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50 (2), 179–211. Ajzen, I., 2009. A Theory of Planned Behavior. Retrieved from http://people. umass.edu/aizen/tpb.html on June 15, 2009. Ajzen, I., Driver, B.L., 1992. Application of the theory of planned behavior to leisure choice. Journal of Leisure Research 24 (3), 207–224. Ajzen, I., Fishbein, M., 1980. Understanding Attitude and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, NJ. Ajzen, I., Madden, T., 1986. Prediction of goal-directed behavior: attitude, intentions, and perceived behavioral control. Journal of Experimental Social Psychology 22, 453–474. Anderson, E.W, Fornell, C., Lehmann, D.R., 1994. Customer satisfaction, market share and profitability. Journal of Marketing 58 (3), 53–66. Asperin, 2007. Exploring Brand Personality Congruence: Measurement and Application in the Casual Dining Restaurant Industry. Unpublished doctoral dissertation, Kansas State University. Assael, H., 1984. Consumer Behavior and Marketing Action. Kent, Boston. Bagozzi, R.P., Yi, Y., 1988. On the evaluation of structural equation models. Journal of the Academy of Marketing Science 16, 74–94. Baloglu, S., McCleary, K.W., 1999. A model of destination image formation. Annals of Tourism Research 26 (4), 868–897. Bansal, H.S., Taylor, S.F., 1999. The service provider switching model (SPSM): a model of consumer switching behavior in the service industry. Journal of Service Research 2 (2), 200–218. Barsky, J.D., 1992. Customer satisfaction in the hotel industry: meaning and measurement. Hospitality Research Journal 16 (1), 51–73. Bitner, M.J., Hubbert, A.R., 1994. Encounter satisfaction versus overall satisfaction versus quality. In: Rust, R.T., Oliver, R.L. (Eds.), Service Quality: New Directions in Theory and Practice. Sage, London, pp. 72–94. Bloemer, J., Ruyter, K., 1998. On the relationship between store image, store satisfaction and store loyalty. European Journal of Marketing 32, 499–513. Chan, W., Wong, K., 2006. Estimation of weight of solid waste: newspapers in Hong Kong hotels. Journal of Hospitality & Tourism Research 30 (2), 231–245. Chang, M.K., 1998. Predicting unethical behavior: a comparison of the theory of reasoned action and theory of planned behavior. Journal of Business Ethics 17, 1825–1834. Cheng, S., Lam, T., Hsu, C.H.C., 2006. Negative word-of-mouth communication intention: an application of the theory of planned behavior. Journal of Hospitality & Tourism Research 30 (1), 95–116. Claver-Cortes, E., Molina-Azorin, J.F., Pereira-Moliner, J., 2007. The impact of strategic behaviors on hotel performance. International Journal of Contemporary Hospitality Management 19 (1), 6–20. Cronin, J.J., Taylor, S.A., 1992. Measuring service quality: a reexamination and extension. Journal of Marketing 56, 55–68.

668

H. Han, Y. Kim / International Journal of Hospitality Management 29 (2010) 659–668

Dalton, G.J., Lockington, D.A., Baldock, T.E., 2008. A survey of tourist attitudes to renewable energy supply in Australian hotel accommodation. Renewable Energy 33, 2174–2185. Diekmann, A., Preisendorfer, P., 2003. Green and Greenback: the behavioral effects of environmental attitudes in low-cost and high-cost situations. Rationality and Society 15 (4), 441–472. D’Souza, C., Taghian, M., 2005. Green advertising effects on attitude and choice of advertising themes. Asian Pacific Journal of Marketing and Logistics 17, 51–66. Department of Environmental Protection, 2001. Greener Accommodation: Saving Money and the Environment. Retrieved from http://www.dep.state.pa.us/ greenerAccom.pdf June 10, 2009. Environmentally Friendly Hotels, 2008, http://www.environmentallyfriendlyhotels. com on June 10, 2009. Fishbein, M.A., Ajzen, I., 1975. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading, MA. Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18, 39–50. Green Hotel Association, 2009. What are Green Hotels? Retrieved from http:// www.greenhotels.com on June 10, 2009. Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., 1998. Multivariate Data Analysis, Eighth ed. Prentice Hall, Upper Saddle River, NJ. Han, H., Back, K., 2008. Relationships among image congruence, consumption emotions, and customer loyalty in the lodging industry. Journal of Hospitality & Tourism Research 32 (4), 467–490. Han, H., Ryu, K., 2006. Moderating role of personal characteristics in forming restaurant customers’ behavioral intentions: an upscale restaurant setting. Journal of Hospitality & Leisure Marketing 15 (4), 25–53. Han, H., Hsu, L., Lee, J., 2009. Empirical investigation of the roles of attitudes toward green behaviors, overall image, gender, and age in hotel customers’ eco-friendly decision-making process. International Journal of Hospitality Management 28, 519–528. Han, H., Hsu, L., Sheu, C., 2010. Application of the theory of planned behavior to green hotel choice: testing the effect of environmental friendly activities. Tourism Management 31, 325–334. Keaveney, S.M., 1995. Customer switching behavior in service industries: an exploratory study. Journal of Marketing 59, 71–82. Kim, S., 2001. E-mail survey response rates: a review. Journal of Computer Mediated Communication 6 (2), 1–20. Kim, W., Ok, C., 2009. The effects of relational benefits of favorable inequity, affective commitment, and repurchase intention in full-service restaurants. Journal of Hospitality & Tourism Research 33 (2), 227–244. Kim, W., Ok, C., Canter, D.D., 2009. Contingency variables for customer share of visits to full-service restaurant. International Journal of Hospitality Management 29, 136–147. Kirk, D., 1995. Environmental management in hotels. International Journal of Contemporary Hospitality Management 7 (6), 3–8. Kotler, P., 1991. Marketing Management: Analysis, Planning, Implementation and Control. Prentice-Hall, Englewood Cliffs, NJ. Lam, T., Hsu, C.H.C., 2004. Theory of planned behavior: potential travelers from China. Journal of Hospitality & Tourism Research 28 (4), 463–482. Lam, T., Hsu, C.H.C., 2006. Predicting behavioral intention of choosing a travel destination. Tourism Management 27, 589–599. Lee, M.J., Back, K., 2007. Association members’ meeting participation behaviors: development of meeting participation model. Journal of Travel & Tourism Marketing 22 (2), 15–33. Lee, M.J., Back, K., 2009. Association meeting participation: a test of competing models. Journal of Travel Research 46, 300–310. Lewis, R.C., Chambers, R.E., 2000. Marketing Leadership in Hospitality. Wiley, New York.

Madden, T., Ellen, P.S., Ajzen, I., 1992. A comparison of the theory of planned behavior and the theory of reasoned action. Personality and Social Psychology Bulletin 18 (2), 3–9. Manaktola, K., Jauhari, V., 2007. Exploring consumer attitude and behavior towards green practices in the lodging industry in India. International Journal of Contemporary Hospitality Management 19 (5), 364–377. Morgan, R.M., Hunt, S.D., 1994. The commitment-trust theory of relationship marketing. Journal of Marketing 58, 20–38. Oh, H., Hsu, C.H.C., 2001. Volitional degrees of gambling behaviors. Annals of Tourism Research 28 (3), 618–637. Oliver, R.L., 1997. Satisfaction: A Behavioral Perspective on the Consumer. McGrawHill, New York. Oliver, R.L., Bearden, W.O., 1985. Crossover effects in the theory of reasoned action: a moderating influence attempt. Journal of Consumer Research 12 (3), 324–340. Parasuraman, A., Zeithaml, V.A., Berry, L.L., 1988. SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing 64 (1), 12–40. Park, J., 2003. Understanding Consumer Intention to Shop Online. Unpublished doctoral dissertation. University of Missouri, Columbia, MO. Perugini, M., Bagozzi, R.P., 2001. The role of desires and anticipated emotions in goal-directed behaviors: broadening and deepening the theory of planned behavior. British Journal of Social Psychology 40, 79–98. Pizam, A., 2009. Green hotels: a fad, ploy or fact of life? International Journal of Hospitality Management 28, 1 editorial. Prendergast, G., Ho, W., 2002. The influence of store image on store loyalty in Hong Kong’s quick service restaurant industry. Journal of Foodservice Business Research 5 (1), 45–59. Quellette, J.A., Wood, W., 1998. Habit and intention in everyday life: the multiple processes by which past behavior predicts future behavior. Psychology Bulletin 124, 54–74. Randall, D.M., Gibson, A.M., 1991. Taking stock: can the theory of reasoned action explain unethical conduct? Journal of Business Ethnics 8 (10), 873–882. Roberts, J.A., 1996. Green consumers in the 1990s: profile and implications for advertising. Journal of Business Research 36, 217–231. Ryu, K., Jang, S., 2006. Intention to experience local cuisine in a travel destination: the modified theory of reasoned action. Journal of Hospitality & Tourism Research 30 (4), 507–516. Ryu, K., Han, H., Kim, T., 2007. The relationships among overall quick-casual restaurant image, perceived value, customer satisfaction, and behavioral intentions. International Journal of Hospitality Management 27 (3), 459–469. Sheppard, B.H., Hartwick, J., Warshaw, P.R., 1988. The theory of reasoned action: a meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research 15, 325–343. Taylor, S.A., Baker, T.L., 1994. An assessment of the relationship between service quality and customer satisfaction in the formation of consumers’ purchase intentions. Journal of Retailing 70 (2), 163–178. Taylor, S., Todd, P., 1995. Assessing IT usage: the role of prior experience. MIS Quarterly 19, 561–568. Ting, D.H., 2004. Service quality and satisfaction perceptions: curvilinear and interaction effect. International Journal of Bank Marketing 22 (6), 407–420. Vallerand, R.J., Deshaies, P., Cuerrier, J., Pelletier, L.G., Mongeau, C., 1992. Ajzen and Fishbein’s theory of reasoned action as applied to moral behavior: a confirmatory analysis. Journal Personality and Social Psychology 62, 98–109. Vandermerwe, S., Oliff, M.D., 1990. Customers drive corporations green. Long Range Planning 23, 10–16. Wolfe, K.L., Shanklin, C.W., 2001. Environmental practices and management concerns of conference center administrations. Journal of Hospitality & Tourism Research 25 (2), 209–216. Yang, Z., Peterson, R.T., 2004. Customer perceived value, satisfaction, and loyalty: the role of switching costs. Psychology & Marketing 21 (10), 799–822. Yesawich, P., 1997. The myth of the loyal quest. Lodging Hospitality 53 (11), 18.