Loyalty - City University of New York
An Online Loyalty Model- The Differences between U.S. and Chinese Users
Author : Dr. TKP Leung- Associate Professor
Address : Department of Management and Marketing
The Hong Kong Polytechnic University
Hung Hom, Kowloon
Hong Kong
Tel no. : (852) 2766-7106
Fax no. : (852) 2766-7142
E-mail : msthomas@polyu.edu.hk
Author : Dr. Ricky Y.K. Chan- Associate Professor
Address : Department of Management and Marketing
The Hong Kong Polytechnic University
Hung Hom, Kowloon
Hong Kong
Tel no. : (852) 2766-7110
Fax no. : (852) 2766-7142
E-mail : msricky@polyu.edu.hk
Author : Prof. Ilan Alon (PhD)- Chair Professor of International Business
Address : Crummer Graduate School of Business,
Rollins College,
1000 Holt Ave.,
2722 Winter Park, Florida 32789
United States of America
Tel no. : (407) 646-1512
Fax no. : (407) 646-1550
E-mail : ialon@rollins.edu
Author: Miss Tam Tsui Wa, Christine- Teaching Associate
Address : Department of Business Administration
Hong Kong Institute of Vocational Education (Chai Wan)
A111, 1/F, Administrative Block, 30 Shing Tai Road, Chai Wan, Hong Kong
Tel : (852) 2595-8137
Fax : (852) 2505-4216
E-mail : twtam@vtc.edu.hk
The authors gratefully acknowledge funding support from the Hong Kong Polytechnic University. All correspondence should be directed to the first author.
ABSTRACT
Using Technology Acceptance Model as a theoretical framework and a combined sample of 570 individuals, this article contributes to the literature by developing, testing, and discussing seven hypotheses relating beliefs and attitudes to behavioral intentions in both the American and Chinese Internet contexts. Five latent variables are used: belief variables include ease of use, perceived usefulness, and enjoyment; the attitude variable includes e-Satisfaction; and the behavioral variable includes e-loyalty. The results largely confirm our model that perceived usefulness, enjoyment and e-Satisfaction positively affect e-loyalty, but reject the hypothesis that ease of use positively affects e-Loyalty. While no differences in cultural assigned meanings were found for the five latent variables, structural differences were found between the American and Chinese groups evaluations of e-Satisfaction and e-Loyalty.
INTRODUCTION
Davis (1986) pioneered the Theory of Reasoned Action (TRA) in social psychology, a widely studied theory to identify the determinants of consciously intended behaviors (Ajzen & Fishbei 1980; Fishbein & Ajzen, 1975). TRA’s most useful application is in Davis’s Technology Acceptance Model (TAM), used to hypothesize user acceptance of system and information technologies in the United States. Since then, numerous studies have demonstrated the academic vigor of TAM behavioral constructs: i.e., perceived usefulness (Igbaria et al. 1996; Philips et al. 1997); perceived ease of use (Dabholkar 1996; Dabholkar et al. 2003; Hoffman & Novak 1996); predicted user acceptance (Gegen & Straub 2000; Adams et al. 2001; Al-Gahtani & King 1999). Subsequent studies conducted by Davies et al. (1992) and others (Igbaria et al. 1994; Starbuck & Webster 1991; Webster & Martocchio 1992) have confirmed that enjoyment is another important behavioral construct that anticipates technology acceptance.
Nevertheless, relying solely on the above findings to generalize user behavior on the internet is problematic. First, these studies were conducted in different computer-related contexts. These contexts are very different from that of the internet environment, which requires high levels of spontaneous interactivity such as online searching and chat room engagement. Second, the aforementioned constructs have not been tested with e-satisfaction and e-loyalty that have become high priorities for scholars and practitioners to evaluate the intensely competitive dynamics of relatively mature markets (Anderson & Srinivasan 2003). Third, some preliminary studies in e-satisfaction and e-loyalty are inconsistent. For instance, Shankar et al. (2003) and Anderson & Srinivasan (2003) found a positive relationship between the two in the e-hospitality and an e-tailing sectors, whereas Miller-Williams (2002) and Taylor & Hunter (2003) found a negative relationship and a non-significant relationship in an e-business-to-business context. Finally, TAM research has not been conducted in the fastest growing but relatively untapped Chinese (PRC) internet market. According to China Daily, China is the second largest internet market globally after the U.S. with 111 million users. Its tiny penetration rate of 8.5% (Anonymous author, .cn accessed on May 17, 2006) showed that the internet is only at the introductory stage of its product life cycle. In contrast, internet usage is very mature in the U.S. According to Terry (1994), U.S. firms started using the internet in 1991 and public access was commercialized by AT&T Jens in September 1993. Usage has increased exponentially in the U.S. since then. Up to July 1995, 68.4% of worldwide internet domains were hosted in America (Ang, 1996). Given the relative paucity of TAM research, a specific study must be conducted to compare the differences between an emerging market like China and a mature market like America. This paper attempts to empirically test TAM’s influences on e-satisfaction and e-loyalty in the U.S. internet market and compare as well as moderate these influences in the Chinese internet market to determine its generalizability in these two important contexts.
OBJECTIVES
This study is an initial examination to model TAM behavioral constructs on e-satisfaction and e-loyalty in a comparative online environment in the U.S. and the Chinese (PRC) market. Specifically, it
• Examines the psychometric properties of TAM constructs on e-satisfaction and e-loyalty;
• Evaluates the relationships between e-satisfaction and e-loyalty;
• Compares the differences of TAM constructs, e-satisfaction and e-loyalty in the U.S. and the Chinese (PRC) markets;
• Provides important comparative insights of online consumer behaviors between the two countries; &
• Suggests strategies for e-commerce operators to effectively design their internet portals or e-commerce websites in the U.S. and the Chinese (PRC) markets.
THEORETICAL FRAMEWORK OF AN ONLINE LOYALTY MODEL
The Theory of Reasoned Action (TRA) (Ajzen & Fishbein 1980; Fishbein & Ajzen 1975) conceptual framework advocates for distinctions among beliefs, attitudes, intentions, and behaviors. According to TRA, a person’s performance of a specific behavior is influenced by his or her behavioral intention (BI); that intention is jointly determined by a person’s attitude (A) and subjective norms (SN) (Al-Gahtani & King 1999). A person’s A and SN are then influenced by his / her beliefs (B). Davis (1989) pioneered the TRA framework in his Technology Acceptance Model (TAM), which focused on modeling user acceptance of system and information technologies. However, Davis (1989) found no significant relationship between the BI and SN constructs; therefore, he excluded the SN construct and only concluded perceived usefulness and perceived ease of use as definitive belief variables influencing A, BI and user acceptance. Based on the work of Fishbein & Ajzen (1975), Thompson, et al. (1991) and others (Al-Gahtani & King 1999; Davis 1993) posited that BI actually deals with future behavior because it is “the person’s subjective probability that he will perform the behavior in question,” whilst user acceptance and usage are behaviors that have already taken place (Fishbein and Ajzen 1975, p. 12). Therefore, Thompson et al. 1991 and others (Davis 1993; King 1999) considered it necessary to drop BI and link attitude directly to actual behavior. Also, Davies et al. (1992) and others (Malone 1981a, 1981b; Holbrook et al. 1984; Igbaria et al. 1994) subsequently found that enjoyment is another influential belief variable on usage intention and behaviors. As a result, three belief constructs are adapted in this study: perceived ease of use, perceived usefulness, and enjoyment.
The attitude construct of this study is captured by e-satisfaction, which is deemed to be appropriate because user experience and judgment can be formed in a context of using an internet portal at the same time (Fournier & Mick 1999; Fornell 1992), whereas the behavioral construct is represented by e-loyalty. The understanding of e-satisfaction and e-loyalty are important to e-commerce and internet operators because retention of loyal customers becomes a crucial, profit-related task in a relatively mature market such as America (Anderson & Srinivasan 2003) and in an emerging market such as China. The conceptualization of the online loyalty model and its relevant hypotheses are explained in the literature review.
LITERATURE REVIEW
e- Loyalty
Customer loyalty, regardless whether it is online (Deighton 1996; Blattberg & Deighton 1996; Hart 1999; Lidsky 1999; Poleretzky 1999) or offline (Bolton 1998; Bolton et al. 2000; Bolton & Lemon 1999), has attracted extensive attention. Both streams of research, nevertheless, come to a consensus that customer loyalty is won through consistent delivery of superior customer experiences (Reichheld & Schefter 2000). Ideally, loyal customers will then convey their enjoyable experiences to many new customers and provide recommendations to them. The ultimate positive outcome of this chain would be a substantial reduction of a given company’s promotional expenses (Reichheld & Schefter 2000). Current customers thus become part of a company’s value chain as they generate free word-of-mouth advertising, capturing extra sales and contributing heavily to the profitability of the service provider (Anderson & Sullivan 1993; Anderson & Mittal 2000). Also, the relative costs of customer retention are substantially less than those of acquisition (Fornell & Wernerfelt 1987; Reichheld 1996). An effective loyalty program is found to positively affect both customer retention and customer share development (Verhoef 2003). The major difference between online and offline customer loyalty, however, is that the rapidly changing pace of internet technology motivates market operators to consistently upgrade their products and services in order to keep their customers loyal (Reichheld & Schefter 2000). Retention of e-customers can only be accomplished by satisfying the perceptions of using a particular portal (Reinchheld & Schefter 2000).
Various definitions describe off-line loyalty as consumer repeat purchasing of the same brand without searching for any brand-related information (Tellis 1988; Newman & Werbel 1973). These definitions, however, suffer from the problem of recording what the consumer does whilst not examining the psychological meaning of loyalty (Oliver 1999). In fact, as Oliver (1999, p. 36) suggests, repeat purchasing is not “true loyalty”. For instance, business operators have been using so-called “loyalty programs” to stimulate re-purchase behavior (Mittal & Lassar 1998). However, 66% of the consumers who participated in a survey admitted that they joined a loyalty program because of the attractive discount offerings, not because of loyalty to a particular brand (The Credit Union Journal Sept 6 2004). Also, with many companies now participating in similar loyalty programs, the issues of whether they can create legitimate competitive advantage and strengthen loyalty relationships between customers and companies emerge (Barnes 2001). Verhoef (2003) and others (Braum, 2002; Rust et al. 2000) proposed that an effective loyalty program should include economic incentives (i.e. discount offerings) creation of close customer ties (i.e. socially oriented programs) to generate customer retention and share development.
Oliver (1999, p. 34) followed a consumer attitude development perspective and defined loyalty as “a deeply held commitment to re-buy or re-patronize a preferred product / service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior.” He further suggested that change in consumer loyalty attitude can be captured in a four-stage continuum: (1) Cognitive loyalty focuses on the brand’s performance aspects, such as product reliability, durability, service effectiveness, efficiency and empathy; (2) affective loyalty is directed toward the brand’s likeability, credibility, trustworthy and judgment; (3) conative loyalty is experienced when the consumer focuses on wanting to re-buy the brand based on social approval, fun, warmth and self respect; and (4) action loyalty is a commitment to the action of re-buying (Oliver 1999; Keller 1993 & 2002).
E-Loyalty is defined as the “consumer’s favorable attitude toward an electronic business resulting in repeat buying behavior” (Anderson & Srinivasan 2003, p. 124). Mathwick (2002) recognized that an internet operator normally builds some cognitive loyalty elements into his/her portal by motivating a group of virtual community members to have a feeling that they “have to” involve in navigating a particular operator’s portal (p. 50), or they will otherwise miss the portal’s distinctive aspects, such as enhanced escapism, intrinsic enjoyment, and entertainment value. Affective loyalty elements are established through community-building infrastructure such as chat rooms, bulletin boards, and sponsored interactive events, locations that afford an opportunity for these members to open up and offer help. These infrastructures are necessary to create a purposeful and attractive community vision (McWilliam 2000). Also, stripped of the constraints of social status and physical appearance, the anonymity of online interaction appears to operate as a social likeability agent that allows people to act more spontaneously than they might create a warmth of feeling in face-to-face interactions (Mathwick 2002; Deighton 1996; Watson et al. 1998). The social approval, self-respect, excitement and warmth of feeling embedded in virtual communication through the portal motivate the community members to enter the conative stage, the resultant online relationships appear to be as intimate, and rich in emotional support as any one experienced in off-line environments (Mathwick 2002; Fischer et al. 1996; Rheigold 1993; Tambyah 1996; Poleretzky 1999). These online relationships will transform active contributors into “the most attractive purchasers” or “action loyalty customers” (Hagel & Armstrong 1997, p. 22). These community members tend to consolidate their purchases with one primary supplier to such an extent that purchasing from the supplier’s site becomes part of their daily routine (Reinchheld & Schefter 2000). The above arguments imply that the internet is an excellent tool to test the concept of “true loyalty” advocated by Oliver (1999, p. 36), because the loyalty continuum suggested by Mathwick (2002) and others (Fischer et al. 1996; Rheigold 1993; Tambyah 1996; Poleretzky 1999) does not involve any financial incentive to motivate users’ re-visits or re-purchases from internet at the final, action loyalty, stage.
e- Satisfaction
Offline customer satisfaction is defined as a “pleasurable fulfillment” (Oliver 1999, p.34) and “an overall evaluation based on the total purchase and consumption experience with a good or service over time” (Anderson et al. 1994, p.54). The management of it has become a strategic consideration for most firms (Anderson & Mattal 2002; Anderson et al. 1994; Coyne 1989; Oliver 1997; Rust et al. 1994). Research has shown that satisfied customers are less motivated than dissatisfied customers to engage in searching and considering a set of brands (Anderson & Mittal 2000; Srinivasan & Ratchford 1991). A delighted customer has little incentive to consider other brands at all (Anderson & Mittal 2000) because the perceived risk associated with choosing a familiar service provider is less than the perceived risk associated with choosing an unfamiliar service provider or a familiar provider who has not met expectations in previous experiences. Evidence from Sweden has suggested that industries tend to generate a higher level of customer satisfaction if they are heavily dependent on satisfaction for repeat business. Products and services that provide high customer satisfaction are less vulnerable to competition. Also, they have a higher proportion of repeat business and higher gross margins (Fornell 1992). Customer satisfaction must be managed with extreme care because somewhat and completely dissatisfied customers are equally unlikely to repurchase a particular firm’s product and services (Anderson & Mittal 2000; Mittal & Kamakura 2001).
Customer satisfaction with off-line delivered products and services has been empirically tested as influencing a buyer’s decision to continue a relationship (Fornell 1992; Hirschman 1970), and conversely reduces unfavorable word-of-mouth and the possibility of divorce from that relationship (Richins 1983; Singh 1988). According to Oliver (1989) and others (Bolton & Lemon 1999), consumers hold pre-consumption product standards, observe and compare actual product performance with these standards, conceptualize confirmation or disconfirmation perceptions, combine these perceptions with standards levels, and then generate summary satisfaction judgments. Customers’ perception of confirmed standards lead to moderate satisfaction, positively disconfirmed (exceeded) standards lead to high satisfaction, and negatively disconfirmed (underachieved) standards lead to dissatisfaction (Churchill & Surprenant 1982; Oliver 1980). However, an overemphasis on pre-consumption standards, feedback from initial product performance, and the formulaic comparison of the two has depicted satisfaction as a cold, cognitive and meaning-deficient phenomenon (Fournier & Mick 1999). Babin & Griffin (1998) argued that satisfaction and dissatisfaction are the human emotional results of a cognitive appraisal process (Van Overwalle et al. 1995). Jones & Sasser Jr. (1995) also noted the importance of the underlying delivery process of a particular product and service. More recently, other commentators (Deighton 1992; Iacobucci et al. 1994; Tse et al. 1990) have proposed that customer satisfaction is a dynamic rather than a static evaluative process: it is a context-dependent process consisting of a multi-modal blend of motivations, cognitions, emotions, and meanings transformed during progressive customer product usage experiences and encounters with an organization (Fournier & Mick 1999; Fornell 1992). Thus, extra-emotional values must be created to achieve total customer satisfaction (Barnes 2001). Griffin (1998) concurred that future satisfaction research should be directed at a better delineation of emotions.
E-Satisfaction is defined as the contentment of a customer with respect to his or her prior purchasing experience with a given electronic commerce firm (Anderson & Srinivasan 2003). Preliminary research has suggested that site security, provision of information or contents, site accessibility and design (Kalyanam & Mcintyre 2002; Szymanski and Hise 2000) are important determinants to generate e-satisfaction. Anderson & Srinivasan (2003) agreed that convenient features like flexibility motivate consumers to shop on the internet. ForeSee Results (2004) found that high quality information or contents provided by a government portal positively affect customer e-satisfaction, which may be due to its compliance with a basic fundamental marketing principle: it narrowly confines its provision of essential governmental news or policies to its targeted customers (Reichheld and Schefter 2000)! These determinants provide some insights on the width and depth of product information contents. Numerous authors (Mittal and Lassar 1998; Oliver 1997; Mittal and Kamakura 2001) have reported that customer satisfaction is a major determinant of loyalty in an off-line environment. Nevertheless, reports of the same in an online environment are very inconsistent. For instance, Miller-Williams (2002) found a negative relationship between them in a business-to-customer environment whilst Taylor & Hunter (2003) found a non-significant relationship in a business-to-business environment. As such, we have to establish a definitive relationship between customer e-satisfaction and e-loyalty in an internet portal. Hence, we hypothesize:
H1: A user’s e-satisfaction positively influences his/ her e-loyalty in an internet portal environment.
Chau et al (2002) noted that American and Chinese internet users have some fundamentally different perceptions of e-satisfaction based on product performance. American users feel more satisfied if a portal has superb ability to search for information, while Chinese users concentrate more on a portal’s provision of abundance of Chinese information in terms of breath and depth and its ability to initialize social communication. These fundamental differences may lead to different perception of e-loyalty. Thus, we propose:
H1a: American and Chinese users have different perception of e-satisfaction on e-loyalty.
Perceived usefulness
Davies (1989) defined perceived usefulness as “the degree to which a person believes that using a particular system would enhance his or her job performance” (p. 320). According to Davies (1989), the key concept is the definition of useful: “capable of being used advantageously” (p. 320). Employees in an organization who are capable of being used effectively to improve its performance will always be rewarded in terms of promotions, bonuses and other means (Pfeffer 1982; Schein 1980). If the same logic is applied to a computer environment, it can be argued that a system high in perceived usefulness is one in which a user has motivation to generate a positive use-performance relationship (Davies 1989). As such, the more often a user finds the information required to his/her advantage, the higher the chance he/she perceives the portal as useful (Atkinson & Kydd 1997). In other words, searching for relevant information to speedily improve work performance is the key usefulness factor.
A user’s motivation can be intrinsic or extrinsic (Vallerand 1997). Intrinsic motivation refers to the performance of an activity for no apparent reinforcement other than the process of performing the activity itself (Teo et al 1999), while extrinsic motivation relates to the incentive of performing a behavior to achieve specific goals / rewards (Deci and Ryan 1987). Venkatesh (2000) and others (Vankatesh & Speier 2000) believed that the perceived-usefulness construct captures the extrinsic motivation in the original TAM model. This construct has been repeatedly confirmed by Davies (1989) and others (Davies et al. 1989; Igberia & Nachman 1990; Szajina 1996) as a vital determinant on computer usage. The question that emerges here is whether this construct be applied to predict user behavior on the internet such as satisfaction and loyalty!
Very limited research has been conducted to investigate the influence of perceived usefulness on e-satisfaction and e-loyalty. Nevertheless, Burke (1997) and others (Dabholkar 1996) noted that motivation generates internet acceptance. Internet users value comprehensive search capabilities within a particular portal and navigate in the internet by moving through linkages to other websites (Evans & Wurster 1999). Perry (1995) also realized that the ability of consumers to have easy and extensive access to useful and accurate information such as textual information, statistics, graphics, and audio features and the ability to interact with three-dimensional product images can further increase usage and satisfaction. Devaraj et al. (2003) noted that the extent of time and effort that a user puts in may affect his or her satisfaction on a particular website. Szajina (1996) argued that when an individual becomes more experienced in using an information technology, usefulness directly determines usage behavior. Thus, we hypothesize that:
H2: A user’s perceived usefulness positively influence his/ her e-satisfaction in an internet portal environment.
American users prefer freely navigating the internet through sites with good layout, respond speed, uncluttered screens, and uncomplicated and useful search paths (Hodge 1997; Patrick 1997; Szymanski & Hise 1999). In contrast, the preferences of Chinese users are limited by the Chinese political and social environments. For instance, the State Council requires all internet content providers (ICPs) to provide upon demand all site-content as well as records of users who have visited their sites for up to 60 days prior to the requests. ICPs are also responsible for policing their own sites for subversive materials (Anonymous author, Agence France Presse 2000). As such, navigation on the internet in China is not truly free. In fact, ICPs in China tend to provide politically insensitive news, and Chinese users are limited to site accessibility of securitized news and sports information, emails, and chat rooms (Zhu & Zhou 2002). Also, the Internet Society of China openly advocated to its members that they should not produce, disseminate or spread information that jeopardizes state security, harms social stability or violates laws and regulations and societal morality (Anonymous author, .cn April 2006). In addition, collective cultural traits imply that Chinese consumers normally respond to aggregated content for specific interests through the internet (Hofstede 1991; Lovelock 2000). For instance, the Chinese government’s recent ban on Nike’s advertising commercial featuring NBA star LeBron James defeating a Chinese national cultural symbol and a source of national pride (i.e., a dragon and a martial arts master) created fiery discussions online (Li 2005). As such, the Chinese political and cultural environments restrict Chinese internet users’ exposure to information which may affect their perceptions of usefulness and satisfaction. Thus, we propose:
H2a: American and Chinese users have different perception of perceived usefulness on e-satisfaction.
The more a user perceives a particular portal as useful, the higher the chance that he or she will re-visit the same portal. Hence, we hypothesize that:
H3: A user’s perceived usefulness influences his/ her e-loyalty in an internet portal environment.
Hart (1999) and Lidsky (1999) noted that switching behaviors usually occur if American internet consumers find a portal which has better performance, while Chau et al. (2002) perceived that Chinese users normally attach to a particular portal once they have received benefits from it. Thus, we propose:
H3a: American and Chinese users have different perception of perceived usefulness on loyalty in an internet environment.
Perceived ease of use
Davies (1989) defined perceived ease of use as “the degree to which a person believes that using a particular system would be free from effort” (p. 320). Swanson (1987) and others (Gefen & Straub 2000) advocated that perceived ease of use measures user assessments of ease of learning which is characterized as ease of control, ease of selection and ease of obtaining information. It influences an individual’s perceived behavioral ability to use information technology (Davis et al. 1989; Hill et al. 1987; Swanson 1982 & 1987; Ajzen 1998). In other words, the less difficulty an individual experiences in performing his / her tasks through a given portal, the more likely it is that an individual can have an enjoyable experience and a good impression of a website (Dabholkar 1996; Hoffman and Novak 1996).
However, many studies of perceived ease of use on intention to use IT generate contradictory results. Some studies suggest that perceived ease of use positively influences intention to use (Davies et al. 1989; Venkatesh and Davies 1994; Rose and Straub 1999), while others propose a negative relationship between them (Davies 1989; Szajina 1994). A potential reason for these inconsistent results may be due to the use of different assumptions in users’ motivations. In fact, the use of a system within an occupational context is hardly voluntary (Adams et al. 2001). For instance, the use of transaction processing and reporting systems is mandatory; its users are “captive,” and factors such as ease of use and usefulness may have very little influence on their intention to use (Adams et al. 2001, p. 233). Thus, captive usage makes the construct of perceived ease of use less likely to uncover relationships with other variables such as intention to use. As such, perceived ease of use must be tested in a genuinely voluntary environment. This line of argument implies that perceived ease of use actually reflect users’ intrinsic motivations to use a particular system (Teo et al. 1999; Venkatesh 2000).
As mentioned above, intrinsic motivation refers to the performance of an activity for no apparent reinforcement other than the process of performing the activity itself (Teo et al. 1999). Use of the internet, in a way, reflects that intrinsic characteristic. Its ease of access and control aspects motivate its users to enter a state of telepresence, which is defined as “the ability to use telecommunication technologies to interactively explore and experience events at a remote site” (Schrum & Berenfeld 1997, p. 46). Users can interact freely and speedily with a particular portal such as Google to access numerous information sources and to download information (Evans & Wurster 1999). In fact, users can actually use a particular internet portal at will without confining themselves to any particular work environment. Only under such an environment can perceived ease of use be honestly reflected. Therefore, we expect that the less difficult it is to perform the tasks involved in searching from a portal, the more likely telepresences and flows can be felt, resulting in an enjoyable experience, which can lead to greater satisfaction with the process of using that portal. Hence, we hypothesize:
H4: A user’s perceived ease of use positively influences his/ her e-satisfaction in an internet portal environment.
Other hidden factors related to perceived ease of use in a cross cultural internet setting are users’ skill levels and their language capacities. Internet technology is relatively mature in America and therefore users in this market do not experience any difficulty in using the internet. They prefer to navigate on a browser that offers a single, integrated, point-and-click interface to a wide variety of information sources (Chau et al. 2002). Also, their use of English language only allows them to freely communicate on the internet. In contrast, Chinese users like using a browser that requires fewer technical skills to navigate (China Internet Network Information Centre 2002) and most of them can only communicate in Chinese. Thus, we propose:
H4a: American and Chinese users have different perception of perceived ease of use on e-satisfaction.
Ease of use experiences can be established through users e-mail and chat room activities, among other things (Vienkatesh 2000; Szajina 1996), to establish a favorable attitude in using a particular portal. Oliver (1999) and others (Keller 1993 & 2002) confirmed that this favorable attitude helps establish loyalty in an off-line environment. Nevertheless, this relationship has not been tested in an online environment. Thus we hypothesize that:
H5: A user’s perceived ease of use positively influences his/ her e-loyalty in an internet portal environment.
According to Poleretzky (1999), American and Chinese users display different levels of trust on the internet. American users have relatively lower trust than their Chinese counterparts. Specifically, American users may shift to another internet portal whenever they find a portal with better performance. Google’s overtaking of Yahoo’s market leader position is a typical example, even though the latter has a relatively long history. In contrast, Poleretzky (1999) recognized that Chinese users will be loyal to a portal if that portal can provide ample, useful Chinese content. Thus, we propose:
H5a: American and Chinese users have different perceptions of perceived ease of use on loyalty in an internet environment.
Perceived enjoyment
It is defined as “the extent to which the activity of using the system is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated” (Davies et al. 1992, p. 1112). Studies on the role of enjoyment in workplace computing (Webster 1989; Webster & Martocchio 1992), computer games (Malone 1981a, 1981b; Holbrook et al. 1984) and word processing program (Davies et al. 1992) suggested that enjoyment is an influential construct on usage intention and behaviors. It is an intrinsic motivation which partially determines an individual’s behavioral response of how much fun he or she will have after doing the activity (Atkinson & Kydd 1997; Turley & Milliman 2000), which is described as spontaneous, curious, inventive and imaginative (Barnett 1991; Lieberman 1977). For instance, it was found that university students reported better moods and involvement in a computer training task when it was labeled as “play” rather than “work” (Webster et al. 1990). So a person who is playful when using the computer is likely to exhibit more of these characteristics than one who is not. Webster & Martocchio (1992) found a positive relationship between user enjoyment and satisfaction in a micro-computer environment. In other words, an individual will display more creative and exploratory behaviors because he/she enjoys the interactions with a computer system (Glynn & Webster 1992). In fact, one of the most commonly used world-wide-web (www) interfaces, Internet Explorer, uses elaborate graphics, animation, and instant feedback that are very similar to those used in computer games to gear an individual into a predisposition of playfulness. As such, those who are more playful are intrinsically motivated to use the www for pure enjoyment rather than for its usefulness (Atkinson & Kydd 1997). The design of many online games is based on a creation of users’ feelings of enjoyment and satisfaction because they can play with colleagues around the world in real time without physical restrictions (Oliveira & Henderson 2003). In an internet environment, Liaw (2002) also found that enjoyment positively influences intention to use. Thus, we hypothesize:
H6: A user’s perceived enjoyment positively influences his/ her satisfaction in an internet portal environment.
Some cultural evidence shows that drastic differences exist between American and Chinese users’ perceptions of enjoyment. American consumers, with their highly individualistic natures, emphasize individual enjoyment when they purchase, while Chinese consumers, with their dominantly collectivistic traits, incline to share enjoyment according to some social norms (Hofstede 1980; Mattila 1999). Thus, we propose:
H6a: American and Chinese users have different perception of perceived enjoyment on satisfaction in an internet environment.
Research on enjoyment and usage is inconclusive. For instance, Igbaria et al (1995) found an insignificant relationship between enjoyment and usage in a group of Finnish computer users, while Igbaria et al. (1996) proposed a positive relationship between the two constructs in a U.S. sample. McGrath & Kelly (1986) and others (Csikszentmihalyi 1975; Sandelands 1988) further suggested that higher enjoyment results in immediate subjective experiences like involvement, and positive moods that actually lead a user into the cognitive loyalty characteristics proposed by Mathwick (2002). Thus, we formulate the following hypothesis:
H7: A user’s perceived enjoyment positively influences his/ her e-loyalty in an internet portal environment.
As mentioned above, American and Chinese users display different levels of trust while on the internet. American users have relatively lower trust than their Chinese counterparts (Poleretzky 1999). As such, we hypothesize that they display different levels of e-loyalty.
H7a: American and Chinese users have different perception of perceived enjoyment on loyalty in an internet environment.
The conceptualized online loyalty model and relevant hypotheses are captured in Figure 1 below.
METHOD
Whilst online loyalty is an emerging research area in the West, it is virtually a new academic topic in China. Nevertheless, numerous commentators have extensively discussed off-line loyalty, and the validity of TAM scales have been repeatedly confirmed in the system information area. The operationalizations of the aforementioned constructs were developed according to Churchill’s (1979) suggested guidelines. Meantime, some procedures must be implemented to ensure their validity in an online situation. Also, extreme care must be given especially when these scales are being adapted to measure online loyalty in China, which is an unfamiliar topic with very limited theoretical support (Hinkin et al. 1997). In view of the above, we adapted a two-stage approach to generate our survey questionnaire.
First, we developed a standardized, semi-structured protocol based on literature review and applied it to 2 Americans and 2 PRC focus groups. These members were mainly exchange students either from the U.S. or the mainland who were recruited through our university recruitment center in Hong Kong. They satisfied the minimum age requirement of 18, which was set by Sudman (1983) in attitude-related research. They all have a minimum of 3 years of online experience. Each cultural group contained only American or Chinese members to allow their exchange of information without a language barrier. An experienced bilingual moderator was responsible for conducting all four focus-group sessions, with the aim of finding out the interviewees’ perceptions or experiences with respect to the five constructs proposed in the literature review section. All focus groups were conducted in English, but we occasionally used Mandarin or Cantonese in the Chinese group to allow for better communication results. Also, we allowed minor variations during the process to accommodate some unique aspects of individual cultural groups and avoid what Merton et al. (1990) called the fallacy of adhering to fixed questions (Morgan 1996). We invited an experienced bilingual Chinese scholar to reconcile the 4 resulting item lists. Based on a number of succinct alignments of all generated items from all groups, we have generated some new items for satisfaction, loyalty and enjoyment constructs. We subsequently formulated our questionnaire in English according to literature review and items generated from the focus group interviews. We asked two U.S.- educated professors who were raised in the PRC to help us in the translation process. One translated the English questionnaire into Chinese, and the other back translated it into English. The translation process stopped when the English and Chinese versions were linguistically equivalent (Chan 2001).
Second, we pre-tested our questionnaire at our university in Hong Kong. We successfully pre-tested our questionnaire on 30 American and 30 Chinese (PRC) students. A careful examination of these questionnaires revealed that virtually all items were well understood by the target respondents, and also that most prompts yielded a diverse range of responses.
Finally, we distributed our final questionnaires through two universities, i.e. one in the U.S. and one in China, targeting respondents with similar demographic profiles in terms of age and user experience as those in our focus group interviews and pre-tests. The respondents in both locations were all full-time workers and part- time course participants at the study centers of the two universities. In a 3-month period ending June 2005, we obtained 271 and 299 responses from America and China respectively. We compared the demographics of the two cultural groups to determine whether significant differences existed. No obvious difference was found (Kunz 1997).
The relevant details of all constructs are discussed below.
E-Loyalty (e-LOY) is defined as “the consumer’s favorable attitude toward an electronic business resulting in repeat buying behavior” (Anderson and Srinivasan 2003, p. 124). The 5 items were derived from the literature (Gremler 1995; Zeithaml et al. 2000) and modified by focus group interviews and expert advice. The finalized items are presented in Appendix A (five 5-point statements anchored with “1= strongly agree” and “5= strongly disagree”).
e-Satisfaction (e-SAT) is defined as “the contentment of the customer with respect to his or her prior purchasing experience with a given electronic commerce firm” (Anderson & Srinivasan 2003, p. 125). The 4 items were derived from the literature (Allard et al. 2001) and modified by focus group interviews and expert advice. The finalized items are presented in Appendix A (five 5-point statements anchored with “1= strongly agree” and “5= strongly disagree”).
Perceived usefulness (USE) is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davies 1989, p. 320). The 5 items were derived from the literature (Davies 1989; Zeithaml et al. 2000) and modified by focus group interviews and expert advice. The finalized items are presented in Appendix A (five 5-point statements anchored with “1= strongly agree” and “5= strongly disagree”).
Perceived ease of use (EOU) is defined as “the degree to which a person believes that using a particular system would be free from effort” (Davies 1989, p. 320). The 5 items were derived from the literature (Davies 1989; Zeithaml et al. 2000) and modified by focus group interviews and expert advice. The finalized items are presented in Appendix A (five 5-point statements anchored with “1= strongly agree” and “5= strongly disagree”).
Perceived Enjoyment (ENJOY) is defined as “the extent to which the activity of using the system is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated” (Davies et al. 1992, p. 1112). The 4 items were derived from the literature (Davies 1992; Zeithaml et al 2000) and modified by focus group interviews and expert advice. The finalized items are presented in Appendix A (five 5-point statements anchored with “1= strongly agree” and “5= strongly disagree”).
Comparison of Means
We compared means of the five constructs between the American and the Chinese cultural groups (Table 1). We could not find any significant difference between the American and the Chinese groups on two constructs—EOU and e-SAT—but we detected significant difference between the two groups on three constructs— USE, ENJOY & e-LOY—either at p=0.05 or 0.1 level. The Chinese group was found to be significantly higher than the American group in USE, and the American group was found to be significantly higher than their Chinese counterparts in ENJOY and e-LOY. The preliminary investigation of means between groups provided sufficient evidence for a subsequent examination of the possible moderating effect of “cultural group” on the significant structural (causal) relationships.
Validation of Measuring Instruments
While due care was taken in developing all the measuring instruments, post-hoc statistical analyses were conducted on the collected survey data for the purposes of validation. Specifically, confirmatory factor analysis (CFA) techniques were employed to validate all the constructs under investigation. In this study, CFA was performed using EQS Windows Version 6.1. Owing to its user-friendly nature and less stringent assumptions on the multivariate normality of data, EQS is considered to be one of the better alternatives to the more traditional LISREL software (Hair et al. 1995).
The CFA results for each of the five constructs under investigation (i.e., EOU, USE, ENJOY, eSAT, and eLOY) are presented in Table 2. These results highlight satisfactory construct reliability and convergent validity for the five constructs. In summary, the reliabilities of these constructs were all above the minimum threshold of 0.70 (Hair et al. 1995), and the indicators of the constructs also loaded significantly as hypothesized at p < 0.05 (Byrne 1994).
In terms of fit indexes, Table 2 reveals that all the relevant χ2 statistics of the five constructs were significant at p < 0.05, which might indicate an inadequate fit of the measurement models (Hair et al. 1995). Given that the χ2 statistic is highly sensitive to sample size (Bagozzi and Foxall 1996; Byrne 1994), other more powerful fit indexes such as the comparative fit index (CFI), normed fit index (NFI), and root mean square error of approximation (RMSEA) were also computed. In summary, the values of these indexes all met the threshold requirements (CFI and NFI > 0.90; RMSEA < 0.10) as suggested by psychometric researchers (Browne and Cudeck 1993; Hair et al. 1995).
In view of the satisfactory CFA results for each of the five constructs, CFA on all of them as a whole was further performed. The relevant CFI and NFI and RMSEA were 0.901, 0.901, and 0.013 respectively, thereby meeting the recommended thresholds. Although the computed χ2 statistic of the five-construct measurement model was significant at p < 0.05, it was considerably lower than that of the null model (2456.081d.f = 214 vs 22265.253d.f .= 253). To further assess the discriminant validity of the constructs, this study followed Fornell & Larcker’s (1981) suggested guideline, involving an examination of whether the correlation estimate between any pair of constructs was significantly different from 1.0. The application of this guideline did not detect any anomalies. Taken together, the CFA results demonstrated satisfactory reliability and validity for all of the constructs under investigation.
Examination of Hypothesized Causal Relationships
After satisfactory reliability and validity had been established for the constructs, investigation on all the hypothesized causal relationships was carried out (Anderson & Gerbing 1988). Owing to the large number of indicators involved, the investigation was done by employing the path analysis technique that requires the summing up of all the scales and using composite reliabilities to fix the error variances of the constructs (Banerjee et al. 2003; Ganesan 1994). The results of the path analysis are shown in Table 3.
The lower part of Table 3 summarizes the degree to which the data fitted the proposed model according to various fit indexes. First, the (2 statistic of the proposed model was much smaller than that of the null model (2.327 Vs 513.699) at p = 0.127. Given that the (2 statistic is highly sensitive to sample size (Bagozzi & Foxall 1996), other more powerful fit indexes such as NFI, CFI, and RMSEA were also computed. As shown in Table 3, the NFI, CFI, and RMSEA values were 0.995, 0.997, and 0.048 respectively. These values met the recommended thresholds (Browne and Cudeck 1993; Byrne 1994, Hair et al. 1995). Moreover, the proposed model was able to explain 33% of the variance of satisfaction (e-SAT), and 27% of the variance of e-loyalty (e-LOY). Overall, the analysis highlighted the fact that the data fitted the proposed model very well.
Table 3 also displays the estimated standardized path coefficients of the proposed model. Except for the path coefficient of EOU(eLOY (insignificant coefficient), all others were significant at p < 0.05, with the sign of influence as hypothesized. Implications of these findings will be discussed in subsequent sections.
Examination of the Possible Moderating Effect of Culture
As mentioned above, the preliminary investigation of means between groups suggests a further examination of the possible moderating effect of “cultural group” on significant structural (causal) relationships. In the context of structural equation modeling, the test of moderating effects for categorical variables is mainly based on the LaGrange Multiplier statistics (i.e., modification indexes) derived from multiple-group comparison (Byrne 1994; Cole et al. 1993). In this study, the comparison was used to examine the moderating effects exerted by the two (the Chinese vs. the U.S.) cultural groups under investigation.
In examining the moderating effect, the issue of measurement invariance was first addressed to ascertain whether the similarity of meanings of the five latent constructs under investigation (i.e., EOU, USE, ENJOY, e-SAT and e-LOY) could be established across the Chinese and the U.S. cultural groups. This was achieved by following the procedure recommended by Steenkamp & Baumgartner (1998). This procedure employs the multi-group CFA technique to test the equality of covariance matrices and mean vectors, both separately and jointly. If the covariance matrices and mean vectors are found to be not invariant, a series of additional tests (e.g. tests of configural, metric and factor-variance invariances) based on the same technique will then be used to further assess whether the constructs are meaningful when compared across groups. In summary, the application of this procedure in the present study did not detect any significant difference between the Chinese and U.S. cultural groups in terms of their assigned meanings within the five latent constructs.
After addressing the issue of measurement invariance, the two cultural groups were then subjected to multi-group comparison using the aforementioned path analysis technique (Banerjee et al. 2003). The comparison was achieved by estimating a mixed model formed by stacking the covariance matrices of the two groups. This method allowed free estimation of the moderated paths while constraining all other corresponding paths to equality (Bentler 1995; Byrne, 1994). In summary, the modification indexes derived from the analysis of the mixed model indicated that the two groups differed significantly in coefficient values for four corresponding paths. The four paths were ENJOY(eSAT (p = 0.000), USE(eLOY (p = 0.001), EOU(eLOY (p = 0.024), and ENJOY(eLOY (p = 0.035). Based on these findings, equality constraints of these four paths were relaxed for free estimation. All the estimated path coefficients and relevant fit statistics of the revised mixed model are also shown in Table 3.
Table 3 highlights that the computed χ2 statistic (3.403; d.f. = 1; p > 0.334), NFI (0.997), CFI (0.999) and RMSEA (0.056) values all met the recommended thresholds, thus providing evidence for a satisfactory fit of the model. As revealed above, the two cultural groups exhibited significant difference in their path coefficients for ENJOY(eSAT, USE(eLOY, EOU(eLOY, and ENJOY(eLOY. For the Chinese cultural group, the estimated standardized path coefficients for ENJOY(eSAT, USE(eLOY, EOU(eLOY, and ENJOY(eLOY were equal to 0.44, 0.03 (insignificant), 0.08 (insignificant), and 0.40, respectively. The corresponding figures for the U.S. cultural group were 0.07 (insignificant), 0.21, 0.12 and 0.18. The Implications of these results will be discussed below.
ANALYSES
According to the results of the overall model presented in Table 3, we found that the three TAM variables, i.e. perceived usefulness (USE), perceived ease of use (EOU) and perceived enjoyment (ENJOY), positively influence user satisfaction online (eSAT). USE is a more effective TAM construct (0.41), followed by EOU (0.22) and ENJOY (0.18), in the generation of eSAT. Also, eSAT has the highest impact (0.41) on eLOY, followed by ENJOY (0.26) and USE (0.23). The only TAM variable that does not have any impact on eLOY is EOU. So, we can conclude that TAM variables are basically intact as they envisage users’ satisfaction and loyalty online.
The American and the Chinese internet users exhibited similar as well as different behaviors. First, both cultural groups almost equally emphasize more on USE (U.S: 0.45 & Chinese: 0.39) than EOU (U.S.: 0.18 & Chinese: 0.20) in the generation of eSAT. Second, the Chinese cultural group heavily relies on ENJOY (0.44) to create eSAT, whereas the American cultural group does not consider ENJOY as an influential variable on eSAT. Third, the Chinese (0.50) rely more heavily on eSAT than their American counterparts (0.39) to engender eLOY. Last, the members of the American cultural group anticipate that every TAM variable has a certain level of impact on eLOY, whereas the Chinese place significantly more emphasis than their American counterparts on ENJOY to produce eLOY; they do not anticipate that USE and EOU have any impact on eLOY at all.
The analyses above reveal that ENJOY is an influential variable to generate user eSAT and eLOY in China. In contrast, USE is the most effective variable in the production of eSAT and ENJOY is not considered as an important variable at all in America. The three TAM variables have certain levels of impact on eLOY from the American perception but ENJOY is considered as the only variable which can generate eLOY from the Chinese perspective. eSAT is found to have strong impacts on eLOY in both contexts, but the Chinese put higher emphasis on eSAT than their American counterparts when online loyalty is considered.
Two potential reasons contribute to the Chinese enjoyment phenomenon. First, the internet has a very low penetration rate and is still in its infancy in the PRC. Chinese internet users’ fascinations with innovative graphic designs and navigation ability are inaudibly exaggerated in the enjoyment variable as it generates satisfaction and online loyalty. Indeed, the enjoyment phenomenon was evidenced in America when the internet emerged as a powerful info-tainment tool in the early 1990s (Adams 1996). Americans were intrinsically motivated to use the www for pure enjoyment rather than for its usefulness (Atkinson & Kydd 1997). Second, active Chinese government interference may limit internet users’ recognitions of its usefulness in searching for useful information and therefore internet users are naturally driven to concentrate solely on its enjoyment aspect. One of the most widely used internet portals in China that is currently listed on the American stock exchange (NASDAQ) is , which is heavily related to enjoyment and entertainment information activities. Another widely used Chinese internet portal that is also listed on NASDAQ is . This portal provides information such as business contacts and news regarding the Chinese market, but the government’s scrutiny of all internet content may limit Chinese users’ access to sensitive information such as anti-government and exotic materials. As such, the only content which Chinese users can routinely access is some non-exotic entertainment materials like music because this activity will not alert the Chinese government’s central policing organ. In contrast, American users are more mature in their recognition of the usefulness of the internet as they search for information in a completely free market. Together with their technological advancement, American internet operators continuously upgrade their systems, a situation that is reflected in internet users’ perceptions among the TAM variables, satisfaction and loyalty.
CONCLUSIONS AND RECOMMENDATIONS
To conclude, internet operators should treat the U.S. & Chinese markets separately because the U.S. market is more mature, and the three TAM variables are indeed more applicable there. Internet operators are strongly advised to strengthen the usefulness features of their portals by increasing their information-search abilities to improve use-performance relationships (Davies 1989) and to reach relevant and useful information quickly (Atkinson & Kydd 1997). Also, improving the ease of use features in terms of interactive graphic design can aid internet operators to engender user satisfaction. Google’s replacement of Yahoo as the second largest internet search engine in the American market reflects its continuous, superior ability to enable searchers to locate useful and relevant information as Google upgrades its use of “natural” links over time (relevancy accessed on Dec 16, 2006). Enjoyment is not an effective construct to generate satisfaction but it, together with usefulness and ease of use, influences online loyalty. As such, internet operators should maintain a focus on enjoyment as a strategic area to improve online loyalty.
In contrast, internet operators in China should concentrate on building up “enjoyment” in their portals because it is a major determinant linked to loyalty from Chinese users’ perspectives. Operators should sustain their investments in the other two variables—i.e. usefulness and ease of use—to ensure user satisfaction. Because the Chinese internet market is unique in the sense that it is under constant surveillance by the Chinese government, the growth of internet content is limited to politically innocuous information, which inevitably restricts internet users as they search for useful information. Therefore, it is logical for internet operators to use enjoyment as a strategic variable to generate user loyalty on the one hand, and to attempt to find relief from the government’s policing actions on the other. The self-policing by ICPs as they attempt to please both state and individual demands leads to a difficult balancing act. The regulated internet market in China makes it difficulty for operators to balance usefulness and enjoyment within an ease-of-use internet context and thereby generate users’ online satisfaction. Recently, Google has attempted a questionable solution. In January 2006, it relocated its major internet server, , from the U.S. to China to further develop its portal under Chinese censorship laws; its company executives were eventually called into U.S. Congressional hearings and compared to Nazi collaborators (Clive 2006). As such, foreign internet operators should be prepared to balance the interests of their users and the requirements of the Chinese government. Also, Chinese users have different internet concepts, and this cultural relativism counts. As Lee, the Google Beijing CEO, explained the Chinese user’s psychology: “I think people would say: 'Hey, U.S. democracy, that's a good form of government. Chinese government, good and stable, that's a good form of government. Whatever, as long as I get to go to my favorite Web site, see my friends, live happily” (Clive 2006).
FURTHER RESEARCH
Internet operations have cultural boundaries, and this study provides a glimpse of how different cultures behave in a TAM context. Further investigations can be done in terms of areas of adaptation vs. standardization of international portals to suit the taste of internet users in different countries. In this vein, we may be able to develop a global positioning strategy for internet operators to adapt to generate users’ online loyalty. This positioning strategy may be related to social and sensory functions (Roth 1995). Further, designing a portal to create action loyalty within an internet community through social approval, fun, warmth and self respect is an urgent consideration for any internet portal operator (Oliver 1999; Keller 1993 & 2002).
China is the second largest internet market, and more research should be done to help internet operators to enter this highly lucrative potential market. Further studies should adopt a lens of culture as a more central focus for analyses (Lynch & Beck 2001). If we look at internet portal activities as a brand, further research can concentrated on how to produce an appropriate brand image in this specific culture so as to win the loyalty of Chinese users and maintain proper positioning of the portals at the same time.
More specific variables such as codes of society (Singh et al. 2003) andlanguage variability may be included in further studies to help smooth the operations of internet portals. Also, differences in beliefs, attitudes, perceptions and internet buying behaviors exist among countries depending upon user experiences and home country or region (Lynch & Beck 2001). These specific avenues provide more ideas for portal designs in a polycentric internet environment.
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Table 1- Mean Scores and Standard Deviation on individual variables
|Group |N |USEa |EOUa |ENJOYb |e-SATb |e-LOYa |
|Chinese Group |299 |2.175 |2.111 |2.076 |2.136 |2.236 |
| | |0.568 |0.517 |0.606 |0.538 |0.633 |
|U.S. Group |271 |1.985 |2.027 |2.296 |2.080 |2.694 |
| | |1.006 |0.610 |1.019 |0.792 |1.232 |
|F-statistics | |7.892 |2.687 |10.027 |0.991 |32.032 |
|p | |0.05d |0.102 |0.002c |0.320 |0.000c |
EOU = Ease of use; USE = Usefulness; ENJOY = Enjoyment; eSAT = e-Satisfaction; and LOY = e-Loyalty
a Mean Score on a five-item five-point scale.
b Mean Score on a four-item five point scale.
c One way ANNOVA test showed that two group means are different from one-another at p ................
................
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