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In this paper, we extend the guidelines of Venkatesh et al. (2013) for mixed-methods research by identifying and integrating variations in mixed-methods research. By considering 14 properties of mixed-methods research (e.g., purposes, research questions, epistemological assumptions), our guidelines demonstrate how researchers can flexibly identify the existing variations in mixed-methods research and proceed accordingly with a study design that suits their needs. To make the guidelines actionable for various situations and issues that researchers could encounter, we develop a decision tree to map the flow and relationship among the design strategies. We also illustrate one possible type of mixed-methods research in information systems in depth and discuss how to develop and validate metainferences as the outcomes of such a study.

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Interaction with technology involves not only externally directed cognition, but also internally directed cognition. Although the information systems (IS) field has made a significant progress toward understanding of how individuals use technology, more emphasis has been given to goal-directed external activity that requires focused external attention and less or no emphasis on goal-directed internal activity called mind wandering. Drawing upon the emerging cognitive neuroscience literature, the current research investigates the relationships between self-regulation, mind wandering, and cognitive absorption. Specifically, we hypothesize there is a U-shape relationship between mind wandering and cognitive absorption. Based on a cross-sectional study of 323 individuals, the results reveal that the relationship between mind wandering and cognitive absorption is curve-linear. As mind wandering increases, cognitive absorption decreases to a certain point, after which, cognitive absorption increases as mind wandering increases. The results also show self-regulation has a significant effect on mind wandering and cognitive absorption.

In today's uncertain and disruptive environment, every firm in the supply chain is susceptible to disruptions that may require high levels of firm resilience. We argue that recent advances in artificial intelligence (AI) may help. This paper expands our understanding of the role of AI in shaping firm resilience to supply chain disruptions and, in turn, enhancing firm performance. In doing so, we conceptualize AI use as a dynamic information processing capability-consisting of three dimensions: coordinating/integration, learning, and strategic competitive response capability-as an antecedent of firm resilience to supply chain disruptions, and firm resilience as a mediation factor that links AI use and firm performance. By analyzing the data gathered using a two-stage survey from 107 companies in Europe, we found AI use has a direct impact on firm resilience, and firm resilience fully mediates the relationship between AI use and firm performance. The findings of this study contribute to IT and supply chain literature.