Shopping destinations and trust – Tourist attitudes: Scale development and validation

Shopping destinations and trust – Tourist attitudes: Scale development and validation

Tourism Management 54 (2016) 490e501 Contents lists available at ScienceDirect Tourism Management journal homepage: www.elsevier.com/locate/tourman ...

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Tourism Management 54 (2016) 490e501

Contents lists available at ScienceDirect

Tourism Management journal homepage: www.elsevier.com/locate/tourman

Shopping destinations and trust e Tourist attitudes: Scale development and validation Miju Choi a, *, Rob Law b, Cindy Yoonjoung Heo c a

School of Hotel and Tourism Management, Faculty of Business Administration, The Chinese University of Hong Kong, Shatin, NT, Hong Kong Special Administrative Region b School of Hotel and Tourism Management, Hong Kong Polytechnic University, 17 Science Museum Road, TST East, Kowloon, Hong Kong Special Administrative Region c ^teli Revenue Management, Ecole ho ere de Lausanne HES-SO/University of Applied Sciences Western Switzerland, Route de Cojonnex 18, 1000 Lausanne 25, Switzerland

h i g h l i g h t s  The present study investigated shopping destinations and trust e tourist attitudes.  This study developed and validated the measurement properties of a scale that measures shopping destination trust.  Results reveal that shopping destination trust consists of nine dimensions.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 August 2015 Received in revised form 7 January 2016 Accepted 8 January 2016 Available online xxx

Shopping is one of the oldest tourist activities and commonly accounts for the majority of travel budgets. However, tourists have expressed concerns regarding the risks they face in shopping destinations. Scholars have suggested that trust is a mechanism for reducing the complexity of human behavior in a situation that involves uncertainty. Therefore, the present study aims to investigate the trust of tourists toward shopping destinations. Specifically, the study attempts to develop and validate the measurement properties of a scale, which measures shopping destination trust. The target sample comprised shopping tourists. Via convenience sampling, 708 usable samples were collected in Hong Kong. Subsequently, purification of the measurement scale, assessment of the latent structure, and scale validation were conducted. Results reveal that shopping destination trust consists of nine dimensions. The present research is expected to shed light on potential research topics in the field of shopping tourism. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Destination marketing Shopping tourism Trust Regulatory focus theory

1. Introduction Shopping has become a determinant factor that affects destination choice (Choi, Heo, & Law, 2015b; Choi, Law, & Heo, 2015a) The emerging concept of shopping tourism is defined as “a contemporary form of tourism fostered by individuals for whom purchasing goods outside of their usual environment is a determining factor in their decision to travel” (United Nations World Tourism Organization, 2014, p.13). Shopping is one of the oldest tourist activities (Choi, Heo, & Law, 2015a, Choi et al., 2015a; Geuens, Vantomme, & Brengman, 2004) and commonly accounts for the majority of travel budgets (Murphy, Moscardo, Benckendorff, & Pearce, 2011). As an example, the Hong * Corresponding author. E-mail addresses: [email protected] (M. Choi), [email protected] (R. Law), [email protected] (C.Y. Heo). http://dx.doi.org/10.1016/j.tourman.2016.01.005 0261-5177/© 2016 Elsevier Ltd. All rights reserved.

Kong Tourism Board (HKTB) (2014) reported that shopping accounts for 61.2% (USD 16.457 billion) of overnight visitor spending patterns, and 90.8% (USD 61.76 billion) of that for same-day visitors. The spending patterns mainly include shopping, hotel bills, meals outside hotels, tours, and entertainment. Therefore, destination marketing organizations (DMOs) devote considerable efforts to develop shopping facilities and options for shopping tourists, because shopping not only increases tourist arrivals (Choi et al., 2015a, 2016b; Choi, Liu, Pang, & Chow, 2008; Rosenbaum & Spears, 2005), but also helps generate jobs and revitalize related industries (e.g., retail and hospitality and tourism industries) (Hsieh & Chang, 2006; Timothy, 2005). The importance of shopping has also elicited considerable research attention. Previous studies have explored various topics in shopping tourism, including shopping motivation (Chang, Yang, & Yu, 2006; Hsieh & Chang, 2006; Michalko & Varadi, 2004; Moscardo,

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2004), tourist shopping satisfaction (Doong, Wang, & Law, 2012; Lin & Chen, 2013; Murphy et al., 2011; Wong & Wan, 2013), and behavioral intentions (Choi et al., 2008; Luo & Lu, 2011; Michalko & Ratz, 2006). Considering that shopping tourism research remains at an early stage, most studies have leaned toward the superficial aspects of shopping. To date, researchers have focused on exploring the phenomenon of tourist shopping and examining the relationship among tourist shopping, its antecedents, and behavioral intentions. Deviating from the mainstream, Yüksel and Yüksel (2007) viewed tourist shopping from a different angle, paying attention to the negative tourist emotions that arise from shopping risks and exploring the antecedents of such risks. The concept of perceived risk is crucial for obtaining a deeper understanding of the trust of tourists toward shopping destinations. Bauer (1960, p. 21) viewed that “consumer behavior involves risk in the sense that any action of a consumer will produce consequences which he cannot anticipate with anything approximating certainty, and some of which at least are likely to be unpleasant.” Similarly, Stone and Grønhaug (1993, p.40) regarded perceived risk as “a state in which the number of possible events exceeds the number of events that will actually occur, and some measure of probability can be attached to them.” Overall, perceived risk is regarded as the subjective perception or a concern of an individual toward uncertainty, which causes unfavorable potential purchase behavior (Cox, 1967; Cunningham, 1967; Horton, 1976). Perceived risk is a multidimensional concept (Bettman, 1973; Cunningham, 1967; Moutinho, 1987; Pinhey & Iverson, 1994; Roehl & Fesenmaier, 1992; Yüksel & Yüksel, 2007). In the hospitality and tourism context, Moutinho (1987) suggested five dimensions as tourist-perceived risks, such as functional, physical, financial, social, and psychological risks; while Roehl and Fesenmaier (1992) divided tourist-perceived risk into seven dimensions, including equipment, financial, physical, psychological, satisfaction, social, and time risks. In the research on safety concerns of Japanese visitors to Guam conducted by Pinhey and Iverson (1994), safety concerns/uncertainty was categorized into seven aspects, including the perceptions on the described safety, sightseeing safety, water sports safety, nightlife safety, beach activity safety, in-car safety, and road safety. Perceived risk is indeed powerful in explaining tourist behavior, because tourists are motivated to avoid negative experiences rather than to maximize utility (Lim, 2003; Mitchell, Davies, Moutinho, & Vassos, 1999; Roehl & Fesenmaier, 1992; Sonmez & Graefe, 1998; Tsaur, Tzeng, & Wang, 1997). It implies that the more shopping risk tourists perceive, the less likely tourists will purchase. Yüksel and Yüksel (2007) synthesized previous studies and conceptualized tourist-perceived risks by categorizing two general types: internal shopping risk and external shopping risk. Internal shopping risk relates to the emotional state of tourists toward a new shopping behavior or concern from customeresalespersons interaction (e.g., receiving inconvenient treatment/services from the salesperson), whereas external shopping risk relates to the perceived uncertainty on shopping destination and vendors. Yüksel and Yüksel (2007) further argued that the risk level varies depending on the amount of shopping budget and the product type (i.e., luxury goods or souvenirs). This variance implies that shopping risk is more fatal for shopping tourists, whose motive for travel is “shopping.” Meanwhile, Chebat and Michon (2003) emphasized the importance of managing shopping risks in a destination. George (2002) added that tourists tend to limit their shopping budget and reschedule their itinerary when they encounter shopping risks. Therefore, destination marketing organizations (DMOs) are required to manage potential shopping risks when promoting shopping venues. However, research on shopping risks and their alternatives is lacking. Trust is regarded as a mechanism for reducing the complexity of

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human behavior in a situation that involves risks and uncertainty (Awad & Ragowsky, 2008; McKnight & Chervany, 2002). HarridgeMarch (2006) emphasized that trust and being trustworthy in the service industry can, in a way, be a differentiator in a competitive market place. If customers have sufficient trust in a company or its products/services, then such trust may outweigh the level of risk they perceive (Grabner-Krauter & Kaluscha, 2003). From this perspective, it is therefore imperative to achieve the right balance between risk and trust. Trust plays a crucial role in purchasing behavior at the shopping destination by lowering the perceived shopping risk (e.g., internal shopping risk and external shopping risk). Tourists, in particular shopping tourists, may be willing to choose trustworthy shopping destinations to minimize potential shopping risk. Consequently, building trust toward shopping destinations may positively affect the shopping behaviors of tourists. Such logic is linked to human nature in terms of decisionmaking. People are generally driven to participate in reasonable behavior without exception. Higgins (1997) explained this tendency using a new psychological perspective, regulatory focus theory (RFT). Higgins (1997) claimed that people have two distinct motivational systems, namely, promotion focus and prevention focus. Promotion focus is related to hopes and accomplishments, whereas prevention focus is concerned with safety and responsibility. These self-regulatory motivational systems are involved in the decision-making process and are focused toward their desired end-states. Applying RFT, shopping tourists are likely to choose trustworthy shopping destinations and adjust their promotion focus (i.e., hopes and accomplishments in shopping) and prevention focus (i.e., security and safety in shopping) accordingly to reach a reasonable decision. This idea reflects the natural human tendency to avoid or minimize risks (Chen & Dhillon, 2003). Despite the importance of trust in promoting shopping destinations, no research has explored this topic comprehensively. Specifically, the underlying dimensions of shopping destination trust (SDT) and the most influential dimensions in forming trustworthy shopping destinations have not been identified. Therefore, the present study aims to investigate the trust of tourists toward shopping destination. In particular, this study attempts to develop and validate the measurement properties of a scale that measures SDT. The findings are expected to broaden research on shopping tourism and are significant because psychological theory is applied to develop a measurement scale. Furthermore, the findings present recommendations to establish effective sales and marketing strategies. 2. Literature review 2.1. Shopping tourism Shopping tourism is a new form of tourism. Most researchers agree that shopping is one of the critical driving forces for tourists to visit destinations. Hsieh and Chang (2006) perceived shopping as the core leisure activity during a trip. Heung and Cheng (2000) also believed that travel is incomplete without shopping. Turner and Reisinger (2001) reinforced such an opinion by arguing that tourists tend to allocate a higher budget for shopping than for other expenses, such as dining, accommodation, or sightseeing. Furthermore, Tosun, Temizkan, Timothy, and Fyall (2007) suggested that a well-managed shopping experience forms a favorable tourist destination image. In addition, studies have attempted to develop a scale that measures tourist shopping satisfaction. Wong and Wan (2013) identified the sub-dimensions of tourist shopping satisfaction and evaluated the relationship among destination facilities (i.e., safety, transportation, location, and cleanliness), tourist shopping satisfaction (i.e., service product and environment satisfaction, merchandise value satisfaction, and service differentiation

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satisfaction), and overall shopping experience. Their findings revealed that satisfied tourists tend to stay long and allocate high budgets during their trips. Scholars have generally agreed that shopping is a key factor that attracts tourists and have mainly highlighted the positive aspects of tourist shopping, namely, antecedents and behavioral intentions. Meanwhile, LeHew and Wesley (2007) argued that the shopping behavior of tourists differ from the regular shopping behavior of individuals in their home countries. Tourists tend to allocate more money to purchase high-quality products from well-known manufacturers or from renowned shopping venues. Littrell et al. (1994) as well as LeHew and Wesley (2007) explained that such behavior is derived from anxiety. That is, tourists are likely to perceive shopping risks. Hence, tourists tend to purchase expensive products from a renowned venue or destination to reduce anxiety and enhance their confidence. Yüksel and Yüksel (2007) conducted a study on shopping risk perceptions by examining how shopping risk perceptions (e.g., internal risk related to new purchasing behaviors and external risk related to shopping destination) influence tourist emotions, satisfaction, and expressed loyalty intentions. Their findings showed that shopping risk perceptions (both internal and external risks) are negatively associated with tourist emotion, satisfaction, and expressed loyalty intentions. In particular, external risks exhibited a stronger negative effect on other variables than did internal risks. Such a finding implies that the anxiety of tourists toward shopping destinations primarily relate to whether the vendors are reliable, a product has high quality, or credit card use is secure. Thus, Yüksel and Yüksel (2007) suggested that destination authorities should exert tremendous efforts to facilitate risk-free shopping environments. Although the negative aspects of shopping and its relevant relationships have been investigated in tourism research, most scholars have focused on the positive aspects of tourist shopping. However, shopping risk is a fatal barrier to the influx of tourists, and thus building trust is one of the most effective methods to reduce shopping risk (Chebat & Michon, 2003; Eroglu & Machleit, 1990). Such a gap in existing research presents the need to develop an SDT to measure tourist trust toward shopping destinations. 2.2. Components of SDT Trust is an abstract concept that explains one of the central aspects of human behavior. In social environments, people need to “know” in advance how other people or the behavior of their peers will influence their benefits. Hosmer (1995, p. 393) defined trust as “the reliance by one person, group, or firm upon a voluntarily accepted duty on the part of another person, group, or firm to recognize and protect the rights and interests of all others engaged in a joint endeavor or economic exchange”. From a psychological standpoint, human nature urges people to embrace pleasure and avoid pain (Aaker & Lee, 2001; Chernev, 2004; Higgins, 1997; Pham & Higgins, 2005). This basic human instinct affects the decision-making process and applies to the fields of marketing, hospitality, and tourism (Jin, He, & Song, 2012). Jin et al. (2012) suggested that prevention-focused customers tend to purchase basic travel packages in an upgrading task to overcome potential risk, that is, to avoid pain, whereas promotion-focused customers tend to purchase full travel packages to fulfill sensory gratification, that is, to maximize pleasure. However, research on investigating the trust of tourists toward shopping destination is lacking. Hence, the present study started by forming possible trust dimensions based on shopping and trust-related research, considering both online and offline trust. Gefen and Straub (2004) argued that trust has four dimensions, namely, benevolence, integrity, predictability, and ability. In their study on consumer trust in B2C ecommerce, they validated a four-dimensional trust scale. Meanwhile, Park, Gunn, and Han (2012) used the dimensions of

benevolence, competence, and integrity to form trust in e-retailing. Similarly, Jarvenpaa, Tractinsky, and Vitale (1999) investigated consumer trust (i.e., benevolence, integrity, and trustworthiness) in an Internet store. According to Giffin (1967) and Blau (1964), benevolence, integrity, and ability are the core components that form customer trust toward online shopping transactions. Benevolence, integrity, competence, and ability are the most frequently used dimensions of trust, as shown by a thorough literature review. Hence, these dimensions were considered included as potential components of SDT. Meanwhile, predictability, reputation, product, and information content are also regarded as components of SDT. For shopping tourists, shopping for products is the most important motivation. The information provided by retailers and DMOs, as well as the reputation of shopping destinations from previous tourists, may affect the decision-making process of shopping tourists. Such an effect ultimately contributes to the formation of trust toward a shopping destination. By examining the nature of trust in buyereseller relationships, Doney and Cannon (1997) proposed that predictability allows consumers to reinforce the trustworthiness of the products. Kelly and Stahelski (1970) also argued that predictability enhances the confidence of consumers, because they know that nothing unexpected may occur. Hence, they regarded these dimensions (i.e., predictability, reputation, product, and information content) as potential components of the trust of consumers. In addition, transaction security and risk avoidance promote trust in the online shopping context. Given that buyers cannot check the product in person, they tend to doubt the quality of the product and transaction security (Camp, 2001; Hoffman, Novak, & Peralta, 1999; Kim, Song, Braynov, & Rao, 2005). Finally, the dimension of liking is considered the last potential component. According to Lau and Lee (1999), liking is one of the influential factors that contribute to product trust because shopping activities during travel tend to arise from hedonic reasons. Based on the above discussion, 11 dimensions (product, predictability, reputation, competence, ability, transaction security, integrity, benevolence, liking, information content, and risk avoidance) are proposed as the components of SDT in the present study. The 11 potential dimensions cover both internal shopping risks (arising from individual psychological status) and external shopping risks (toward shopping destination, vendors, and products). Appendix A displays the dimensions and relevant literature. 3. Method 3.1. Scale development A multi-staged development study was conducted to develop a measurement scale for SDT. This procedure was suggested by Churchill (1979) and Hinkins (1995) in the context of marketing constructs. In terms of establishing measurement reliability, the present study adopted the guidelines established by Anderson and Gerbing (1988), as well as previous scale development studies (Choi & Sirakaya, 2005; Kim, Ritchie, & McCormick, 2012; Wong & Wan, 2013). The following sections provide the specific procedures in the scale development process. 3.1.1. Specification of construct domains and generation of initial item pool A total of 48 SDT items related to the 11 construct domains were initially generated from in-depth interviews, along with a review of previous trust research on the shopping activities of customers. A total of 10 shopping tourists visiting Hong Kong were invited for an in-depth interview. Interviewees were asked to answer what aspects of trust make shopping destination reliable. Open-ended questions were provided. Most of their answers were consistent with those in previous literature. By combining items from the two sources, 11

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potential dimensions were determined including benevolence (3 items), integrity (4 items), competence (6 items), predictability (6 items), ability (4 items), transaction security (4 items), information content (2 items), reputation (6 items), product (6 items), liking (3 items), and risk avoidance (4 items). Neither a measurement scale nor a conceptual model of the SDT that can assist in developing the construct domains and the associated scale items was found in the existing literature. Thus, initial items were drawn from previous related research. Accordingly, modifications were made to fit the items to the context of shopping tourism. For example, the original item from “integrity,” namely, “the company is honest in its dealing with its customers,” was modified to “retail shop employees in Hong Kong are honest in their dealing with tourists.” 3.1.2. Refinement of instruments The initial item pool was sent to a panel of experts to obtain content validity (DeVellis, 2003). The definitions of each construct domainwere provided at the beginning of the expert evaluation sheet. The expert panel, comprising five tourism and hospitality scholars, reviewed each item thoroughly and assessed the applicability and representativeness of the measurement items toward the associated construct by choosing an appropriate value on a scale of 1 (highly inapplicable) to 6 (completely applicable). The initial items were then modified based on the experts' constructive comments on the scale. Finally, 48 SDT items were included for data collection based on the consensus of experts. Appendix B shows the refined measurement items. 3.1.3. Data collection The present study attempts to investigate the trust of tourists toward shopping destinations by developing an SDT scale. The initial questionnaire comprised three parts. The first part was related to the general shopping behavior of tourists during their trip, which includes frequency, main shopping item, shopping budget, companion, length of stay, and travel mode. In the second part, 48 items related to SDT were provided. Responses were recorded on a Likert-type scale, with the anchors being strongly disagree ¼ 1 and strongly agree ¼ 7. The socio-demographic information of respondents was captured in the third part. The target sample was limited to shopping tourists. Two screening questions were selected based on the definition of shopping tourism (UNWTO, 2014). First, respondents were instructed to answer the dichotomy item “I travel to Hong Kong with the major purpose of shopping.” Respondents who ticked the “Yes” box were exclusively invited to answer the second item “Please provide the top three travel activities that you participated in.” Respondents who included shopping in their response to the second question were regarded as shopping tourists. The study site, Hong Kong, is widely reputed as a “shopping paradise.” The HKTB (2014) reported that shopping accounts for 61.2% of overnight visitor spending patterns. A self-administered on-site survey was carried out for the data collection. Twenty undergraduate student helpers were hired as interviewers. All the interviewers have good command of English and Mandarin Chinese and adequate experience in on-site data collection. The data collection sites included various spots in Hong Kong, including Kowloon Park, Hung Hom Train Station, Time Square, Stanley Market, and The Hong Kong International Airport. At the beginning of the pilot test, the interviewers were mainly dispatched to famous shopping centers, such as Harbour City, SOGO Department Store, Time Square, and IFC. However, these sites were almost impossible to conduct an on-site survey because of overcrowding and the lack of seats where respondents could fill in the questionnaire, among other factors. A low response rate was thus obtained. The main survey was carried out for about three months, from early April 2014 to mid-July 2014. Data collection was conducted near shopping malls, such as Kowloon Park (10 min walking distance from

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Harbour City, which is spacious and has several benches) and Stanley Plaza (one of the popular tourist destinations in Hong Kong; it is also spacious and has several benches). Interviewers gently intercepted tourists while passing out incentives to ask them if they were willing to participate in the survey. A total of 708 usable questionnaires were collected from among 800 distributed questionnaires. The incentives, such as iced bottles of water, Starbucks coupons, and Pacific Coffee coupons, played a big role in the data collection and resulted in a high response rate (88.5%) in a limited time. Table 1 displays the profile of respondents. Gender is relatively well distributed, with 56.5% male participants and 43.6% female participants. Approximately one third of the respondents (36.0%) belonged to the 36 to 45-year-old age group. In terms of nationality, Chinese tourists accounted for 48.6% of the participants, whereas non-Chinese tourists formed 51.4% of the sample. These figures imply that respondents are unbiased toward tourists from China, which is the largest source market for Hong Kong tourism. 3.1.4. Purifying the measurement scale Churchill (1979) emphasized that scale development should maintain replicability, which is a prerequisite for reliability. Accordingly, the reliability of the developed SDT scale was obtained. Item-to-total correlations were computed for all 48 SDT items. This practice is one of the verified procedures based on previous scale development studies (Choi & Sirakaya, 2005; Hosany & Gilbert, 2009; Hung & Petrick, 2010; Kim et al., 2012). To purify the scale, items with low or no correlation (r value less than 0.3) with the total score were eliminated, resulting in 46 items out of the original 48. Subsequently, Cronbach's alpha was applied to examine internal consistency reliability. The reliability of the 46-item scale is 0.974, indicating that the 46-item SDT scale is highly reliable. EFA was then employed on the remaining 46 items using principal component analysis with varimax rotation methods to identify the dimensionality of SDT (see, Table 2). The appropriateness of factor analysis was verified using the KaisereMeyereOlkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity, which indicated KMO values of 0.956, expressed as “marvelous” (statistic > 0.9) by Kaiser (1974). The Bartlett's test of sphericity was 42926.972 (p < 0.001), indicating that the factor analysis was proper. A final nine-factor model was extracted, with the remaining 42 items accounting for 85.150% of the total variance. The items included factor 1 (product), factor 2 (predictability), factor 3 (reputation), factor 4 (competence), factor 5 (ability), factor 6 (integrity), factor 7 (transaction security), factor 8 (benevolence), and factor 9 (liking). Four risk-avoidance items were eliminated because they indicated factor loadings lower than 0.4. Cronbach's alpha coefficients for individual SDT factors ranged from 0.941 to 0.972, indicating highly reliable results. 3.1.5. Assessment of the latent structure Confirmatory factor analysis (CFA) was employed using the covariance matrix to verify the factor structure extracted from the previous EFA. The data set (n ¼ 708) was divided into two 354-case subsamples (i.e., a calibration sample and a validation sample) using Statistical Package for Social Sciences 20.0. Table 3 displays the summary of the CFA results. The overall fit of model was acceptable. In particular, the degrees of freedom ratio (i.e., c2/df) was 1.706, Bentler's (1992) comparative fit index (CFI) was 0.971, Bentler and Bonett (1980) non-normed fit index (NNFI) was 0.932, and root mean square error of approximation (RMSEA) was 0.045. Following the established criteria (CFI > 0.90, NNFI > 0.95, and RMSEA < 0.08; Bentler, 1992; Hu & Bentler, 1999), the CFA results appropriately represented the measurement model. In addition, as shown in Table 4, construct and discriminant validity concern were not found [i.e., composite reliabilities > 0.7, average variance

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Table 1 Profiles of respondents (n ¼ 708). Profile Category Gender

Male Female 25 or below 26e35 36e45 46e55 56e65 66 or above Middle school High school Bachelor's degree Graduate/post-graduate degree Working Housewife Student Retired Others Chinese Non-Chinese Less than US$5001 US$5001e10,000 US$10001e15,000 US$15001 or above

Age

Education level

Occupation

Nationality Monthly household Income

Frequency

Percentage

399 309 66 122 255 132 76 57 55 219 316 118 432 122 61 90 3 344 364 160 396 124 28

56.4 43.6 9.3 17.2 36.0 18.6 10.7 8.1 7.8 30.9 44.6 16.7 61.0 17.2 8.6 12.7 0.4 48.6 51.4 22.6 55.9 17.5 4

Table 2 EFA results of SDT items. Factor

No. of items

Eigenvalue

% of Variance

Cumulative %

a

Product Predictability Reputation Competence Ability Integrity Transaction security Benevolence Liking

6 6 6 6 4 4 4 3 3

5.820 5.575 5.568 5.274 3.703 3.673 3.607 3.079 2.870

12.653 12.119 12.103 11.465 8.050 7.986 7.842 6.693 6.238

12.653 24.772 36.875 48.340 56.390 64.370 72.218 78.911 85.150

0.972 0.968 0.962 0.944 0.945 0.941 0.961 0.972 0.952

extracted (AVE) > 0.5, Hair, Black, Babin, & Anderson, 2010]. 3.2. Scale validation 3.2.1. Validation of the developed scale Table 5 shows the CFA results of the validation sample (n ¼ 354). As with the previous calibration sample (n ¼ 354), the measurement model of the validation sample was found to be statistically significant. The results indicated a good model fit to the data c2/ df ¼ 2.296, p < 0.001, CFI ¼ 0.949, NNFI ¼ 0.914, IFI ¼ 0.949, and RMSEA ¼ 0.061. The validity test showed that no construct validity and discriminant validity concern representing each dimension of AVE was above 0.05 (see Table 6). 3.2.2. Second-order factor model To investigate the hierarchical relationships among the suggested constructs, SDT, and whether the nine dimensions were statistically related to a higher order of construct, a second-order CFA was required. Prior to a second-order CFA test, the two 354-case subsamples (i.e., a calibration sample and a validation sample) were combined and a CFA test was conducted. CFA results showed that the measurement model is theoretically acceptable, indicating c2/ df ¼ 3.416, p < 0.001, CFI ¼ 0.951, NNFI ¼ 0.932, IFI ¼ 0.951, and RMSEA ¼ 0.058. The validity test results showed that no construct validity and discriminant validity concern representing each dimension of AVE was above 0.05 (the range was from 0.744 to 0.922). Then, a second-order CFA was conducted using the nine dimensions as the indicators and the SDT as a latent variable. The

results showed that the second-order CFA measurement model was statistically significant, indicating c2/df ¼ 3.487, p < 0.001, CFI ¼ 0.948, NNFI ¼ 0.928, IFI ¼ 0.948, and RMSEA ¼ 0.059. In the validity test results, no construct validity indicated 0.522, suggesting that the sub-dimensions of the SDT shared common variances. 3.2.3. Invariance test Kline (2011) emphasized that an invariance test indicates whether a set of indicators assesses the same variables among different groups and ultimately enhances the validity of the measurement model. In this study, a chi-square statistic was employed in a multi-group invariance test. One group was tested to evaluate multi-group invariance (i.e., gender: male and female). The results showed that chi-square differences among groups were not statistically significant, implying that the current measurement model was statistically acceptable and suitable (i.e., Unconstrained: Chisquare ¼ 8756.7478, df ¼ 3990; Fully constrained: Chisquare ¼ 8833.5, df ¼ 4055; Difference: Chi-square ¼ 76.753, df ¼ 65, p ¼ 0.151). 4. Discussion and conclusions The present study attempts to develop a scale to measure the trust of tourists toward shopping destinations. Following the procedure set by Churchill (1979) and Hinkins (1995) for scale development, the present study succeeded in developing and validating an SDT scale consisting of nine dimensions (i.e., benevolence, product, predictability, reputation, competence, integrity,

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Table 3 CFA results (calibration sample, n ¼ 354). Factor

SRW

Product Dependable products Functional products Good quality Reliable products Workmanship Durable products Predictability Predictable shopping environment Correct anticipation Uncertain shopping environment Consistent shopping environment Inconsistent shopping quality Expected shopping environment Reputation Good reputation Convenient location Reliable place Unreliable reputation Unreliable place Negative comments Competence Competitive shopping environment Best shopping destination Effectiveness Needs fulfillment Other shopping destinations Accomplishment Ability Identifying shopping needs Competent shopping destination Understanding on shopping needs Service excellence Transaction security Sufficient technical capacity Safe transmission of personal information Great security concern Identity of retail shops Integrity Honest retailers Ethical retailers Consistent advertisement Inaccurate advertisement Benevolence Sincere retailers Helpful retailers Caring retailers Liking Like the shopping destination Favorite shopping destination Prefer other shopping destinations

CR

AVE

MSV

ASV

0.972

0.855

0.278

0.223

0.969

0.838

0.315

0.239

0.962

0.810

0.315

0.220

0.946

0.744

0.353

0.239

0.947

0.818

0.250

0.158

0.962

0.862

0.287

0.247

0.941

0.801

0.353

0.236

0.973

0.922

0.301

0.220

0.952

0.869

0.275

0.227

0.914 0.920 0.935 0.947 0.947 0.882 0.933 0.938 0.945 0.916 0.935 0.818 0.937 0.927 0.906 0.901 0.912 0.810 0.912 0.915 0.893 0.821 0.806 0.822 0.958 0.970 0.925 0.747 0.924 0.946 0.901 0.943 0.820 0.927 0.917 0.911 0.985 0.952 0.943 0.927 0.935 0.935

Note: c2 ¼ 1335.615 degree of freedom (p < 0.001); CFI ¼ 0.971; IFI ¼ 0.971; NFI ¼ 0.932; RMSEA ¼ 0.045; SRW: standardized regression weights; CR: composite reliability; AVE: average variance extracted; MSV: maximum shared squared variance; ASV: average shared squared variance.

Table 4 Construct inter correlations (calibration sample, n ¼ 354).

Benevolence Product Predictability Reputation Competence Ability Transaction security Integrity Liking

Benevolence

Product

Predictability

Reputation

Competence

Ability

Transaction security

Integrity

Liking

0.960 0.453 0.549 0.402 0.543 0.358 0.468 0.491 0.456

0.924 0.460 0.527 0.486 0.439 0.486 0.402 0.509

0.915 0.561 0.473 0.333 0.466 0.508 0.524

0.900 0.415 0.349 0.489 0.481 0.492

0.863 0.375 0.525 0.594 0.463

0.904 0.500 0.389 0.406

0.929 0.536 0.500

0.895 0.451

0.932

transaction security, ability, and liking). In particular, “ability,” “integrity,” “benevolence,” and “liking” were found to be the major influential dimensions that drive SDT.

This outcome implies that tourists perceive Hong Kong as a competent shopping destination that provides a shopping environment consistent with that being advertised. The findings are

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Table 5 CFA results (validation sample, n ¼ 354). Factor

SRW

Product Dependable products Functional products Reliable products Good quality Workmanship Durable products Predictability Correct anticipation Predictable shopping environment Uncertain shopping environment Consistent shopping environment Inconsistent shopping quality Expected shopping environment Reputation Good reputation Convenient location Unreliable reputation Unreliable place Reliable place Negative comments Competence Other shopping destinations Best shopping destination Effectiveness Competitive shopping environment Needs fulfillment Accomplishment Integrity Honest retailers Ethical retailers Inaccurate advertisement Consistent advertisement Transaction security Sufficient technical capacity Safe transmission of personal information Identity of retail shops Great security concern Ability Competent shopping destination Identifying shopping needs Understanding on shopping needs Service excellence Benevolence Helpful retailers Sincere retailers Caring retailers Liking Like the shopping destination Prefer other shopping destinations Favorite shopping destination

CR

AVE

MSV

ASV

0.972

0.855

0.327

0.270

0.968

0.835

0.361

0.275

0.964

0.819

0.361

0.270

0.944

0.739

0.460

0.314

0.961

0.860

0.460

0.273

0.959

0.853

0.342

0.291

0.937

0.792

0.267

0.210

0.976

0.931

0.372

0.246

0.955

0.876

0.356

0.298

0.899 0.924 0.953 0.938 0.956 0.874 0.943 0.933 0.943 0.923 0.928 0.804 0.933 0.951 0.930 0.933 0.892 0.779 0.772 0.915 0.890 0.930 0.782 0.856 0.894 0.940 0.940 0.934 0.926 0.944 0.941 0.881 0.964 0.946 0.927 0.695 0.960 0.985 0.949 0.940 0.926 0.941

Note: c2 ¼ 1797.432 degree of freedom (p < 0.001); CFI ¼ 0.949; IFI ¼ 0.949; NFI ¼ 0.914; RMSEA ¼ 0.061; SRW: standardized regression weights; CR: composite reliability; AVE: average variance extracted; MSV: maximum shared squared variance; ASV: average shared squared variance.

Table 6 Construct inter correlations (calibration sample, n ¼ 354).

Benevolence Product Predictability Reputation Competence Integrity Transaction Security Ability Liking

Benevolence

Product

Predictability

Reputation

Competence

Integrity

Transaction security

Ability

Liking

0.965 0.484 0.526 0.440 0.610 0.483 0.472 0.414 0.510

0.924 0.539 0.531 0.547 0.439 0.550 0.484 0.572

0.914 0.601 0.515 0.487 0.499 0.404 0.597

0.905 0.496 0.510 0.550 0.434 0.571

0.860 0.678 0.585 0.446 0.573

0.927 0.573 0.479 0.496

0.923 0.517 0.557

0.890 0.476

0.936

consistent with those reported in previous studies on shopping and trust in the marketing context (Chen & Dhillon, 2003; Gefen, 2002; Jarvenpaa, Tractinsky, & Vitale, 1999; Mayer & Davis, 1999). Gefen

(2002, p. 40) argued that trust is “the product of a set of trustworthiness beliefs and these beliefs are primarily beliefs about the ability, integrity, and benevolence of the trusted party.” In the

M. Choi et al. / Tourism Management 54 (2016) 490e501

research on consumer trust in e-commerce, Chen and Dhillon (2003) identified competence, integrity, and benevolence as key dimensions forming trust in the Internet vendor. Research findings also show that tourists are convinced by psychological factors, such as “reputation.” For example, tourists tend to form trust toward a shopping destination when others tell them positive comments about the destination. Previous research confirmed that word of mouth (WOM) influences the attitude (Cheung, Lee, & Rabjohn, 2008; Doh & Hwang, 2009) and future behavior of recipients. Managing positive WOM is expected to be a key point in attracting potential tourists. Another meaningful point indicates that the dimension “liking” is as important as the other dimensions that form SDT. Liking denotes “a certain fondness one party has towards another party because the party finds the other party pleasant and agreeable” (Lau & Lee, 1999, p. 349). In general, “liking” is not a core dimension in shopping- and trust-related literature. Only few studies regard “liking” as a dimension of trust (Beatty, Mayer, Coleman, & Reynolds, 1996; Lau & Lee, 1999; Murphy, 1999; Swan, Bower, & Richardson, 1999; Webster, 1968). According to Bennett (1996), “liking” is the first stage in initiating not only a personal relationship but also a business relationship. For example, consumers must like a certain brand or service before consumption. Lau and Lee (1999) stated “When a consumer likes a brand, the consumer is bound to find out more about it, setting the stage for trusting it” (p.349). Taylor, Peplau, and Sears (1994, p.349) also argued that “traits that generate likeability have been found to emphasize sincerity, dependability, trustfulness, thoughtfulness, and consideration, all of which are connected with trust.” Shopping during travel driven by a hedonic aspect is different from daily shopping driven by a utilitarian aspect. Mannell and Kleiber (1997) emphasized that hedonism is an integral part of leisure and shopping. Otto and Ritchie (1996) also emphasized that hedonism can influence the emotions and future behaviors of tourists. In the same vein, “liking” represents a hedonic aspect and is likely to contribute to the trust of tourists toward shopping destinations. A major contribution of this study is the establishment of a new construct, namely, SDT based on RFT, which examines the decisionmaking process by adjusting individual regulatory fits (i.e., promotion focus and prevention focus). Given that no prior research has been conducted on SDT and on the development of a SDT scale, this study represents the first empirical examination of such concept. Current research findings confirm that RFT is applicable in the context of shopping tourism. Findings show that all dimensions are successfully grouped under SDT. This result implies that shopping tourists are likely to choose trustworthy shopping destinations and adjust their promotion focus (i.e., hopes and accomplishments in shopping) and prevention focus (i.e., security and safety in shopping) accordingly to reach a reasonable decision. The RFT is reflected in suggested dimensions, such as “predictability,” “reputation,” “competence,” “transaction security,” and “liking.” For example, the dimension “transaction security” is mainly grounded on prevention focus. This idea reflects the natural human tendency to avoid or minimize risks (Chen & Dhillon, 2003). In addition, the scope of shopping tourism is broadened. Shopping tourism is a new form of tourism; hence, this topic remains at the initial stage of tourism research. The findings of the present study are expected to shed light on this aspect of shopping tourism research. Furthermore, the findings not only fill the gaps of previous studies on shopping tourism, but also provide recommendations for DMOs. From a managerial perspective, this study presents a direction for gaining competitive advantage by attracting shopping tourists to a shopping destination through the development of trust. Many destinations expend efforts to attract shopping tourists because of the potential economic effect that these tourists bring (Santos & Cabral

497

Vieira, 2012). For example, Dubai has been hosting the Dubai Shopping Festival every year since 1996 (Anwar & Sohail, 2004), and Istanbul and Macau have been encouraging more people to shop by organizing their respective shopping festivals since 2011. Therefore, the competition among shopping destinations is becoming fiercer. Given that tourists shop for items at trustworthy destinations, the trust of people in a shopping destination is an important factor in shopping tourism. This study assumes that SDT plays an important role in the shopping behavior of tourists. It contributes to DMOs by identifying the key dimensions of shopping destination trust. By reducing the complexity of human behavior in uncertain situations, trust has become the most important factor in business transactions. Trust reduces the perceived risk during transactions and reflects the human characteristic of avoiding or minimizing risk. Shopping tourists tend to stay longer in the destination and spend a higher portion of their budget on shopping compared with non-shopping tourists (UNWTO, 2014). As the expenditures of shopping tourists increase, their concerns on ability and competence also increase. Therefore, DMOs are advised to promote their destination as a reliable place in which tourists can shop worry-free. Despite the efforts to conduct a sound research, this study has several limitations that should be acknowledged. Several suggestions for future research are also proposed. The first limitation is related to the convenience sampling method adopted in the study. The respondents were approached on the basis of their availability and/or accessibility. Although the convenience sampling method is the most feasible approach for an on-site tourist survey, this technique has been criticized for its bias. The major disadvantage of this procedure lies in the ability of the collected data to represent an entire population, which may lead to criticisms related to attempted generalization and inference making. Different types of shopping attractions were considered, and the survey was conducted at different times of the day and days of the week within two months to reduce bias. Future studies with a similar design should be conducted in other tourist destinations to generalize the research findings. Next, considering that tourists intend to shop for different types of products (i.e., luxury goods and non-luxury goods), the study does not explain the same level of trust towards shopping destination. According to Yüksel and Yüksel (2007, p711), “risk is context specific and that different types of shopping (e.g., grocery, jewelry, souvenir, and clothing) may involve different types of risk. Thus, risk-reduction measures may not have equal level of impact on every shopping context.” This definition implies that a tourist who purchased expensive jewelry and watches may have a higher level of trust toward shopping destination than a tourist who purchased a can of milk powder. Therefore, future research is expected to target more specific shopping tourists categorized by types of shopping items to gain a deeper understanding of SDT. Last but not least, the current study overlooks situational factors (e.g., geographical distancedlong-haul and short haul) and personal factors (e.g., gender, age, frequency of visit, etc.). Roehl and Fesenmaier (1992) asserted that situational factors and types of risk influence tourist response. Although the respondents (i.e., shopping tourists who traveled to Hong Kong primarily for shopping) were thoroughly selected using two screening questions, approximately half of the respondents (n ¼ 344, 48.6%) were from Mainland China, whereas the other half were from other countries (n ¼ 364, 51.4%). Hong Kong is renowned as one of the popular shopping destinations for Mainland China. Frequency of visit and geographical distance may influence the level of trust for long-haul tourists and short-haul tourists. According to Chang and Chung (2005), the level of trust in shopping is likely to be enhanced across groups with significant social distance or geographical distance. For future research, controlling those variables is expected to get more solid outcomes.

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M. Choi et al. / Tourism Management 54 (2016) 490e501

Appendix A. Component of trust related to shopping

Dimension

Relevant literature

Benevolence

Hwang & Kim, 2007; Jarvenpaa et al. 1999; Jones, Wilikens, Morris, & Masera, 2000; Josang, 1999; Kim & Benbasat, 2006; Kim, Xu, and Koh 2004; Kumar, Scheer, & Steenkamp, 1995; Lee & Turban, 2001; Lu, Zhao, & Wang, 2010; McKnight & Chervany, 2002; McKnight, Choudhury, & Kacmar, 2002; McKnight, Kacmar, & Choudhury, 2004; Pavlou & Dimoka, 2006; Pavlou & Fygenson, 2006; Railsback, 2001; Ridings, Gefen, & Arinze, 2002; Roy, Dewit, & Aubert, 2001; Shankar, Urban, & Sultan, 2002; Su & Manchala, 1997; Swan, Bowers, and Richardson 1999; Tan & Theon, 2001; Hwang & Kim, 2007; Jarvenpaa et al., 1999; Jones et al., 2000; Josang, 1999; Kim & Benbasat, 2006; Kim, Xu and Koh 2004; Kumar et al., 1995; Lau & Lee, 1999; Lee & Turban, 2001; Lu et al., 2010; McKnight & Chervany, 2002; McKnight et al., 2002; McKnight et al., 2004; Noteberg, Christiaanse, & Wallage, 2003; Pavlou & Dimoka, 2006; Pavlou & Fygenson, 2006; Railsback, 2001; Ridings et al., 2002; Roy et al., 2001; Su & Manchala, 1997; Swan, Bowers, and Richardson 1999; Benassi, 1999; Bhimani, 1996; Chen & Dhillon, 2003; Friedman, Kakn, & Howe, 2000; Garbarino & Lee, 2003; Grabner-Krauter, Kaluscha, & Fladnitzer, 2006; Hawes, Rao, & Baker, 1993; Jones et al., 2000; Josang, 1999; Kim & Benbasat, 2006; Kim, Xu and Koh 2004; Lau & Lee, 1999; Lee & Turban, 2001; McKnight & Chervany, 2002; McKnight et al., 2002; McKnight et al., 2004; Pavlou & Fygenson, 2006; Railsback, 2001; Shankar et al., 2002; Su & Manchala, 1997; Swan, Bowers, and Richardson 1999 Ba, 2001; Benassi, 1999; Bhimani, 1996; Gefen & Straub, 2004; Grabner-Krauter et al., 2006; Jones et al., 2000; Josang, 1999; Lau & Lee, 1999; McKnight et al., 2002; McKnight et al., 2004; Railsback, 2001; Schurr & Ozanne, 1985; Su & Manchala, 1997; Tan & Theon, 2001 Ambrose & Johnson, 1998; Ang, Dubelaar, & Lee, 2001; Dwyer & Oh, 1987; Ganesan, 1994; Gefen, 2000; Gefen, Karahanna, & Straub, 2006; Gefen & Straub, 2004; Hwang & Kim, 2007; Pavlou & Dimoka, 2006; Ridings et al., 2002; Roy et al., 2001; Zhou & Tian, 2010 Atchariyachanvanich & Sonehara, 2008; Camp, 2001; Hoffman et al., 1999; Kim et al., 2005; Kim, Williams, and Lee 2004; Park, 2004; Pugliese & Halse, 2000; Shankar et al., 2002; Travica, 2002 Alba et al., 1997; Chow & Holden, 1997; Elofson & Robinson, 1998; Fung & Lee, 1999; Hill, 1990; Janal, 1997; Kim et al., 2005; Kim, Williams, and Lee 2004; Peterson & Balasubramanian, 1997 Ba & Pavlou, 2002; Ba, Whinston, & Zhang, 1999; Benassi, 1999; Bhimani, 1996; Khazanchi & Sutton, 2001; Kim et al., 2005; Lau & Lee, 1999; Salam, Rao, & Pegels, 1998 Alba et al., 1997; Chow & Holden, 1997; Elofson & Robinson, 1998; Fung & Lee, 1999; Hill, 1990; Kim et al., 2005; Peterson & Balasubramanian, 1997 Beatty et al., 1996; Lau & Lee, 1999; Murphy, 1999; Swan et al., 1999; Webster, 1968 George, 2002; Jarvenpaa, 1996; Lohse & Spiller, 1999; Miyazaki & Fernandez, 2000; Palmer, Bailey, & Faraj, 2000

Integrity

Competence

Predictability Ability Transaction security Information content Reputation Product Liking Risk avoidance

Appendix B. Refined measurement items

Constructs and items Benevolence Sincere retailers Helpful retailers Caring retailers Integrity Consistent advertisement Inaccurate advertisement Honest retailers Ethical retailers Competence Best shopping destination Other shopping destinations Competitive shopping environment Effectiveness Needs fulfillment Accomplishment Predictability Predictable shopping environment Correct anticipation Inconsistent shopping quality Consistent shopping environment Uncertain shopping environment Expected shopping environment Ability Competent shopping destination Understanding on shopping needs Identifying shopping needs Service Excellence Transaction security Safe transmission of personal information Great security concern Sufficient technical capacity Identity of retail shops Information content Meeting personal information needs Adequate information

Hong Kong retailers act in my best interest. If I need help, Hong Kong retailers do their best to help me. Hong Kong retailers are concerned about my well-being. Hong Kong Hong Kong Retail shop Retail shop

provides a shopping environment consistent with that being advertised. advertises shopping products that it does not offer. employees in Hong Kong are honest in their dealings with tourists. employees in Hong Kong are ethical.

Hong Kong is the best destination for a shopping trip. Other shopping destinations are better than Hong Kong. Hong Kong offers a better shopping environment than other destinations. I can do my shopping more effectively in Hong Kong than in other destinations. Hong Kong meets my shopping needs better than other shopping destinations. I accomplish my shopping tasks in Hong Kong more easily than in other destinations. When I visit Hong Kong for shopping, I know exactly what to do. I can always correctly anticipate how Hong Kong will be as a shopping destination. Hong Kong does not offer consistent shopping quality for tourists. Hong Kong provides a consistent shopping environment. I cannot always be sure of the shopping environment in Hong Kong each time I visit. I know how Hong Kong is going to provide a shopping environment for me. Hong Hong Hong Hong

Kong Kong Kong Kong

is a competent shopping destination. as a shopping destination understands my shopping needs. as a shopping destination knows my shopping needs. as a shopping destination knows how to provide excellent service.

Retail shops in Hong Kong have mechanisms that ensure safe transmission of the personal information of shoppers. Retail shops in Hong Kong show great concern for the security of any transaction. Retail shops in Hong Kong have sufficient technical capacity. I am sure of the identity of retail shops in Hong Kong when I shop. Shopping information in Hong Kong adequately meets my informational needs. Shopping information in Hong Kong is adequate.

M. Choi et al. / Tourism Management 54 (2016) 490e501

499

(continued ) Constructs and items Reputation Good reputation Unreliable reputation Unreliable place Reliable place Convenient location Negative comments Product Reliable products Workmanship Good quality Functional products Dependable products Durable products Liking Like the shopping destination Prefer other Shopping Destinations Favorite shopping destination Risk avoidance Concerns Uncertainty Feeling uncomfortable Risk

Hong Kong has a good reputation as a shopping destination. Hong Kong has an unreliable reputation as a shopping destination. Other people have told me that Hong Kong is not a reliable place for a shopping trip. Other people have told me that Hong Kong is a reliable place for a shopping trip. Hong Kong has a reputation as a convenient shopping destination. I have heard negative comments about Hong Kong as a shopping destination. Products purchased in Hong Kong are highly likely to be reliable. Products purchased in Hong Kong appear to have exquisite workmanship. Products purchased in Hong Kong appear to be of very good quality. I consider products purchased in Hong Kong to be very functional. Products purchased in Hong Kong are extremely likely to be dependable. Products purchased in Hong Kong seem to be durable. I like Hong Kong as a shopping destination. I prefer other shopping destinations over Hong Kong. Hong Kong is my favorite shopping destination. I have concerns when shopping at a new destination. I feel uncertain about shopping at an untrustworthy destination. I become uncomfortable in new situations. Shopping in a new environment is risky.

Note: 1) The measurement scales used in the current study are derived from previous literature. 2) Three items of “Benevolence” were modified from Park et al. (2012); two items of “Integrity” were modified from Lau and Lee (1999); six items of “Competence” were modified from Lau and Lee (1999); six items of “Predictability” were modified from Lau and Lee (1999); four items of “Ability” were modified from Gefen and Straub (2004); four items of “Transaction Security” were modified from Ranganathan and Ganapathy (2002); two items of “Information content” were modified from Kim et al. (2004); six items of “Reputation” were modified from Lau and Lee (1999); six items of “Product” were modified from Kennedy, Ferrell, and LeClair (2001); three items of “Liking” were modified from Lau and Lee (1999); four items of “Risk avoidance” were modified from Yoon (2009).

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Miju Choi, Ph.D. is a Lecturer at the School of Hotel and Tourism Management, Faculty of Business Administration, The Chinese University of Hong Kong. Her research interests include cross-cultural research as well as in tourist psychology in shopping tourism.

Rob Law, Ph.D. is a Professor at the School of Hotel and Tourism Management, the Hong Kong Polytechnic University. His research interests are information management and technology applications.

Cindy Yoonjoung Heo, Ph.D. is an Assistant Professor at ^telie re de Lausanne, the Revenue Management, Ecole ho HES-SO/ University of Applied Sciences Western Switzerland. Her research interests include revenue management, service marketing, strategic management, and restaurant management.