Understanding users’ switching from online stores to mobile stores

Abstract

Compared to online stores, mobile stores as an emerging application have received less adoption among users. This may be for the reason that they are locked into the relationship with online stores and are unwilling to switch from online stores to mobile stores. From a dual perspective of enablers and inhibitors, this research examined user switching from online stores to mobile stores. Enablers include trust transfer and flow, whereas the inhibitor is switching barrier. The results indicate that user switching receives a dual influence from both enablers and inhibitors. Among them, trust transfer has the largest effect on switching intention. These results imply that service providers need to be concerned with both aspects of enablers and inhibitors in order to facilitate user switching from online stores to mobile stores.

Keywords

online stores mobile stores switching costs trust transfer.

Service providers need to promote user trust transfer and offer an engaging experience to users while curbing the effects of switching barriers.

Introduction

Mobile Internet has been developing rapidly in the world. According to a report issued by the China Internet Network Information Center (CNNIC) in July 2013, the number of mobile Internet users in China has exceeded 464 million, accounting for 78.5 percent of its Internet population (591 million) (CNNIC, 2013). Attracted by the great market potential, service providers have released a variety of mobile applications and services, such as mobile instant messaging, mobile games and mobile stores. They expect users to widely accept and use these services, so that they can achieve competitive advantage and make profits. However, some services have received limited adoption among users. For example, as a transaction application, mobile stores have only been used by 16.5 percent of mobile Internet users in China (CNNIC, 2013). In the US, this figure is 29 percent (Oracle, 2011). Nevertheless, mobile stores have received great attention from enterprises. For example, Taobao, which is the largest online consumer-to-consumer website in China, has released mobile Taobao. Other well-known online stores such as Jingdong, Amazon and Dangdang have also built their sites on the mobile Internet. Due to the low adoption rate of mobile stores, service providers need to understand the factors affecting user behavior, and adopt effective measures to facilitate mobile shopping usage.

With the help of mobile networks and devices, the mobile Internet has freed users from temporal and spatial constraints and enabled them to make purchases at any time from anywhere. This provides great convenience to users and may promote their usage. However, users may lack trust in mobile stores, which are novel to users. They may doubt whether mobile stores can protect their payment security and personal information privacy. In addition, they may obtain a poor experience due to the constraints of mobile networks and devices, such as slow responses and inconvenient input. Further, users have widely adopted online stores (45.9 percent of adoption rate) (CNNIC, 2013). If they plan to switch to mobile stores, they may face high switching costs, such as learning costs and habit change. These problems may inhibit user adoption of mobile stores.

Extant research has paid attention to identifying the factors affecting user adoption of mobile shopping (Ko et al., 2009; Lu and Su, 2009; Zhou, 2013). Factors such as perceived value and perceived usefulness are found to affect mobile shopping intention. However, user switching from online stores to mobile stores has seldom been examined. As noted earlier, online stores have been popular among users. If users are locked into the relationship with online stores, they may be unable to switch to mobile stores. This may prevent their adoption and usage of mobile shopping. The purpose of this research is to examine user switching from online stores to mobile stores by integrating a dual perspective of enablers and inhibitors. Enablers include trust transfer and flow, which represent an optimal experience, whereas the inhibitor is switching barrier. Enablers pull users to switch to mobile stores, whereas inhibitors push them back to use online stores. Kim and Son (2009) adopted a dedication-constraint perspective to examine post-adoption of online services. Liu et al. (2011) examined mobile user loyalty through a lens of pull-in forces (relationship quality) and push-back forces (switching barrier). Consistent with these studies, our research also adopted a dual paradigm to examine user switching from online stores to mobile stores.

The rest of this paper is organized as follows. We develop the research model and hypotheses in section 2. Section 3 describes instrument development and data collection. We present results in section 4, which is followed by a discussion of these results in section 5. Section 6 presents the implications and limitations.

Research model and hypotheses

Mobile shopping user adoption

As an emerging service, mobile shopping user adoption has received some attention from researchers. Information systems theories such as the technology acceptance model are often used as the theoretical bases. Lu and Su (2009) reported that enjoyment, usefulness, compatibility and anxiety affect mobile shopping intention. Ko et al. (2009) suggested that usefulness, enjoyment, instant connectivity and ease of use affect perceived value, which further affects adoption intention of mobile shopping in Korea. Kuo et al. (2009) noted that service quality and perceived value affect satisfaction, which in turn affects post-purchase intention of mobile value-added services. Zhou (2013) found that trust, flow and perceived usefulness predict mobile purchase intention. Lai et al. (2012) adopted a push-pull-mooring perspective to examine consumer switching behavior towards mobile shopping. They found that mooring forces such as trust, privacy and security have the strongest effects on switching intentions.

As we can see from these studies, although mobile shopping adoption has received attention from researchers, user switching from online stores to mobile stores has seldom been examined. This research tries to fill the gap.

Trust

Trust reflects a willingness to be vulnerable based on positive expectation toward another party’s future behavior (Mayer et al., 1995). Trust includes three dimensions: ability, integrity and benevolence (Hwang and Lee, 2012). Ability means that service providers have the necessary knowledge and expertise to fulfill their tasks. Integrity means that service providers keep their promises and do not deceive users. Benevolence means that service providers are concerned with users’ interests, not just their own benefits.

Due to the uncertainty and risk of online transactions, trust has received considerable attention in the e-commerce context (Beldad et al., 2010). Factors related to consumers, websites, companies and third parties are identified to affect online trust. Similar to online business, mobile business may also involve great uncertainty and risk (Lu et al., 2011). For example, mobile networks are vulnerable to hacker attack and information interception. Mobile devices may be infected by viruses and worms. These problems increase users’ perceived risk of mobile shopping. They need to build trust in order to mitigate perceived risk and facilitate their usage. Extant research has identified the effect of trust on mobile user behavior in the contexts of mobile payment (Chandra et al., 2010), mobile banking (Lin, 2011), mobile Internet sites (Lee, 2005; Li and Yeh, 2010), and mobile commerce technologies (Vance et al., 2008).

In this research, we are mainly concerned with trust transfer from online stores to mobile stores. As both online stores and mobile stores are often owned by the same vendor, when users have developed trust in online stores, they may transfer their trust to mobile stores (Stewart, 2003). That is, they believed that mobile stores have the same ability and integrity to provide quality information and services to them as do online stores. This may help facilitate their switching from online stores to mobile stores. Lai et al. (2012) found that trust as a mooring force has a strong effect on consumer switching intention toward mobile shopping. Thus, we suggest:

H1: Trust transfer from online stores to mobile stores positively affects user switching intention.

Flow

Flow reflects a holistic sensation that people feel when they act with total involvement (Csikszentmihalyi and Csikszentmihalyi, 1988). Hoffman and Novak (1996) defined flow as a state that is characterized by: (1) a seamless sequence of responses facilitated by machine interactivity; (2) intrinsic enjoyment; (3) a loss of self-consciousness; and (4) self-reinforcement. Flow reflects a balance between users’ skills and challenges. When users’ skills exceed challenges, they feel bored. In contrast, when challenges exceed skills, users feel anxious. When skills and challenges are below the threshold values, users feel apathy. Only when both skills and challenges exceed the threshold values and have a good fit will users experience flow.

As a broad and elusive concept, flow consists of various components, such as perceived enjoyment, perceived control and concentration (Hoffman and Novak, 2009). Perceived enjoyment reflects the enjoyment and pleasure associated with using mobile stores. Perceived control reflects the feelings of control over the activity and surrounding environment. Concentration reflects user immersion and involvement in using mobile stores. Due to its significant effect on user behavior, flow has been examined in various contexts, such as instant messaging (Zaman et al., 2010), sports team websites (O’Cass and Carlson, 2010), e-learning (Ho and Kuo, 2010), virtual worlds (Animesh et al., 2011), and online shopping (Guo and Poole, 2009). Recently, flow has also been examined in mobile contexts, which include mobile TV (Jung et al., 2009), mobile games (Ha et al., 2007), and mobile instant messaging (Zhou and Lu, 2011).

When users obtain flow in accessing mobile stores, they are immersed in the activity and feel great enjoyment. This optimal experience may facilitate their switching to mobile stores. In contrast, if they obtain a poor experience such as unstable connections and slow responses, they may feel annoyed. They may also feel a lack of control over mobile shopping. This may undermine their experience and decrease their switching intention. Extant research has reported the effect of flow on user adoption of various mobile services, such as mobile TV (Jung et al., 2009), mobile instant messaging (Zhou and Lu, 2011), and mobile games (Ha et al., 2007). Consistent with these studies, we propose,:

H2: Flow associated with using mobile stores positively affects user switching intention.

Trust transfer may have an effect on flow. Trust provides a subjective guarantee that users acquire expected outcomes in future (Gefen et al., 2003). That is, users believe that service providers have the ability and integrity necessary to provide a compelling experience to them. In addition, trust can mitigate perceived risk and increase perceived control. This may help obtain flow experience. On the contrary, if users do not believe that mobile stores are as trustworthy as online stores, they may feel great uncertainty and risk, which further undermine their experience of using mobile stores (Zhou, 2013). Hence:

H3: Trust transfer positively affects flow.

Ubiquitous connection and contextual offering

Ubiquitous connection means that users can access mobile stores to make purchases at any time from anywhere. With the help of mobile networks and devices, users can acquire ubiquitous information and services. This provides great convenience to users. Nevertheless, users may encounter problems such as slow responses and unstable connections during mobile shopping. These problems may decrease users’ trust in service providers. They may feel that service providers do not have enough ability and benevolence to provide ubiquitous services to them. Lee (2005) suggested that ubiquitous connection as a component of interactivity affect user trust in mobile sites. In addition, users expect to acquire a fluid mobile shopping experience. If they frequently encounter connection interruptions, they may feel anxiety and cannot obtain enjoyment associated with accessing mobile stores (Ko et al., 2009). So:

H4: Ubiquitous connection positively affects trust transfer.

H5: Ubiquitous connection positively affects flow.

Contextual offering means that service providers offer personalized information and services to users based on their preferences and locations (Lee, 2005). Service providers can use location-based services to acquire users’ locations, and then push relevant information to them based on their locations and preferences (Xu et al., 2009). This personalized information may better meet users’ demand and increase their trust in service providers. They may feel that service providers are concerned with their interests. Ho and Chau (2013) noted the effect of localization (similar to contextual offering) on user trust in mobile merchants. In addition, contextual information and services also help improve users’ experience, as it is relatively difficult for them to search for information on the mobile Internet. Contextual offering enables users to obtain relevant information without much effort (Zhao et al., 2012). Thus, we suggest:

H6: Contextual offering positively affects trust transfer.

H7: Contextual offering positively affects flow.

Switching costs and switching barrier

Switching costs reflect the expected costs of switching from a current service provider to an alternative one (Ray et al., 2012). Switching costs consist of multiple components: uncertainty costs, transition costs, sunk costs and loss costs (Kim, 2011). Ray et al. (2012) noted that online users’ switching costs are composed of vendor-related costs and user-related costs. Vendor-related costs include benefit-loss costs, service-uncertainty costs and brand-relationship costs, whereas user-related costs include search costs, transfer costs and learning costs. Extant research has reported the significant effect of switching costs on user behavior. Kim (2011) noted that switching costs affect user resistance to enterprise systems implementation. Deng et al. (2010) stated that switching costs affect user loyalty toward mobile instant messaging.

Switching barrier reflects the extent to which users experience a sense of being locked into a relationship (Tsai and Huang, 2007). When the switching costs are expensive, users may face great switching barrier if they plan to switch from online stores to mobile stores. That is, the high switching costs have locked users into the relationship with online stores. Extant research has identified the effect of switching costs on user loyalty toward mobile instant messaging (Deng et al., 2010) and e-retailing (Wang et al., 2011). Thus:

H8: Switching costs positively affect switching barrier.

Switching barrier may affect switching intention. When users are locked into the relationship with online stores, they cannot easily switch to mobile stores and conduct mobile purchases. Compared to the effects of trust transfer and flow as enablers of switching intention, switching barrier acts as an inhibitor. Liu et al. (2011) reported the effect of switching barrier as a push-back force on mobile user loyalty. Thus, we state:

H9: Switching barrier negatively affects switching intention.

Figure 1 presents the research model.

Figure 1.

Research model.

Method

The research model includes seven factors. Each factor was measured with multiple items. All items were adapted from extant literature to improve content validity. These items were first translated into Chinese by a researcher. Then another researcher translated them back into English to ensure consistency. When the instrument was developed, it was tested among five users that had online and mobile shopping experience. Then, according to their comments, we revised some items to improve the clarity and understandability. The final items and their sources are listed in the Appendix.

Items of ubiquitous connection and contextual offering were adapted from Lee (2005). Items of ubiquitous connection reflect the fact that users can conduct mobile purchases at any time from anywhere. Items of contextual offering reflect the timely and personalized information offered to users. Items of switching costs were adapted from Tsai et al. (2006) to measure the effort, time cost and losses incurred from switching. Items of trust were adapted from Lee (2005) to reflect the trustworthiness and integrity of mobile stores. Items of flow were adapted from Lee et al. (2007) to measure enjoyment, concentration and perceived control. Items of switching barrier were adapted from Tsai et al. (2006) to reflect the life disruption and limited choices derived from switching. Items of switching intention were adapted from Anton et al. (2007) to measure user intention to switch from online stores to mobile stores.

Data were collected at the service outlets of China Mobile and China Unicom, which are two main telecommunication operators in China. Users go to these service outlets to pay fees, print invoices and open new services. They often need to wait for some minutes before they are served, due to the large number of users. This allowed us to interview them and collect data. We contacted users and inquired whether they had online and mobile shopping experience. Then we asked those with positive answers to complete the questionnaire based on their usage experience. We scrutinized all responses and dropped those with too many missing values. As a result, we obtained 243 valid responses. Among them, 50.6 percent were male and 49.4 percent were female. A majority of them (about 87.2 percent) were between 20 and 29 years old. The frequently used mobile stores include mobile Taobao, mobile Jingdong, and mobile Dangdang, three leading mobile stores in China.

We conducted two tests to examine common method variance. First, we conducted a Harmon’s single-factor test (Podsakoff and Organ, 1986). The results indicated that the largest variance explained by any individual factor is 12.23 percent. Thus, none of the factors can explain the majority of the variance. Secondly, we modeled all items as the indicators of a factor representing the method effect, and re-estimated the model (Malhotra et al., 2006). The results indicated a poor fitness. For example, the goodness of fit index (GFI) is 0.581 (<0.90). The root mean square error of approximation (RMSEA) is 0.19 (>0.08). The results of both tests suggest that common method variance is not a significant problem in our research.

Results

Following the two-step approach recommended by Anderson and Gerbing (1988), we first examined the measurement model to test reliability and validity. Then we examined the structural model to test research hypotheses and model fitness.

First, we conducted confirmatory factor analysis to examine validity. Validity includes convergent validity and discriminant validity. Convergent validity measures whether items can effectively reflect their corresponding factor, whereas discriminant validity measures whether two factors are statistically different. Table 1 lists the standardized item loadings, the average variance extracted (AVE), and the composite reliability (CR). As listed in the table, most item loadings are larger than 0.7. The T values indicate that all loadings are significant at 0.001. Each AVE exceeds 0.5 and CR exceeds 0.7. Thus, the scale has good convergent validity (Gefen et al., 2000). In addition, all Cronbach Alpha values are larger than 0.7, suggesting good reliability (Nunnally, 1978).

Table 1.

Standardized item loadings, AVE, CR and Alpha values.

Factor	Item	Standardized loading	AVE	CR	Alpha
Ubiquitous connection (UC)	UC1	0.855	0.74	0.89	0.89
	UC2	0.924
	UC3	0.796
Contextual offering (CO)	CO1	0.757	0.65	0.85	0.84
	CO2	0.865
	CO3	0.788
Switching costs (SC)	SC1	0.732	0.62	0.83	0.82
	SC2	0.921
	SC3	0.694
Trust transfer (TRU)	TRU1	0.829	0.70	0.87	0.86
	TRU2	0.945
	TRU3	0.717
Flow (FLOW)	FLOW1	0.679	0.66	0.85	0.85
	FLOW2	0.845
	FLOW3	0.897
Switching barrier (SB)	SB1	0.693	0.59	0.81	0.81
	SB2	0.830
	SB3	0.777
Switching intention (INT)	INT1	0.699	0.58	0.80	0.80
	INT2	0.808
	INT3	0.765

To examine discriminant validity, we compared the square root of AVE and factor correlation coefficients. As listed in Table 2, for each factor, the square root of AVE is significantly larger than its correlation coefficients with other factors. This suggests good discriminant validity (Gefen et al., 2000).

Table 2.

The square root of AVE (listed at diagonal) and factor correlation coefficients.

	UC	CO	SC	TRU	FLOW	SB	INT
UC	0.860
CO	0.540	0.805
SC	–0.245	–0.102	0.789
TRU	0.493	0.522	–0.262	0.836
FLOW	0.563	0.486	–0.164	0.520	0.812
SB	0.139	0.016	0.176	–0.116	0.051	0.769
INT	0.408	0.531	–0.275	0.580	0.459	–0.251	0.759

Secondly, we employed structural equation modeling software LISREL 8.7 to estimate the structural model. Figure 2 presents the results. Table 3 lists the recommended and actual values of some fit indices. Except for GFI, other fit indices have better actual values than the recommended values. This indicates the good fitness of the research model (Gefen et al., 2000). The explained variance of trust transfer is 43.3 percent, of flow, 40.8 percent, of switching barrier 3.1 percent, and of switching intention 42 percent.

Figure 2.

The results estimated by LISREL.

Table 3.

The recommended and actual values of fit indices.

Fit indices	chi²/df	GFI	AGFI	CFI	NFI	NNFI	RMSEA
Recommended value	<3	>0.90	>0.80	>0.90	>0.90	>0.90	<0.08
Actual value	2.31	0.861	0.819	0.955	0.924	0.947	0.064

(Note: chi²/df is the ratio between Chi-square and degrees of freedom, GFI is Goodness of Fit Index, AGFI is the Adjusted Goodness of Fit Index, CFI is the Comparative Fit Index, NFI is the Normed Fit Index, NNFI is the Non-Normed Fit Index, RMSEA is Root Mean Square Error of Approximation)

Discussion

As shown in Figure 2, all hypotheses are supported. Ubiquitous connection and contextual offering have effects on trust transfer and flow, both of which further affect switching intention. In addition, switching costs affect switching barrier, which has a negative effect on switching intention.

Compared to ubiquitous connection, contextual offering has a larger effect (the path coefficient is 0.51) on trust transfer. When users receive personalized information and services from service providers, they may feel that service providers are benevolent and concerned with their interests. This may help transfer their trust from online stores to mobile stores. It is worth noting that to offer contextual information and services, a service provider needs to collect and utilize user location information. This may arouse users’ privacy concerns and increase their privacy risk (Xu et al., 2012). Thus, service providers need to obtain users’ permission before sending contextual information to them. Otherwise, users may feel that their privacy is violated and have low trust in service providers. Contextual offering also affects flow. Contextual offering provides the optimal information and services to users based on their location and preferences. This may reduce users’ effort spent on information search, increase their perceived control and help them obtain a compelling experience.

The results indicate that ubiquitous connection has a significant effect (the path coefficient is 0.37) on flow. In addition, the effect of ubiquitous connection on flow is larger than the effect of contextual offering on flow. Users expect to conduct mobile purchase at any time from anywhere. If service connections are unstable or unavailable, they cannot be immersed in using mobile stores and obtain enjoyment. This may undermine their experience. Ubiquitous connection also affects trust transfer. This suggests that when users obtain ubiquitous information and services during mobile shopping, they may build trust in mobile stores. Ubiquitous connection may act as a trust signal, as offering ubiquitous information and services to users entails service providers’ effort and investment of resources. Thus, service providers should ensure that their systems provide reliable and quality connections to users.

Switching costs significantly affect switching barrier. This suggests that high switching costs may lock users into existing relationships with online stores. Switching costs include sunk costs, learning costs and artificial costs (Chen and Hitt, 2002). Sunk costs reflect the effort and time spent on using online stores. Learning costs reflect the effort and time spent on learning to use mobile stores. Artificial costs reflect the lost benefits derived from switching, such as discounts, convenience and member points. Service providers can offer easy-to-use mobile stores to users to decrease their learning costs. They also need to be concerned with artificial costs, such as using rewards and points to encourage user switching.

Trust transfer has a significant effect on flow. Trust ensures that service providers have the ability and integrity to offer a compelling experience to users. If users lack trust in service providers, they will not expect to obtain a good experience from mobile stores. The results indicate that trust transfer, flow and switching barrier affect switching intention. Among them, trust transfer and flow have positive effects on switching intention and they act as switching enablers, whereas switching barrier has a negative effect on switching intention and it acts as a switching inhibitor. Further, among these three factors, trust transfer has the largest effect (the path coefficient is 0.44) on switching intention. This highlights the need to transfer users’ trust from online stores to mobile stores in order to facilitate their switching.

Implications and limitations

From a theoretical perspective, this research integrated a dual perspective of enablers and inhibitors to examine user switching from online stores to mobile stores. As noted earlier, extant research has been concerned with user adoption of mobile shopping, and has seldom examined user switching from online stores to mobile stores. However, if users have been locked into the relationship with online stores, they may be unable to switch to mobile stores and conduct mobile purchases. Thus, it is necessary to study user switching from online stores to mobile stores. This research tries to fill the gap and provides a new perspective of understanding mobile user behavior. In addition, our results suggest that user switching receives a dual influence from both enablers and inhibitors. Enablers include trust transfer and flow, whereas the inhibitor is switching barrier. Among these factors, trust transfer has the largest effect on switching intention. These results extend previous research that focused on the single effects of enablers on user behavior, and provide a fuller picture of mobile user shopping behavior. Future research can generalize our results to other contexts, such as mobile payment.

From a managerial perspective, our results imply that service providers need to be concerned with both enablers and inhibitors to facilitate user switching from online stores to mobile stores. On the one hand, they need to promote user trust transfer and offer an engaging experience to users. Specifically, trust transfer has the largest effect on switching intention. Our results suggest that service providers can offer contextual information and services to users to facilitate trust transfer from online stores to mobile stores. They also need to present ubiquitous connections to users to improve their usage experience. On the other hand, service providers need to curb the effects of switching barrier. They can reduce switching costs, such as learning costs and artificial costs, to decrease switching barrier, and facilitate user switching from online stores to mobile stores.

This research has the following limitations. First, we conducted this research in China, where mobile business is developing rapidly but still in its early stage. Thus, our results need to be generalized to other countries that have developed mobile business. Secondly, besides the factors identified in our research, there exist other factors possibly affecting switching intention, such as satisfaction and perceived usefulness. Future research can examine their effects. Third, we mainly conducted a cross-sectional study. However, user behavior is dynamic. Thus, a longitudinal research may provide more insights into user behavior development.

Footnotes

Appendix: Measurement scale and items

Acknowledgement

This work was supported by grants from the National Natural Science Foundation of China (71371004, 71001030), and a grant from Zhejiang Provincial Key Research Base of Humanistic and Social Sciences in Hangzhou Dianzi University (ZD04-2014ZB).

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