How dynamic capabilities enable Chinese SMEs to survive and thrive during COVID-19: Exploring the mediating role of business model innovation.

As a response to the damage caused by the spread of COVID-19, the Chinese government has implemented severe quarantine measures that have greatly affected the operational patterns of small and medium-sized enterprises (SMEs). This paper explores the critical role of dynamic capabilities (DCs) in helping Chinese SMEs manage crises, adjust their business strategies, and mitigate the uncertainty caused by the epidemic. Although the importance of DCs in promoting organizational resilience is well recognized, academic research on their specific contributions to business model innovation (BMI) and SME performance improvement during crises remains scarce. Our study fills this gap by pioneering the development and empirical testing of a microintegrated mediation model linking DCs, BMI and organizational performance. By surveying 257 Chinese SMEs severely affected by a pandemic, we verify our hypotheses using partial least squares structural equation modeling (PLS-SEM). Our results strongly show a positive relationship between DCs and BMI and SME performance. In addition, we found that BMI plays a partial mediating role in the interrelationship between DCs and SME performance. Our findings clarify the critical role of BMI as a channel through which SMEs' DCs can be transformed into higher performance in the face of sudden crises. Thus, our results not only contribute to the broader discussion of strategic management and organizational theory but also provide theoretical and practical insights into the mechanisms by which SMEs can increase their flexibility and resilience in a crisis. Thus, our results not only contribute to the broader discussion of strategic management and organizational theory but also provide theoretical and practical insights into the mechanisms by which SMEs can increase their flexibility and resilience in a crisis. Importantly, this study suggests policy and market strategies that can support SMEs in leveraging DCs and BMI for sustained performance, thereby contributing valuable insights for policymakers and business leaders aiming to fortify economic stability and growth in the face of global health emergencies.

A review and analysis of the business model innovation literature.

Business model innovation (BMI) is an emerging field that has attracted much attention from scholars and practitioners. However, the literature on BMI is fragmented and inconsistent, lacking a comprehensive and systematic framework. This study aims to fill this gap by conducting a literature review of 272 peer-reviewed articles on BMI published between 2010 and 2022. We analyze the articles based on six dimensions: antecedents, processes, types, barriers, outcomes, and moderators/mediators of BMI. We synthesize the findings and propose an integrated theoretical model that captures the complex relationships among these dimensions. We also identify several research gaps and directions for future studies. This study contributes to the BMI literature by providing a clearer understanding of this phenomenon and offering practical guidance for various types of businesses.

Changes in the use of mobile devices during the crisis: Immediate response to the COVID-19 pandemic.

We analyze the smartphone usage behavior of individuals against the background of the spread of the coronavirus disease (COVID-19) to classify usage behaviors and examine the factors that lead to change. Specifically, we examine the differences in smartphone usage between the first wave and the second wave of the epidemic in Japan. On average, the frequency of use increased, especially during the first wave of the epidemic. Next, we classify the changes in usage behavior and examine the differences between individuals whose smartphone usage time increased and those whose usage time decreased. Our analysis using personal characteristics as explanatory variables suggests that demographic variables may explain behavioral changes. We were able to classify the factors into three categories: positive factors that promote an increase in usage time, negative factors that promote a decrease, and variation factors that promote fluctuations.