Why do smartphone shoppers help others on websites? The effects of attachments on reciprocal altruism

Abstract

Why do people help others on mobile shopping sites, even when members have never met? To answer the research question, this study applies attachment theory to investigate the relationships between formation variables (trust, enjoyment, and timesaving), transfer variables (site, group, and interpersonal attachments), and an outcome variable (reciprocal altruism). An online survey was conducted of shoppers who had purchased tourism products with smartphones. The results show that trust, enjoyment, and timesaving have significant effects on site attachment, which, in turn, have a significant effect on both group and interpersonal attachments. Interpersonal attachment also has a highly significant effect on group attachment. Reciprocal altruism is influenced by both site and interpersonal attachments. The findings not only provide theoretical contributions to the tourism literature but also practical contributions to the tourism industry.

Keywords

mobile shopping site smartphone attachments reciprocal altruism tourism products attachment theory

Trust, timesaving, and enjoyment are important elements of mobile shopping for tourism products.

Introduction

Currently, more consumers use smartphones for shopping-related activities (Chen et al. 2013) and mobile shopping than ever before (Zhou 2014). While retail sales in South Korea (hereafter Korea) increased by 9.2% from 2010 to 2011, mobile shopping, including shopping for travel, restaurants, and leisure activities, increased by 633% in the same period (Choi 2012). Use of digital wallets for purchases made using mobile devices in Korea has led to rapid changes in e-commerce, and changes in the tourism industry have been the most rapid (Han 2012). The world’s 6.5 billion mobile phone subscribers become vocal and active detractors if they have negative experiences while making purchases on their devices (Marketwire 2012). Conversely, they are willing helpers and advocates when their experiences are positive (Marketwire 2012).

Since mobile shopping on smartphones is already a large and still rapidly growing market (Chen et al. 2013, Choi 2012, and Zhou 2014), obtaining a more complete understanding of consumers’ needs, motivations, and online behaviors will benefit managers. It would also be useful for researchers to obtain a more complete understanding of online consumer behavior (Kim, Chung, and Lee 2011; Kim et al. 2012; Kim, Lee, and Chung 2013).

Chu et al. (2010) find that grocery shoppers exhibit significant differences between online and offline shopping behaviors. Furthermore, online and offline shoppers express different content in word of mouth messages (Scarpi, Pizzi, and Visentin 2014). These findings demonstrate that consumers behave differently in those two shopping contexts and that consumers have different motivations for shopping online than for shopping offline. Offline shopping behavior is well researched but mobile shopping, especially mobile shopping for tourism products, is under-researched.

Reviews written and posted on online social media by users who have recently traveled facilitate travel by others who intend to travel or who are organizing their own trips (Parra-Lopez et al. 2011). Responding to questions posed online by other travelers or potential travelers also helps other travelers. Such postings can create powerful attachments to websites, groups, and individuals (Ren et al. 2012) and are a form of altruism (Ren, Kraut, and Kiesler 2007).

Reciprocal altruism, the act of consumers helping each other, is a key aspect in websites’ successes (Ma and Chan 2014). Accordingly, it would be useful to identify why consumers engage in online reciprocal altruism, to understand consumers’ motivations for engaging in online reciprocal altruism, and to evaluate the factors that influence consumers to engage in online reciprocal altruism. Hence, this study examines motivations for engaging in reciprocal altruism in the online environment. This study proposes a model aimed at assessing the motivations that lead to reciprocal altruism.

Theoretical framework

Definition of attachment theory and attachment transfer process

Attachment theory conceptualizes the propensity of human beings to make strong affectional bonds to particular others and objects (Ainsworth 1989; Bowlby 1977). Several studies have applied attachment theory to the significant role of attachments in keeping members connected to online communities and the subsequent success of the associated businesses (e.g., Chung and Koo 2012; Fiedler and Sarstedt 2010; Ren, Kraut, and Kiesler 2007; Ren et al. 2012). Furthermore, researchers find group and interpersonal attachments of online communities significantly influence reciprocity (e.g., Fiedler and Sarstedt 2010; Ren, Kraut, and Kiesler 2007).

Several studies (e.g., Fiedler and Sarstedt 2010; Mikulincer et al. 2005; Mikulincer and Shaver 2005; Ren, Kraut, and Kiesler 2007) suggest that attachment is an important factor for altruistic behaviors. Nevertheless, researchers do not differentiate between site, group, or interpersonal attachments in the context of mobile shopping for tourism products. This study seeks to determine the relative roles of these three styles of attachments and the importance of each style of attachment on reciprocal altruism between mobile tourism shoppers.

Reciprocal altruism between members of online sites is a key aspect in websites’ successes (Ma and Chan 2014; Ren, Kraut, and Kiesler 2007). Fiedler and Sarstedt (2010) and Ren, Kraut, and Kiesler (2007) claim that group and interpersonal attachments have significant roles in reciprocal altruism in online environments. Reciprocal altruism can be explained by the motivation to help others in social networks (Leider et al. 2009; Mohtashemi and Mui 2003). Social media are one type of social network and allow tourists to contribute actively by providing a very easy way of sharing information and helping other people buy tourism products (Parra-Lopez et al. 2011).

The immense popularity of social media is creating a change in consumer shopping behavior (Hsiao et al. 2010). That trend, known as social function in shopping behavior, is defined as opinions and recommendations of users of online sites that provide consumers with information on new products of interest to them and that assists them in making shopping decisions (Hsiao et al. 2010). Because of the social influence and the number of users of social networks, companies need to encourage and maintain social functions among their customers and the relatives and friends of their customers in order to boost sales (Liébana-Cabanillas, Sánchez-Fernández, and Muñoz-Leiva 2014). Shoppers enjoy social functions while shopping (Kim, Kim, and Huang 2014), implying that retailers interested in reaching and satisfying those shoppers need to encourage consumers to engage in social activities. When consumers perceive online reviews and messages (e-word of mouth) as helpful for achieving a social function, they express a better attitude toward reading e-word of mouth messages (Reichelt, Sievert, and Jacob, 2014).

Online communities can fulfill social functions by allowing users to interact with others who share an area of interest. Importantly, the perceived trustworthiness of e-word of mouth is one of the benefits of the social functions of websites (Reichelt, Sievert, and Jacob, 2014). Moreover, researchers (e.g., Hsiao et al. 2010; Kim, Kim, and Huang 2014; Liébana-Cabanillas, Sánchez-Fernández, and Muñoz-Leiva 2014) advocate that social functions on shopping sites have significant effects on shoppers. Because of the increasing importance of social influence in shopping, encouraging, and maintaining social activities among consumers is essential for companies to be successful (Liébana-Cabanillas, Sánchez-Fernández, and Muñoz-Leiva 2014).

Attachment transfer is defined as the process of transferring the primary attachment figure and the attachment-related functions from caregivers in childhood to peers later in life (Freeman and Brown, 2001). Attachment theory has been broadened to include subsequent developmental periods and attachment figures, including focusing on the composition of attachment networks during the adult years (Doherty and Feeney 2004). A secure attachment is a bond that engages high levels of hope for both self and the relationship, high levels of trust in others, high levels of self-disclosure, and high levels of satisfaction with the relationship (Welch and Houser 2010). Sometimes attachments are transferred from one person or object to another; this attachment transfer process is associated with differences in attachment style (Freeman and Brown 2001; Friedlmeier and Granqvist 2006). Someone with a predisposition to form supportive and trustworthy relationships with others will be more willing and able to forge an attachment with a friend or a romantic partner (Mayseless 2004). Trust between individuals is positively associated with the attachment transfer (Fraley and Davis 1997); thus trust is related to attachment transfer. The extent of attachment transfer is associated with participants’ positive affect (Zhang, Chan, and Teng 2011). The attachment transfer process has become an emerging topic in the research of behaviors, however, most studies conducted on the transfer process are carried out in off-line relationships. This study aims to examine whether this process of attachment transfer could be generalized to mobile shopping sites for smartphone users. Next we examine the variables (i.e., trust, enjoyment, and timesaving) involved in the formation of attachments.

Definitions of formation variables for attachment

Trust

Sirdeshmukh, Singh, and Sabol (2002) define trust as expectations held by consumers that service providers are dependable and can be relied on to deliver on their promises. Based on Morgan and Hunt (1994), successful relationship marketing requires strong trust and commitment (e.g., psychological attachment). Trust is a significant factor in attachment to brands (Jahn, Gaus, and Kiessling 2012; Louis and Lombart 2010), and since websites are de facto brands (McWilliams 2000), trust is likely to be a significant factor in site attachment. Garbarino and Johnson (1999) describe customer trust in an organization as customer confidence in the quality and reliability of the services offered. To customers, trust and commitment (e.g., attachment) are important determinants of intentions to repurchase (Garbarino and Johnson 1999). In online shopping environments for tourism products, trust contributes to increased loyalty (Kim, Chung, and Lee 2011). Through trust transfer processes, trust also contributes to increasing online shopping for tourism products (Kim et al. 2012; Kim, Lee, and Chung 2013). Because of the significant role that trust plays in the context of tourism online shopping (Kim, Chung, and Lee 2011; Kim et al. 2012; Kim, Lee, and Chung 2013), and because of the significance of trust in the attachment transfer process as outlined above, trust is examined in this study as a variable influencing the formation of attachments to mobile shopping sites for tourism products.

Enjoyment

Enjoyment is a powerful response to a broad range of experiences and affects perceptions and behaviors in many ways (Cyr, Head, and Ivanov 2006; Ha, Yoon, and Choi 2007; Hong and Tam 2006). Hong and Tam (2006) define enjoyment as delight derived from using something. Shopping enjoyment has a strong influence on consumers’ emotional attachments to retailers (Childers et al. 2001; Vlachos et al. 2010) and is a significant predictor of loyalty to brick-and-mortar stores (Johnson et al. 2014). Enjoyment is the single greatest factor in explaining intention to adopt mobile data services (Hong and Tam 2006). In mobile commerce, enjoyment plays a critical role, and the design of the interface is central in determining the level of enjoyment experienced by users (Cyr, Head, and Ivanov 2006). Enjoyment has a greater effect than any other single factor on intention to play mobile broadband wireless access games (Ha, Yoon, and Choi 2007), suggesting that enjoyment may be the most important factor in whether a user returns to a mobile site. Since enjoyment of mobile environments has a substantial role in users returning to sites (Cyr, Head, and Ivanov 2006; Ha, Yoon, and Choi 2007; Hong and Tam 2006), this study examines enjoyment as a variable leading to the formation of attachments to mobile shopping sites for tourism products.

Timesaving

Timesaving is the primary benefit online shopping offers over traditional retail environments because saving time is an important goal (Punj 2012) and is a major determinant of positive attitude toward online shopping (Alreck et al. 2009; Cho 2004). Timesaving positively influences shoppers’ experiences (Lloyd et al. 2014), implying that more timesaving results in stronger attachments to shopping sites. Timesaving results from taking less time to visit multiple retail stores and less time for browsing through alternatives (Cho 2004). A well-supported decision-making process in the e-commerce channel also leads to consumers’ timesaving (Kohli, Devaraj, and Mahmood 2004). Since consumers perceive online shopping to be a timesaving practice, online merchants promote this aspect of online shopping (Alreck et al. 2009). In line with the important role that timesaving has in the online shopping context (Alreck et al. 2009; Cho 2004; Kohli, Devaraj, and Mahmood 2004; Punj 2012), timesaving in this study is treated as a variable in the formation of attachments to mobile sites for shopping for tourism products.

While these three factors of trust, enjoyment, and timesaving have been demonstrated to be significant to consumers in brick-and-mortar situations, little theoretically based research has examined how or why these factors are significant in explaining mobile shoppers’ attachments to sites, groups, or members. Accordingly, this research investigates the influence of these factors on attachments and consequently on reciprocal altruism. Next we examine three types of attachments and attachment transfer.

Definitions of attachments and transfer of attachment variables

Site attachment

Online sites with high levels of site attachment are more likely than sites with low levels of attachment to have users who interact with others to maintain the infrastructure, generate information, and provide social and emotional support to other users (Ren, Kraut, and Kiesler 2007). Fiedler and Sarstedt (2010) evaluate the influence of numerous antecedents on site attachment as well as the mediating role of site attachment in explaining consumer behavior. To facilitate knowledge sharing on social networking sites it is important to foster a sense of attachment (Chung and Koo 2012). Since the majority of people who visit online website communities contribute little and leave quickly, community members’ strong attachments to their online sites are crucial to the success of those sites (Ren et al. 2012). Accordingly, site attachment in this study is considered to be developed through positive experiences (e.g., trust, enjoyment, timesaving).

Group and interpersonal attachment

According to social psychologists, people join groups for one of two reasons, either they like the group as a whole, or they like individuals within the group. A group formed by people who like the group as a whole is referred to as a common-identity group while a group formed by people who like individuals within the group is referred to as a common-bond group (Prentice, Miller, and Lightdale 1994). Research into online behaviors shows that people join online communities for the same two reasons (Chung and Koo 2012; Fiedler and Sarstedt 2010; Ren, Kraut, and Kiesler 2007; Ren et al. 2012).

Group attachment is defined as the attachment an individual feels to a group and results in the identification of the individual with the group as a whole, its goal, and its purposes (i.e., a common-identity group) (Sassenberg 2002). Individuals in common-identity groups share something in common, such as attending the same university or belonging to the same church. Interpersonal attachment is defined as the bonds between individual group members (Sassenberg 2002). An affectional bond (i.e., an interpersonal attachment) is defined as “a relatively long-lasting tie in which the partner is important as a unique individual and is interchangeable with none other” (Ainsworth 1989: 711). In common-bond groups the members have interpersonal attachments and are attached to individual members rather than to the entire group (Prentice, Miller, and Lightdale 1994).

Those who feel a common-identity (identity-based) attachment to a community are more likely to engage in generalized reciprocity (help with the expectation that incidents of helping behavior will balance out over time) (Ren, Kraut, and Kiesler 2007). Members who have bond-based (common-bond) attachments to a group are more likely to help each other (direct reciprocity) but are less likely to help unless they know the other person or feel obligated to return a favor that they have received in the past (Ren, Kraut, and Kiesler 2007). Common-identity attachment is the primary driver for user behaviors in online communities, while common-bond attachment has a more limited influence in this context (Fiedler and Sarstedt 2010).

While network externality (the number of members), social interaction, enjoyment of helping, and self-image expression significantly affect the two styles of attachments (i.e., common-identity and common-bond), the two styles of attachments significantly affect the extent of knowledge sharing online (e.g., reciprocal altruism) (Chung and Koo 2012). Whereas participants’ visit-frequencies and self-reported attachments increase in both identity and bond conditions, participants in common-identity groups and who have access to group profiles and exposure to their groups’ activities visit their communities twice as frequently as those in common-bond groups (Ren et al. 2012). Thus, group attachment in this study is feelings of identification with the group resulting from attachment to mobile shopping sites for tourism products, and is thought to make reciprocal altruism more likely. Additionally, interpersonal attachment in this study is feelings of identification with individual users resulting from attachment to mobile shopping sites for tourism products, and also makes reciprocal altruism more likely.

Definition of Reciprocal Altruism

Altruism is the opposite of selfishness and is defined as a behavior that enhances the well-being of others at the expense of one’s own well-being; altruism affects quality of life because altruistic individuals are concerned about both their own well-being and the well-being of others (Stark 1989). Building on this foundation, Leider et al. (2009) say that altruism consists of three components: baseline altruism toward randomly selected strangers, directed altruism that favors friends over random strangers, and the motivation of the prospect of future interactions. According to White and Peloza (2009), altruism can be motivated purely by the desire to help others or by more egoistic reasons (e.g., self-serving motives), which explains why charities sometimes combine other-benefit and self-benefit appeals.

Reciprocal altruism creates powerful ties between people. This phenomenon has been documented in the fields of psychology (Cialdini et al. 1997), economics (Leider et al. 2009), marketing (White and Peloza 2009), and tourism (Fennell 2006; Jacob and Gueguen 2012; Uriely et al. 2002). Cialdini et al. (1997) suggest that empathic concern (e.g., compassion, tenderness, soft heartedness, sympathy), manifested as helping others, signals an emotional reaction (e.g., attachment) that ultimately leads to reciprocal altruism. Altruism in online environments has received considerable attention (e.g., Mohtashemi and Mui 2003; Parra-Lopez et al. 2011). Because of the demonstrated importance of reciprocal altruism, in this study it is treated as an outcome influenced by group and interpersonal attachments.

Hypothesis development

Relationships between formation variables and site attachment

The close relationship of trust to affective commitment demonstrated by Morgan and Hunt (1994) and Garbarino and Johnson (1999) implies that trust is important for the formation of attachments. In their attachment model, Thau et al. (2007) assert that trust is related to feelings of attachment of individuals to both organizations and their members. Furthermore, a more recent study shows that the positive influence of trust is significant in customer attachment to brands (Louis and Lombart 2010). Regarding the effect of trust on attachment, Jahn, Gaus, and Kiessling (2012) find that trust is a key driver of brand attachment. Drawing on the above literature review, this study proposes the following hypothesis:

H₁ : Trust in mobile sites has a significant and positive effect on site attachment to the sites.

With regard to the relationship between enjoyment and attachment, enjoyment is considered a major determinant of attachment in intimate relationships (Bowlby 1951). In the context of interactive media usage, enjoyment is a direct positive determinant of affective evaluations (Childers et al. 2001). Grocery retailers providing enjoyable shopping experiences are at an advantage in building ties with positive affect with their customers (Childers et al. 2001) because shopping enjoyment positively influences consumers’ emotional attachments to firms (Vlachos et al. 2010). Since enjoyment is a significant predictor of attachment in the retail environment, this study proposes the following hypothesis:

H₂ : Enjoyment of mobile sites has a significant and positive effect on site attachment to the sites.

Saving time is an important motive of online consumers (Punj 2012). Kohli, Devaraj, and Mahmood (2004) find strong support that online consumers’ decision-making processes are mediated by time savings, which lead to consumers’ greater channel satisfaction and the benefit of timesaving evokes positive attitudes (e.g., affective judgment) toward online shopping (Cho 2004). Since consumers are pressed for time, when they shop online “they want to get on, get done, and get off, having spent a minimum amount of time” (Alreck et al. 2009: 3). Since timesaving is important as a goal in itself, and as an antecedent of positive attitudes toward online shopping, this study proposes the following hypothesis:

H₃ : Timesaving on mobile sites has a significant and positive effect on site attachment to the sites.

Relationships among site, group, and interpersonal attachments

Common-identity theory makes predictions about the causes and consequences of attachments to groups (Ren, Kraut, and Kiesler 2007). People engaging in an on-topic online chat can be classified as a common-identity group (Sassenberg 2002). Sassenberg (2002) supports not only the importance of common-identity groups but also suggests that group attachment is related to consumers’ behaviors. Based on Ren, Kraut, and Kiesler (2007), behaviors of members of common identity groups are consequences of those members’ attachments to online communities. The greater the attachment to an online site, the more willingly the users attach themselves to online community groups associated with the site (Choi 2013; Ma and Chan 2014). Similarly, the greater the perceived attachment of individual online users to social media sites the greater their perceived attachment to online groups on the sites (Ma and Chan 2014). Thus, this study proposes the following hypothesis:

H₄ : Site attachment to mobile sites has significant and positive effects on group attachment to the sites.

Group and interpersonal attachments can be leveraged by increasing attachments to online sites (Ren et al. 2012). According to Fiedler and Sarstedt (2010), members’ bonds with each other on online sites strengthen members’ identities with those sites. Many sites that were originally identity-based have introduced features that nurture social interactions between their members (Fiedler and Sarstedt 2010), implying that interpersonal attachments influence group attachments. Drawing on the above literature, this study proposes the following hypothesis:

H₅ : Interpersonal attachment to mobile sites has significant and positive effects on group attachment to the sites.

Common-bond theory predicts the causes and consequences of attachments to individual group members (Ren, Kraut, and Kiesler 2007). People engaging in off-topic online chats can be classified as common-bond groups (Sassenberg 2002). Based on Ren, Kraut, and Kiesler (2007), common-bond groups are a consequence of members’ attachments to other group members of online communities. Sassenberg (2002) shows the importance of common-bond groups and that interpersonal attachments in such groups are related to consumers’ behaviors.

Attachment to an online site has a positive and significant impact on individuals’ intentions to create attachments to online communities associated with that site (Choi 2013). The stronger an individual’s attachment to a social network site, the more the individual is attached to the relationship with other individuals who use the site (Ma and Chan 2014). Based on these findings, this study proposes the following hypothesis:

H₆ : Site attachment to mobile sites has significant and positive effects on interpersonal attachments to the sites.

Relationships among group attachment, site attachment, interpersonal attachment, and altruism

As our understanding of attachment grows, it becomes important to distinguish between different styles of attachments and to understand the implications of stronger attachments. According to Mikulincer et al. (2005) and Mikulincer and Shaver (2005), attachment is positively correlated with altruistic behaviors but these researchers do not differentiate between group attachment, interpersonal attachment, or site attachment. We think these three styles of attachment have different consequences for altruism, which leads us to investigate the strengths of these relationships and their consequent impacts on altruism. People who are committed to online community sites, such as open source software developers or members of electronic knowledge networks, are more likely than others to engage in reciprocal altruism because they are attached to the communities (groups) (Fiedler and Sarstedt 2010; Ren et al. 2012). Hence, we propose:

H₇ : Group attachment to mobile sites has a significant and positive effect on reciprocal altruism.

According to Thomson and Johnson (2006), reciprocal altruism refers to social interactions in which one person does something nice (makes a sacrifice) for the other with the expectation of receiving something nice (a reward) in return; thus there is mutuality or bidirectionality of rewards. Stronger social attachments are positively related to increasing altruistic reciprocity (Thomson and Johnson, 2006). The connection between attachment and altruistic behavior is most likely to occur in close relationships (Cialdini et al. 1997). Attachment promotes altruistic behaviors (Mikulincer and Shaver 2005) and this connection of attachment to reciprocal altruism works similarly in different environments (e.g., western and eastern cultures) (Mikulincer et al. 2005). Since visitors with high levels of site attachment are more likely to interact with others on the site and to actively help others on the site (Ren, Kraut, and Kiesler 2007; Ren et al. 2012) we propose that higher levels of site attachment result in higher levels of reciprocal altruism. Hence:

H₈ : Site attachment to mobile sites has a significant and positive effect on reciprocal altruism between users of the sites.

Similar reasoning applies to people who have relationships with specific individuals (interpersonal attachments) on websites. Fiedler and Sarstedt (2010), Ren, Kraut, and Kiesler (2007), and Ren et al. (2012) find that reciprocal altruism can occur at the dyadic level (i.e., when one’s giving is reciprocated by the other). This leads us to propose:

H₉ : Interpersonal attachments on mobile sites have a significant and positive effect on reciprocal altruism.

Based on the hypotheses above, the research model in Figure 1 is proposed. This model examines relationships between trust, enjoyment, timesaving, site attachment, group attachment, interpersonal attachment, and reciprocal altruism among consumers of online tourism shopping sites.

Figure 1.

Proposed research model

Method

Measurement

A preliminary list of items to measure trust, enjoyment, timesaving, attachment, and altruism was generated from previous research. Specifically, to measure trust, four items were adopted from previous studies (Kim, Chung, and Lee 2011; Kim et al. 2012) (e.g., “The mobile shopping site has integrity”). Four items used to measure enjoyment were all adopted from Cyr, Head, and Ivanov (2006) and Ha, Yoon, and Choi (2007) (e.g., “Shopping for tourism products on the mobile site is enjoyable”). To measure timesaving, we adopted four items from Kohli, Devaraj, and Mahmood (2004) and Punj (2012) (e.g., “Less time was spent in making this purchase from the mobile shopping site than would have been spent on conventional shopping sites”). Site attachment was measured with four items adopted from Choi (2013), Ma and Chan (2014), and Ren et al. (2012) (e.g., “I like to shop on the mobile shopping site more than on other sites”). To measure group attachment, we obtained four items directly from Chung and Koo (2012), Ren, Kraut, and Kiesler (2007) and Ren et al. (2012) (e.g., “I feel like the mobile shopping site is a part of me”). Interpersonal attachment was measured with five items adopted from Fiedler and Sarstedt (2010), Ren, Kraut, and Kiesler (2007), and Ren et al. (2012) (e.g., “I would like to interact with users of the mobile shopping site in the future”). In particular, to measure reciprocal altruism we developed seven items based on Fiedler and Sarstedt (2010), Leider et al. (2009), Ren, Kraut, and Kiesler (2007), and White and Peloza (2009) (e.g., “I share my opinion of tourism products on the mobile shopping site with others”). All of these 32 items were measured on a 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7).

Once the survey items were generated, four academic experts in tourism management information systems were invited to check the content validity of the items. Also, four field professionals in mobile shopping for tourism products were consulted to confirm whether the measurement items were suitable for assessing the respective constructs among mobile tourism shoppers. Then, a pretest was conducted with 50 people who had purchased tourism products using smartphones in the previous year. Based on the results of these three steps, four items (i.e., one item from trust, one item from site attachment, and two items from interpersonal attachment) were eliminated from the questionnaire and five items were rephrased to fit the tourism mobile shopping context in Korea. Two items of site attachment were rephrased: “I feel the mobile shopping site is an ideal place to shop,” and “I will do my best to recommend the mobile shopping site to others,” and three items of group attachment were rephrased: “I feel like the mobile shopping site is a part of me,” “I am a typical user of the mobile shopping site,” and “It would feel very good to be described as a typical user of the mobile shopping site.”

Data collection

To collect data we employed a top-ranking Korean survey firm (Embrain). Online invitations to participate in the survey were sent to 50,234 potential respondents chosen at random from a panel of approximately 980,000 Koreans. Confirmations of email addresses were received from 2,832 respondents and 2,267 respondents connected to the questionnaire. A qualifying question asked if the respondents had purchased tourism products through smartphones in the prior year and responses were collected only from those who responded in the affirmative (502 respondents). To help respondents’ understanding, the definition of tourism products was provided in detail on the first page of the questionnaire; the definition stated that tourism products include overseas and domestic travel arrangements, hotels and accommodations, tickets for festivals and events, leisure and recreation activities, admissions to resorts and theme parks, spas and adventure tourism, airline and transportation tickets, and coupons for tourism products. Also, the meaning of a mobile shopping site for tourism products (a website used to purchase or book tourism products through smartphones) was presented at the beginning of the questionnaire, as was the meaning of mobile devices (e.g., smartphones).

At the beginning, each respondent was asked to provide the name of a mobile site from which the respondent had recently purchased or booked a tourism product. The name of the site mentioned by the respondent appeared on the questionnaire screen for every subsequent question. Of the 337 completed responses received from qualified subjects (67.1% response rate), 332 questionnaires were coded for analysis after the data were checked for outliers (Hair et al. 2010). According to Cook, Heath, and Thompson (2000), despite the popularity of online surveys in terms of fast response and cost-effectiveness, the online response rates tend to be relatively low. Response rates for e-mail surveys are reported as ranging from 6% to 73% compared with response rates for mail ranging from 27% to 56% (Weible and Wallace 1998). Therefore we conclude the response rate for this study is acceptable.

Analysis

The collected data were analyzed with AMOS 18.0 structural equation modeling (SEM) software (Arbuckle 2009). SEM is designed to evaluate how well a proposed model or hypothetical construct explains the collected data (Hair et al. 2010). The two-step approach advanced by Anderson and Gerbing (1992) was used for data analysis, first testing convergent validity and discriminant validity of the measurement model, followed by testing the research hypotheses and structural model. Normality of the data was tested by using maximum-likelihood estimation (MLE) in AMOS; this is a robust method to assess normality (Kline 2011). As shown in Table 2, the absolute values of skewness and kurtosis range from 0.007 to 0.648 and from 0.21 to 0.866, respectively; both fall within conventional criteria of multivariate normality (Kline 2011). In addition, MLE was employed to test our research model because MLE is more efficient and unbiased than other common methods when the assumption of multivariate normality is met (Hair et al., 2010). Moreover, MLE is a flexible approach to parameter estimation where the most likely parameter values to achieve the best model fit are found (Hair et al., 2010).

Results

Profile of the respondents

As shown in Table 1, female respondents (60.2%) outnumbered males (39.8%). Slightly more than half of the respondents (51.2%) fell into the 20-29 year old bracket and about one quarter (25.3%) fell in the 30-39 year old bracket. Respondents who were attending university or who had university degrees comprised a majority of the respondents (66.8%). Single respondents (71.1%) outnumbered married respondents (28.9%). Approximately half the respondents (51.7%) had incomes more than US$2,000 per month and 21.0% had incomes greater than US$4,000 per month. Students were the largest proportion of respondents (36.1%) and office workers comprised the second largest proportion (28.7%).

Table 1.

Demographic characteristics of respondents

Characteristics	N(332)	%(100)
Gender
Male	132	39.8
Female	200	60.2
Age
Below 20	44	13.3
20 - 29	170	51.2
30 - 39	84	25.3
40 - 49	25	7.5
Over 50	9	2.7
Education
Middle/high school	52	15.7
2-year college	58	17.5
University	194	58.4
Graduate school	28	8.4
Marital status
Single	236	71.1
Married	96	28.9
Monthly personal income
Less than US$1,000	83	25.1
$1,000 - $1,999	77	23.2
$2,000 - $2,999	62	18.7
$3,000 - $3,999	40	12.0
$4,000 - $4,999	41	12.3
More than $5,000	29	8.7
Occupation
Professional	50	15.1
Business person	22	6.6
Services	10	3.0
Office worker	95	28.7
Civil servant	15	4.5
Homemaker	16	4.8
Student	120	36.1
Other	4	1.2

Table 2.

Results of factor analysis and assessment of normality

Construct	Items	Factor loadings	Skew-ness	Kurto-sis
Trust	1. The mobile shopping site has integrity.*	-	-	-
	2. The mobile shopping site is reliable.	0.817	-0.439	0.812
	3. The mobile shopping site is trustworthy.	0.790	-0.337	0.714
Enjoyment	1. Shopping for tourism products on the mobile site is enjoyable.	0.860	-0.264	0.206
	2. Shopping for tourism products on the mobile site is pleasurable.*	-	-	-
	3. Shopping for tourism products on the mobile site is fun.	0.904	-0.463	0.842
	4. Shopping for tourism products on the mobile site is interesting.	0.824	-0.317	0.362
Timesaving	1. Less time was spent in making this purchase from the mobile shopping site than would have been spent on conventional shopping sites.	0.799	-0.396	-0.019
	2. Shopping for tourism products from the mobile shopping site saves time.	0.849	-0.648	0.866
	3. By purchasing tourism products from the mobile shopping site I can save more time than I could by using other shopping options.	0.837	-0.587	0.532
	4. Purchasing tourism products from the mobile shopping site is the right choice if saving time is considered.	0.827	-0.341	0.505
Site	1. I like to shop on the mobile shopping site more than on other sites.	0.774	-0.624	0.548
attachment	2. I feel the mobile shopping site is an ideal place to shop.	0.683	-0.299	0.252
	3. I will do my best to recommend the mobile shopping site to others.*	-	-	-
Group	1. I feel like the mobile shopping site is a part of me.	0.792	-0.236	-0.165
attachment	2. I am a typical user of the mobile shopping site.*	-	-	-
	3. Belonging to the mobile shopping site is very important for me.	0.849	-0.256	-0.301
	4. It would feel very good to be described as a typical user of the mobile shopping site.	0.771	-0.326	-0.007
Interpersonal attachment	1. I would like to interact with users of the mobile shopping site in the future.	0.836	-0.151	-0.641
	2. I feel very close to the other users of the mobile shopping site.	0.915	-0.007	-0.780
	3. Many users of the mobile shopping site have influenced my thoughts or behaviors.	0.711	-0.295	-0.671
Reciprocal altruism	1. I share my opinion of tourism products on the mobile shopping site with others.	0.851	-0.573	-0.195
	2. I write reviews of tourism products on the mobile shopping site to assist others, as do they.	0.820	-0.418	-0.120
	3. I write evaluations of tourism products on the mobile shopping site for others in the same way they do.	0.862	-0.380	-0.149
	4. I inform others of my opinions of tourism products on the mobile shopping site in the same way they do.	0.813	-0.533	0.376
	5. I post information about tourism products available on the mobile shopping site for friends to see on my mobile social network site.	0.746	-0.424	-0.021
	6. I let others know about the tourism products available on the mobile shopping site by posting comments to my mobile social network site, as do they.	0.819	-0.326	-0.225
	7. I let friends know about my reviews of tourism products on the mobile shopping site through my mobile social network site, as do they.	0.861	-0.365	0.056

* The item was deleted following confirmatory factor analysis.

Exploratory factor analysis

In order to measure reciprocal altruism, items were developed and tested using exploratory factor analysis. In terms of the reliability of the measurement scale for reciprocal altruism, Cronbach’s alpha for the seven items used is 0.936, indicating reliability (Nunnally and Bernstein 1994). Accordingly, further statistical analysis is appropriate using this scale. For the adequacy of exploratory factor analysis (EFA), inspection of the correlation matrix and the anti-image correlation was performed. The results show that the data matrix is well suited to factor analysis, based on the overall significance of the correlation matrix with Bartlett’s test of sphericity (p < 0.000) and the Kaiser– Meyer–Oklin (KMO) measure of sampling adequacy (0.936) (Nunnally and Bernstein 1994). The results of the EFA show that all factor loadings are greater than 0.7, the total eigenvalue is 5.080, and the total variance explained is 72.58%. Thus, these reciprocal altruism items are employed as exogenous constructs in the structural equation model proposed in this study.

Confirmatory factor analysis

Confirmatory factor analysis (CFA) involves revision of measurement models by dropping items that share high degrees of residual variance with other items (Arbuckle 2009). AMOS 18.0 was used for this purpose and, as a result, four items were dropped (Babin and Boles 1998). Model fit for the measurement model is good [χ² = 379.364/df = 231 ≤ 3.0 (Hayduck 1987), p < 0.001; goodness-of-fit index (GFI) = 0.916 ≥ 0.9 (Bagozzi and Yi 1988); adjusted goodness-of-fit index (AGFI) =0.891 ≥ 0.8 (Scott 1994); normed fit index (NFI) =0.933 ≥ 0.9 (Bentler and Bonett 1980); comparative fit index (CFI) =0.973 ≥ 0.9 (Bagozzi and Yi 1988); root mean square error of approximation (RMSEA) is 0.044 at the 90% confidence interval ≤ 0.08 (Bagozzi and Yi 1988)]. Results show that every index exceeds recommended values, indicating an adequate fit to the collected data.

Convergent validity was checked using three criteria. First, the standardized factor loading of each item must be statistically significant and greater than 0.7 (Gefen, Straub, and Boudreau 2000). Second, the composite reliability (CR) and Cronbach’s α for each construct must be larger than 0.6 (Nunnally 1967). Third, the average variance extracted (AVE) for each construct must exceed 0.5 (Fornell and Larcker 1981). The standardized factor loadings are all significant and greater than 0.7. The CR for all constructs exceeds 0.7 and Cronbach’s α for all constructs exceeds 0.6. The AVE for each construct is greater than 0.5 (see Tables 2 and 3). Therefore, the convergent validity of the constructs is supported (Bagozzi and Yi 1988). The discriminant validity of the measurement model is checked by comparing the square root of the AVE for each construct with the correlations between each construct and the other constructs. If the square root of the AVE is greater than the correlations between each construct and the other constructs, discriminant validity is indicated (Fornell and Larcker 1981). As shown in Table 3, the square root of the AVE for each construct exceeds the correlations between that construct and the other constructs. Therefore, discriminant validity is established.

Table 3.

Tests of convergent and discriminant validity

Construct	1	2	3	4	5	6	7	Mean	S.D.
1. Trust	0.804							5.023	0.886
2. Enjoyment	0.532	0.863						5.152	0.934
3. Timesaving	0.403	0.464	0.828					4.946	0.998
4. Site attachment	0.522	0.548	0.400	0.730				4.886	0.935
5. Group attachment	0.346	0.360	0.436	0.556	0.805			4.061	1.202
6. Interpersonal attachment	0.249	0.258	0.347	0.388	0.695	0.825		3.612	1.325
7. Reciprocal altruism	0.283	0.382	0.353	0.452	0.594	0.602	0.825	4.188	1.163
AVE	0.646	0.745	0.686	0.533	0.648	0.681	0.681
Composite reliability (CR)	0.785	0.898	0.897	0.694	0.846	0.863	0.937
Cronbach’s α	0.783	0.897	0.896	0.686	0.843	0.862	0.936

Note: The diagonal elements in boldface in the “correlation of constructs” matrix are the square root of the AVE. All correlations are significant at p < 0.01.

Structural model

Table 4 shows the estimates from the structural modeling. The model-fit indices for the structural model provide evidence of a good model fit (χ² = 406.560, df = 240, p < 0.001; GFI = 0.910; AGFI = 0.887; NFI = 0.928; CFI = 0.969; RMSEA = 0.046). Figure 2 shows the standardized path coefficient, path significance, and variance explained (R² ) for each path; with the exception of H₇, all hypotheses are supported by the path analysis results.

Figure 2.

Path analysis results

Table 4.

Standardized structural estimates and tests of the hypotheses

Hypothesis	Path		Estimate (t-value)	Test result
H₁	Trust → Site attachment		0.345 (4.115)	Supported
H₂	Enjoyment → Site attachment		0.352 (4.472)	Supported
H₃	Timesaving → Site attachment		0.199 (3.049)	Supported
H₄	Site attachment → Group attachment		0.410 (7.041)	Supported
H₅	Interpersonal attachment → Group attachment		0.615 (10.570)	Supported
H₆	Site attachment → Interpersonal attachment		0.479 (7.094)	Supported
H₇	Group attachment → Reciprocal altruism		0.032 (0.232)	Not Supported
H₈	Site attachment → Reciprocal altruism		0.298 (3.413)	Supported
H₉	Interpersonal attachment → Reciprocal altruism		0.507 (4.815)	Supported
	R²
	Site attachment	0.576
	Group attachment	0.787
	Interpersonal attachment	0.230
	Reciprocal altruism	0.532

For site attachment, 57.6% of the variance in site attachment is explained by the direct effects of trust, enjoyment, and timesaving. More than three quarters of the variance in group attachment (R² = 0.787) is explained by the direct effects of site attachment and interpersonal attachment, and 23.0% of the variance in interpersonal attachment is explained by the direct effect of site attachment. Additionally, more than half of the variance in reciprocal altruism (R² = 0.532) is explained by the direct effects of group attachment, site attachment, and interpersonal attachment.

Hypotheses 1, 2, and 3 postulate positive relationships among trust, enjoyment, timesaving, and site attachment. The results of hypothesis testing indicate that site attachment is positively influenced by trust (β = 0.345, p < 0.001), enjoyment (β = 0.352, p < 0.001), and timesaving (β = 0.199, p < 0.01). Thus, Hypotheses 1, 2, and 3 are supported.

Hypotheses 4, 5, and 6 posit structural relationships between site attachment, interpersonal attachment, and group attachment. Site attachment is found to have a positive effect on group attachment (β = 0.410, p < 0.001), thus supporting Hypothesis 4. Interpersonal attachment is found to have a positive effect on group attachment (β = 0.615, p < 0.001), supporting Hypothesis 5. Site attachment has a positive and significant effect on interpersonal attachment (β = 0.479, p < 0.001); thus, supporting Hypothesis 6.

Finally, Hypotheses 7, 8, and 9 posit that reciprocal altruism is associated with group attachment, site attachment, and interpersonal attachment, respectively. Results indicate that reciprocal altruism is positively influenced by site attachment (β = 0.298, p < 0.001) and interpersonal attachment (β = 0.507, p < 0.001). Thus, Hypotheses 8 and 9 are supported. However, the path from group attachment to reciprocal altruism is not significant (β = 0.032, n.s.), so Hypothesis 7 is not supported.

Discussions and conclusion

This study applies attachment theory to examine the relationships between formation variables (trust, enjoyment, and timesaving), transfer variables (site, group, and interpersonal attachments), and an outcome variable (reciprocal altruism). The results demonstrate that trust, enjoyment, and timesaving have significant effects on site attachment, which, in turn, significantly influences group and interpersonal attachments, and reciprocal altruism. Interpersonal attachment also has significant effects on group attachment and reciprocal altruism. Contrary to our expectation, group attachment is not shown to have a significant effect on reciprocal altruism. Based on theoretical and practical insights from the findings, tourism academics and marketers can leverage mobile sites that engage consumers through formation and transfer of attachments that, in turn, encourage reciprocal altruism.

Theoretical implications

This study provides several theoretical contributions to the tourism literature. First, trust is found to promote site attachment, which in turn influences group and interpersonal attachments and reciprocal altruism, extending the results of Jahn, Gaus, and Kiessling (2012) and Louis and Lombart (2010). The second contribution is to show the significant effects of enjoyment on site attachment that are vital to group and interpersonal attachments and reciprocal altruism, extending the findings of Childers et al. (2001) and Vlachos et al. (2010). Another theoretical finding is that timesaving has a significant effect on site attachment, which leads to group and interpersonal attachments and reciprocal altruism, broadening the results of Alreck et al. (2009) and Cho (2004). Thus, the findings of our study add new insights to the body of knowledge on the effects of the variables of trust, enjoyment, and timesaving on site attachment in ways that can help manage mobile shopping sites for success.

Further, this study builds on the work of Ren, Kraut, and Kiesler (2007) and Ren et al. (2012) and illustrates the effects of interpersonal attachment on group attachment, implying that interpersonal attachment is vital to the success of mobile sites. Additionally, this research shows that site and interpersonal attachments significantly increase member altruism, demonstrating the effectiveness of the attachment formation and transfer approach, extending the findings of Doherty and Feeney (2004), Fraley and Davis (1997), and Freeman and Brown (2001). Contrary to our expectation, the study reveals some challenges for future research on the relationship between group attachment and reciprocal altruism, which were not shown to have significant effects. This result implies that mobile shoppers for tourism products are likely to get help from and give help to other individual members on the site. This study is the first to show how attachment transfer theory can be used to gain insights into consumers’ use of and relationships to mobile shopping sites for tourism products in order to predict shoppers’ reciprocal altruism with other members of the sites.

Practical implications

The findings of this study offer several practical implications for tourism operators. In the competitive tourism shopping space, inducing site attachment through trust, enjoyment, and timesaving could create a competitive advantage through creation of high levels of customers’ group and interpersonal attachments. For example, tourism operators can increase site attachment by designing their shopping sites to be reliable, to be pleasurable, and to waste less time. Additionally, operators could include unedited reviews (i.e., e-word of mouth) by verified customers and a rating system (for example 1-5 stars) for the purchase process and a separate rating for the product or service purchased.

The attachment transfer process has practical implications through which tourism managers can enhance their marketing strategies for attracting and keeping shoppers. The findings suggest that tourism businesses should encourage their customers to engage in altruistic behaviors; this could be done directly by asking customers to post reviews and answer questions from other customers, and also by increasing site attachment by making the sites fun for customers to shop and reminding customers that the site is the best place to shop.

Raising interpersonal attachment can help make mobile sites successful. Accordingly, tourism businesses should design their mobile sites in ways that enhance interpersonal attachments between users of the site. To increase interpersonal attachment, tourism practitioners must elevate site attachment; this can be done by remaining trustworthy, by making it easy and quick for customers to make purchases resulting in timesaving, and by making the sites enjoyable and fun to shop. Stronger site attachment is important because customers are more attached to other members when those customers have stronger attachments to the sites. Moreover, tourism marketers should raise interpersonal attachment by stimulating their customers to interact with other users by encouraging behaviors that result in their users feeling close to other users, and by reminding users that others depend on users’ comments and reviews to make informed decisions on the mobile shopping site.

In order to boost reciprocal altruism among shoppers, tourism managers should motivate their customers to share their opinions and experiences, write reviews and evaluations, and provide information to other users of those mobile sites. Specifically, managers must make it easy for their mobile shopping customers to share opinions, reviews, and evaluations of tourism products with others. Tourism operators can link their sites to widely used social networks such as Facebook and Twitter so their customers can let friends know about products, services, reviews, and other items of interest. Accordingly, marketers should encourage their customers to post information and comments about their tourism purchases on other social network sites (i.e., both mobile and non-mobile social network sites).

Limitations and future directions

Caution should be used in applying these findings to other than smartphone users as some of the findings may not apply to other types of mobile devices. Further research is needed in order to determine whether users of other mobile devices (e.g., tablets) generate similar results. Future research is needed to determine whether attachments and reciprocal altruism can be more easily fostered in various stages of travel. For example, there may be differences between consumers whose travels are some time in the future, those who are very close to their travel dates, those who are currently traveling, and those who have completed their travels. In the future, researchers should explore a broader set of theoretical concepts, such as social network or social exchange theories depending on customers’ involvements in mobile social network sites. Lastly, this study was performed in Korea and it is not known if the results apply to travelers in other countries and from other cultures.

Concluding remarks

This research demonstrates that attachment transfer theory can be successfully applied in understanding reciprocal altruism. Furthermore, we find that trust, timesaving, and enjoyment are important elements of mobile shopping for tourism products and influence users’ site, group, and interpersonal attachments and engagement in reciprocal altruism. These findings have important practical implications for mangers of mobile tourism sites and theoretical implications for researchers seeking to understand attachment and altruism among shoppers using smartphones to purchase mobile tourism products.

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