Purpose: Big data analytics capability (BDAC) can affect firm performance in several ways. The purpose of this paper is to understand how BDA capabilities affect firm performance through supply chain resilience in the presence of the risk management culture.Design/methodology/approach: The study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. 167 responses were collected and analyzed using partial least squares in SmartPLS3. The respondents were generally senior IT executives with education and experience in data and business analytics.Findings: The results show that BDA capabilities increase supply chain resilience as a mediator by enhancing innovative capabilities and information quality, ultimately leading to improved firm performance. In addition, the relationship between supply chain resilience and firm performance is influenced by risk management culture as a moderator.Originality/value: The present study contributes to the relevant literature by demonstrating the mediating role of supply chain resilience between the BDA capabilities relationship and firm performance. In this context, some theoretical and managerial implications are proposed and discussed.

Purpose: The purpose of this paper is to focus on understanding how social influence and personality of individuals differentiate between users’ social network fatigue and discontinuance behavior. Furthermore, the most common discontinuance behavior among users was investigated. Design/methodology/approach: The research model was tested with the data from 163 Instagram users based on online and offline surveys. The partial least squares method was used to test the proposed hypotheses of this study. Findings: The results indicate that social influence affects users’ discontinuance behavior and social network fatigue. Social network fatigue is greater in users with higher reported social influence compared to those with a lower one. Moreover, in response to social network fatigue, users prefer to keep their activities under control instead of switching to alternative social network sites (SNSs) or a short break in social network activities. Practical implications: By achieving a better understanding of users’ feeling and behaviors, social network providers may codify their strategies more efficiently. Originality/value: The study is novel in exploring users’ SNS fatigue and their discontinuance behavior by integrating social influence and personality. The authors defined a new concept of effect of social influence on social network fatigue. Additionally, the authors examined which discontinuance behaviors in individuals were more prevalent.

Mitigating wastes, manufacturers must make the best decisions when it comes to reusing and recycling returned products. As unsatisfactory products are not going to be bought by customers, managers would be faced with a paradoxical decision on reusing or recycling these products. The proposed framework demonstrates how to analyse positive/negative feedback from consumers to form the most effective disposition decision strategies for managers in reverse logistics by means of sentiment analysis algorithms. Applying the framework, companies will be able to extract, categorise, and analyse their consumers’ opinion and sentiment to make a strategic decision in reverse logistics to minimise returned products, waste, inventory, and cost, while maximising efficiency, profit, SC sustainability, and customer satisfaction. While the framework is broad enough to be used in different industries, such as the electronics and automobile, the probability of biased opinion that may arises by limitation in considering a specific language or location has been greatly reduced. This paper focuses on social media data to optimise the decision-making process in reverse logistics through a big data analysis approach. In this research, a case study was conducted on Apple mobile phones Twitter data, including models and features.