Why do customers switch? More satiated or less satisfied

International Journal of Hospitality Management 37 (2014) 159–170

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

Why do customers switch? More satiated or less satisfied Jeong-Yeol Park 1 , SooCheong (Shawn) Jang ∗ School of Hospitality and Tourism Management, Purdue University, Marriott Hall, 900 West State Street, West Lafayette, IN 47907, USA

a r t i c l e Keywords: Satiation Satisfaction Switching intention Service quality Atmospheric quality Food quality

i n f o

a b s t r a c t This study investigated whether restaurant customers switch to other restaurants due to satiation or diminished satisfaction. To achieve its objectives, this study extended well-known relationships among perceived quality, satisfaction, and behavioral intentions by including satiation and further examined the role of satiation on switching intentions. The results of this study showed that satiation was negatively associated with satisfaction but differed from diminished satisfaction. Further, the study results endorsed that satiation significantly influenced switching intentions, whereas satisfaction did not. This supports that customers switch to other restaurants not because they are less satisfied but because they are satiated. Regarding the relationship between perceived quality and satiation, service quality and food quality considerably reduced satiation levels, whereas physical surroundings were associated with an increase in satiation. In addition, satiation fully mediated the relationship between perceived quality and switching intentions, which emphasizes the importance of satiation in customer switching intentions. Findings and implications are provided in the main body of this paper. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction An ample body of literature has suggested that satisfaction is a direct antecedent of various forms of behavioral intentions, such as repurchase intentions and switching intentions (Oliver, 1993, 1999, 2009). Customer satisfaction can be increased when he or she receives high quality products or services. Thus, increasing satisfaction through better products or services is accepted as an essential step leading to customers’ repurchase intentions and/or reducing switching intensions. Contrary to the notion that satisfied customers show higher levels of repurchase intentions (Oliver, 1993, 1999, 2009), recent studies in consumer research have suggested that even satisfied customers may eventually stop repeat patronage (Verhoef, 2003; Agustin and Singh, 2005; Seiders et al., 2005; Jang and Feng, 2007). Academically, why satisfied consumers exhibit switching behaviors can be explained from two theoretical perspectives. The first is optimal arousal theory, which explains that people have a satisfactory (i.e., optimal) level of arousal to when they consume products or services and they switch when they cannot achieve this optimal level of arousal (Zuckerman, 1969). More specifically, consumers perceive a certain level of arousal after each consumption experience, and repeated consumption causes a decrease in arousal levels. Once perceived arousal falls short of its optimal level,

∗ Corresponding author. Tel.: +1 765 496 3610; fax: +1 765 494 0327. E-mail addresses: [email protected] (J.-Y. Park), [email protected] (S. Jang). 1 Tel.: +1 765 404 6788. 0278-4319/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijhm.2013.11.007

consumers switch brands or products (Zuckerman, 1969). Second, Mehrabian and Russell (1974) suggested that affect is important in determining approach and avoidance behaviors. In other words, changes in arousal levels and affect are critical in explaining switching behaviors. Holistically, considering both optimal arousal theory (Zuckerman, 1969) and Mehrabian and Russell’s (1974) conceptual framework, satisfaction may not fully account for customers’ switching behaviors because satisfaction is largely based on a cognitive evaluation (i.e., disconfirmation between expectations and actual performance), which may not reflect the change in arousal compared to previous consumption experiences (Oliver, 1999). Hence, to fully understand consumers’ switching behaviors, it is necessary to consider additional constructs, such as satiation, which can capture both affective evaluations and changes in arousal (Coombs and Avrunin, 1977). The term satiation refers to the decline in overall enjoyment after repeated consumption (Coombs and Avrunin, 1977; Redden, 2008; Galak et al., 2009). More specifically, satiation can be understood as a combination of two opposite affects (i.e., positive and negative affects) after repeated experiences, as well as reflecting the change in overall enjoyment at the same time. Thus, from the perspective that switching behaviors can be explained by changes in arousal level (Zuckerman, 1969) and affect (Mehrabian and Russell, 1974), satiation may provide a more clear explanation for consumers’ switching behaviors (Carrol et al., 1982; McAlister, 1982). Marketing studies have stressed the importance of controlling consumers’ switching behaviors because it reduces marketing costs significantly (Rust and Zahorik, 1993; Mittal and Lassar, 1998). In order to manage switching behaviors, efforts have been made to

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examine the role of satiation in various consumption experiences (Redden, 2008; Galak et al., 2009). Despite its importance and the possibility that satisfied customers may exhibit switching behaviors, previous literature and the restaurant industry have put their efforts toward identifying how to increase satisfaction in order to keep their customers’ loyalty levels high (Voss et al., 2010). The main goal of this study is to identify the role of satiation within the conceptual relationships among perceived quality, satisfaction, and behavioral intentions focusing on restaurant customers. More specifically, the objectives of this study are: (1) to investigate whether or not satiation can be seen as the direct opposite of satisfaction (i.e., diminished satisfaction), (2) to examine the roles of both satiation and satisfaction in explaining customers’ switching intentions, and (3) to identify which dimensions of perceived quality stimulate or reduce customers’ satiation in restaurants. The results of this study are expected to provide important implications to the academic literature, as well as the industry. Theoretically, this study would contribute to the current literature by arguing satiation is a different construct from satisfaction. Also, if the role of satiation in increasing customers’ switching intention is determined, then this study would provide evidence that satiation should be utilized when the main concern of a study is set to find antecedents of switching intentions. Empirically, the results of this study would help establish marketing strategies, suggesting that controlling satiation would be more important than increasing satisfaction in order to reduce customers’ switching intentions. Lastly, by identifying specific dimensions of perceived quality that increase or decrease satiation levels, restaurant marketers could utilize the results of this study to provide dining experiences that control customers’ satiation levels.

2. Literature review 2.1. Perceived quality–satisfaction–behavioral intentions Lazarus (1991) suggested that customers’ attitudes are linked to behavioral intentions via the following sequence: appraisal → emotional response → coping response. Later, Bagozzi (1992) applied this framework to explain how attitude is related to behavioral intentions. Specifically, individuals typically engage in activities (i.e., repurchasing a product) due to a desire to achieve certain outcomes. If one’s appraisal of a certain activity indicates that he or she achieved the planned outcome, desire–outcome fulfillment occurs. That is followed by an affective response, such as satisfaction (Bagozzi, 1992). After the affective response (i.e., satisfaction), a coping response follows. This coping response refers to the development of favorable behavioral intentions toward the product or switching to another product or brand in an effort to maintain or increase the level of satisfaction. The relationship among appraisal, emotional response, and coping has been applied to the service literature by extending the link as perceived quality → satisfaction → behavioral intentions (Gotlieb et al., 1994). In general, perceived quality refers to a consumer’s appraisal of a product’s overall excellence or superiority (Zeithaml, 1988; Verhoef, 2003; Agustin and Singh, 2005; Seiders et al., 2005). More specifically, consumers purchase a product (i.e., a desire–outcome unit) to receive a certain level of quality, which is the appraisal (Zuckerman, 1969; Parasuraman et al., 1988; Brown and Swartz, 1989; Bolton and Drew, 1991). As a sequence, satisfaction follows perceived quality as an affective response to the quality appraisal (McAlister, 1982; Bagozzi, 1992). Thus, satisfaction is defined as a cognitive and affective reaction to an experience or consumption (Rust and Oliver, 1994). In addition, satisfaction is an antecedent of behavioral intentions (i.e., a coping behavior),

which is accepted as the subjective probability that an individual will take a particular action (Fishbein and Ajzen, 1974, 1975). In this study, we did not hypothesize the relationships among perceived quality, satisfaction, and behavioral intentions (i.e., switching intentions). However, all the paths have been included in the analysis model and a separate model has been estimated to verify these relationships. 2.2. Satiation The term satiation refers to a decrease in overall enjoyment after repeated exposure to the same stimulus (Coombs and Avrunin, 1977; Oliver, 1993; Redden, 2008; Poor et al., 2012). From the perspective of appetite, satiation often refers to a physiological phenomenon such as feeling full, but it is more common to understand satiation as a psychological process (Cronin and Taylor, 1992; Redden and Galak, 2013). Satiation has been studied mostly as a phenomenon called “sensory-specific satiety.” For example, people tend to desire a particular food less after eating it, with little change in their desire for foods they have not eaten (Rolls et al., 1981; Carrol et al., 1982; McAlister, 1982). This drop in desire also extends to other foods with the same color, shape, or flavor (Rolls et al., 1982; Rust and Zahorik, 1993; Mittal and Lassar, 1998). Satiation is almost inevitable for every consumption or experience, such as food (Buttle and Burton, 2002; Redden, 2008), sensory experiences like massage (Nelson and Meyvis, 2008), or even social experiences like friendship (Galak et al., 2009). Generally, a repeated consumption experience or repeated exposure to the same stimulus is viewed as the main cause of satiation (Coombs and Avrunin, 1977). Satiation can be explained by the psychological concept known as two-factor theory (Berlyne, 1970). It posits that the affective consequences of exposure to a stimulus are a function of learning and satiation. Specifically, as an individual is exposed to a novel stimulus he or she begins to learn about it. During this learning process, individuals perceive positive affect, such as joy, excitement, and so forth. However, after repeatedly being exposed to or experiencing the same stimulus he or she starts to perceive negative affect, such as boredom or tedium. Positive affect is stronger during the early stages of exposure to a novel stimulus, but repeated exposure can cause negative affect to increase rapidly. In other words, satiation can be understood as an interaction between positive and negative affect (Berlyne, 1970) and people eventually become satiated once they are exposed to the same stimulus repeatedly. In addition to the notion that satiation is a psychological response, satiation can be understood from an economic perspective as well, for example the diminishing law of marginal utility. Generally, utility refers to the value that individuals gain from consumption or goods of all kinds, such as purchasing a toaster oven or dining at a restaurant (Greene and Baron, 2001). Marginal utility refers to the difference in utility between previous experiences and current consumption, and it is known to decrease or diminish as the frequency of consumption increases. During early consumption experiences utility increases for consumers, but the marginal utility diminishes as the frequency of consumption increases. Consumers’ overall utility is maximized when marginal utility becomes zero, and consumption beyond this point can cause a decrease in overall utility. In other words, consumers may perceive satiation once marginal utility becomes zero. According to these two viewpoints, the two-factor theory and the law of diminishing marginal utility, repeated consumption experiences cause satiation. In the restaurant industry, consumers are exposed to similar kinds of restaurants or even the same franchise (or chain) restaurants repeatedly. In other words, restaurant customers may perceive satiation after dining at the same restaurant repeatedly because they do not have much to learn about the specific

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restaurant (Berlyne, 1970) and their overall utility is decreased (Baucells and Sarin, 2010). 2.3. Comparing satiation and satisfaction As components related to customer behavioral intentions (i.e., repurchase and/or switching intentions) satiation and satisfaction are somewhat similar. Satisfaction is conceptualized as cognitive and affective reactions to a certain experience or form of consumption (Rust and Oliver, 1994). The cognitive component of satisfaction is based on Lewin’s (1938) expectancy-disconfirmation theory (Oliver, 1980; Churchill and Surprenant, 1982; Tse and Wilton, 1988; Yi, 1990). More specifically, satisfaction is derived when positive disconfirmation occurs (i.e., the post hoc performance exceeds the level of a priori expectations). Inversely, if the perceived performance falls short of expectations (i.e., a negative disconfirmation) the customer perceives dissatisfaction. This subjective assessment of disconfirmation causes satisfaction-related emotions, such as pleasure or excitement (Oliver, 1993). Similarly, satiation is a subjective psychological response (Redden, 2008; Redden and Galak, 2013) that incorporates both positive affect and negative affect (Berlyne, 1970). As mentioned earlier, satisfaction and satiation are known to influence consumers’ behaviors or behavioral intentions (Oliver, 1993; Taylor and Baker, 1994; Redden, 2008; Galak et al., 2009). For instance, satisfaction is known to drive consumers’ repurchase intentions because it reflects the affective response that determines consumers’ coping responses (Bagozzi, 1992). The positive relationship between satisfaction and behavioral intentions, such as repurchase intentions, has been extensively identified in marketing and restaurant studies (Bai et al., 2008; Liu and Jang, 2009; Heung and Gu, 2012). Along similar lines, satiation encourages consumers’ behavioral intentions, especially switching intentions, because satiated consumers do not receive additional utility from further consumption of a particular product (Baucells and Sarin, 2007, 2010). That is, the more satiated consumers become, the more they seek to switch to other products or brands. Even though these two constructs have common factors, it is not evident that they are really similar. In short, satiation is caused by repeated exposure to the same product (McAlister, 1982; Redden, 2008; Galak et al., 2009), whereas satisfaction is largely based on a cognitive evaluation of the disconfirmation between prior expectations and actual performance (Oliver and Bearden, 1985; Oliver, 1994). That means the primary causes for each construct are different. Furthermore, satiation reflects a change in overall enjoyment compared with previous enjoyment, as explained by the diminishing law of marginal utility (Baucells and Sarin, 2007; Galak et al., 2009; Redden and Haws, 2013). However, satisfaction is a rather transaction-specific evaluation (Cronin and Taylor, 1992). In sum, satisfaction and satiation share not only similarities (i.e., affective responses and impacts on behavioral intentions) but also have major differences (i.e., fundamental causes and the ability to mirror changes in affect). Thus, this study proposes that: H1 . Satiation is inter-correlated with satisfaction but it is not the same as diminished satisfaction. 2.4. Switching intentions Consumers’ switching behaviors, in general, have been considered as the opposite of consumer loyalty (Dick and Basu, 1994; Bolton et al., 2004). Specifically, switching behaviors have been conceptualized as an economic phenomenon where a customer ceases patronizing a particular supplier (Stewart, 1998), whereas switching intentions have been defined as the possibility of transferring existing transactions with a company to a competitor (Dekimpe

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et al., 1997; Bolton et al., 2004). Previous literature on loyalty classified antecedents of customers’ switching behaviors into two different groups: economic or cognitive and social/affective (Bolton et al., 2004; Geyskens and Steenkamp, 2000). Economic determinants focus on the economic value of the relationship with the firm, including aspects such as (economic) satisfaction, perceived price levels, and price-value ratios (Bolton and Lemon, 1999). Comparably, social determinants consider more social and affective aspects, such as trust and commitment (i.e., psychological attachment) (Verhoef, 2003; Verhoef et al., 2002). In terms of the service industry, various factors are known to cause customers’ switching intentions or behaviors. For instance, Keaveney (1995) identified eight extrinsic causal precursors to switching behaviors, such as price, inconvenience, core service failure, service encounter failure, response to service failure, competition, ethical problems, and involuntary switching. Comparably, customers’ intrinsic factors, such as satisfaction, are also an important variable in reducing switching intentions or behaviors (Athanassopoulos, 2000; Mittal and Kamakura, 2001; Antón et al., 2007; Jung and Yoon, 2012). That is, both intrinsic and extrinsic factors may affect switching intentions (Raju, 1984; Buttle and Burton, 2002). Among these various factors, satisfaction has been considered the most important component in decreasing customers’ switching intentions (Oliver, 1981; Bearden and Teel, 1983). For instance, Ganesh et al. (2000) argued that customers’ dissatisfaction was the most essential factor for switching service providers. In a similar vein, Han et al. (2011) argued that satisfaction always leads to low switching intentions, which stresses the negative relationship between satisfaction and switching intentions. 2.5. Relationship between satiation and switching intentions As noted earlier, satisfaction has been suggested as one of the most influential factors in reducing customers’ switching intentions. However, recent literature in consumer research suggests that even satisfied customers may also show high levels of switching intentions or switching behaviors (Verhoef, 2003; Agustin and Singh, 2005; Seiders et al., 2005; Jang and Feng, 2007). For instance, Steenkamp and Baumgartner (1992) argued that customers did not engage in switching behaviors due to dissatisfaction but in order to experience novel products. In other words, other constructs than mere satisfaction may influence customers’ switching intentions or behaviors. Consumers’ switching behaviors can be explained by the arousal level they perceive after a consumption experience (Zuckerman, 1979; Raju, 1980; Carrol et al., 1982). Specifically, individuals engage in consumption behaviors to be aroused, which is closely related to being excited, alert, or stimulated (Ladhari, 2008). Each individual has his or her own optimal level of arousal to continue the consumption behaviors (Zuckerman, 1979). However, after experiencing or consuming the same product repeatedly, perceived arousal from each consumption occasion decreases because customers start to perceive negative affect, such as boredom or tedium. This suggests that a gap exists between optimal arousal and perceived arousal as they consumption continues. That is, consumers seek novel alternatives to achieve arousal closer to their optimal level because this gap becomes greater after each consumption experience (Berlyne, 1960; Hansen, 1972; McAlister, 1982; O’Donohue and Geer, 1985). Additionally, consumers’ emotional responses are also known to result in switching behaviors (Mehrabian and Russell, 1974). Based on the conceptual framework suggested by Mehrabian and Russell (1974), stimuli (S) influence an individual’s emotional state (O), which in turn influences an approach or avoidance response (R). They posited that consumers experience emotional states in

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response to stimuli, such as arousal and pleasure. These emotional responses result in two contrasting behaviors: approach or avoidance. Approach behavior involves a desire to stay, explore, and affiliate with others in the environment (Booms and Bitner, 1982), whereas avoidance behavior includes escaping from the environment (Donovan and Rossiter, 1982). Furthermore, positive emotions stimulate approach behaviors, whereas negative emotions induce avoidance behaviors. These two concepts indicate that consumers’ switching behaviors or intentions are caused by changes in arousal level and emotions (i.e., positive and negative emotions). From the perspective that satiation reflects a change in overall enjoyment (Redden, 2008; Galak et al., 2009), switching can also be caused by increased negative emotions, such as satiation. Therefore, this study hypothesized: H2 . Satiation positively influences restaurant customers’ switching intentions. 2.6. Relationship between perceived quality and satiation The relationship between perceived quality and satiation can be explained by appraisal theory (Scherer, 1999), which claims that emotions are elicited and differentiated on the basis of a person’s subjective evaluation or appraisal of the personal significance of a situation, object, or event based on a number of dimensions or criteria (Scherer, 1999). Specifically, appraisal theories posit that emotions are determined by the evaluation or interpretation that arises after comparing one’s actual state with his or her desired state (Bagozzi et al., 1999). If customers perceive or appraise an experience as higher quality than expected, it induces positive emotions (i.e., joy, pleasure, and so forth). Based on the notion that satiation is an interaction between positive and negative affects (Berlyne, 1970), this results in a decrease in satiation due to increased positive affect (Namkung and Jang, 2007; Walsh et al., 2011). Thus, it can be inferred that experiencing high quality in a service setting would stimulate positive emotions, which would eventually suppress satiation. Satiation is known to occur at the level of sensory-specific stimuli (Rolls et al., 1982; Rolls, 1986). In a restaurant setting, many stimuli could influence customers’ emotional states. These stimuli encompass both tangible and intangible features of the restaurant. According to Jang and Namkung (2009) the dining experience is comprised of various stimuli that can be categorized into three different categories – product attributes (i.e., food quality), service aspects (i.e., service quality), and physical environment (i.e., atmospheric quality). As for product attributes, previous studies have noted that the most essential part of the restaurant experience is food quality and it influences customer satisfaction (Johns and Tyas, 1996; Kivela et al., 1999; Raajpoot, 2002). For instance, providing excellent quality food could result in reduced satiation, because positive appraisal of food quality produces positive emotions (Jang and Namkung, 2009). Thus, it can be hypothesized that: H3a . Perceived food quality negatively influences restaurant customers’ satiation levels. As for the service aspect, the role of service quality has been extensively researched in the service marketing literature (Parasuraman et al., 1988; Rust and Oliver, 1994). Because services in the hospitality industry rely heavily on service providers’ interpersonal skills (Nikolich and Sparks, 1995), the interaction between customer and service provider can have a substantial impact on consumers’ evaluations of restaurant services (Jang and Namkung, 2009). Similar to food quality, providing excellent service quality would reduce satiation as well. Extending the concept of appraisal theory (Bagozzi et al., 1999), high quality

Fig. 1. Conceptual model.

service stimulates customers’ positive emotions, which, in turn, reduces satiation. Thus, if a customer perceives excellent service quality in a restaurant, his or her satiation level would decrease. Based on these rationales, it can be hypothesized that: H3b . Perceived service quality negatively influences restaurant customers’ satiation levels. Generally, consumers desire two different values from consumption – utilitarian and hedonic value. In terms of the restaurant experience, utilitarian value includes fulfilling hunger or enjoying food without waiting, whereas hedonic value is comprised of fun, excitement, joy, or receiving high-class hospitality (Holbrook and Hirschman, 1982; Ha and Jang, 2010). That is, having high quality food and receiving prompt service are related to utilitarian value. On the other hand, atmospheric quality is closely associated with hedonic value (Kotler, 1973; Ha and Jang, 2010). Moreover, positive affect from hedonic value decreases more rapidly after continuous consumption compared to its utilitarian counterpart. Utilitarian value does not change much in terms of overall utility with additional consumption (Rolls, 1986; McSweeney and Swindell, 1999; Baucells and Sarin, 2010). It may be evident that positive emotions are derived from the positive appraisal of atmospheric quality. However, if customers perceive higher levels of atmospheric quality in a restaurant, they might feel satiated more quickly than those who perceive the atmospherics as lower quality because their arousal level would quickly fall short of its optimal level. Thus, this study hypothesized: H3c . Perceived atmospheric quality positively influences restaurant customers’ satiation levels. The theoretical and operational models for this study are depicted in Figs. 1 and 2. 3. Methodology 3.1. Research instrument The main objective of this study was to examine the role of satiation within the traditional relationships among perceived quality–satisfaction–behavioral intentions, especially when restaurant switching behaviors are involved. To fulfill this objective, a self-administered questionnaire was utilized. The survey included questions regarding perceived quality, customer satisfaction, satiation with dining experiences, switching intentions, and demographic information (i.e., gender, age, income, etc.). All items except demographic information were measured by a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree). In order to measure restaurant customers’ satiation, this study slightly modified variables developed by previous studies (Redden, 2008; Galak et al., 2013; Redden and Galak, 2013). In previous studies, satiation was measured after each consumption experience because these studies were conducted as lab experiments (e.g., Redden, 2008; Galak et al., 2013; Redden and Galak, 2013). However, in reality it is nearly impossible to track or record all past consumption experiences. Thus, it was necessary to ask about participants’ satiation levels compared to previous consumption

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Fig. 2. Proposed operational model.

experiences. For example, “the dining experience at this restaurant was not as enjoyable as previous visits,” “the dining experience at this restaurant was not as exciting as previous visits,” and “I feel tired of dining at this restaurant.” The measurement items for perceived service quality were adopted from the DINSERV scale (Stevens et al., 1995). We measured customers’ perceptions of food quality by assessing food related attributes (Raajpoot, 2002; Sulek and Hensley, 2004; Namkung and Jang, 2007). To capture the perceived quality of the physical environments, this study utilized measurement items used in previous studies (e.g., Bitner, 1992; Jang and Namkung, 2009). For satisfaction, this study utilized happiness, pleasantness, and overall satisfaction regarding the dining experience (e.g., Carpenter, 2008; Ha and Jang, 2013). Three measurement items were adopted from Antón et al. (2007), Dekimpe et al. (1997), and Hirschman (1970) for switching intentions. For example, “I would like to visit another restaurant,” “I would like to have a dining experience at another restaurant,” and “I would like to dine at another restaurant.” Measurement items used in this study are summarized in Appendix A. Correlations among the constructs used in this study are provided in Appendix B.

3.2. Sample and data collection To fulfill the research goal, this study defined the population as general restaurant customers in the U.S. The sample was set as customers who had visited either an upscale or casual dining restaurant multiple times. Other restaurants, such as fast food restaurants, were not included in this study. This is because customers are already habituated (McSweeney and Murphy, 2000), implying that they may not perceive overall enjoyment after eating meals at these restaurants. For data collection, a web-based survey was conducted by an online marketing research firm in the U.S. The company randomly distributed the survey questionnaire to their panel members. To recruit participants to the survey, the company sent out an invitation to their panel members nationwide. At the very beginning of the survey, we included one screening question, which asked whether or not a participant had multiple dining experiences at the same restaurant within two weeks. Two weeks was chosen to minimize possible memory bias. Also, there would not be many participants who visited the same upscale or casual dining

restaurant multiple times within a shorter period than two weeks. Those with no multiple consumption experiences were redirected to the end of the survey without answering further questions. 600 surveys were collected and 531 usable responses were utilized for analysis after eliminating errors and missing values. 3.3. Data analysis Data analysis followed the two-step SEM process suggested by Anderson and Gerbing (1988). In the first step, the fit and construct validity of the proposed measurement model were tested. Specifically, confirmatory factor analysis (CFA) was utilized to test the first hypothesis: the correlation and independence between satisfaction and satiation. Once the validity of all constructs was identified, structural equation modeling (SEM) was used to assess the overall fit of the proposed model and test hypotheses H2 and H3 . 4. Results 4.1. Sample profile Among 531 respondents, 53.3% were female and 47.3% were male. Further, the average age of respondents was 41.8 years old. In terms of marital status, 61% of respondents were married, whereas 23.0% were single. Regarding education level, 31.6% of the sample graduated from college/university (i.e., 4-year), and 33.1% graduated from a 2-year college. For occupation, 35% of respondents had white-collar jobs, while 21.3% were either retired or unemployed. For annual household income, 35% of respondents had annual household incomes between $50,000 and $100,000 (US Dollars). The average annual household income was $87,797. Detailed information on the descriptive statistics is provided in Table 1. 4.2. Common method bias Due to the fact that all data were self-reported and collected through the same questionnaire during the same period of time with cross-sectional research design, common method variance could be examined (Podsakoff and Organ, 1986; Podsakoff et al., 2003; MacKenzie and Podsakoff, 2012). In general, common method variance is known as a variance or bias that is attributed

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Table 1 Descriptive statistics. Variable

Descriptive

Frequency

Gender

Female Male

283 248

53.3% 46.7%

Age

18–19 20–29 30–39 40–49 50–59 Over 60

16 124 114 107 92 78

3.0% 23.4% 21.5% 20.2% 17.3% 14.7%

Marital status

Single Married Other

122 319 90

23.0% 60.1% 16.9%

Education

High school College (2 year) College/university (4 year) Graduate school and above

84 176 168 103

15.8% 33.1% 31.6% 19.4%

Occupation

White-collar Blue-collar Pink-collar Student Retired/unemployed Other

186 42 32 42 113 116

35.0% 7.9% 6.0% 7.9% 21.3% 21.8%

Income

$0–25,000 $25,001–50,000 $50,000–100,000 $100,001–150,000 $150,001–200,000 $200,001+

65 117 186 84 52 27

12.2% 22.0% 35.0% 15.8% 9.8% 5.1%

531

100.0%

Total

Percent

to the measurement method rather than the constructs of interest, and it may cause systematic measurement error and further bias the estimate of the true relationship among theoretical constructs. Method variance can either inflate or deflate observed relationships between constructs, thus leading to both Type I and Type II errors (Podsakoff and Organ, 1986; Avolio et al., 1991; Crampton and Wagner, 1994; Podsakoff et al., 2003; Spector, 2006; MacKenzie and Podsakoff, 2012). Common method variance can be detected by various types of analysis. For instance, if a substantial amount of common method variance is present, either (a) a single factor will emerge from the factor analysis, or (b) one general factor will account for the majority of the covariance among the variables (Greene and Organ, 1973; Schriesheim, 1979; Organ and Greene, 1981; Andersson and Bateman, 1997; Aulakh and Gencturk, 2000). To identify the common method variance, exploratory factor analysis by principal components factor was conducted. The results suggested that three distinctive factors (i.e., eigen value greater than 1) were identified and these three factors explained 73% of the total variance (Table 2). Second, CFA analysis with singlefactor model did not fit the data well (2 = 4356.400, df = 136, RMSEA = .242, CFI = .521, NFI = .514). Additionally, additional analysis to identify the presence of common method variance was conducted (Podsakoff et al., 2003; MacKenzie and Podsakoff, 2012). Specifically, items are allowed to load on their theoretical constructs, as well as on a latent common methods variance factor. The

results suggested that common method variance only accounted for 3.61% of variance. While the results of these analyses do not preclude the possibility of common method variance, they do suggest that common method variance is not of great concern and, thus, is unlikely to confound the interpretation of results.

4.3. Measurement model In order to assess the overall fit of the measurement model, confirmatory factor analysis (CFA) was conducted. In general, model 2 is sensitive to the sample size (Hair et al., 2010). Since this study analyzed more than 500 samples, 2 value was significant at a 1% level (2118 = 370.461, p < 0.000; 2 /df = 3.140). Thus, it was necessary to consider other goodness-of-fit indices. First of all, the root mean square error of approximation (RMSEA), which is generally utilized to correct for the tendency of the 2 goodness-of-fit test statistic to reject models with a large sample or a large number of observed variables, was .064. Previous literature suggested that a RMSEA value between .05 and .08 with a 95% confidence interval provides a good fit (Chen et al., 2008; Hair et al., 2010). Additionally, normed fit index (NFI), which is a ratio of the difference in the 2 value between the fitted model and a null model divided by the 2 value for the null model, was .959. Further, the comparative fit index (CFI) was .971. Overall, goodness of fit indicators (i.e., RMSEA, NFI, and CFI) suggested that the proposed model fit the data well. Convergent validity, which posits that indicators of a specific construct should converge or share a high proportion of variance in common, was tested by various methods. First, the size of the factor loadings was examined. It is commonly suggested that a .7 or higher standardized loading estimate indicates that each item converges on a common point (Hair et al., 2010). As shown in Table 3, all standardized factor loadings ranged from .793 to .940, implying that the measurement had convergent validity (Anderson and Gerbing, 1988). Second, the average variance extracted (AVE) was calculated, which is considered the mean variance extracted for the items loading on a construct and a summary indicator of convergence. The results showed that the AVEs ranged from .741 to .894, which are higher than the .5 cutoff. Third, the value for construct reliability (CR) was estimated. The cutoff value for construct reliability is .7 or higher (Hair et al., 2010). All CR values in this study were higher than .7, ranging from .745 to .954. Additionally, to check the internal consistency, this study used Cronbach’s alpha values for all six constructs. The alpha values ranged from .885 to .962, which exceeded the .70 minimum requirement for internal consistency as suggested by Nunnally (1978). In sum, convergent validity for each construct was confirmed. Discriminant validity, which tests whether or not a construct is truly distinct from other constructs, was measured by comparing the squared correlations of the other constructs to the AVEs (Fornell and Larcker, 1981). The results suggested that all AVE values were greater than the squared correlations between constructs, indicating adequate discriminant validity (Table 4). The results of CFA suggest that even though satiation and satisfaction are correlated they are distinct constructs, providing partial information for H1 .

Table 2 Common method variance test. Factor

1 2 3

Extraction sums of squared loadings

Rotation sums of squared loadings

Eigen value

% of variance

Cumulative %

Eigen value

% of variance

Cumulative %

8.836 3.165 1.315

49.091 17.585 7.305

49.091 66.676 73.981

5.124 4.873 3.320

28.464 27.070 18.447

28.464 55.534 73.981

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Table 3 CFA results. Cronbach’s ˛

Construct reliability

AVE

0.900

0.897

0.758

– 25.248*** 22.863***

0.885

0.872

0.741

0.898 0.908 0.888 0.783

– 31.973*** 30.418*** 23.569***

0.923

0.905

0.758

SATIS-1 SATIS-2 SATIS-3

0.940 0.958 0.938

– 46.806*** 42.934***

0.962

0.954

0.894

Satiation

SAT-1 SAT-2

0.944 0.868

– 22.585***

0.900

0.745

0.822

Switching intention

SI-1 SI-2 SI-3

0.911 0.798 0.934

– 15.689*** 23.735***

0.891

0.813

0.780

Constructs

Items

Std. loading

Food quality

FQ-1 FQ-2 FQ-3

0.860 0.888 0.864

t-Value 26.771*** 25.615***

Service quality

SQ-1 SQ-2 SQ-3

0.884 0.860 0.837

Atmospheric quality

AQ-1 AQ-2 AQ-3 AQ-4

Satisfaction

*** p < 0.001. ** p < 0.01. * p < .05.

Table 4 Discriminant validity.

Service quality Atmospheric quality Food quality Satisfaction Satiation Switching intention

1

2

3

4

5

6

(.741) .416 .520 .729 .162 .047

(.758) .623 .425 .027 .001

(.758) .521 .097 .026

(.894) .187 .051

(.822) .333

(.780)

Note: The diagonal numbers in parentheses represent average variance extracted (AVE). The remaining numbers show squared correlations.

4.4. Structural model Prior to confirming the model for further interpretation, we examined the correlation between constructs (Appendix B). The results suggested that there was high correlation among quality dimensions and satisfaction. In other words, there is a possibility of multicollinearity. In general, multicollinearity reduces the overall R2 and negatively affects the statistical significance tests of coefficients by inflating the variance of independent variables (Hair et al., 2010). Despite the possibility of multicollinearity, the

hypothesized paths were all significant and showed the same sign as the correlation coefficients, except the ones between satisfaction and switching intentions, atmospheric quality, and satiation. These can be interpreted as suppression effects, which can be described as instances when the “true” relationship between the dependent and independent variables has been hidden in the bivariate correlation (Hair et al., 2010). Thus, multicollinearity may not be a problem in this analysis. Structural equation modeling was used to test the hypotheses. The goodness-of-fit statistics of the proposed model suggested that the model reasonably fits the data (2120 = 388.441, 2 /df = 3.237, RMSEA = .065, CFI = .970, NFI = .957). The structural results of the proposed model with standardized path coefficients for significant relationships are depicted in Fig. 3. As reported in Table 5, satiation and satisfaction are significantly correlated. The sign of the correlation is negative. Thus, together with the discriminant analysis results, it is possible to conclude that satiation is negatively related to satisfaction but that the two are not the same, as proposed in H1 . Thus, H1 was supported. Further, the hypothesized relationship between satiation and switching intentions (H2 ) was supported. In other words, diners’

Table 5 SEM results.

H1 H2 H3a H3b H3c

Path coefficients

SE

t-Value

Result

Satiation Satiation FQ SQ AQ

↔ → → → →

Satisfaction Switching intention Satiation Satiation Satiation

−0.205 0.599 −0.236 −0.433 0.308

0.040 0.046 0.148 0.111 0.110

−3.563*** 11.608*** −2.507** −5.745*** 3.810***

Supported Supported Supported Supported Supported

Satisfaction FQ SQ AQ FQ SQ AQ

→ → → → → → →

Switching intention Satisfaction Satisfaction Satisfaction Switching intention Switching intention Switching intention

0.014 0.149 0.703 0.072 −0.065 0.003 0.119

0.123 0.063 0.051 0.046 0.123 0.135 0.093

0.150 2.586** 14.179*** 1.476 −0.736 0.026 1.544

– – – – – – –

Model fit: 2120 = 388.441, 2 /df = 3.237, RMSEA = .065, CFI = .970, NFI = .957. SE denotes standard error. *** p < 0.001. ** p < 0.01. * p < .05.

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Fig. 3. SEM results. 2118 = 370.461, 2 /df = 3.140, RMSEA = .064, CFI = .971, NFI = .959. *** for p < 0.001, ** for p < 0.01, and * for p < .05.

Table 6 Verification of perceived quality → satisfaction → switching intention.

Satisfaction FQ SQ AQ FQ SQ AQ

→ → → → → → →

Switching intention Satisfaction Satisfaction Satisfaction Switching intention Switching intention Switching intention

Path coefficient

SE

t-Value

−0.216 0.168 0.681 0.080 −0.176 −0.087 0.317

0.134 0.061 0.049 0.045 0.139 0.139 0.103

−2.056* 2.997** 13.985*** 1.674 −1.770 −0.798 3.696***

Model fit: 2 = 325.855, df = 93, p < .000, RMSEA = 0.069, CFI = .971, NFI = .960. SE denotes standard error. *** p < 0.001. ** p < 0.01. * p < .05.

switching intentions increased as their satiation levels increased. The hypothesized relationship between perceived quality and satiation (H3 ) was assessed by examining the influence of food quality, service quality, and atmospheric quality. Perceived food quality and service quality had significant negative effects on satiation, implying that H3a and H3b were supported. Further, the quality of the physical surroundings increased satiation. Thus, H3c was also supported. The relationship between perceived quality–satisfaction– behavioral intentions was not hypothesized, but these relationships were verified using a separate model (Table 6). The goodness of fit indicators suggested that the model fit the data well (2 = 325.855, df = 93, 2 /df = 3.504, RMSEA = 0.069, CFI = .971, NFI = .960). The results suggested that perceived quality dimensions significantly (i.e., food quality and service quality) or marginally (i.e., atmospheric quality) increased satisfaction. Further, satisfaction significantly decreased switching intentions.

4.5. The mediating role of satiation In general, a variable may be said to function as a mediator to the extent that it accounts for the relationship between the predictor and the criterion (Baron and Kenny, 1986). To test for mediation, it

Fig. 4. Mediation analysis.

was necessary to estimate the following three regression equations (Fig. 4): MV = 1 IV + 1

(1)

DV = 2 IV + 2

(2)

DV = 2 IV + ˇ1 MV + 2

(3)

To establish mediation, the following conditions must hold. First, the independent variable must affect the mediator in the first equation (i.e.,  1 = / 0 in Eq. (1)). Second, the independent variable must be shown to affect the dependent variable in the second equa/ 0 in Eq. (2)). Third, the mediator must affect the tion (i.e.,  2 = dependent variable in the third equation (i.e., ˇ1 = / 0 in Eq. (3)). If these conditions all hold in the predicted direction, then the effect of the independent variable on the dependent variable must be less in the third equation than in the second. Perfect mediation holds if the independent variable has no effect when the mediator is controlled (i.e.,  2 = 0 and ˇ1 = / 0 in Eq. (3)). The first condition (i.e., perceived quality → satiation) was satisfactory (i.e.,  FQ = −.292, p < .001;  SQ = −.444, p < .01;  AQ = .377, p < .01). The second condition (i.e., perceived quality → switching intention) was also satisfactory (i.e.,  SQ = −.276, p < .001;  AQ = .300, p < .01;  FQ = −.197, p < .05). When satiation was included as a mediator, the paths between perceived quality and switching intentions were not significant ( FQ = −.077,  SQ = −.013,  AQ = .130), but the path from satiation to switching intentions was significant (i.e., ˇSatiation = −.572, p < .01). This implies that satiation is fully mediated by the relationship between perceived quality and switching intentions. Lastly, the difference in 2 values between the constrained model (279 = 387.704) and the mediating model (278 = 247.353) was significant (21 = 140.351, p < .001), demonstrating that the mediating model was a significant improvement over the constrained model. This supports the

J.-Y. Park, S. Jang / International Journal of Hospitality Management 37 (2014) 159–170

significant mediating role of satiation in the relationship between perceived quality and switching intentions. The indirect effect of perceived quality on switching intentions was also examined to explain the role of satiation. The indirect effects of each quality dimension were: −.136 for food quality, −.227 for service quality and .172 for atmospheric quality. Further, the direct effect of satiation on switching intentions was .571, which was greater than the indirect effects of the quality dimensions. This result emphasizes the role of satiation as a mediating variable for switching, suggesting that managerial strategies should be directed toward reducing satiation by providing excellent quality food and service to prevent customers’ switching behaviors.

5. Conclusion 5.1. Summary and implications Until recently, many studies have verified the relationships among perceived quality–satisfaction–behavioral intentions in various service settings, such as restaurants (Namkung and Jang, 2007; Kim et al., 2009) and tourism destinations (Baker and Crompton, 2000). However, recent studies in consumer research pointed to the possibility that satisfied customers may still switch to other products or brands (Verhoef, 2003; Agustin and Singh, 2005; Seiders et al., 2005). Thus, this study intended to contribute to the literature by examining restaurant customers’ switching intentions through the concept of satiation. First, the results of confirmatory factor analysis and structural equation modeling suggested that satiation was significantly correlated with satisfaction, but the two constructs differ. Second, satiation significantly influenced consumers’ switching intentions, whereas satisfaction turned out not to impact switching intentions. This result indicated that satiation is a more effective variable in explaining consumers’ switching behaviors than satisfaction. Three, higher quality food and service reduces satiation, but physical surroundings were associated with an increase in satiation. Lastly, this study identified the significant role of satiation in the relationship between perceived quality and switching intentions. The results of this study provide important theoretical implications. First, some past studies asserted that satisfaction significantly reduces switching intentions (Bansal and Taylor, 1999; Antón et al., 2007). In this study, we verified this relationship by estimating a separate model, and found that satisfaction indeed reduced switching intentions (ˇsatisfaction = −.216, p < .05). However, the results of this study suggested satisfaction was not able to successfully explain switching intentions when satiation was present within the model. Satiation provided a clearer explanation of switching intentions (ˇsatiation = 0.599, p < .001) than satisfaction, and the impact of satiation on switching intentions was more than two times greater than satisfaction. Based on the perspective that satiated consumers may seek variety, or switch (Zuckerman, 1979), this study suggested that satiation should be considered when the main concern of a study is to identify the antecedents of switching intentions. Second, this study identified that satiation is a different construct from satisfaction. By definition, satisfaction includes two different dimensions – overall evaluation of the experience and emotional response after consumption (Oliver, 1999). Comparably, satiation was defined as a decrease in enjoyment after consumption (Redden, 2008; Galak et al., 2009). Even though both constructs were designed to account for consumers’ emotional or affective responses after consumption, this study showed that these two constructs measure different things. According to the balance theory (Bradburn, 1969), positive affect (i.e., excitement or pleasure) is not necessarily inversely correlated with its counterpart (i.e., boredom). Specifically, satisfaction measurement

167

items are utilized to evaluate positive affects (i.e., happiness and pleasantness) for specific consumption experiences, but satiation captures the decrease in overall enjoyment compared to previous consumption experiences. In other words, satisfaction may not fully capture the difference in overall enjoyment compared to previous consumption experiences, implying that satiation is not the direct opposite of satisfaction. This point adds an important implication to both satisfaction and satiation literature in that satiation and satisfaction should not be equated. Third, Keaveney (1995) suggested that there are eight different causes for customers’ switching behaviors, especially in the service industry (e.g., price, inconvenience, core service failure, and so forth). These can be identified as extrinsic causes, but the results of this study demonstrated that consumers’ intrinsic motivations (i.e., being satiated) can also influence switching intentions. Overall, this study provides an important theoretical implication by finding that satiation is a critical variable in explaining consumers’ switching intentions. In addition to theoretical implications, this study provides significant managerial implications as well. From the previous research, it was identified that customers’ revisiting (or revisit intentions) can be stimulated by increasing satisfaction. We strongly suggest that restaurant managers should look for ways to increase satisfaction, such as providing high quality food, service, and physical surroundings. However, the results of this study found that customers’ revisits stimulate satiation, which accelerates consumers’ switching intentions. Thus, we suggest that increasing satisfaction is an effective way to stimulate revisit intentions, and controlling satiation is an essential way to reduce switching intentions. In order to provide specific implications to increase or decrease satiation and eventually reduce customers’ switching intentions, this study included the three most common restaurant quality dimensions (i.e., food, service, and atmospheric quality). Among the three dimensions, food quality was found to reduce satiation. Due to the fact that food quality can be considered a critical aspect of restaurants in terms of both increasing satisfaction and reducing satiation, it is strongly suggested that restaurant marketers should make an effort to provide high quality food. More specifically, managers should consider presenting their food in a more visually appealing way as well as, such as using more fresh ingredients to increase customers’ positive affects or emotions toward the food. Another way to reduce satiation can be to provide a variety of food on the menu. It is known that variety can reduce customers’ satiation (Galak et al., 2009). For instance, providing many choices for side dishes that come with an entrée can provide a feeling of variety, which would reduce satiation. However, managers should not overlook the importance of food quality. By doing so, they would reduce satiation and eventually avoid losing precious customers to rival restaurants. High quality service was found to reduce satiation levels as well. More specifically, high quality service reduced satiation at double the rate of food quality. This implies the importance of human resources in reducing satiation. The restaurant industry is largely based on service. Compared to reduced service restaurants (e.g., fast food restaurants, coffee shops, etc.), casual or upscale restaurants heavily rely on human services. This means that from greeting a customer to the check out, restaurant customers cannot complete their dining experience without service. Simply, reducing satiation can be achieved by sincerely greeting customers. In other words, restaurant managers should consider ways to increase the positive emotions of customers in order to reduce satiation. For instance, well educated and kind employees could contribute to significantly decreasing satiation levels. Interestingly, this study found that atmospheric quality could increase satiation. This finding could provide an important message

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for owners and marketers of upscale restaurants. In general, operators of upscale restaurants should invest a significant amount of money in order to provide exceptional physical surroundings that can distinguish them from competitors. However, if operators are not able to frequently renovate dining environments, customers may feel satiated quickly after revisiting. Thus, to reduce satiation upscale restaurant managers should pay attention to the quality of the physical surroundings. Practically, restaurateurs can mitigate a decrease in satiation by changing table layouts, aromas, or small interior decorations. Thus, efforts based on the results of this study can contribute to maximizing restaurant operators’ profits, as well as maximizing customers’ utility. 5.2. Limitations and future research suggestions Even though this study provided new research findings regarding the role of satiation in consumers’ switching behaviors, limitations are unavoidable. First of all, it is still controversial whether customer exits are revocable or irrevocable. In other words, even though participants declared high intentions to switch, this does not imply that they will never revisit the same restaurant again. Thus, to understand restaurant customers, future studies should consider actual switching behaviors. Second, this study only hypothesized that dimensions of perceived quality were influential factors on satiation. However, there are other variables that can increase or decrease satiation. Potential moderators of the relationship between satiation and behavioral intentions may also exist. Thus, future studies can consider more predictors as well as possible moderating variables. Last but not least, even though we verified that common method variance was not a great concern in this study, the possibility of its presence cannot be completely neglected. Thus, future studies should consider a longitudinal design to avoid common method variance issues. Appendix A. Measurement items Construct

Label

Measurement items

Food quality

FQ-1

5.99 The presentation of the food was visually attractive The food I ate was fresh 6.13 The food I ate was served at the 6.18 appropriate temperature

1.12

5.88 The restaurant provided prompt and quick service The restaurant’s employees 5.97 could answer your questions well The overall quality of employee 6.10 service was good

1.19

5.61

1.24

5.50

1.22

5.57

1.20

5.92

1.03

6.08 SATIS-1 I was happy with the dining experience at this restaurant 6.06 SATIS-2 I was pleased with the dining experience at this restaurant SATIS-3 Overall, I was satisfied with the 6.12 dining experience at this restaurant

1.12

2.79

1.81

3.02

1.77

FQ-2 FQ-3

Service quality

SQ-1 SQ-2

SQ-3

Atmospheric quality

AQ-1 AQ-2 AQ-3 AQ-4

Satisfaction

Satiation

SAT-1

SAT-2

The interior design was visually appealing The table setting was visually appealing The decorations were visually appealing The overall quality of the dining environment was good

The most recent dining experience was not as enjoyable as previous visits The most recent dining experience was not as exciting as previous visits

Mean SD

1.00 0.98

1.12

1.08

1.11 1.09

Construct

Label Measurement items

Switching intention

SI-1

SI-2 SI-3

Mean SD

I would like to consider another restaurant for the next time I have considered dining at other restaurants next time I intend to switch to another restaurant next time

4.31

1.59

4.36

1.54

3.85

1.57

Note: SD denotes standard deviation.

Appendix B. Correlations among constructs

Service quality Atmospheric quality Food quality Satisfaction Satiation Switching intention

1

2

1 .645*** .721*** .854*** −.403*** −.216***

1 .789*** .652*** −.165*** −.035

3

4

5

1 −.433*** −.225***

1 .577***

6

1 .722*** −.311*** −.161***

1

***

p < 0.001. ** p < 0.01. * p < .05.

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