The literature pertaining to digital transformation, digital innovation, digital infrastructure, and digital platforms heavily relies on digital technology. Information Systems (IS) literature defines digital technologies as technologies that incorporate bitstrings (digital object). However, we contend that digital technologies are complex, multilayered information systems that surpass the simplicity of bitstrings. Bitstrings have existed since the inception of computers, and the contemporary use of bitstrings in IS literature is inconsistent with Computer Science and Engineering literature. We theorize artifact delineation—the capacity to provide unique interfaces and capabilities to distinct users—and coalescence—the ability to virtually integrate multiple IT architectural layers of complex information systems to conceal the details from users—to extend the core on digital technology, but more importantly to provide a better understanding of the complex nature of digital technologies for IS researchers and other domains that borrow from IS.

Business Intelligence (BI) and analytics are often bundled together in the Information Systems (IS) literature as BI&A. We argue that BI and analytics are different information systems that are used differently, require different tools and technical skills, and are targeted at different audiences. We posit that this bundling is not relevant to the practice, rather the result of a fashion wave inspired by Gartner technology trends. We argue that such misrepresentation disassociates IS research from practice. We performed a pilot qualitative study as well as secondary data analysis on job data from Indeed.com to validate our arguments. The paper contributes to the IS research by critiquing the status quo on BI&A, which has existed since 2012. Implications of the unbundling of BI and analytics for future research and academics are discussed.

While there is an explosion in data analytic activity, certain behavioral aspects of a data analyst’s modeling efforts are not well understood. Through the lens of effort minimization theory, this research considers how data analysts make decisions to engage with more data as a part of their modeling process, and the effects that modeling tools have on these decisions. Previous research found that decision aids have no effect on reducing effort in terms of the amount of information referenced and processed. However, our results indicate that there are indeed significant differences in the amount of data analyzed with less effort-intensive modeling methods, and increasing amounts of available data. These results offer new implications for effort minimization research and organizations engaged in data analytics.

The increasing number of devices connected to the internet paves the way for the new era of the internet of things (IoT) computing in which almost every object surrounding us can be transformed into smart entities by equipping objects with sensors, actors, and connectivity technologies. In this study, we seek to examine the consumer acceptance of IoT in smart home context. Drawing on the unified theory of acceptance and use of technology (UTAUT2), we propose an integrative theoretical model that extends UTAUT2 and offers a more comprehensive picture of the factors influencing IoT acceptance and use. The results show that performance expectancy, effort expectancy, social influence, hedonic motivation, and price value are significant predictors of smart home acceptance and use. The results also show that trust and security risk play a significant role in smart home acceptance. We conclude with a discussion of the theoretical and practical implications.

As organizations manage their reputations on social media, the role of individual stakeholders in responding to online criticism about the organization becomes increasingly important. Building on the organizational identification literature, this study examines the role of organizational identification and organizational identity orientation in motivating stakeholder responses to organization-directed criticism on social media. Analysis of survey data reveals that organizational identification is positively related to individuals’ propensity to respond to negative content and that individuals with stronger organizational identification are likely to respond in a more cooperative manner. Organizational identity orientation moderates the relationship. The study’s implications for research and practice are discussed.

As online social networking permeates all aspects of personal and professional lives, users of social networking sites (SNSs) are more motivated than ever to manage their online identities to project a favorable impression of themselves to online audiences. This research builds on the boundary management perspective to gain a better understanding of online identity management practices by examining the relationship between characteristics of the online social network, including cognitive homogeneity and social tie variety and the use of identity management practices such as segmentation and self-enhancement. The proposed research model is tested using survey data. The findings suggest cognitive homogeneity is positively related to the use of both identity management practices, segmentation and self-enhancement, whereas social tie variety is positively related to segmentation, but not self-enhancement practices. We conclude with implications of the study results for research and practice, as well as a discussion of directions for future research.

This exploratory study provides a validated, parsimonious research model of antecedents of the perception of the quality of decisions made using business intelligence support. Findings provide insight into little investigated avenues such as the role of problem space complexity in perceived decision quality as well as indicate a more complex interplay among the antecedents of decision quality than heretofore examined. For example, results suggest that there may be a tipping point for which information quality and use of the system support higher perceived decision quality. In addition, these findings provide a direction for future research to generate deeper, more meaningful contributions in our collective understanding of how business intelligence (BI) serves to improve the quality of decision-making.