Internet of Things (IoT) and blockchain are complex digital technologies characterized by a strong connection and dependency between individuals, data, and information technology (IT). A socio-technical perspective that captures the dynamic interaction of social, legal, and technical artifacts is essential to understanding the IoT and blockchain phenomena. This review provides direction for employing a social-technical perspective across IoT and blockchain studies. The findings lead to new insights for developing a theoretical framework, research model, and research agenda to investigate IoT and blockchain technology. Practitioners and institutions can use these findings to develop strategies to promote the adoption of IoT and blockchain technologies through specific technical, legal, and social strategies. Overall, this research is unique in its attempt to trace new trajectories for future IS scholars and practitioners facing the complexity of IoT and blockchain.

This research investigates the social, legal, and technological factors that affect the adoption of Internet of Things wearable devices (WIoT) among young adults during and before a healthcare crisis. The study develops a technological acceptance model by integrating the Technological-Personal-Environmental (TPE) framework with new constructs, i.e. regulatory environment, epidemic ecosystem, perceived social usefulness, and WIoT devices’ technical characteristics. A research survey is developed and distributed to undergraduate and graduate students in a major university located in the USA. The research hypotheses are tested by using partial least square modeling. Results of the analysis lead to significant contributions for both academics and practitioners.

Social commerce is a new development in e-commerce and social networks. Consumers belonging to various social communities of interest often rely on information generated by these communities to make online purchase decisions. This empirical research study explores the relational and security aspects of social commerce. It looks at technology-enabled customer relationship quality, privacy, and security protection as determinants of social commerce decisions. The results show that the web experience and online convenience delivered through the social commerce provider website positively influence the perceived customer relationship quality as a driver of social commerce purchase and sharing behavior. Additionally, the study finds that while perceived security protection is still a significant influencer of social commerce decisions, perceived privacy protection is no longer a primary customer concern.

The recent influx in generation, storage, and availability of textual data presents researchers with the challenge of developing suitable methods for their analysis. Latent Semantic Analysis (LSA), a member of a family of methodological approaches that offers an opportunity to address this gap by describing the semantic content in textual data as a set of vectors, was pioneered by researchers in psychology, information retrieval, and bibliometrics. LSA involves a matrix operation called singular value decomposition, an extension of principal component analysis. LSA generates latent semantic dimensions that are either interpreted, if the researcher's primary interest lies with the understanding of the thematic structure in the textual data, or used for purposes of clustering, categorization, and predictive modeling, if the interest lies with the conversion of raw text into numerical data, as a precursor to subsequent analysis. This paper reviews five methodological issues that need to be addressed by the researcher who will embark on LSA. We examine the dilemmas, present the choices, and discuss the considerations under which good methodological decisions are made. We illustrate these issues with the help of four small studies, involving the analysis of abstracts for papers published in the European Journal of Information Systems.