Understanding the omnichannel shopping intention of offline-focused consumers
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Cite this article:
Gao, M., &
Huang, L.
(2025). Understanding the omnichannel shopping intention of offline-focused consumers.
Social Behavior and Personality: An international journal,
53(6),
e14383.
Abstract
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While digital transformation and omnichannel retailing are becoming prominent in the retail industry, consumers are still oriented toward physical stores. Therefore, we examined offline-focused consumers’ omnichannel shopping intention by incorporating channel fluency and personalization into the theory of planned behavior. Furthermore, we examined whether the effects of channel fluency and perceived personalization on attitude consistency and perceived behavioral control were moderated by offline channel usage frequency. We surveyed 2,163 Uniqlo consumers and analyzed their responses using structural equation modeling. The results showed that channel fluency and perceived personalization predicted attitude consistency and perceived behavioral control. Attitude consistency, subjective norms, and perceived behavioral control predicted offline-focused consumers’ omnichannel shopping intention. Further, offline channel usage frequency positively moderated the effect of channel fluency on perceived behavioral control, and negatively moderated the effect of personalization on perceived behavioral control. Implications are discussed for theory and practice.
Omnichannel retailing is a retail strategy that provides a seamless customer experience by integrating a wide variety of channels and customer touchpoints (M. Gao & Huang, 2021). Omnichannel retailing relies on the internet and is relatively more acceptable to e-commerce retailers (Cui et al., 2021). However, moving from e-commerce to omnichannel retailing is not easy for brick-and-mortar retailers. For example, Macy’s saw a decline in sales at its physical stores as it moved to omnichannels (The Plant, n. d.) The root cause of the omnichannel failure of brick-and-mortar retailers is attributed to the lack of consumer perspective, amid multiple reasons including lack of technical support, experience, and market share (Ye et al., 2018). Existing research has focused mainly on omnichannel consumers who are proficient in using various channels (Shen et al., 2018; Shi et al., 2020), yet recent studies have found that most consumers still shop with a focus on online or offline channels (Neslin, 2022; Valentini et al., 2020). Consumers from several countries, such as Japan, the UK, Australia, and the US, continue to shop primarily offline, even after experiencing the COVID-19 pandemic impact (Diep, 2022; Tighe, 2022). Zhang et al. (2022) found that offline-focused consumers, who primarily use physical stores and promotions to purchase, are more inclined than are online consumers to purchase high-value products. Therefore, there is an urgent need for research focused on the omnichannel shopping intentions of offline-focused consumers, a group that has been understudied in previous research.
In this study we employed the theory of planned behavior (TPB; Ajzen, 1991) to predict offline-focused consumers’ omnichannel shopping intention. The definition of omnichannel shopping intention is the extent to which consumers intend to utilize multiple channels to make purchases at omnichannel retailers (M. Gao & Huang, 2021). As consumers transition between channels during their omnichannel shopping journey and interact differently across these channels, attitude consistency, which is defined as the extent to which consumers’ evaluations of a service remain constant when switching between channels (M. Gao & Huang, 2024), is a better reflection of consumer perception during the omnichannel shopping process than is a single assessment (Swoboda & Winters, 2021). Therefore, we replaced attitude in the TPB with attitude consistency to better fit the omnichannel retailing context, proposing the following research question:
Research Question 1: How does the theory of planned behavior, which consists of the dimensions of attitude consistency, subjective norms, and perceived behavioral control, predict offline-focused consumers’ omnichannel shopping intention?
The TPB is a context-specific collection of external beliefs (Pavlou & Fygenson, 2006). Therefore, it is necessary to expand this theory by incorporating external beliefs into the context of omnichannel retailing. Offline-focused consumers seek an easy shopping experience; thus, the attribute of seamlessness in omnichannel retail is crucial (Valentini et al., 2020). This philosophy of seamlessness is reflected in the construct of channel fluency (Lin et al., 2022). Furthermore, compared with online-focused shoppers, offline-focused shoppers are more likely to enjoy personalized services (Haridasan & Fernando, 2018). On the basis of the characteristics of a seamless and personalized omnichannel customer experience, in this study we focused on two important external beliefs: channel fluency and perceived personalization (Bullock, 2019; Shi et al., 2020). Our second research question was as follows:
Research Question 2: How do channel fluency and perceived personalization promote omnichannel shopping intention through attitude consistency and perceived behavioral control?
In addition, studies have found that consumers who develop the habit of using offline channels perceive higher switching costs (Cambra-Fierro et al., 2020). Consumers’ perception of the omnichannel shopping experience may vary depending on the frequency of use of offline channels. Therefore, we focused on offline channel usage frequency in proposing our third research question:
Research Question 3: How does offline channel usage frequency moderate the influence of channel fluency and perceived personalization on attitude consistency and perceived behavioral control?
In summary, we investigated the impact of channel fluency and perceived personalization on attitude consistency and perceived behavioral control; the impact of attitude consistency, subjective norms, and perceived behavioral control on omnichannel shopping intention; and the moderating role of offline channel usage frequency on the relationship between external beliefs (i.e., channel fluency and perceived personalization) and perceptions (i.e., attitude consistency and perceived behavioral control).
Channel Fluency and Perceived Personalization
The TPB identifies attitude, subjective norms, and perceived behavioral control as factors that drive behavioral intention (Ajzen, 1991, 2002). According to the TPB, attitudes are generated by behavioral beliefs, subjective norms are formed by normative beliefs, and perceived behavioral control results from control beliefs (Ajzen, 2002). These beliefs reflect the fact that the information that people possess is related to the performance of a particular behavior (Ajzen, 2011). Previous research has proposed decomposing derived beliefs in specific contexts to extend the TPB and ensure it offers a high degree of explanation of behavior (Zapkau et al., 2015).
For offline-focused consumers’ omnichannel shopping, we concentrated on two external beliefs: channel fluency and perceived personalization. Channel fluency is defined as the perceived smoothness with which a customer transitions between the multiple channels offered by an omnichannel retailer (Shen et al., 2018). Studies have shown that perceived fluency contributes to positive affect and attitude (Cassab & MacLachlan, 2006; Mainardes et al., 2020). Further, Lin et al. (2022) recognized that the perceived fluency between channels is a key factor in demonstrating seamlessness. When offline-focused consumers switch channels with no extra effort, their subjective cognition and feelings remain consistent throughout the transition. Therefore, we examined whether channel fluency contributes to consumers’ consistent attitudes during the omnichannel process. Thus, we formulated the following hypotheses:
Hypothesis 1: The channel fluency perceived by offline-focused consumers will positively predict attitude consistency in omnichannel retailing.
Xu and Jackson (2019) found that channel uniformity positively affects the perceived behavioral control of consumers. In omnichannel shopping, consumers who are familiar with using one channel are more confident and capable of using another, reflecting consumers’ self-efficacy in the channel-switching process (Huang et al., 2023). Sun et al. (2020) demonstrated that channel integration enhances consumer self-efficacy when engaging in omnichannel shopping. Hence, we predicted that channel fluency would increase offline-focused consumers’ confidence in mastering the omnichannel-shopping approach, and we proposed the following hypothesis:
Hypothesis 2: The channel fluency perceived by offline-focused consumers will positively predict perceived behavioral control in the context of omnichannel retailing.
Offline-focused (vs. online-focused) consumers have a greater need for personalized attention and are more likely to enjoy personalized services (Haridasan & Fernando, 2018; Riegger et al., 2021). Perceived personalization is defined as the degree to which consumers perceive retailers as providing personalized attention (Shi et al., 2020). In physical stores, omnichannel retailers provide personalized real-time services to consumers based on customer data (Oberoi et al., 2017). Consequently, offline-focused consumers who shop in physical stores are more likely to use mobile channels for personalized discounts and recommendations, ignoring the perceived dissonance of channel switching (Tyrväinen et al., 2020). We anticipated that for offline-focused consumers, personalized services offered by omnichannel retailers would reduce the perceived cost of channel switching and make it easier to maintain a consistent attitude. Hence, we proposed the following hypothesis:
Hypothesis 3: Perceived personalization will positively predict the attitude consistency of offline-focused consumers during omnichannel shopping.
The personalized service offered by a retailer is both an incentive for consumers and a sign of consumer empowerment, as personalized services recommend products based on consumer preferences (Hsia et al., 2020). Xiao and Benbasat (2018) found that consumers who received personalized recommendations had greater confidence in their choices, fewer information searches, and lower cognitive effort. H. Y. Kim et al. (2019) demonstrated that personalization enhances perceived behavioral control to help consumers make choices more easily. An omnichannel retail environment reduces the uncertainty and risk for offline-focused consumers in meeting their individual requirements. Therefore, we proposed the following hypothesis:
Hypothesis 4: Personalization perceived by offline-focused consumers will positively predict perceived behavioral control in omnichannel retailing.
Attitude Consistency
If a consumer’s attitude toward a behavior is positive, the consumer will tend to perform that behavior (Ajzen, 1991). Lazaris et al. (2022) showed that consumers’ affect responses and cognition of omnichannel atmosphere positively influence their purchase intention. Sun et al. (2020) found that satisfaction with omnichannel services has a positive impact on omnichannel service usage. In contrast, mixed emotions may lead consumers to avoid or leave stores (Penz & Hogg, 2011). Offline-focused consumers are likely to use channels other than the physical stores of omnichannel retailers only if they maintain a consistent attitude in their shopping process. As such, we proposed the following hypothesis:
Hypothesis 5: Positive attitude consistency will positively predict offline-focused consumers’ omnichannel shopping intention.
Subjective Norms
Subjective norms represent social influence, usually from family, relatives, and friends (Juaneda-Ayensa et al., 2016). Previous studies have provided empirical evidence demonstrating the positive influence of subjective norms on behavioral intentions such as purchase, usage, and investment intentions (Chi et al., 2012; Ibrahim & Arshad, 2017; Shin & Hancer, 2016). In the context of omnichannel retail, subjective norms may be generated through various channels and affect consumer decision making. Mosquera et al. (2018) confirmed the positive influence of social influence on the intention to use smartphones in store. Therefore, we proposed the following hypothesis:
Hypothesis 6: Subjective norms will positively predict offline-focused consumers’ omnichannel shopping intention.
Perceived Behavioral Control
Perceived behavioral control represents the ability and confidence to use multiple channels during a single purchase and is a driver of consumer adoption of omnichannel shopping (Chiu et al., 2011). Further, perceived behavioral control emphasizes the belief that one has the necessary resources and opportunities to perform specific behaviors (Ajzen, 1991). Arora and Sahney (2018) concluded that perceived behavioral control is a core factor in adopting webrooming behavior. Sun et al. (2020) found that omnichannel self-efficacy positively affects omnichannel service usage. Therefore, we proposed that perceived behavioral control, which is the perceived ease and control of omnichannel shopping, would play a central role in predicting offline-focused consumers’ omnichannel shopping intention (Xu & Jackson, 2019). As such, we proposed the following hypothesis:
Hypothesis 7: Perceived behavioral control will positively predict offline-focused consumers’ omnichannel shopping intentions.
Moderating Effect of Offline Channel Usage Frequency
Channel usage frequency can be used to measure consumer preferences and habits. Long-standing habits trigger inertia and automaticity, and rarely involve conscious behavior (Olsen et al., 2013). Offline-focused consumers are considered conservative and passive in channel transitions (Dholakia et al., 2010; Lu et al., 2011). Therefore, in this study we focused on the moderating effect of offline channel usage frequency, that is, the frequency of using physical stores for information and purchase (Flavián et al., 2020).
A high frequency of offline channel usage indicates that consumers are highly dependent on physical stores. Beauchamp and Ponder (2010) revealed that offline-focused consumers have lower perceptions of access, search, and transaction convenience than online consumers do. Consequently, consumers who frequently utilize offline channels have a reduced need for alternative channels and require less channel fluency. While channel fluency positively affects attitude consistency and perceived behavioral control, increased frequency of offline channel usage weakens this effect. Hence, we proposed the following hypotheses:
Hypothesis 8: Increasing the frequency of offline channel use will weaken the positive effect of channel fluency on attitude consistency in omnichannel retail settings.
Hypothesis 9: Increasing the frequency of offline channel use will weaken the positive effect of channel fluency on perceived behavioral control in omnichannel retail settings.
Personalized services in physical stores primarily track consumer behavior in real time through customers’ smartphones and use this information to quickly offer customized recommendations and discounts (Dekimpe et al., 2020). However, offline-focused consumers are often repulsed by services that require them to sacrifice personal privacy to enjoy benefits (Choi & Park, 2006). Additionally, in physical stores, consumers receive one-on-one service from store associates, which reduces the need for personalized digital services. We speculated that the positive effect of personalization on attitude consistency and perceived behavioral control might diminish when consumers frequently use offline channels. Therefore, we proposed the following hypotheses:
Hypothesis 10: Increasing the frequency of offline channel usage will weaken the positive effect of perceived personalization on attitude consistency in omnichannel retail settings.
Hypothesis 11: Increasing the frequency of offline channel usage will weaken the positive effect of perceived personalization on perceived behavioral control in omnichannel retail settings.
Figure 1 demonstrates the research model of the present study.
Figure 1. Research Model
Method
Participants and Procedure
This study was approved by the ethics committee of Zhejiang Wanli University. We distributed a survey to Uniqlo customers in Japan through Yahoo Crowdsourcing, and rewarded users who met the survey criteria and completed it. We received 2,215 responses and removed 52 that had the same scores for all items, retaining the remaining 2,163 responses for analysis. Table 1 shows the demographic characteristics of the respondents.
We collected data through an online survey, which began with two questions: “Are you a customer of Uniqlo’s physical stores?” and “Have you visited Uniqlo’s online stores, including the mobile app and website?” We considered respondents who replied “yes” to the first question as the target group. Respondents chose one of three options to answer the second question: barely visited, occasionally visited, or frequently visited.
The next part of the survey included 23 items addressing the variables in the research model. We adjusted all variable measures from the existing literature according to their conceptual fit, and modified the items after parallel and back-translation to make them more suitable for the context of Japan. We invited 10 researchers (five master’s students and five colleagues) to participate in the pretest; on the basis of their feedback, the survey was modified to improve the validity of the content. All items were measured using a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), except for the items on channel usage frequency, which were measured from 1 (I never use it) to 7 (I use it every time I make a purchase).
In the title of the second part of the questionnaire, we explained the concept of omnichannel shopping as follows: “Seamless and uninterrupted shopping by integrating multiple channels of the retailer, both online and offline.” The final section of the survey collected data on the demographic characteristics of the participants.
Table 1. Demographic Profile of the Participants
Note. N = 2,163. JPY 1 = USD 0.007.
Channel Fluency
The measure of channel fluency was adapted from Shen et al. (2018) by replacing “omnichannel service” with “omnichannel retailer.” It consists of four items, with a sample item being “The omnichannel retailer allows tasks to continue smoothly across different channels.”
Perceived Personalization
We assessed perceived personalization with three items from Gao and Huang (2023), who based their work on Tyrväinen et al. (2020). A sample item is “The omnichannel retailer provides me with shopping recommendations according to my purchase records across different channels.”
Attitude Consistency
We measured attitude consistency with four items from Shen et al. (2018). A sample item is “My attitude with the service remains the same when I shop across different channels.”
Subjective Norms
We measured subjective norms with three items from Juaneda-Ayensa et al. (2016). A sample item is “People whose opinions I value use different channels, choosing whichever is the most convenient at any given time.”
Perceived Behavioral Control
We measured perceived behavioral control with three items from Hansen et al. (2004) and Shin and Hancer (2016). A sample item is “Omnichannel shopping is easy for me.”
Omnichannel Shopping Intention
We measured omnichannel shopping intentions with three items from M. Gao and Huang (2024). A sample item is “I intend to use omnichannel shopping in the future.”
Usage Frequency
We measured channel usage frequency using three items from Flavián et al. (2020). A sample item is “How often do you make purchases in brick-and-mortar stores?”
Research Setting
We conducted a survey among Uniqlo’s customers in Japan for the following reasons: First, Japanese consumers prefer to shop at physical stores (Ryall, 2019). Although e-commerce retail has become increasingly popular in Japan, physical stores remain the preferred choice. Second, according to the company’s website (https://corp.supcolo.jp/uniqlo/) Uniqlo is well known in Japan and attracts consumers of all ages. Uniqlo has successfully applied an omnichannel business model, centering on physical stores and integrating online channels such as websites, mobile applications, and social platforms, to provide customers with convenient and personalized services. Approximately 95% of Uniqlo’s customers visit physical stores when shopping. Uniqlo’s physical stores place great emphasis on understanding customer feedback on products and services through employee–customer interactions.
Results
Common Method Bias
We employed confirmatory factor analysis to detect common method bias (Rodríguez-Ardura and Meseguer-Artola, 2020). We added an unmeasured latent factor to the confirmatory factor analysis model, linking this factor to all observed items, and constraining its variance to 1 so that all paths would be equal. The results showed that the common variance, estimated as the square of the coefficient of each path, was 24.4%, which is below the 50% threshold (Eichhorn, 2014). Therefore, common method bias in this study was not a significant concern.
Validation of the Measurement Model
Tables 2 and 3 show the assessment of the reliability and validity of the model. Cronbach’s alpha values for all variables were higher than .70, demonstrating that the scales were reliable (Nunnally, 1978). Convergent validity was verified by all item loadings exceeding .70, all composite reliability values of all variables exceeding .70, and all average variance extracted values exceeding .50, as shown in Table 2 (Hair et al., 2010).
Discriminant validity was assessed by calculating the square root of average variance extracted for each latent variable. These exceeded the cross-correlations between the variables, as shown in Table 3 (Fornell & Larcker, 1981).
Table 2. Reliability and Validity of Variables
Note. CR = composite reliability; AVE = average variance extracted.
Table 3. Correlation Analysis of Study Variables
Note. Values on the diagonal are the square roots of average variance extracted.
** p < .01. *** p < .001.
Validation of the Structural Model
We used structural equation modeling to test the hypotheses, provide global fit statistics, and simultaneously analyze multiple relationships among the variables (Kline, 2016). First, we tested the base model without a moderator. The fit indices were as follows: χ2 = 1377.875, df = 160, Tucker–Lewis index (TLI) = .952, comparative fit index (CFI) = .960, root-mean-square error of approximation (RMSEA) = .059, standardized root-mean-square residual (SRMR) = .029. Schermelleh-Engel et al. (2003) suggested that the significance of χ2 should not be emphasized too much because as the sample size increases, the value of χ2 will increase. Other fit indices, such as CFI and TLI, were higher than .90, and RMSEA and SRMR did not exceed .08, thus meeting the recommended levels (Hair et al., 2010) and revealing that the structural model had acceptable goodness of fit.
Hypothesis testing revealed that channel fluency significantly and positively influenced attitude consistency and perceived behavioral control, thus supporting Hypotheses 1 and 2. Perceived personalization positively influenced attitude consistency and perceived behavioral control, thus supporting Hypotheses 3 and 4. Furthermore, attitude consistency, subjective norms, and perceived behavioral control positively influenced omnichannel shopping intention, thus supporting Hypotheses 5, 6, and 7. The results are shown in Table 4.
Table 4. Structural Equation Modeling of the Base Model
Note. *** p < .001. † p < .10.
We investigated the moderating effect of offline channel usage frequency using the latent moderated structural equation method, which involves implementing an iterative maximum likelihood estimation procedure; therefore, it does not show a substantial bias in estimating standard errors (Klein & Moosbrugger, 2000). This approach can accurately test interaction effects (Cheung & Lau, 2017). The moderating effect results are shown in Table 5. Offline channel usage frequency did not significantly moderate the effects of channel fluency and perceived personalization on attitude consistency; thus, Hypotheses 8 and 10 were not supported. Offline channel usage frequency significantly and positively moderated the impact of channel fluency on perceived behavioral control, and significantly and negatively moderated the impact of perceived personalization on attitude consistency. Therefore, Hypothesis 9 was not supported, whereas Hypothesis 11 was supported.
Table 5. Results of Structural Equation Modeling with Offline Channel Usage Frequency as a Moderator
Discussion
Theoretical Implications
The present study has contributed to the omnichannel literature by examining the important factors that drive omnichannel shopping among offline-focused consumers. In contrast to most previous studies of omnichannel consumers, this study targeted offline-focused consumers because consumers in many regions and countries still predominantly shop in physical stores. First, on the basis of the characteristics of seamless and personalized services offered by omnichannel retailing, we selected channel fluency and perceived personalization as two important external beliefs in omnichannel retailing. We found that channel fluency and perceived personalization were behavioral beliefs that produced attitude consistency, as well as control beliefs that produced perceived behavioral control. These results are in line with the findings of Hsia et al. (2020), H. Y. Kim et al. (2019), and Sun et al. (2020). Therefore, this study provides new insights into the omnichannel retail literature by considering consumers’ different beliefs.
Second, we found that the TPB can be made more relevant to the omnichannel retail context by replacing attitude with attitude consistency. We obtained empirical evidence that attitude consistency, subjective norms, and perceived behavioral control determined offline-focused consumers’ omnichannel shopping intention, which is in line with previous studies (Arora & Sahney, 2018; Gao & Huang, 2024; Mosquera et al., 2018). The finding that perceived behavioral control played a determining role in omnichannel shopping intention is consistent with the role of the self-regulatory strategy of channel migration intention in promoting omnichannel shopping behavior (Ajzen, 2011). This also indicates that omnichannel shopping is not completely controlled by individual subjective evaluations or the influence of others; rather, it is governed primarily by the capabilities and resources of offline-focused consumers in the omnichannel retail context. Hence, perceived behavioral control is crucial in omnichannel retailing and must be considered.
Third, this study contributes to omnichannel management research by considering the moderating role of offline channel usage frequency. The habits or preferences of people making offline channel purchases are generally believed to hinder multi- and omnichannel processes (Dholakia et al., 2010; Lu et al., 2011). However, our empirical results indicated that a high frequency of offline channel usage did not completely hinder omnichannel shopping. We found that offline channel usage frequency enhanced the positive effect of channel fluency on perceived behavioral control and weakened the positive effect of personalization on perceived behavioral control. Therefore, under different conditions, offline channel usage frequency can be a barrier as well as a motivating factor.
Practical Implications
We have several recommendations to make from the perspectives of omnichannel strategies and customer relationship management. First, our findings suggest that omnichannel retailers should adopt a smooth and personalized strategy. We recommend that omnichannel retailers strengthen the fluency between channels, such as by establishing quick links between various customer touchpoints, so that customers can cross channels effortlessly. Retailers can post links and QR codes to online channels in physical stores and train in-store associates to unobtrusively guide offline-focused consumers to switch channels (McKenzie et al., 2018). In addition, maintaining the consistency of retailer information and connecting customer information between channels is conducive to the transfer of tasks, thereby enhancing customer-perceived channel fluency.
Furthermore, the provision of personalized services enables retailers to distinguish themselves within the highly competitive retail industry and attract special attention from customers. Hence, omnichannel retailers should establish perceived personalization among customers (Tyrväinen et al., 2020). They should invest in in-store intelligence technologies to track consumers’ shopping paths as they enter the store and should push offers in real time (Ylilehto et al., 2021). Retailers must be aware of offline-focused consumers’ privacy concerns and the level of technological capability of consumers to use personalized services (Jain et al., 2021; Riegger et al., 2021).
In addition, the moderating results we obtained regarding of offline channel usage frequency indicate that omnichannel retailers should guide offline-focused consumers to make omnichannel shopping trips while considering the extent to which consumers rely on physical stores. Therefore, we suggest that retailers group customers together in an omnichannel setting and deploy different customer relationship management strategies for different customer groups (Kinard & Capella, 2006). Omnichannel retailers must clarify the scope of privacy and improve consumer knowledge before offering personalized services (Strycharz et al., 2019).
Limitations and Future Research Directions
Several limitations apply to this study. First, we collected cross-sectional data from a single source. Future studies could consider collecting data from multiple sources. Second, it is necessary to consider whether other external beliefs exist in the omnichannel retail context. Third, as the sample comprised Japanese consumers who are more accustomed to shopping in physical than online stores, this study used offline channel usage frequency as a moderator, which necessitates caution in generalizing the results other cultural contexts. Future research could consider channel preferences or usage as moderators. Further, future research on consumer behavior in the omnichannel settings could consider the different influencing factors of different consumers to extend understanding of the interaction of these variables.
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Juaneda-Ayensa, E., Mosquera, A., & Sierra Murillo, Y. S. (2016). Omnichannel customer behavior: Key drivers of technology acceptance and use and their effects on purchase intention. Frontiers in Psychology, 7, Article 1117. https://doi.org/10.3389/fpsyg.2016.01117
Kim, H. Y., Song, J. H., & Lee, J.-H. (2019). When are personalized promotions effective? The role of consumer control. International Journal of Advertising, 38(4), 628–647. https://doi.org/10.1080/02650487.2019.1593721
Kim, J. K., Cho, Y. H., Kim, W. J., Kim, J. R., & Suh, J. H. (2002). A personalized recommendation procedure for Internet shopping support. Electronic Commerce Research and Applications, 1(3–4), 301–313. https://doi.org/10.1016/S1567-4223(02)00022-4
Kim, S., Connerton, T. P., & Park, C. (2022). Transforming the automotive retail: Drivers for customers’ omnichannel BOPS (buy online & pick up in store) behavior. Journal of Business Research, 139, 411–425. https://doi.org/10.1016/j.jbusres.2021.09.070
Kinard, B. R., & Capella, M. L. (2006). Relationship marketing: The influence of consumer involvement on perceived service benefits. Journal of Services Marketing, 20(6), 359–368. https://doi.org/10.1108/08876040610691257
Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65(4), 457–474. https://doi.org/10.1007/BF02296338
Kline, R. B. (2016). Principles and practices of structural equation modelling (4th ed.). Guilford Press.
Kwon, K., & Kim, C. (2012). How to design personalization in a context of customer retention: Who personalizes what and to what extent? Electronic Commerce Research and Applications, 11(2), 101–116. https://doi.org/10.1016/j.elerap.2011.05.002
Lazaris, C., Vrechopoulos, A., Sarantopoulos, P., & Doukidis, G. (2022). Additive omnichannel atmospheric cues: The mediating effects of cognitive and affective responses on purchase intention. Journal of Retailing and Consumer Services, 64, Article 102731. https://doi.org/10.1016/j.jretconser.2021.102731
Lin, S.-W., Huang, E. Y., & Cheng, K.-T. (2022). A binding tie: Why do customers stick to omnichannel retailers? Information Technology and People, 36(3), 1126–1159. https://doi.org/10.1108/ITP-01-2021-0063
Lu, Y., Cao, Y., Wang, B., & Yang, S. (2011). A study on factors that affect users’ behavioral intention to transfer usage from the offline to the online channel. Computers in Human Behavior, 27(1), 355–364. https://doi.org/10.1016/j.chb.2010.08.013
Mainardes, E. W., Rosa, C. A. M., & Nossa, S. N. (2020). Omnichannel strategy and customer loyalty in banking. International Journal of Bank Marketing, 38(4), 799–822. https://doi.org/10.1108/IJBM-07-2019-0272
McKenzie, B., Burt, S., & Dukeov, I. (2018). Introduction to the special issue: Technology in retailing. Baltic Journal of Management, 13(2), 146–151. https://doi.org/10.1108/BJM-01-2018-0032
Mosquera, A., Juaneda-Ayensa, E., Olarte-Pascual, C., & Pelegrín-Borondo, J. (2018). Key factors for in-store smartphone use in an omnichannel experience: Millennials vs. nonmillennials. Complexity, 1, Article 1057356. https://doi.org/10.1155/2018/1057356
Neslin, S. A. (2022). The omnichannel continuum: Integrating online and offline channels along the customer journey. Journal of Retailing, 98(1), 111–132. https://doi.org/10.1016/j.jretai.2022.02.003
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill Book Company.
Oberoi, P., Patel, C., & Haon, C. (2017). Technology sourcing for website personalization and social media marketing: A study of e-retailing industry. Journal of Business Research, 80, 10–23. https://doi.org/10.1016/j.jbusres.2017.06.005
Olsen, S. O., Tudoran, A. A., Brunsø, K., & Verbeke, W. (2013). Extending the prevalent consumer loyalty modelling: The role of habit strength. European Journal of Marketing, 47(1), 303–323.
The Plant. (n.d.). Omnichannel – Latest success stories. What is a ‘winning system?’ https://the-plant.com/articles/omnichannel-case-study
Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115–143. https://doi.org/10.2307/25148720
Penz, E., & Hogg, M. K. (2011). The role of mixed emotions in consumer behaviour: Investigating ambivalence in consumers’ experiences of approach‐avoidance conflicts in online and offline settings. European Journal of Marketing, 45(1/2), 104–132. https://doi.org/10.1108/03090561111095612
Riegger, A.-S., Klein, J. F., Merfeld, K., & Henkel, S. (2021). Technology-enabled personalization in retail stores: Understanding drivers and barriers. Journal of Business Research, 123, 140–155. https://doi.org/10.1016/j.jbusres.2020.09.039
Rodríguez-Ardura, I., & Meseguer-Artola, A. (2020). Editorial: How to prevent, detect and control common method variance in electronic commerce research. Journal of Theoretical and Applied Electronic Commerce Research, 15(2), 1–5. https://doi.org/10.4067/S0718-18762020000200101
Ryall, J. (2019, April 22). How online giants lose ground in Japan to physical retail stores. MyNEWS. https://sc.mp/fpqa?utm_source=copy_link&utm_medium=share_widget&utm_campaign=3007111
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74.
Shen, X.-L., Li, Y.-J., Sun, Y., & Wang, N. (2018). Channel integration quality, perceived fluency and omnichannel service usage: The moderating roles of internal and external usage experience. Decision Support Systems, 109, 61–73. https://doi.org/10.1016/j.dss.2018.01.006
Shi, S., Wang, Y., Chen, X., & Zhang, Q. (2020). Conceptualization of omnichannel customer experience and its impact on shopping intention: A mixed-method approach. International Journal of Information Management, 50, 325–336. https://doi.org/10.1016/j.ijinfomgt.2019.09.001
Shin, Y. H., & Hancer, M. (2016). The role of attitude, subjective norm, perceived behavioral control, and moral norm in the intention to purchase local food products. Journal of Foodservice Business Research, 19(4), 338–351. https://doi.org/10.1080/15378020.2016.1181506
Strycharz, J., van Noort, G., Helberger, N., & Smit, E. (2019). Contrasting perspectives – Practitioner’s viewpoint on personalised marketing communication. European Journal of Marketing, 53(4), 635–660. https://doi.org/10.1108/EJM-11-2017-0896
Su, Q., & Chen, L. (2015). A method for discovering clusters of e-commerce interest patterns using click-stream data. Electronic Commerce Research and Applications, 14(1), 1–13. https://doi.org/10.1016/j.elerap.2014.10.002
Sun, Y., Yang, C., Shen, X.-L., & Wang, N. (2020). When digitalized customers meet digitalized services: A digitalized social cognitive perspective of omnichannel service usage. International Journal of Information Management, 54, Article 102200. https://doi.org/10.1016/j.ijinfomgt.2020.102200
Swoboda, B., & Winters, A. (2021). Effects of the most useful offline-online and online-offline channel integration services for consumers. Decision Support Systems, 145, Article 113522. https://doi.org/10.1016/j.dss.2021.113522
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Valentini, S., Neslin, S. A., & Montaguti, E. (2020). Identifying omnichannel deal prone segments, their antecedents, and their consequences. Journal of Retailing, 96(3), 310–327. https://doi.org/10.1016/j.jretai.2020.01.003
Xiao, B., & Benbasat, I. (2018). An empirical examination of the influence of biased personalized product recommendations on consumers’ decision making outcomes. Decision Support Systems, 110, 46–57. https://doi.org/10.1016/j.dss.2018.03.005
Xu, X., & Jackson, J. E. (2019). Examining customer channel selection intention in the omni-channel retail environment. International Journal of Production Economics, 208, 434–445. https://doi.org/10.1016/j.ijpe.2018.12.009
Ye, Y., Lau, K. H., & Teo, L. K. Y. (2018). Drivers and barriers of omni-channel retailing in China: A case study of the fashion and apparel industry. International Journal of Retail and Distribution Management, 46(7), 657–689. https://doi.org/10.1108/IJRDM-04-2017-0062
Yen, B. P.-C., & Kong, R. C. W. (2002). Personalization of information access for electronic catalogs on the web. Electronic Commerce Research and Applications, 1(1), 20–40. https://doi.org/10.1016/S1567-4223(02)00004-2
Ylilehto, M., Komulainen, H., & Ulkuniemi, P. (2021). The critical factors shaping customer shopping experiences with innovative technologies. Baltic Journal of Management, 16(5), 661–680. https://doi.org/10.1108/BJM-02-2021-0049
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Jain, G., Paul, J., & Shrivastava, A. (2021). Hyper-personalization, co-creation, digital clienteling and transformation. Journal of Business Research, 124, 12–23. https://doi.org/10.1016/j.jbusres.2020.11.034
Juaneda-Ayensa, E., Mosquera, A., & Sierra Murillo, Y. S. (2016). Omnichannel customer behavior: Key drivers of technology acceptance and use and their effects on purchase intention. Frontiers in Psychology, 7, Article 1117. https://doi.org/10.3389/fpsyg.2016.01117
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Kim, S., Connerton, T. P., & Park, C. (2022). Transforming the automotive retail: Drivers for customers’ omnichannel BOPS (buy online & pick up in store) behavior. Journal of Business Research, 139, 411–425. https://doi.org/10.1016/j.jbusres.2021.09.070
Kinard, B. R., & Capella, M. L. (2006). Relationship marketing: The influence of consumer involvement on perceived service benefits. Journal of Services Marketing, 20(6), 359–368. https://doi.org/10.1108/08876040610691257
Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65(4), 457–474. https://doi.org/10.1007/BF02296338
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Kwon, K., & Kim, C. (2012). How to design personalization in a context of customer retention: Who personalizes what and to what extent? Electronic Commerce Research and Applications, 11(2), 101–116. https://doi.org/10.1016/j.elerap.2011.05.002
Lazaris, C., Vrechopoulos, A., Sarantopoulos, P., & Doukidis, G. (2022). Additive omnichannel atmospheric cues: The mediating effects of cognitive and affective responses on purchase intention. Journal of Retailing and Consumer Services, 64, Article 102731. https://doi.org/10.1016/j.jretconser.2021.102731
Lin, S.-W., Huang, E. Y., & Cheng, K.-T. (2022). A binding tie: Why do customers stick to omnichannel retailers? Information Technology and People, 36(3), 1126–1159. https://doi.org/10.1108/ITP-01-2021-0063
Lu, Y., Cao, Y., Wang, B., & Yang, S. (2011). A study on factors that affect users’ behavioral intention to transfer usage from the offline to the online channel. Computers in Human Behavior, 27(1), 355–364. https://doi.org/10.1016/j.chb.2010.08.013
Mainardes, E. W., Rosa, C. A. M., & Nossa, S. N. (2020). Omnichannel strategy and customer loyalty in banking. International Journal of Bank Marketing, 38(4), 799–822. https://doi.org/10.1108/IJBM-07-2019-0272
McKenzie, B., Burt, S., & Dukeov, I. (2018). Introduction to the special issue: Technology in retailing. Baltic Journal of Management, 13(2), 146–151. https://doi.org/10.1108/BJM-01-2018-0032
Mosquera, A., Juaneda-Ayensa, E., Olarte-Pascual, C., & Pelegrín-Borondo, J. (2018). Key factors for in-store smartphone use in an omnichannel experience: Millennials vs. nonmillennials. Complexity, 1, Article 1057356. https://doi.org/10.1155/2018/1057356
Neslin, S. A. (2022). The omnichannel continuum: Integrating online and offline channels along the customer journey. Journal of Retailing, 98(1), 111–132. https://doi.org/10.1016/j.jretai.2022.02.003
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill Book Company.
Oberoi, P., Patel, C., & Haon, C. (2017). Technology sourcing for website personalization and social media marketing: A study of e-retailing industry. Journal of Business Research, 80, 10–23. https://doi.org/10.1016/j.jbusres.2017.06.005
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The Plant. (n.d.). Omnichannel – Latest success stories. What is a ‘winning system?’ https://the-plant.com/articles/omnichannel-case-study
Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115–143. https://doi.org/10.2307/25148720
Penz, E., & Hogg, M. K. (2011). The role of mixed emotions in consumer behaviour: Investigating ambivalence in consumers’ experiences of approach‐avoidance conflicts in online and offline settings. European Journal of Marketing, 45(1/2), 104–132. https://doi.org/10.1108/03090561111095612
Riegger, A.-S., Klein, J. F., Merfeld, K., & Henkel, S. (2021). Technology-enabled personalization in retail stores: Understanding drivers and barriers. Journal of Business Research, 123, 140–155. https://doi.org/10.1016/j.jbusres.2020.09.039
Rodríguez-Ardura, I., & Meseguer-Artola, A. (2020). Editorial: How to prevent, detect and control common method variance in electronic commerce research. Journal of Theoretical and Applied Electronic Commerce Research, 15(2), 1–5. https://doi.org/10.4067/S0718-18762020000200101
Ryall, J. (2019, April 22). How online giants lose ground in Japan to physical retail stores. MyNEWS. https://sc.mp/fpqa?utm_source=copy_link&utm_medium=share_widget&utm_campaign=3007111
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74.
Shen, X.-L., Li, Y.-J., Sun, Y., & Wang, N. (2018). Channel integration quality, perceived fluency and omnichannel service usage: The moderating roles of internal and external usage experience. Decision Support Systems, 109, 61–73. https://doi.org/10.1016/j.dss.2018.01.006
Shi, S., Wang, Y., Chen, X., & Zhang, Q. (2020). Conceptualization of omnichannel customer experience and its impact on shopping intention: A mixed-method approach. International Journal of Information Management, 50, 325–336. https://doi.org/10.1016/j.ijinfomgt.2019.09.001
Shin, Y. H., & Hancer, M. (2016). The role of attitude, subjective norm, perceived behavioral control, and moral norm in the intention to purchase local food products. Journal of Foodservice Business Research, 19(4), 338–351. https://doi.org/10.1080/15378020.2016.1181506
Strycharz, J., van Noort, G., Helberger, N., & Smit, E. (2019). Contrasting perspectives – Practitioner’s viewpoint on personalised marketing communication. European Journal of Marketing, 53(4), 635–660. https://doi.org/10.1108/EJM-11-2017-0896
Su, Q., & Chen, L. (2015). A method for discovering clusters of e-commerce interest patterns using click-stream data. Electronic Commerce Research and Applications, 14(1), 1–13. https://doi.org/10.1016/j.elerap.2014.10.002
Sun, Y., Yang, C., Shen, X.-L., & Wang, N. (2020). When digitalized customers meet digitalized services: A digitalized social cognitive perspective of omnichannel service usage. International Journal of Information Management, 54, Article 102200. https://doi.org/10.1016/j.ijinfomgt.2020.102200
Swoboda, B., & Winters, A. (2021). Effects of the most useful offline-online and online-offline channel integration services for consumers. Decision Support Systems, 145, Article 113522. https://doi.org/10.1016/j.dss.2021.113522
Tighe, D. (2023). Percentage of consumers that shop online and offline on a weekly basis in the U.S., UK, and Australia in 2023. Statista. https://www.statista.com/statistics/1257243/consumers-that-shop-online-and-offline-each-week
Tyrväinen, O., Karjaluoto, H., & Saarijärvi, H. (2020). Personalization and hedonic motivation in creating customer experiences and loyalty in omnichannel retail. Journal of Retailing and Consumer Services, 57, Article 102233. https://doi.org/10.1016/j.jretconser.2020.102233
Valentini, S., Neslin, S. A., & Montaguti, E. (2020). Identifying omnichannel deal prone segments, their antecedents, and their consequences. Journal of Retailing, 96(3), 310–327. https://doi.org/10.1016/j.jretai.2020.01.003
Xiao, B., & Benbasat, I. (2018). An empirical examination of the influence of biased personalized product recommendations on consumers’ decision making outcomes. Decision Support Systems, 110, 46–57. https://doi.org/10.1016/j.dss.2018.03.005
Xu, X., & Jackson, J. E. (2019). Examining customer channel selection intention in the omni-channel retail environment. International Journal of Production Economics, 208, 434–445. https://doi.org/10.1016/j.ijpe.2018.12.009
Ye, Y., Lau, K. H., & Teo, L. K. Y. (2018). Drivers and barriers of omni-channel retailing in China: A case study of the fashion and apparel industry. International Journal of Retail and Distribution Management, 46(7), 657–689. https://doi.org/10.1108/IJRDM-04-2017-0062
Yen, B. P.-C., & Kong, R. C. W. (2002). Personalization of information access for electronic catalogs on the web. Electronic Commerce Research and Applications, 1(1), 20–40. https://doi.org/10.1016/S1567-4223(02)00004-2
Ylilehto, M., Komulainen, H., & Ulkuniemi, P. (2021). The critical factors shaping customer shopping experiences with innovative technologies. Baltic Journal of Management, 16(5), 661–680. https://doi.org/10.1108/BJM-02-2021-0049
Zapkau, F. B., Schwens, C., Steinmetz, H., & Kabst, R. (2015). Disentangling the effect of prior entrepreneurial exposure on entrepreneurial intention. Journal of Business Research, 68(3), 639–653. https://doi.org/10.1016/j.jbusres.2014.08.007
Zhang, J. Z., Chang, C.-W., & Neslin, S. A. (2022). How physical stores enhance customer value: The importance of product inspection depth. Journal of Marketing, 86(2), 166–185. https://doi.org/10.1177/00222429211012106
Figure 1. Research Model
Table 1. Demographic Profile of the Participants
Note. N = 2,163. JPY 1 = USD 0.007.
Table 2. Reliability and Validity of Variables
Note. CR = composite reliability; AVE = average variance extracted.
Table 3. Correlation Analysis of Study Variables
Note. Values on the diagonal are the square roots of average variance extracted.
** p < .01. *** p < .001.
Table 4. Structural Equation Modeling of the Base Model
Note. *** p < .001. † p < .10.
Table 5. Results of Structural Equation Modeling with Offline Channel Usage Frequency as a Moderator
This work was supported by the Key Research Center of Philosophy and Social Science of Zhejiang Province—Modern Port Service Industry and Creative Culture Research Center.
The authors declare no conflicts of interest.
The data that support the findings of this study are available on request from the corresponding author.
Mengjia Gao, The Logistics and E-Commerce School, Zhejiang Wanli University, 8 Qianhu S. Road, Yinzhou, Ningbo, Zhejiang, 315104, People’s Republic of China. Email: [email protected]
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