From storefront to screen: an in-depth analysis of the dynamics of online for offline retailing

The rapid advancement of technology has revolutionized the retail landscape, leading to innovative business models like O2O and omnichannel retail. However, the online for offline (O4O) model, which leverages online capabilities to enhance the physical shopping experience, remains relatively underexplored. O4O differs from O2O by deeply integrating online and offline channels to create a seamless and enhanced in-store experience, using online data and technologies to optimize offline customer interactions. Examples such as Musinsa's Mutan service, Amazon Go, Amazon bookstores, and Freshippo illustrate the successful application of O4O strategies. This study aims to investigate the key factors that drive consumer behavior in the O4O context, specifically focusing on the determinants of shopping intention and continuance intention.

The study utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) as its theoretical framework. UTAUT posits four key determinants of technology use: performance expectancy, effort expectancy, social influence, and facilitating conditions. The researchers leverage existing literature on UTAUT and its application in various contexts (e.g., mobile shopping, digital payment, e-commerce) to formulate hypotheses regarding the influence of these constructs on continuance intention and shopping intention within the O4O context. Previous studies have shown the importance of these factors in technology adoption and usage, but their specific impact on O4O remains largely uninvestigated. The study also introduces innovativeness as a moderating variable, considering its potential influence on the relationship between social influence and continuance intention.

The study employed a quantitative approach, collecting data from 272 consumers familiar with O4O platforms. Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS software was used to analyze the data. PLS-SEM was chosen due to its suitability for smaller sample sizes, its focus on prediction, its ability to handle complex models, and its robustness against multicollinearity. The measurement model was validated for convergent validity, reliability (Cronbach's alpha and composite reliability), and discriminant validity (Fornell-Larcker criterion and HTMT ratio). The structural model was then evaluated to test the hypotheses using bootstrapping with 5,000 subsamples. The demographic characteristics of the sample (gender, age, education) are described, providing context for the results.

The PLS-SEM analysis revealed the following key findings: 1. **Performance expectancy** significantly and positively influenced both continuance intention and shopping intention. Consumers who perceive the O4O platform as useful are more likely to continue using it and make purchases. 2. **Effort expectancy** significantly and positively influenced continuance intention but not shopping intention. Ease of use is crucial for user retention but not necessarily for driving purchases. 3. **Social influence** significantly and positively influenced continuance intention but not shopping intention. Social norms and peer influence are important for continued usage but not directly for purchase decisions. 4. **Facilitating conditions** significantly and positively influenced continuance intention but not shopping intention. The availability of resources and support is key for retention but not directly for driving purchases. 5. **Continuance intention** significantly and positively influenced shopping intention, indicating that continued use of the platform leads to increased purchases. 6. **Innovativeness** did not significantly influence continuance intention or shopping intention directly. However, it showed a marginally significant moderating effect on the relationship between social influence and continuance intention, suggesting that highly innovative consumers may be less influenced by social norms in their continued use of the platform. The R-squared values indicate that the model explained 73.8% of the variability in continuance intention and 71.0% of the variability in shopping intention.

The findings largely support the hypotheses related to performance expectancy, effort expectancy (on continuance intention), social influence (on continuance intention), and facilitating conditions (on continuance intention). The unexpected finding that effort expectancy and social influence do not significantly influence shopping intention suggests that other factors beyond ease of use and social pressure are important drivers of purchasing decisions in O4O contexts. The significant impact of continuance intention on shopping intention highlights the importance of user retention strategies. The marginal moderating effect of innovativeness on the relationship between social influence and continuance intention suggests a complex interplay between individual characteristics and social influence in technology adoption. The study's findings contribute to a better understanding of the factors that drive consumer behavior in the evolving O4O retail landscape.

This study offers several key theoretical and managerial contributions. Theoretically, it extends the UTAUT model by examining its applicability in the O4O context and by incorporating innovativeness as a moderator. Managerially, the findings emphasize the importance of focusing on performance expectancy and facilitating conditions to improve user retention, while acknowledging that driving shopping intention might require a more holistic approach addressing various factors beyond ease of use and social influence. Future research could investigate the role of other factors (e.g., trust, perceived value, product assortment) in influencing shopping intention in O4O contexts and explore the moderating effect of innovativeness in more detail.

The study's limitations include its reliance on a specific sample of consumers in South Korea, which might limit the generalizability of the findings. The cross-sectional nature of the data limits the ability to establish causal relationships. Future research should consider employing longitudinal studies and diverse samples to address these limitations.