Journal of Data and Information Science, 2018, 3(2): 78-104
doi: 10.2478/jdis-2018-0010
The Effects of B2C Interaction on Customer Loyalty
Xinyue Yang, Qinjian Yuan
School of Information Management, Nanjing University, Nanjing 210023, China
 Cite this article:
Xinyue Yang, Qinjian Yuan. The Effects of B2C Interaction on Customer Loyalty. Journal of Data and Information Science[J], 2018, 3(2): 78-104 doi:10.2478/jdis-2018-0010

Abstract:

Purpose: This research attempts to examine the relationship between B2C interaction and customer loyalty in Business-to-Customer (B2C) context from a new perspective of the interactive tool.Design/methodology/approach: The scale for B2C interactive tools is of seven dimensions: efficiency, security, fulfillment, mobility, community, cultivation, and customization. A model reflecting the influences of these attributes on customer loyalty is developed and empirically examined based on data collected from 265 B2C customers. Findings: Results reveal that the fulfillment, mobility, community, and customization of B2C interactive tools can enhance customer loyalty directly and significantly. Efficiency and security, serving as the premise for possible purchase behavior, facilitate fulfillment. In addition, cultivation promotes the formation of customization, which directly strengthens customer loyalty.Research limitations: Models considering individual-level indicators and combined with classic loyalty mechanisms in B2C context may lead to a deeper understanding of the tested effects of interaction on customer loyalty.Practical implications: To strengthen B2C interaction and further cultivate loyal customers, making interactive tools more fundamental, flexible, and personalized is critical for B2C enterprises. Originality/value: This study proposes a new perspective from interactive tools when measuring the relationship between B2C interaction and customer loyalty, and offers a useful theoretical lens and reasonable explanations for investigating customer loyalty in B2C e-commerce context.

Key words: B2C e-commerce) ; Interaction) ; Interactive tool) ; Customer loyalty)
1 Introduction

Web 2.0 offers B2C enterprises unprecedented opportunities to understand their customers better. As the strongest trait of web 2.0, interaction has become increasingly prominent due to the more open Internet and widely-shared information and has tremendous potential for strengthening bonds with customers. McKnight et al.’s (2002) study of customer relationships in e-commerce found that interaction shows the positive attitude of online suppliers to consumers and customers’ interaction with online sellers has significant effects on their trust in sellers. Wu and Hsing (2006) found that consumers’ perceived interaction shapes their attitudes and purchase behavior. Levy et al. (2011) proved that the interaction with consumers promoted by enterprise could help to increase the satisfaction and enjoyment of consumers, thereby improving its relationship marketing performance. Koufaris et al. (2001) believe that the degree of online interaction positively affects the consumers’ shopping pleasure and concentration of attention. This enjoyable experience helps to maintain a good relationship with consumers, and thus strengthen their loyalty.

Interaction serves as one of the most prominent features of the online shopping environment. Online enterprises provide consumers with interactive tools that are conducive to attracting consumers’ attention and enable consumers to perceive and experience online shop interaction and enterprises (Wu and Hsing, 2006). For average customers, it is nearly impossible to talk with B2C enterprises face to face. It is the interactive tools provided by B2C enterprises that play the role of the direct communicator. In B2C interaction, users can quickly and accurately find the information they need through various interactive tools and can get answers timely when encountering problems. Good interactive tools bring more information to the customer while providing real-time feedback on customer demand. By promptly resolving the user’s problems, B2C companies can narrow the distance with customers, improve customer service levels, enhance customer satisfaction and even stimulate customers’ desire to purchase (Yang et al., 2012).

In this case, the features and attributes B2C interactive tools owned are perceived and evaluated by customers. According to Burgoon et al. (2000), structural characteristics are represented in certain communication tools and approaches, and the experience characteristics produce the perception of interaction process, during which the structural characteristics play a role. The structural features of interactive tools that can support interaction are the material basis for interaction. With such
a foundation, users can use the medium (i.e. interactive tools) to interact and get experience and perception of B2C enterprises. If customers become interested in and attached to several interactive tools of one B2C enterprise, it is reasonable to believe that this enterprise is highly appreciated and embraced by customers, which may result in high loyalty eventually. The analysis of B2C interactive tools is to serve for the process of user demand induction, information search, purchase decision, and online purchase, in order to ultimately improve the quality of interaction and customer loyalty (Yang et al., 2012). Therefore, we take a new approach, adopt the perspective of B2C interactive tools here and address the research questions: What are the attributes of B2C interactive tools? How do the influences of B2C interaction work on customer loyalty? Specifically, by studying the influence of
the common attributes of interactive tools on customer loyalty, we have clarified the attributes that significantly influence customer loyalty, thus indicating the improvement trends of future B2C interactive tools and further promoting the development of B2C interaction.

2 Theoretical Background
2.1 Customer Loyalty

Early views of customer loyalty focus on customers’ behavior, a unidimensional perspective, using observed data to measure brand or customer loyalty. Tucker (1964) viewed customer loyalty as consisting of three-time continuous repeated purchases toward one brand. Except for purchase frequency, Kuehn (1962) and Cunningham (1956) regarded customer loyalty as the probability of product repurchase and the proportion of purchase toward a favorable brand, respectively. More directly, there may be a linear functional relationship between purchase frequency toward a certain brand and customer loyalty (Ha, 1998). Although providing an early scientific understanding of customer loyalty, these studies equate continuous patronage of customers to customer loyalty and show only direct performances rather than underlying causes.

Noting that repeat purchases may reflect situational constraints such as perceived lack of alternatives (Storbacka et al., 1994) and strengthening loyalty is not a matter of simply cutting prices or adding product attributes (Reichheld, 1993), researchers turn to attitudinal perspective at the psychological level. Attitude orientation represents the degree of consumers’ positive psychological tendency to the service providers (Jacoby & Chestnut, 1978). It could also manifest the commitment of customers’ selecting the enterprise as the first choice to buy services and actively recommending it to people around (Gremler & Brown, 1996).

In response to both preference attitude and behavior performance, later studies constructed integrated frameworks to explore customer loyalty. Griffin (1995) cross-compared low and high relative attachment with high and low repeat purchase frequencies and divided customer loyalty into four types. Customer loyalty could be considered as the degree to which a customer exhibits repeat purchase behavior, possesses a positive attitudinal disposition toward the provider, and considers using only this provider when a need for this service arises (Gremler & Brown, 1996). Following this trend, researchers nowadays generally refer customer loyalty to both preference attitude and behavior performance.

The rapid development of e-commerce deepens the concept of customer loyalty into e-loyalty, a term specific to e-commerce context. E-loyalty is related to customers’ repeat visiting rates to e-commerce sites, representing the level of interaction between online sellers and customers (Smith, 2000). Although e-loyalty is unique in its manifestation with regard to customer behavior (Gommans et al., 2001), the theoretical basis of classic customer loyalty and e-loyalty is similar. If consumers like one e-commerce site very much and often go shopping on this site, the consumers are e-loyalty to the e-commerce site (Srinivasan et al., 2002). Loyalty in e-commerce context can also be divided into attitudinal loyalty, represented by customer’s positive word of mouth, and behavioral loyalty, represented by customer repurchase intention (Stokes, 2014). For research purpose, customer loyalty in this research could be defined as the combination of customer’s favorable attitudes toward one B2C enterprise and repeat purchasing behaviors through various kinds of interactive tools.

2.2 B2C Interaction and Interactive Tools

The concept of interaction could be traced back to interactivity, a virtue related to communication and interaction. It reflects the degree to which participants in an interaction process could exchange roles and have control over their mutual discourse (Rogers, 1995), as well as the extent to which the communicator and the audience respond to each other’s communication need (Ha & James, 1998). The Internet has given new meanings to interactivity, covering the comprehensive and systematic interaction between communication participants, communication medium, and information elements. Liu and Shrum (2002) summarized studies on the interactivity of different scholars, and proposed that “interactivity is the degree to which two or more communication parties can act on each other, on the communication medium, and on the messages and also the degree to which these influences are synchronized during interaction.” This process-related variable relies on web interaction and shows the quality or condition of interaction (Liang et al., 2010).

Web interaction, involved with interactive tools, tools or devices, allows various entities to engage in mediated communication (Varadarajan et al., 2010). Bauer et al. (2002) suggested that Internet-related technologies, especially interaction, could be used as tools to build relationships with customers. The enterprise and consumer are the main participants of interaction, the interactive tool is the medium, and the information exchanged by participants is the purpose of the interaction. In online commerce, interaction represents the high level of engagement and communication between the buyers and the sellers and emphasizes immediate and mutual communication (Bao et al., 2016). Increased interaction improves the perception of social presence and empowerment, thus leading to higher satisfaction and probability of revisits (Dholakia et al., 2000). The level of customers’ overall satisfaction is partially determined by user perceived interactivity during communication (Lee, 2017). Merrilees and Fry (2003) also pointed out that a more general and comprehensive role of interaction is to create a complete online shopping experience.

It is of high strategic relevance in dynamic and competitive environments to build strong customer relationships (Aquilani et al., 2016), therefore B2C enterprises like Amazon and JD.com have developed various kinds of interactive methods, tools, and strategies, trying hard to get in touch with their consumers effectively and present idealized images of themselves. The interactive performance of interactive tools has a positive effect on the perception of interaction and the improvement of the customer relationship. For example, interactive websites lead to customers’ higher favorability toward the websites and greater flow state intensity (Sicilia, Ruiz, & Munuera, 2005). Common interactive tools and their features are summarized in Table 1. By adopting and developing all kinds of interactive tools, B2C enterprises change and improve the interactive experience of consumers, thus improving customer loyalty.

Table 1

Several common interactive tools.

Interactive tools (i.e. the medium or the role of the machine in human-machine interaction) play a key role in the interaction. Interactivity is embodied in the various functions of interactive tools and cannot be separated from the use of specific interactive tools (Mcmillan, 2000; Straubhaar & Larose, 1997). The popularity of various tools reflects the innovation of e-commerce interaction compared to traditional business interaction. Interpersonal interaction cannot happen face to face and therefore it is inseparable from the use of interactive tools (Levy et al., 2011; Liu & Shrum, 2002; Mcknight et al., 2002; Wu & Hsing, 2006). In a series of interactive processes such as searching for needs, exchanging information, and conducting transactions in interactive tools, consumers will form a certain degree of perception and judgment on these tools and online suppliers based on the process, and further develop their attitudes towards online suppliers. Whether exploring the evaluation of online B2C interaction or studying purchase behavior and trust tendency based on B2C interaction (Arnould & Price, 1993; Koufaris et al., 2001), researchers generally measure certain characteristics of interactive tools (especially websites and virtual communities) to address interaction-related problems.

Since B2C interaction is a multidimensional and complex concept (Liu & Shrum, 2002; Rafaeli & Sudweeks, 1997), B2C interaction in this study is considered as the process of information acquisition, exchange, and transfer between B2C companies and consumers, using interactive tools as a medium and for the purpose of trading goods or services. Its manifestation is embodied in various kinds of interactive tools, the instruments adopted, provided and developed by B2C enterprises and mainly used for buyer-seller interaction and customer relationship improvement. Some of the interactive tools also allow communication among customers, such as virtual community and instant messaging. Previous researchers have developed quality evaluation models of single and specific interactive tools (mainly websites) when measuring customer loyalty (Parasuraman et al., 2005; Yoo & Donthu, 2001). Interactive tools adopted by different B2C enterprises also have been empirically compared (Cova & Pace, 2006). However, little research explores how to measure interactive tools as a whole in B2C context, which is one of the research objects of this research.

3 Research Model and Hypothesis

Given the fact that different interactive tools share some common attributes (Cyr, 2008), seven significant attributes of B2C interaction are extracted and constructed in the theoretical model (shown in Figure 1).


Figure 1.

Theoretical Model.

3.1 Fundamental Interaction Attributes

Efficiency, security, and fulfillment are fundamental attributes of B2C interaction.

Efficiency, one of the most traditional and functional attributes for almost all kinds of applications and systems, represents the extent to which users can easily use technical systems without too much effort (Davis, 1989). It influences the user’s attitude towards the technology system and is also seen as the user’s desire to use the technology system. To be more specific in B2C context, efficiency refers to the consumer’s assessment of how easy it is to use it or how much effort is required when using an interactive tool (Koufaris & Hampton-Sosa, 2004), and can be measured with ease of finding a product, easy learning, and ease of use.

Increasingly fast-paced life leads to customers’ more attention to the convenience of shopping and communicating, making it a basis for B2C enterprises to attract and retain customers. Since B2C transactions are complex and daunting, customers could experience considerable difficulty in navigating pages and might be unlikely to complete desired transactions without efficiency (Jiang et al., 2016). Efficient interactive tools make it easy and quick for customers to find and buy the products they want. It is believed to have a direct impact on shopping attitudes and, in turn, on possible purchasing behavior (Ruyter, Dellaert, & Monsuwé, 2004). When consumers visit websites or virtual communities, open apps or other communication tools, what they need to learn first is how to use these interactive tools. They may not have the patience to explore, because the purpose of consumers is not to investigate technology and mechanism but to obtain more efficient and more useful information, facilitate purchase decision and implement purchase behavior. In addition, since the service provided by suppliers represent the corresponding suppliers in B2C e-commerce, when consumers use its interactive tools, we can infer that consumers’ evaluation of the efficiency of interactive tools will affect their acceptance of suppliers.

Besides efficiency, security has also drawn considerable attention in the e-retailing literature. Security influences customers’ sense of safety and trust towards online sellers (Wolfinbarger & Gilly, 2003) and serves as the technical function and defaulted feature embedded in almost every type of interactive tools. It indicates the degree to which customers believe what they are using is safe from intrusion and personal information is protected (Parasuraman et al., 2005). This concept can be divided into privacy (the protection of personal information) and so-called security (the protection of users from the risk of fraud and financial loss), and has been empirically proven to exert strong impact on customers’ attitudes toward the use of online financial services (Montoya-Weiss et al., 2003). For interactive tools especially websites, security has been found one of the key dimensions of success and the best predictor of transactional intent (Ranganathan & Ganapathy, 2002). Other interactive tools are also supposed to protect customer’s personal information and the security of the transaction process (Alzola & Robaina, 2005) Security, along with efficiency can be seen the reflection of system quality when measuring the interactivity of interactive tools from the quality factors (Ahn, Ryu, & Han, 2004). Related to trust in completing e-transactions, security is considered the little direct effect on customer loyalty (Jiang et al., 2016), but leads to reliable e-commerce transaction completion.

Fulfillment is the interactive attribute which transaction completion directly relies on. It has something common with reliability and features the technical function of interactive tools and the accuracy of service promises, billing, and product information (Zeithaml et al., 2002). Referring to the ability to make promises about order delivery and fulfill item availability, fulfillment has been proven one of the important dimensions of increasing customer loyalty (Parasuraman et al., 2005). Since users can use interactive tools to complete the transaction, get the experience of fulfillment, and form their perception of B2C enterprises, fulfillment promises the purchasing behavior and serves as one of the fundamental features of B2C interaction. Interactive tools with the attribute of fulfillment can perform the promised service dependably and accurately and offer the provision of reliable information and reliable service (Barnes & Vidgen, 2008). It reflects the system quality and information quality of interactive tools, ensuring customers’ vivid experience as well as facilitating purchasing decision (Ahn et al., 2004). Websites with the attribute of fulfillment are found to strongly predict customer loyalty and attitudes toward online sellers (Wolfinbarger & Gilly, 2003; Parasuraman et al., 2005). To achieve successful transaction, most of the interactive tools should have the ability to make promises about products and related matters, whether they are mobile applications (which can be seen as mobile “websites”) or purchase links embedded in virtual communities and instant messaging.

Before the fulfillment of B2C interaction, efficiency and security make sense. When customers feel interaction efficient and security, they are likely to use interactive tools to complete a purchase. Then there will be the chance for B2C enterprises to make continuing communications with customers and implement other advanced or personalized interactive functions. All of these reasons lead us to propose that:

H1. The fulfillment (FU) of B2C interaction has a positive influence on consumer loyalty (CL).

H1a. The efficiency (EF) of B2C interaction positively affects the fulfillment (FU) of B2C interaction.

H1b. The security (SE) of B2C interaction positively affects the fulfillment (FU) of B2C interaction.

3.2 Flexible Interaction Attributes

Nowadays, the call for B2C interaction is accessible at any time, any place and on any device. New types of e-commerce transactions could be conducted through mobile interactive tools, using wireless networks and other wired e-commerce technologies, such as phones and laptops. Using mobile interactive tools, business activities could be implemented without the limitation of time and space, thus increasing possible transactions. Clarke (2001) stresses the importance of mobility and suggests that mobile devices offer users the ability to perform transactions from virtually any location on a real-time basis, independent of the users’ location. The proliferation of mobile interaction is creating an unparalleled opportunity for B2C enterprises to leverage the benefits of mobility since users could conduct business in real time via mobile devices (Ngai & Gunasekaran, 2007). It is necessary to not only improve mobile applications but also make other interactive ways compatible with the mobile environment, such as full-established web pages and virtual communities shown on mobile terminals. Interactive tools with the attribute of mobility are promising since mobility allows more chances to cultivate customer loyalty. Hence, we propose the hypothesis as follow:

H2. The mobility (MO) of B2C interaction positively and directly influences consumer loyalty (CL).

Out of the common interest and need, people gather to communicate and exchange information, which highlights the importance of communities online. Interaction with community provides B2C enterprises chances to know customers much better than before. Enterprises’ success lies in such understanding of customer preferences and habits, which comes from the comprehensive grasp of user consumption information, and in the ways of gathering information (Kaplan & Haenlein, 2012). Online providers often offer user interactive forums and comment sections on their sites to allow readers to express their opinions and perspectives and to interact with other users (Rowe, 2015). In addition, the enterprise-sponsored virtual community is a good place where consumers get help in knowledge and experience from other consumers. Different users can publish their views on certain goods and services, their own consumption experience and evaluation of online suppliers, seek opinions and suggestions from other consumers and online sellers. The impact of web communities has been tested to show a significantly positive influence on consumers’ purchase intentions (Brengman & Karimov, 2012). It is consistent with the finding that customer satisfaction and loyalty could be affected by community integration (McAlexander et al., 2003). Therefore, we hypothesize that:

H3. The community (CO) of B2C interaction is positively associated with consumer loyalty (CL).

3.3 Customer Personalization Attributes

Cultivation is the extent to which relevant information and incentives are embedded in interactive tools by B2C enterprises to its customers, for extending the breadth and depth of their purchases over time (Srinivasan et al., 2002). Taking full advantage of cultivation-related technologies like databases and market surveys, B2C enterprises could analyze the behaviors and features of customer consumption, thus increasing customers’ repeat purchase frequency. Mulvenna et al. (2000) emphasized the importance of the appropriate and relevant responses to the customer’s behavior during interaction with customers. Desired information and cross-selling offers are provided and spread through B2C interaction to strengthen user stickiness and cultivate loyal customers. Amazon.com, a case in point, uses questionnaires or collaborative filtering to recommend books, videos, music, and toys (Dholakia et al., 2000). If interactive tools can provide consumers with recommendations and information that suits them, such as business recommendations, product suggestions, online responses to their questions and other intimate services, the quality of interaction and visitor volume will be increased (Ghose & Dou, 1998). In the meantime, such a cultivation cycle continuously enhances the retailer’s knowledge base regarding the customer, lessening the customer’s incentive to defect to another e-retailer who has to build such knowledge base from scratch.

With the deepening cultivation toward customers, B2C enterprises enter into the customization stage, which is about how much and how easily interactive tools could be tailored to individual customers’ preferences, histories, and ways of shopping (Zeithaml et al., 2002). The purpose of personalization is to provide customers with what they want to avoid a needle-in-a-haystack problem. Customized interactive tools give customers personal attention and do a pretty good guessing what kinds of things customers might want and make suggestions (Wolfinbarger & Gilly, 2003). By personalizing their shopping experience (Alba, 1997) or allowing customers’ change about the appearance look of interactive tools, interactive tools can provide them with more customized services. Customization requires “understanding the individual” and stresses enterprises’ empathy with customers to provide the right products, prices, and content (Barnes & Vidgen, 2008). It is the ultimate goal of cultivation and can be seen as the function of interaction (Straubhaar & Larose, 1997). In other words, enterprises conduct effective cultivation to analyze customers, while customization requires recognizing customers. Enterprises provide personalized products and services to customers and provide customers with customized trading platforms and decision-making platforms to further meet the individual needs of customers, enhance the customer’s sense of identity and preferences of the enterprise, and further enhance customer loyalty (Wu, 2000). Customized interactive tools are an effective instrument for achieving not only higher customer satisfaction, but also higher customer loyalty (Coelho & Henseler, 2012).

Based on these reasons, we propose that:

H4a. The cultivation (CUL) of B2C interaction has direct and positive effects on the customization (CUS) of B2C interaction.

H4. The customization (CUS) of B2C interaction positively influences consumer loyalty (CL).

4 Methodology
4.1 Sample

An online questionnaire survey was carried out to collect data. The respondents were actual online shoppers and questionnaire links were sent via SNS websites, e-mails, and instant messengers. We received 343 detailed questionnaires from 450 questionnaires sent. According to Comrey (1988), a sample of at least 200 subjects is sufficient when numbers of items in the questionnaire are less than 40. After dropping those with missing values and obvious regularity, we finally found 265 questionnaires valid and yielded a valid response rate of 77.3 percent, fulfilling the expected requirements.

Table 2 shows the demographic information of the respondents. The main figure of respondents consists of some obvious labels, such as young adult (80.8%) and high education level (94.7%). It is consistent with common sense that young college students are the most active e-retailing consumer groups and conduct many personal online businesses using websites and mobile applications. Besides, 63.4% of respondents reported normal purchase frequency monthly and 8.3% of respondents purchased more than eight times a month. Since the reliability test on collected data is also well acceptable (shown in Table 3), we believe that the sample is representative and qualified.

Table 2

Descriptive Statistics.

4.2 Instrument Construction

Eight constructs are measured in this research, including efficiency (EF), security (SE), fulfillment (FU), mobility (MO), community (CO), cultivation (CUL), customization (CUS) and customer loyalty (CL). All constructs are adapted from previous researchers and modified according to B2C context. Each construct consists of at least three items in order to control the possible common-method bias of respondents and filter out untrustworthy replies. The item questionnaire is measured on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Appendix A depicts the detailed scale items.

Items of constructs capture respondents’ perceptions of B2C interactive tools as a whole. The construct of overall customer loyalty includes following five dimensions: general assessment, a rough attitude towards a B2C enterprise; preference and priority, deep emotional love for and attachment to one enterprise (Reichheld & Sasser, 1990); switching cost, the degree to which customers do not want to give up paid time, money and energy to develop a new transaction relationship; word-of-mouth promotion, the extent to which a customer says positive things about the B2C enterprise to other people (Dick & Basu, 1994); repeat purchase intention, the most common behavioral performance of loyal customers.

5 Statistical Analysis and Model Evaluation

In this study, partial least squares structural equation modeling (PLS-SEM) is chosen as the analytical approach. It is a structural path estimation approach that has the capability of working with unobservable latent variables and can account for measurement error in the development of latent variable (Aibinu & Al-Lawati, 2010). PLS-SEM is used in this study for three reasons: it does not presume any distributional form of measured variables hence it is suitable for data from unknown distributions; it is more appropriate for exploration than confirmation (S. Yang, Liu, & Wei, 2016); it is suitable when the sample size is relatively small. The theoretical model (shown in Figure 1) was analyzed using SmartPLS 3.0.

5.1 Outer Reflective Model Evaluation

Consideration of formative and reflective outer model modes is important when using PLS-SEM because two approaches to measurement are based on different principles and therefore require different evaluation measures. In this study, the latent variables represent the common factor of several specific observable variables (Lohmöller, 1989), suggesting that the indicators are caused by the construct and the arrows point from the construct to the indicators (Hair et al., 2014). Therefore, we use a reflective model here and reflective indicators are regressed on the latent variable scores when estimating outer weights (Hair et al., 2012). Assessment of reflective outer models involves reliability and validity suggested by prior research (Henseler et al., 2009).

First, Cronbach’s alpha and composite reliability were calculated to assess the reliability. The benchmark for composite reliability score and Cronbach’s Alpha is the same, requiring a value of 0.7 or higher. As shown in Table 3, the composite reliability and Cronbach’s α coefficients of the constructs are all significant, indicating that the measurement scales used in this study are reliable.

Table 3

Construct reliability and convergent validity.

Second, the convergent validity was evaluated with standard loadings and average variance extracted (AVE) values. Based on the output (shown in Table 3) from SmartPLS 3.0, all items have standard loadings above 0.70 (with t-value more than 1.96 and significant p-value) except observed variable CL3 (i.e. “I may not consider switching to another B2C enterprise.”) whose load value is 0.627. Since low loadings might bias the estimates of the parameters linking the latent variables, items with low loadings should be reviewed and perhaps dropped (Nunnally, 1967). Due to statistics inferring and reasonable consideration, we removed CL3 from the CL dimension. Support was provided for convergent validity since each item had outer loadings above 0.70 (Hair et al., 2014). We retested the reliability and convergent validity, the updated result showed that C.R. for CL dimension increased to 0.870 and AVE increased to 0.626. For all latent variables, estimates of AVEs are above 50%, thus indicating good convergent validity and internal consistency in the measurement model. Besides, the significant standardized loadings together with Cronbach’s alpha strongly prove the appropriate convergent validity of model instrument (Raines-Eudy, 2000).

Third, the discriminant validity was accessed by comparing the square root of the AVEs for each construct and its correlation coefficients with other constructs. Table 4 shows that there is no correlation between any two latent variables larger than the square root AVEs of the two latent variables, supporting that items belonging to different dimensions are hardly correlated with each other. Furthermore, Table B1 in Appendix B lists the cross-loading matrix, a criterion generally considered more liberal in terms of discriminant validity (Henseler et al., 2009). The requirement that internal loadings of each construct should be higher than the cross-loadings on other constructs is met. Hence, discriminant validity was verified.

Table 4

AVE & Construct Correlations.

5.2 Inner Model Testing

Common criterions for inner model assessment are the coefficient of determination (R2), cross-validated redundancy (Q2) and path coefficients. First, R2 represents the amount of explained variance of each endogenous latent variable (Hair et al., 2012). In this research, R2 for construct CL, CUS and FU are respectively 0.432, 0.267 and 0.565. The acceptable level of R2 depends on specific research contexts and R2 more than 0.33 is acceptable, thus describing moderate and even higher levels of predictive accuracy.

Second, Q2 combines cross-validation and function fitting and is used to assess an individual construct’s predictive relevance for the model by omitting selected inner model relationships and computing changes in the criterion’s estimates (Hair et al., 2012). Q2 for construct CL, CUS and FU are respectively 0.246, 0.187 and 0.405, larger than zero, indicating the path model’s predictive relevance for these constructs (Henseler et al., 2009).

Third, bootstrapping in SmartPLS 3.0 was used to test the significance of path coefficients, which could provide evidence of the inner model’s quality with t-value statistics and corresponding p-values (Chin et al., 2003). As bootstrapping results (shown in Table 3) indicated, all dimensions exhibit a relatively high t-value statistic level, ranging from 2.521 for CUS→CL to 11.736 for CUL→CUS. T-value greater than 1.96 indicates that p-value is less than 0.05 (Chin et al., 2003), and thus the parameter estimation gets significant support at the probability of 95%.

Test results of the research model are displayed in Figure 2. Efficiency and security explain a relatively significant amount (56.5 percent) of variation in fulfillment, and the proposed model explains 43.2 percent of the variation in customer loyalty. Most of the paths proposed are highly significant, with p-values less than 0.05. As expected, fulfillment is positively influenced by efficiency (β=0.468, p<0.001), security (β=0.403, p<0.001), supporting H1a and H1b, respectively. The direct effect of fulfillment on customer loyalty is also significant (β=0.229, p<0.001), supporting hypothesis H1. The positive effects of community (β=0.269, p<0.001) and mobility (β=0.217, p<0.01) on customer loyalty are also confirmed (H2 and H3 supported). As for customer personalization, cultivation (β=0.517, p<0.05) is positively associated with customization and hypothesis H4a is supported. Finally, customization has a significantly positive direct effect on customer loyalty (β=0.140, p<0.001, H4 supported).


Figure 2.

Research Model Results.^Notes. *p<0.05; **p<0.01; ***p<0.001.

6 Results and Discussion
6.1 Interactive Antecedents of Customer Loyalty

Research results indicate that the interactive tool is truly a new and appropriate perspective on explaining interaction and customer loyalty in B2C context, with all hypotheses found to be supported. For online shopping, the interactive features of interactive tools provide consumers with effective purchase decision-making assistance, which greatly eases the traditional problems and conflicts that consumers face in information acquisition and information processing during purchase decision-making process. In other words, in the manipulation and interaction with interactive tools, consumers quickly obtain rich information about suppliers and their products and services, thereby reducing the reliance of purchase decision on memory and inferred information, helping consumers improve the quality and efficiency of their shopping decisions and at the same time forming customers’ loyalty towards online suppliers.

We removed CL3 (i.e. “I may not consider switching to another B2C enterprise.”) from CL dimension due to two reasons. First, new B2C enterprises spring up endlessly, whose attractive discounts for first-time users are worth trying, even the loyalist may be distracted by these big publicity stunts for a while. Second, customers tend to use several B2C enterprises at the same time, with one favorite and others needed for specific needs (e.g. Customers using JD.com may turn to Amazon for purchasing e-books). Hence, we believe that CL3 cannot precisely reflect customer loyalty in our context. Although CL3 was deleted, another four observed variables could reflect customer loyalty with well-performed standard loadings, composite reliability, and Cronbach’s alpha.

The confirmed relationship between B2C interaction and customer loyalty leads to a further question: under what mechanisms does B2C interaction affect customer loyalty? It is obvious that there exists a significant difference between classic mechanisms (i.e. customer satisfaction, customer trust and switching costs have impacts on customer loyalty) and interaction effects. Interaction effects focus on the attributes embedded in B2C interactive tools, while classic mechanisms feature abstraction and universality. Attributes extracted from B2C interactive tools may exert an impact on customer loyalty through customer satisfaction, customer trust, and switching costs. Fundamental interaction attributes, such as efficiency, aim at meeting customers’ functional needs and making them satisfied, as the premise of possible purchases. The security of information provided by the website is proven as an important determinant of the trust dimension (Choi & Mai, 2018). It is consistent with Jiang et al.’s (2016) finding that the lack of confidence motivated by the perceived absence of security in the online business environment is a stumbling block to the growth of e-commerce. Fulfillment enables reliable transactions with customers and mobility makes sure they keep up with the same B2C enterprise, further strengthening transaction bonds. Increased switching costs attribute partially to the active community, frequent cultivation, and precise customization, that could make customers far attached to a specific B2C enterprise.

In our interaction-to-loyalty model, four factors directly affecting customer loyalty are fulfillment, mobility, community, and customization. Among them, the path coefficients of mobility explained the highest variance of customer loyalty (p≤0.001). It corresponds with the promising future of mobile electronic commerce. The proliferation of wireless capability has created an emerging opportunity for e-commerce businesses to expand beyond the traditional limitations of the fixed-line personal computer (Clarke, 2001). Enterprises who seize this business opportunity can easily get a head start in keeping the attention of mobile customers. As for the community, customers have enjoyed the benefits from others’ comments and advice when looking through the “find” or “share” sections displayed in the interactive tools offered by B2C enterprises. Therefore, the importance of community should be emphasized especially in website interface design (Rayport & Jaworski, 2003). However, the enterprise-based virtual community is in its infancy, B2C enterprises are supposed to build more active virtual communities, strengthen community interaction mechanisms, and encourage consumers to communicate with others.

6.2 Fundamental Interaction Attributes

It is notable that the effect of fulfillment cannot do without the fundamental role of efficiency and security, which serve as the basis for establishing and developing interactive tools such as websites. These two attributes explain a relatively significant amount (56.5 percent) of variation in fulfillment.

Though previous researches link efficiency and security with customer loyalty (Chiou, 2004; Wolfinbarger & Gilly, 2003), this study argues that efficiency and security have more direct effects on fulfillment, a basic indicator of loyalty development. Efficiency determines whether customers will use and continue to use the interaction of one enterprise. An interactive tool should be easy to use on the technical level, and effectively display what customers want, ensuring consumers can understand how to handle it and enjoy the convenience and speed of the shopping process. Future developers should pay attention to the design and construction of the of interactive tools and design a clear navigation and positioning system to help customers understand where they are and how to obtain accurate and timely information about what they need. Good efficiency is key to attracting customers and provides guarantees for possible purchase behavior, namely fulfillment. The more efficient interaction is, the more engaging experience customers get, and the more satisfied they feel with the B2C enterprise (Alkhouli, 2017). High satisfaction leads to the fulfillment of purchase behavior and the probability of revisits to interactive tools.

As for security, we argue that it relates to customer loyalty more closely at the early development stage of E-retailing when most B2C interactive tools are not fully developed. It is natural and necessary that previous researchers took security in research scale when evaluating some kinds of interactive tools and online loyalty (Parasuraman et al., 2005). When B2C interactive tools gradually meet or exceed certain security standard, higher security did not mean higher loyalty, because customers paid less attention to this already solved problem. Besides, customers have developed familiarity with B2C e-retailing. For example, the security of websites may not be critical for more frequent users and experience may indeed mitigate concerns about security (Wolfinbarger & Gilly, 2003). Nowadays, it is recognizable that basic security requirements are embedded in the design and development process of every type of B2C interactive tools, helping interactive tools to run smoothly and reliably.

6.3 Customer Cultivation and Customization

The positive and direct effect of cultivation on customization (H4a) is as great as expected. Various cultivation approaches of B2C interaction make it possible to build a more comprehensive understanding of customers. Extensive cultivation provides a pre-condition for advanced customization.

On the other hand, the potential problems caused by cultivation require specialized and efficient customization for customers. Almost all B2C enterprises require customers’ e-mail account for registration. It is undoubtedly convenient and customers will get timely information about product discount and special offers, but notification emails will gradually add up to the mailbox. Most customers feel inundated with meaningless and junk notifications when using B2C interactive tools. In addition, although links to relevant pages and information do offer something interesting, it costs time and energy to locate truly useful information and hence consumers are likely to ignore such messages. All these troubles point to the importance and necessity of customization. It allows customers to choose products and service they are truly interested in, set notification options and push frequency, and even change the look and style of interactive tools. Since customization focuses on what the customer really wants, it would be reasonable for customers whose different needs and preferences have been fully met to be “locked in” certain B2C enterprise.

7 Implications and Future Research
7.1 Implications for Theory and Practice

First, interaction is an appropriate research perspective in B2C e-commerce context, confirmed with our finding that interaction attributes have positive effects on customer loyalty. In order to raise customer loyalty, B2C enterprises are supposed to improve the efficiency, fulfillment, security, mobility, community, cultivation
and customization of interactive tools. Through improved interactive tools, B2C enterprises increase the interaction between consumers and information, making consumers feel tools more controllable and convenient, and on the other hand increase their interaction with consumers and interaction among consumers themselves, making consumers feel stronger personalization and community. Enterprises should not only meet certain needs but also make the interactive tools pleasurable, making customers more inclined to stay loyal (Pullman & Gross, 2004).

Second, though the effects of efficiency and security are not considered as direct predictors of customer loyalty in this study, they play an important role in fulfillment, which premises possible repeat purchase behavior. Added attention should be paid to these fundamental interaction attributes because B2C enterprises who cannot reach the foundational standards will be eliminated easily in the fierce competition.

Third, the positive effects of mobility and community on customer loyalty correspond to the development of modern communication technology and social networks. Mobility and community should be carefully taken into consideration in the subsequent phases of B2C interaction design and implementation processes.

Finally, the significance of cultivation and customization raises a new concern. The nature of cultivation provides fertile ground for customization, while annoying ads, banners, and other webpage junk raise requirements for customization. B2C enterprises are encouraged to optimize personalized content and improve push settings, making customers attached to interactive tools they are using frequently.

In a word, more fundamental, flexible and personalized interaction may probably create huge returns.

7.2 Limitation and Future Research

Our work is subject to a number of limitations. First, it is difficult to assess customers’ actual loyalty behavior when using B2C interactive tools. However, there has been well-established attitude theories supporting the fact that the stronger the behavior intention is, the more likely the behavior tends to happen (Ajzen, 1991).

Second, based on our findings, a comprehensive framework involved with B2C interaction and customer loyalty is developed and tested. Future study may seek to integrate the interaction model with classic mechanisms, such as customer satisfaction, customer trust, and switching in B2C context.

Third, individual-level variables related to the use of B2C interactive tools may have impacts. Out of the control of the B2C retailer, certain individual-level variables (such as customer inertia) may also influence customer loyalty. Future work that involves individual-level factors in the setting of B2C interaction is wanted.

Finally, larger and more diverse samples may offer a clearer analysis of research goals. However, prior studies have proven that the use of PLS-SEM approach is able to alleviate the problem with small sample size. Additionally, although young college students conduct numerous personal online business, the diverse sample might reexamine the significant effects.

8 Conclusion

This study introduced a new interactive perspective on understanding determinants of customer loyalty in B2C context. As the popularity of interaction rises, increasing numbers of B2C enterprises are utilizing various kinds of interactive tools to improve communication with customers and cultivate customer loyalty. However, empirical research exploring interactive tools as a whole is rare, and this study aims at filling this void. Based on previous research, seven significant attributes of B2C interactive tools have been extracted to construct the model. PLS-SEM analytical method is adopted and data is collected from 265 online B2C customers with the experience of adopting interactive tools to purchase online. As analysis results demonstrate, the interaction can explain loyalty properly in B2C context, using the perspective of analyzing the attributes of the interactive approach. Customer loyalty is predicted directly by fulfillment, mobility, community, and customization. Fulfillment, in turn, is based on the precondition of efficiency and security, which may lead directly to possible purchase behavior. Successful cultivation contributes to customization. By providing more fundamental, flexible and personalized interactive tools, B2C enterprises can enhance interaction with customers, and eventually strengthen customer loyalty significantly.

Author Contributions

X.Y. Yang (commonaslight@outlook.com, corresponding author) proposed the research question, designed the methods, and wrote the manuscript. Q.J. Yuan (yuanqj@nju.edu.cn) designed the methods and revised the manuscript.

Appendix A: The Detailed Scale Items

Appendix B: Cross-loadings

Acknowledgements

This research is supported in part by the Social Science Foundation of Jiangsu (Grant No.: 17TQB003). We also thank the anonymous reviewers for their constructive comments, which helped us improve the manuscript.

The authors have declared that no competing interests exist.

References

[1.]
Ahn T., Ryu S., & Han I. (2004). The impact of the online and offline features on the user acceptance of Internet shopping malls. Electronic Commerce Research & Applications, 3(4), 405-420.
[Cite within: 2]
[2.]
Aibinu A.A.,&Al-Lawati , A.M. (2010). Using PLS-SEM technique to model construction organizations’ willingness to participate in e-bidding. Automation in Construction, 19(6), 714-724.
DOI:10.1016/j.autcon.2010.02.016      Magsci     URL    
The push for the use of e-bidding is on the increase. In many countries, e-bidding has become part of the public procurement reform. However, previous studies have reported the low uptake of e-bidding in the construction industry. This study develops a theoretical structural model representing the impact six latent variables on the willingness of construction organizations to participate in e-bidding. Data from a questionnaire survey of 64 respondents from clients, contractors, consultants, and suppliers organizations was used to analyze the model. The result of the data analysis suggests that perceived barriers, cost, the perceived benefits of e-bidding and security concerns are the factors influencing willingness participate in e-bidding with perceived barriers being the most significant influencing factor. The perception about the adequacy of the existing traditional paper-based tendering and the perception about the non-readiness of business partners are the underlying barriers that need to be addressed when introducing e-biding. This study also discovers that when the respondents concern about the cost of e-bidding is high, their reported willingness to participate in e-bidding is higher when the perceived benefits is high that when the perceived benefits is low. To enhance the participation of the industry in e-bidding, public sector and large private clients would need to take leadership by making bidder's capability for e-bidding and e-project management at all project phases a criteria in bid evaluation. E-bidding developers and promoters need to use test project cases to monitor, measure, and document the benefits of e-bidding. To disseminate the perceived benefits, promoters would need to work closely with industry professional associations to develop promotional programmes linked to continuous professional development (CPD) points. The analytical approach used in this study should assist other researchers who might wish to address a similar research problem under a similar data and model constraints.
[Cite within: 1]
[3.]
Ajzen I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
DOI:10.1016/0749-5978(91)90020-T      URL    
ABSTRACT Given the lack of unequivocal findings on person-career fit, this investigation aims to gain insight into the role of cognitive styles in understanding students&rsquo; career preferences by two complementary studies. In study 1, we examined whether students (n = 84) with different cognitive styles differ in their entrepreneurial attitudes. Results showed a strong positive correlation between the creating style and the overall occupational status choice index, which implies a preference to become self-employed. No significant correlations were found between this index and the knowing and the planning style respectively. A more detailed look at the occupational status choice sub-indexes showed a positive correlation for the knowing style with the &lsquo;economic opportunity&rsquo; index, for the planning style with &lsquo;security&rsquo; and &lsquo;participation in the whole process&rsquo;, and for the creating style with &lsquo;career&rsquo;, &lsquo;challenge&rsquo;, &lsquo;economic opportunity&rsquo;, &lsquo;autonomy&rsquo;, &lsquo;authority&rsquo;, and &lsquo;self-realisation&rsquo;. No significant differences in overall occupational status choice were found in terms of gender, degree option, or family background in entrepreneurship. Study 2 focused on the link between students&rsquo; career anchors and their cognitive styles and personality profile (n = 275). We found for the knowing style a positive correlation with &lsquo;pure challenge&rsquo;, for the planning style a positive correlation with &lsquo;lifestyle&rsquo; and &lsquo;security/stability&rsquo; and a negative one with &lsquo;autonomy/independence&rsquo;, and for the creating style a positive correlation with &lsquo;entrepreneurial creativity&rsquo; and &lsquo;pure challenge&rsquo; and a negative one with &lsquo;security/stability&rsquo;. Hierarchical regression analyses showed that cognitive styles and personality traits could predict people&rsquo;s career anchors to a certain extent. These findings are particularly relevant for career counselling services of higher education institutions and for selection and recruitment policies of organ
[Cite within: 1]
[4.]
Alkhouli S. (2017). The effect of banks website service quality and e-satisfaction on e-loyalty: An empirical study on swedish banks. International Journal of Business and Management, 13(1), 1-12.
DOI:10.13140/RG.2.2.29483.64802      URL    
Abstract The research is providing an empirical evidence of the association between website SQ and e-satisfaction, and the impact of both on e-loyalty in Swedish banks.
[Cite within: 1]
[5.]
Alzola L.M.,& Robaina , V.P. (2005). SERVQUAL: Its applicability in electronic commerce B2C. Quality Management Journal, 12(4), 46-57.
[Cite within: 1]
[6.]
Aquilani B., Serpico E., Ruggieri A., & Silvestri C. (2016). Offline and online customer satisfaction in B2C markets: Towards an overall customer satisfaction framework. In web-based service: Concepts, methodologies, tools, and applications (pp. 377-430). Hershey, PA, USA: IGI Global.
DOI:10.1111/j.1749-6632.1987.tb51406.x      URL    
No abstract is available for this article.
[Cite within: 1]
[7.]
Arnould E.J.,& Price , L.L. (1993). River magic: Extraordinary experience and the extended service encounter. Journal of Consumer Research, 20(1), 24-45.
[Cite within: 1]
[8.]
Bao H., Li B., Shen J., & Hou F. (2016). Repurchase intention in the Chinese e-marketplace: Roles of interactivity, trust and perceived effectiveness of e-commerce institutional mechanisms. Industrial Management and Data Systems, 116(8), 1759-1778.
DOI:10.1108/IMDS-07-2015-0296      URL    
Abstract Purpose - Retaining customers is very important for the survival of e-commerce sellers. The purpose of this paper is to investigate the roles of computer-mediated communication (CMC) tools, interactivity, trust and perceived effectiveness of e-commerce institutional mechanisms (PEEIM) in influencing customer's repurchase intention in the Chinese online e-commerce marketplace. Design/methodology/approach - The research model is empirically tested using survey data analyzed with partial least squares structural equation modeling. Findings - This study confirms the positive relation between customer satisfaction and trust in the seller, which further contributes to repurchase intention. Results also support the positive influences of effective use of an instant messenger and feedback system on customer perceived interactivity, which helps enhance trust in the seller. PEEIMdemonstrates interesting results regarding itsmoderating effects. Research limitations/implications - In the future, researchers can extend the study to other e-commerce platforms and take trust transfer effects and product categories into consideration. Practical implications - This study highlights the importance to manage trust, PEEIM, interactivity and CMC tools in e-commerce platforms, assisting practitioners to develop appropriate business strategies and processes to retain customers. Originality/value - This study extends previous investigations by integrating trust and PEEIM with interactivity and testing the model in the context of the Chinese online marketplace.
[Cite within: 1]
[9.]
Barnes S.J.,& Vidgen , R.T. (2008). An integrative approach to the assessment of e-commerce quality. Journal of Electronic Commerce Research, 3, 114-127.
URL    
Abstract WebQual is a method for assessing the quality of Web sites. The method has been developed iteratively through application in various domains, including Internet bookstores and Internet auction sites. In this paper we report on the application of a new version of WebQual to Internet bookstores: Amazon, BOL, and the Internet Bookshop. WebQual draws on previous work in three areas: Web site usability, information quality, and service interaction quality to provide a rounded framework for assessing e-commerce offerings. Although WebQual is grounded in the subjective impressions of Web site users, the data collected lends itself to quantitative analysis and the production of e-commerce metrics such as the WebQual Index. The reliability of the instrument is examined and core constructs of Web site quality identified using factor analysis. The role of WebQual in assessing an organization's e-commerce capability is discussed.
[Cite within: 2]
[10.]
Bauer H.H., Grether M., & Leach M. (2002). Building customer relations over the Internet. Industrial Marketing Management, 31(2), 155-163.
DOI:10.1016/S0019-8501(01)00186-9      Magsci     URL    
<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">While previous marketing activities were primarily focused on increasing market shares in terms of a mass marketing based on single transactions, the past few years saw a paradigmatic shift towards relationship marketing. This change in viewpoint is based on the finding that the establishment and maintenance of long-term relationships have a decisive influence on corporate success. In this study, the authors present a critical theoretical and empirical analysis of the contribution the Internet can make to successful relationship marketing. The study focuses on the influence that important characteristics of the World Wide Web (WWW), such as its interactive structure and constant availability of information, can have on key variables of relationship marketing, i.e., commitment, satisfaction and trust. For example, the authors provide empirical proof of potential withdrawal of trust on the part of the customers if their expectations with regard to these characteristics are not met.</p>
[11.]
Brengman M.,& Karimov , F.P. (2012). The effect of web communities on consumers’ initial trust in B2C e-commerce websites. Management Research Review, 35(9), 791-817.
DOI:10.1108/01409171211256569      URL    
Purpose - The purpose of this paper is to test the effectiveness of the mere integration of social network applications to provide a signal concerning the “trustworthiness” of an unfamiliar e-vendor in order to enhance subsequent purchase intentions. Design/methodology/approach - To investigate the impact of web communities on consumers' initial trust beliefs (i.e. ability, benevolence and integrity), a 2×3 between-subjects full factorial online experiment was carried out, using a fictitious web site for a gift gadgets selling company, manipulating it for inclusion or exclusion of a “social networking site”, and for inclusion or exclusion of “a corporate blog (text only blog, photo and text blog, or no blog)”. Data were obtained from 226 online shoppers. Findings - Although the authors could not reveal any effects of the integration of social network applications on “ability” beliefs, it was possible to demonstrate their capacity to “signal” “benevolence” and “integrity”, which in turn have a significant impact on purchase intentions. Unfamiliar e-retailers may foster perceptions of “integrity” by utilizing text-blogs into their web sites, but they should avoid embedding facial photos of shop representatives in the blog. If e-retailers want to make use of “a corporate blog with facial photo”, it is recommended to combine it with the integration of a social networking site such as Facebook in order to boost perceptions of “benevolence”. Research limitations/implications - The simple integration of “social network applications” can affect “initial trust beliefs” towards unfamiliar e-tailers and subsequent “purchase intentions”, but it appears essential to utilize just the right cue combination in order to obtain the desired effect. The effectiveness of integrating a social network application may vary according to the type and may affect different trust beliefs (benevolence, integrity). Originality/value - An important issue in e-commerce remains how trust is developed between consumers and e-retailers. This paper investigates the use of different web communities and the influence of their integration in the commercial web site on consumers' initial trust beliefs in the online environment. The findings will help business managers to understand how social media should be used to lead to optimal results.
[Cite within: 1]
[12.]
Chin W.W., Marcolin B.L., & Newsted P.R. (2003). A psrtial least squares latent variable modeling approach for measuring interaction effects: Results from a monte carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217.
[Cite within: 2]
[13.]
Chiou J.S. (2004). The antecedents of consumers’ loyalty toward Internet service providers. Information & Management, 41(6), 685-695.
DOI:10.1016/j.im.2003.08.006      URL    
Internet popularity is growing at an impressive rate. Sooner or later, every consumer comes face to face with the decision of choosing an Internet Service Provider (ISP). This study developed and empirically tested a model examining the antecedents of consumer loyalty toward ISPs. It incorporated the concept of expected technology change into the model to examine its effects on the formation of ISP loyalty intention. Confirmatory Factor Analysis (CFA) was performed to examine the reliability and validity of the measurement model, and the structural equation modeling techniques were used to evaluate the casual model. Based on an internet survey in Taiwan, this study showed that perceived value is very important in generating overall customer satisfaction and loyalty intention toward an ISP, and that perceived trust of an ISP enhances perceived value, overall satisfaction, and loyalty intention. However, the study demonstrated that future ISP technology expectancy exerted a negative influence on a consumer鈥檚 overall satisfaction and loyalty intention toward their ISP.
[Cite within: 1]
[14.]
Choi Y.,& Mai , D.Q. (2018). The sustainable role of the e-trust in the B2C e-commerce of ietnam. Sustainability, 10(1), 291.
[Cite within: 1]
[15.]
Clarke I.I. (2001). Emerging value propositions for m-commerce. Journal of Business Strategies, 18(2), 133-148.
[Cite within: 1]
[16.]
Coelho P.,& Henseler , J. (2012). Creating customer loyalty through service customization. European Journal of Marketing, 46, 331-356.
DOI:10.1108/03090561211202503      URL    
Purpose – Although practitioners and scholars alike embrace service customization as a possibly powerful management instrument, its impact on customer relationships as well as contingencies for its effective application are not well understood. Drawing from relationship marketing and exchange theory, this paper aims to develop a model of customer relationship outcomes of service customization and the efficacy of service customization. Design/methodology/approach – Two large-scale, representative, cross-sectional studies in different service industries based on the European Customer Satisfaction Index framework are conducted, and PLS path modeling is applied to test the model. Findings – Customization increases perceived service quality, customer satisfaction, customer trust, and ultimately customer loyalty toward a service provider. Customization has both direct and mediated effects on customer loyalty and interacts with the effects of customer satisfaction and customer trust on loyalty. Research limitations/implications – Service customization is a viable instrument for relationship marketing. Its efficacy depends on customer satisfaction and customer trust. While this study solely focuses on the impact of service customization, future research could assess the relative importance of service customization in the presence of other relationship marketing instruments. Practical implications – Service providers can use service customization as an effective instrument for achieving not only higher customer satisfaction, but also higher customer loyalty. Service customization is most effective for companies that have deficits in satisfying their customers, while at the same time their customer relationships are characterized by a high level of trust. These results help managers to decide upon resource allocation to enhance customer satisfaction, trust and loyalty. Originality/value – This paper investigates the simultaneous effects of service customization on customer loyalty and other relationships variables and offers new insights relatively to the nature and size of customization effects. It fills an important gap in the knowledge of customization outcomes, and clarifies under which circumstances service customization is most effective. The paper is of great value for service providers that face the decision whether to customize their offering or not.
[Cite within: 1]
[17.]
Comrey A.L. (1988). Factor-analytic methods of scale development in personality and clinical psychology. Journal of Consulting and Clinical Psychology, 56(5), 754-761.
[Cite within: 1]
[18.]
Cova B.,& Pace , S. (2006). Brand community of convenience products: New forms of customer empowerment—the case “my Nutella The Community.” European Journal of Marketing, 40(9/10), 1087-1105.
DOI:10.1108/03090560610681023      URL    
ABSTRACT Purpose – To analyse the power that a virtual brand community exerts over a brand of a mass-marketed convenience product. To draw implications about the strategy that a company can employ facing this power shift. To track emerging trends in virtual brand communities applied to convenience product (as opposed to niche or luxury goods). Design/methodology/approach – Case study of the web community “my Nutella The Community” promoted by the firm Ferrero in Italy. The study applied multiple methods and was conducted through interviews with key informants, netnography and document analysis. Findings – The virtual community that gathers around a convenience product brand shows a new form of sociality and customer empowerment: it is not based on interaction between peers, but more on personal self-exhibition in front of other consumers through the marks and rituals linked to the brand. The company should play the role of non-intrusive enabler of these personal expressions, reducing its control over the brand's meanings. Originality/value – The literature on brand community has traditionally focused on communities born around niche or luxury brand (Harley Davidson, Mercedes, Saab). The paper deals with a mass marketed convenience product like Nutella (the worldwide famous hazelnut spread), showing noteworthy differences that would advance current knowledge on brand communities and customer empowerment.
[Cite within: 1]
[19.]
Cunningham R.M. (1956). Brand loyalty-what, where, how much. Harvard Business Review, 34(1), 116-128.
URL    
CiteSeerX - Scientific documents that cite the following paper: Brand Loyalty-what, where, how much
[20.]
Cyr D.(2008). Modeling web site design across cultures: relationships to trust, satisfaction, and e-loyalty. Journal of Management Information Systems, 24(4), 47-72.
[Cite within: 1]
[21.]
Davis F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Mis Quarterly, 13(3), 319-340.
DOI:10.2307/249008      URL    
Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions for these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and stream-lined, resulting in two six-item scales with reliabilities of.98 for usefulness and.94 for ease of use. The scales exhibited high convergent, discriminant, and factorial validity. Perceived usefulness was significantly correlated with both self-reported current usage (r=.63, Study 1) and self-predicted future usage (r=.85, Study 2). Perceived ease of use was also significantly correlated with current usage (r=.45, Study 1) and future usage (r=.59, Study 2). In both studies, usefulness had a significantly greater correlation with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecedent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.
[Cite within: 1]
[22.]
Dholakia R.R., Zhao M., Dholakia N., & Fortin D. (2000). Interactivity and revisits to websites: A theoretical framework. American Marketing Association. Conference Proceedings (pp. 108-115).
[Cite within: 2]
[23.]
Dick A.S.,& Basu , K. (1994). Customer loyalty: Toward an integrated conceptual framework. Journal of the Academy of Marketing Science, 22(2), 99-113.
[Cite within: 1]
[24.]
Ghose S.,& Dou , W. (1998). Interactive functions and their impacts on the appeal of Internet presence sites. Journal of Advertising Research, 38(2), 29-43.
[Cite within: 1]
[25.]
Gommans M., Krishman K.S., & Scheffold K.B. (2001). From brand loyalty to e-loyalty: A conceptual framework. Journal of Economic & Social Research, 3, 43-58.
[Cite within: 1]
[26.]
Gremler D.D.,& Brown , S.W. (1996). Service loyalty: Its nature, importance, and implications. QUIS V: Advancing service quality: A global perspective (pp. 171-181). New York: International Service Quality Association.
[Cite within: 2]
[27.]
Griffin J. (1995). Customer loyalty: How to earn it, how to keep it. New York:Jossey-Bass.
[28.]
Ha C.L.(1998). The theory of reasoned action applied to brand loyalty. Journal of Product & Brand Management, 7(1), 51-61.
[29.]
Ha L.,& James , E.L. (1998). Interactivity reexamined: A baseline analysis of early business web sites. Journal of Broadcasting & Electronic Media, 42(4), 457-474.
DOI:10.1080/08838159809364462      URL    
This study reexamined the concept of interactivity and proposed that interactivity be defined as the extent to which the communicator and the audience respond to each other's communication need. Interactivity was construed as consisting of five dimensions: 1) playfulness, 2) choice, 3) connectedness, 4) information collection, and 5) reciprocal communication. Web sites for products, services, and retail outlets appeared to differ significantly in terms of connectedness, information collection, and reciprocal communication.
[Cite within: 2]
[30.]
Hagel J.(1999). Net gain: Expanding markets through virtual communities. Journal of Interactive Marketing, 13(1), 56-65.
[31.]
Hair J.F., Sarstedt M., Hopkins L., & Kuppelwieser V.G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106-121.
[Cite within: 2]
[32.]
Hair J.F., Sarstedt M., Ringle C.M., & Mena J.A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414-433.
DOI:10.1007/s11747-011-0261-6      Magsci    
Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research-despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also give an overview of the interdependencies between researchers' choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.
[Cite within: 3]
[33.]
Henseler J., Ringle C.M., & Sinkovics,R .R.(2009). The use of partial least squares path modeling in international marketing. Social Science Electronic Publishing, 20(4), 277-319.
[Cite within: 3]
[34.]
Homsud S.,& Chaveesuk , S. (2014). An integrated model of customer repurchase intention in B2C e-commerce. In 2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE)(pp. 1-6).
[35.]
Jacoby J.,& Chestnut , R. (1978). Brand loyalty: Measurement and management. New York: John Wiley & Sons Incorporated.
[36.]
Jiang L., Jun M., & Yang Z. (2016). Customer-perceived value and loyalty: How do key service quality dimensions matter in the context of B2C e-commerce? Service Business, 10(2), 301-317.
[Cite within: 3]
[37.]
Kaplan A.M.,& Haenlein , M. (2012). The Britney Spears universe: Social media and viral marketing at its best. Business Horizons, 55(1), 27-31.
DOI:10.1016/j.bushor.2011.08.009      Magsci    
Britney Spears is one of the most successful female recording artists in contemporary music, world-wide. Herein, we analyze how Britney Spears and her team of employees rely on social media applications to communicate around this pop icon, and create and maintain her celebrity brand image. Specifically, we look at the use of social media during the launch of her single 'Hold It Against Me' and the associated album Femme Fatale in early 2011. The interplay of postings on Twitter, You Tube, and Facebook-combined with comments on her webpage, BritneySpears.com-can be seen as a prime example of social media usage to support new product introductions. (C) 2011 Kelley School of Business, Indiana University. All rights reserved.
[Cite within: 1]
[38.]
Koufaris M.,& Hampton-Sosa , W. (2004). The development of initial trust in an online company by new customers, 41(3), 377-397.
DOI:10.1016/j.im.2003.08.004      URL    
Abstract Lack of trust in online companies is a primary reason why many web users do not shop online. This study proposes a model to explain how new customers of a web-based company develop initial trust in the company after their first visit. The model is empirically tested using a questionnaire-based field study. The results indicate that perceived company reputation and willingness to customize products and services can significantly affect initial trust. Perceived web site usefulness, ease of use, and security control are also significant antecedents of initial trust. Finally, we found no support for the hypothesized effect of individual customer trust propensity on initial trust.
[Cite within: 1]
[39.]
Koufaris M., Kambil A., & Labarbera P.A. (2001). Consumer behavior in web-based commerce: An empirical study. International Journal of Electronic Commerce, 6(2), 115-138.
DOI:10.1080/10864415.2001.11044233      URL    
Electronic commerce challenges companies to design electronic systems and interactions that retain customers and increase sales. This exploratory study examines the impact of consumer experience and attitudes on intention to return and unplanned purchases on-line. It also examines how certain consumer and Web site factors influence the on-line consumer experience. The study finds that perceived control and shopping enjoyment can increase the intention of new Web customers to return, but seemingly do not influence repeat customers to return. It also finds that a Web store that utilizes valueadded search mechanisms and presents a positively challenging experience can increase customers' shopping enjoyment. Further, the more often customers return to a Web store, the more their shopping enjoyment is determined by their product involvement. Customers with low need specificity (i.e., who do not know what they are looking for) are more likely to use value-added search mechanisms. Finally, neither perceived control nor shopping enjoyment has any significant impact on unplanned purchases.
[Cite within: 2]
[40.]
Kuehn A.A. (1962). Consumer brand choice as a learning process. Journal of Advertising Research, 2(4), 10-17.
[41.]
Lee J. (2017). The assessment tool for user perceived interactivity from ACG website interactivity on imagination. Communications in Computer and Information Science, 713, 57-65.
[Cite within: 1]
[42.]
Levy S.E., Getz D., & Hudson S. (2011). A field experimental investigation of managerially facilitated consumer-to-consumer interaction. Journal of Travel & Tourism Marketing, 28(6), 656-674.
DOI:10.1080/10548408.2011.603633      URL    
Positive social interactions among consumers are considered increasingly important elements in corporate relationship marketing efforts. There is scant empirical research, however, demonstrating the effect of consumer interactions facilitated by service industry managers on consumer evaluations and behavioral intentions. This study employs a field experimental methodology utilizing four half-day cultural heritage tours (n = 156) to examine consumer-to-consumer (C-to-C) interactions within the group travel context. Research findings demonstrate that managerially facilitated C-to-C interactions significantly increase tour member satisfaction and enjoyment. Theoretical and managerial implications are discussed, and calls are made for more field experimental methodologies to be used in tourism research.
[Cite within: 2]
[43.]
Liang H.N., Parsons P.C., Wu H.-C., & Sedig K. (2010). An exploratory study of interactivity in visualization tools: “Flow” of interaction. Journal of Interactive Learning Research, 21(1), 5-45.
[Cite within: 1]
[44.]
Liu Y.,& Shrum, L.J. (2002). What is interactivity and is it always such a good thing? Implications of definition, person, and situation for the influence of interactivity on advertising effectiveness. Journal of Advertising, 31(4), 53-64.
DOI:10.1080/00913367.2002.10673685      URL    
Most perceptions of interactivity are that it is an inherently good thing, that it will change marketing and advertising as we know it. However, though there are obvious and intuitive advantages to interactivity, it may pose a disadvantage for marketing and advertising in some conditions. In this article, we explore the nature of interactivity and its underlying processes to determine the conditions in which interactivity may be both useful and detrimental in an advertising context. We first discuss the multidimensional nature of the interactivity construct as it has appeared in the literature. We then provide a concrete conceptualization and definition of interactivity that encompasses these various dimensions. We argue that inconsistencies between the definitions and operationalizations found in previous studies make it difficult to draw firm conclusions about the role of interactivity but that these inconsistencies can be at least partly explained by a focus on the different dimensions of interactivity. Finally, drawing on theory and research in cognitive, social, and personality psychology, we suggest that the influence of interactivity on advertising effectiveness may be a function of both the person and the situation. We offer a program of research, in the form of testable propositions, to explore these boundary conditions and discuss implications for Internet advertising strategy.
[Cite within: 2]
[45.]
Lohmöller J.B. (1989). Predictive vs.structural modeling: PLS vs.. ML. In Latent Variable Path Modeling with Partial Least Squares (pp. 199-226). Springer.
[Cite within: 1]
[46.]
McAlexander J.H., Kim S.K., & Roberts S.D. (2003). Loyalty: The influences of satisfaction and brand community integration. Journal of Marketing Theory and Practice, 11(4), 1-11.
DOI:10.1080/10696679.2003.11658504      URL    
This paper empirically explores the relative impacts of satisfaction, brand community integration, and consumer experience on customer loyalty as expressed by future purchase intentions and behavior. Data drawn from qualitative research and a survey of 1000 patrons of a Native American casino who indicate a willingness to engage informal marketing relationships indicate that satisfaction yields to brand community integration as a key driver of loyalty. We discuss important implications of the findings for marketing theory and practice.
[Cite within: 1]
[47.]
Mcknight D.H., Choudhury V., & Kacmar C. (2002). Developing and validating trust measures for e-Commerce: An intgrative typology. Information Systems Research, 13(3), 334-359.
[Cite within: 1]
[48.]
Mcmillan S.J. (2000). Interactivity is in the eye of the beholder: Function, perception, involvement, and attitude toward the web site (pp. 71-78). Proceedings of the 2000 Conference of the American Academy of Advertising.
[Cite within: 1]
[49.]
Merrilees B.,& Fry , M. (2003). E-trust: the influence of perceived interactivity on e-retailing users. Marketing Intelligence & Planning, 21(2), 123-128.
[50.]
Montoya-Weiss M.M., Voss G.B., & Grewal D. (2003). Determinants of online channel use and overall satisfaction with a relational, multichannel service provider. Journal of the Academy of Marketing Science, 31(4), 448-458.
[Cite within: 1]
[51.]
Mulvenna M., Anand S., & G. Büchner A. (2000). Personalization on the net using web mining: Introduction. Comm Acm, 43(8), 122-125.
[52.]
Ngai E.,W. T,& Gunasekaran, A.(2007). A review for mobile commerce research and applications. Decision Support Systems, 43(1), 3-15.
DOI:10.1016/j.dss.2005.05.003      Magsci    
<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Although a large volume of literature is available on mobile commerce (m-commerce), the topic is still under development and offers potential opportunities for further research and applications. Since the subject is at the stage of development, a review of the literature on m-commerce with the objective of bringing to the fore the state-of-art in m-commerce research and applications will initiate further research on the growth of m-commerce technologies. This paper reviews the literature on m-commerce and applications using a suitable classification scheme to identify the gap between theory and practice and future research directions. The 149 m-commerce articles are classified and the results of these are presented based on a scheme that consists of five distinct categories: m-commerce theory and research, wireless network infrastructure, mobile middleware, wireless user infrastructure, and m-commerce applications and cases. A comprehensive list of references is presented. We hope that the findings of this research will provide useful insights into the anatomy of m-commerce literature and be a good source for anyone who is interested in m-commerce. The paper also provides some future directions for research.</p>
[Cite within: 1]
[53.]
Nunnally J.C.(1967). Psychometric theory. McGraw-Hill, New York.
[Cite within: 1]
[54.]
Parasuraman A., Zeithaml V.A., & Malhotra A. (2005). ES-QUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213-233.
DOI:10.1177/1094670504271156      URL    
Abstract Using the means-end framework as a theoretical foundation, this article conceptualizes, constructs, refines, and tests a multiple-item scale (E-S-QUAL) for measuring the service quality delivered by Web sites on which customers shop online. Two stages of empirical data collection revealed that two different scales were necessary for capturing electronic service quality. The basic E-S-QUAL scale developed in the research is a 22-item scale of four dimensions: efficiency, fulfillment, system availability, and privacy. The second scale, E-RecS-QUAL, is salient only to customers who had nonroutine encounters with the sites and contains 11 items in three dimensions: responsiveness, compensation, and contact. Both scales demonstrate good psychometric properties based on findings from a variety of reliability and validity tests and build on the research already conducted on the topic. Directions for further research on electronic service quality are offered. Managerial implications stemming from the empirical findings about E-S-QUAL are also discussed.
[Cite within: 6]
[55.]
Pullman M.E.,& Gross , M.A. (2004). Ability of experience design elements to elicit emotions and loyalty behaviors. Decision Sciences, 35(3), 551-578.
DOI:10.1111/j.0011-7315.2004.02611.x      URL    
ABSTRACT Experience design, an approach to create emotional connection with guests or customers through careful planning of tangible and intangible service elements, has gained popularity in many hospitality and retail businesses. With ever-increasing competition, service providers seek to develop loyalty by aggressively designing, continuously innovating, and managing their customer experiences. This article explores the relationship between different service elements designed to create enhanced experience and customer loyalty. In addition, it looks at emotional responses as mediating factors between the physical and relational elements and loyalty behaviors. A model is proposed and tested with a VIP hospitality tent for an internationally renowned touring circus. Results of the study indicate that while a few design elements directly affect loyalty behavior, the relationship between most design elements and loyalty behavior is strongly mediated by eliciting certain types of emotional behavior. This connection has implications for the focus of service managers' efforts in different environments.
[Cite within: 2]
[56.]
Rafaeli S.,& Sudweeks , F. (1997). Networked interactivity. Journal of Computer-Mediated Communication, 2(4).
[Cite within: 1]
[57.]
RainesEudy R.(2000). Using structural equation modeling to test for differential reliability and validity: An empirical demonstration. Structural Equation Modeling A Multidisciplinary Journal, 7(1), 124-141.
[Cite within: 1]
[58.]
Ranganathan C.,& Ganapathy, S. (2002). Key dimensions of business-to-consumer web sites. Information & Management, 39(6), 457-465.
URL    
CiteSeerX - Scientific documents that cite the following paper: Key Dimensions of Business-to-User Web Sites
[Cite within: 1]
[59.]
Rayport J.F.,& Jaworski , B. (2003). Introduction to E-commerce. Boston: McGraw-Hill.
[Cite within: 1]
[60.]
Reichheld F.F. (1993). Loyalty-based management. Harvard Business Review, 71(2), 64-73.
[61.]
Reichheld F.F.,& Sasser , J.W. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68(5), 105-111.
[Cite within: 2]
[62.]
Rogers E.M.(1995). Diffusion of innovations. New York: Free Press.
[Cite within: 1]
[63.]
Rowe I. (2015). Deliberation 2.0: Comparing the deliberative quality of online news user comments across platforms. Journal of Broadcasting & Electronic Media, 59(4), 539-555.
[Cite within: 1]
[64.]
Ruyter K.D., Dellaert B.G.C., & Monsuwé T.P.Y. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1), 102-121.
[65.]
Sicilia M., Ruiz S., & Munuera J.L. (2005). Effects of interactivity in a web site: The moderating effect of need for cognition. Journal of Advertising, 34(3), 31-44.
[Cite within: 1]
[66.]
Smith E.R. (2000). E-loyalty: How to keep customers coming back to your website. New York: HarperBusiness Publishers.
URL    
Includes bibliographical references and index
[Cite within: 1]
[67.]
Srinivasan S.S., Anderson R., & Ponnavolu K. (2002). Customer loyalty in e-commerce: an exploration of its antecedents and consequences. Journal of Retailing, 78(1), 41-50.
DOI:10.1016/S0022-4359(01)00065-3      URL    
This paper investigates the antecedents and consequences of customer loyalty in an online business-to-consumer (B2C) context. We identify eight factors (the 8Cs—customization, contact interactivity, care, community, convenience, cultivation, choice, and character) that potentially impact e-loyalty and develop scales to measure these factors. Data collected from 1,211 online customers demonstrate that all these factors, except convenience, impact e-loyalty. The data also reveal that e-loyalty has an impact on two customer-related outcomes: word-of- mouth promotion and willingness to pay more.
[Cite within: 3]
[68.]
Stokes C.(2014). Understanding a proposed model of customer loyalty formation in B2C e-commerce. International Journal of Future Computer and Communication, 3(3), 191-196.
[Cite within: 1]
[69.]
Storbacka K., Strandvik T., & Grönroos C. (1994). Managing customer relationships for profit: the dynamics of relationship quality. International Journal of Service Industry Management, 5(5), 21-38.
[Cite within: 1]
[70.]
Straubhaar J.,& Larose , R. (1997). Communications media in the information society. Belmont, CA: Wadsworth Publishers.
[Cite within: 2]
[71.]
Tucker W.T. (1964). The development of brand loyalty. Journal of Marketing Research, 1(3), 32-35.
URL    
[72.]
Varadarajan R., Srinivasan R., Vadakkepatt G.G., Yadav M.S., Pavlou P.A., Krishnamurthy S., & Krause T. (2010). Interactive technologies and retailing strategy: A review, conceptual framework and future research directions. Journal of Interactive Marketing, 24(2), 96-110.
DOI:10.1016/j.intmar.2010.02.004      Magsci     URL    
During the past decade, a number of interactive technologies, including the Internet, have fundamentally transformed how retailers compete in the marketplace. In a similar vein, emerging interactive technologies can be expected to significantly alter the retailing landscape through their impact on retailing strategy and operations. Furthermore, it is conceivable that certain emerging interactive technologies will be perceived by some retailers as enablers (tools to more effectively compete in the marketplace) and by other retailers as disruptors of the present ways of doing business. Interactive technologies can either be generic, a technology that is readily available from an information technology (IT) vendor and is widely adopted by retailers, or proprietary. An interactive technology that is proprietary can enable a firm to generate economic rents from the innovation for an extended duration of time. Investing in a generic interactive technology, however, may be perceived as a cost of doing business for a retailer, and not a potential source of sustainable competitive advantage. However, a retailer's complementary resource endowments may enable the retailer to more effectively leverage a generic technology relative to its competitors and thereby achieve a sustainable competitive advantage. In this paper, we review the related literature, develop a process model delineating the mechanisms by which an interactive technology can affect and necessitate changes in retailers' strategies and identify directions for future research.
[Cite within: 1]
[73.]
Wolfinbarger M.,& Gilly , M.C. (2003). eTailQ: dimensionalizing, measuring and predicting etail quality. Journal of Retailing, 79(3), 183-198.
[Cite within: 5]
[74.]
Wu C.,& Hsing, S .S. (2006). Less is more: How scarcity influences consumers’ value perceptions and purchase intents through mediating variables. Journal of American Academy of Business, 9(2), 125-132.
URL    
[Cite within: 3]
[75.]
Wu G.M.(2000). The role of perceived interactivity in interactive and processing. The University of Texas, Austin.
[Cite within: 1]
[76.]
Yang L.M., Li T., & Li J. (2012). The Interactive Strategy of B2C E-Commerce Website. Advanced Engineering Forum, 6-7(5), 978-984.
[Cite within: 2]
[77.]
Yang S., Liu Y., & Wei J. (2016). Social capital on mobile SNS addiction: A perspective from online and offline channel integrations. Internet Research, 26(4), 982-1000.
[Cite within: 1]
[78.]
Yoo B.,& Donthu , N. (2001). Developing a scale to measure the perceived quality of an Internet shopping site (SITEQUAL). Quarterly Journal of Electronic Commerce, 2(1), 31-47.
[Cite within: 1]
Resource
PDF downloaded times    
RichHTML read times    
Abstract viewed times    

Share
Export

External search by key words

B2C e-commerce     
Interaction     
Interactive tool     
Customer loyalty     

External search by authors

Xinyue Yang    
Qinjian Yuan    

Related articles(if any):