Cyberloafing is a prevalent phenomenon in organisations that may cause serious problems. In previous studies, cyberloafing has been studied as planned and intentional behaviour. Interestingly, we have observed that cyberloafing frequently happens when employees use smartphones to assist with work tasks, but they may not necessarily intend to engage in this activity. We distinguish between active and passive cyberloafing and focus on studying the latter. Based on dual-systems theory, we introduce the construct of mindfulness in the exploration of the antecedences and internal mechanism of passive cyberloafing. We conducted a research survey with employees who use smartphones to assist with work tasks and collected 474 valid questionnaires to examine our research model. The results showed that dual systems are associated with cyberloafing by influencing mind-wandering, and mindfulness significantly decreased mind-wandering when using a smartphone. This study analyses cyberloafing from an innovative neuroscience perspective and contributes to the literature by proposing a new classification of cyberloafing behaviour, reveals the internal mechanism of how dual systems impact an individual’s behaviour, and introduces mindfulness as an antecedent factor of dual systems.

This paper investigates the impact of big data analytics capability (BDAC) on organizational agility under the moderating effect of BDAC–business alignment and its impact on performance through organizational agility. Data from a matched-pair survey of business, data technology, and financial executives in 161 organizations were used to examine the proposed research model. This paper used partial least squares–structural equation modeling and hierarchical component analysis to examine the data. The results suggest a positive mediation role of organizational agility in the relationship between big data analytics capability and organizational performance, except that the mediation effect of operational adjustment agility on BDAC and market performance is not statistically significant. This study also finds that alignment between the business strategy and the big data analytics strategy enhances the relationship between BDAC and market responsiveness agility. It proposes a new perspective which is to realize the value of BDAC in enhancing agility and performance.

While perceived waiting time can undermine user evaluation and cause application abandonment, there is little scientific research on waiting in mobile applications. This paper incorporates three mobile interactivity features (ubiquitous connectivity, active control, and responsiveness) into the model and examines the mediating role of cognitive absorption and the moderating role of perceived procedural justice between these features and perceived waiting time in a short-waiting application. The researchers empirically examine the model using data from 468 uses of the ride-sharing mobile application. The results reveal that mobile interactivity can directly and indirectly (via cognitive absorption) lead to more tolerance in perceived waiting time. The findings elicit several implications for theories and practice.

To make a comprehensive literature review and identify the development trends, this study maps the intellectual structure of the research information management based on co-keyword analysis, the 2-tuple linguistic technique, and social network analysis. This study reveals the intellectual structure by analyzing the topological structure, conceptual structure, and strategic diagram. From the perspective of topological structure, the research of the information management field can be divided into three layers including the nucleus layer, middle layer, and marginal layer. In terms of the conceptual structure, the research of information management can be divided into four sub-fields including health information management, information systems, information technology, and information management application. The four subfields can be repartitioned into seven clusters by using a 2-tuple linguistic model, which means that the 2-tuple linguistic model can improve co-keyword analysis.