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During shocks, residents and businesses rely upon the government to ensure health, safety, and the continuity of services. The government’s ability to respond depends upon how well it utilizes its data resources and builds digital resilience. Yet governments often fail to integrate data from different agencies to respond effectively to shocks. We conceptualize digital resilience as a dynamic capability (DC). Although the DC framework provides a theoretical basis, it is unclear what actions managers can take to build DC. Through process tracing, we examine how the Commonwealth of Virginia (COVA) built DCs and rebounded from two shocks—the opioid crisis and the COVID-19 pandemic. COVA managers leveraged statewide data assets, built routines to disseminate data, and reconfigured operational capabilities to build three DCs— relationship building, intelligence creation, and value extraction. Data functioned as the “protein” to build the digital resilience “muscle.” We found that the relationship building DC leveraged the operational capabilities of data management, integration, and governance structure to foster data sharing, the intelligence creation DC leveraged analytics, and the value extraction DC converted analytics into cost savings, revenue generation, and new services. Whereas COVA built robust digital resilience by facilitating data sharing, the agencies exploited data assets to develop scalable solutions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is increasingly transforming the way we work, live, and travel. IoT devices collect, store, analyze, and act upon a continuous stream of data as a by-product of everyday use. However, IoT devices need unrestricted data access to fully function. As such, they invade users' virtual and physical space and raise far-reaching privacy challenges that are unlike those examined in other contexts. As advanced IoT devices, connected cars offer a unique setting to review and extend established theory and evidence on privacy and data sharing. Employing a sequential mixed methods design, we conducted an interview study (n=120), a survey study (n=333), and a field experiment (n=324) among car drivers to develop and validate a contextualized model of individuals' data sharing decisions. Our findings from the three studies highlight the interplay between virtual and physical risks in shaping drivers' privacy concerns and data sharing decisions-with information privacy and data security emerging as discrete yet closely interrelated concepts. Our findings also highlight the importance of psychological ownership, conceptualized as drivers' feelings of possession toward their driving data, as an important addition to established privacy calculus models of data sharing. This novel perspective explains why individuals are reluctant to share even low-sensitivity data that do not raise privacy concerns. The psychological ownership perspective has implications for designing incentives for data-enabled services in ways that augment drivers' self-efficacy and psychological ownership and thereby encourage them to share driving data. These insights help reconcile a fundamental tension among IoT users-how to avail the benefits of dataenabled IoT devices while reducing the psychological costs associated with the sharing of personal data. [ABSTRACT FROM AUTHOR]

Blockchain technology has the promise of transforming security and trust in digital transactions. However, concerns about technical complexity and the benefits of deployment have blunted its adoption. We examine factors that influence managerial intention to adopt blockchain technology. We extend the fit-viability model (FVM) and develop a value-based technology adoption model through an empirical study of 242 managers mostly in medical and financial industries. Managers in such organizations are likely to consider fit and viability in adopting blockchain technology to store and protect data. Drawing upon Fit-Viability and Task-Technology Fit models, and the Unified Theory of Acceptance and Use of Technology (UTAUT), we test a model with Partial Least Squares (PLS) to assess managers' intention to adopt blockchain technology. Our findings indicate that functional and symbolic benefits have positive impact on managers' assessment of task-technology fit. Furthermore, viability is an important criterion in adopting blockchain technology. [ABSTRACT FROM AUTHOR]

Blockchain is an emerging technology that enables two or more entities to conduct secure transactions. As blockchain makes the transaction ledger open to public, could competitors learn from the dyads of transactions between suppliers and its competitors? After a blockchain transaction is executed, it cannot be altered because the transaction information is encrypted. [Extracted from the article]

The article seeks to define theories in flux as they pertain to management information systems with a focus on the alleged impacts of big data, machine learning, and artificial intelligence. Criteria for methodological rigor for theories in flux are identified including generalizability, replicability, and parsimony.

The treatment of chronic diseases consumes 86% of U.S. healthcare costs. While healthcare organizations have traditionally focused on treating the complications of chronic diseases, advances in information technology (IT) and analytics can help clinicians and patients manage and slow the progression of chronic diseases to result in higher quality of life for patients and lower healthcare costs. We build on prior research to introduce the notion of temporal displacement of care (TDC), in which IT and analytics create healthcare value by displacing the time at which providers and patients make interventions to improve healthcare outcomes and reduce costs. We propose that healthcare value is created by strategic actions taken at specific points-in-time during the treatment process. Our theoretical development identifies TDC mechanisms through which IT and analytics displace later high cost interventions in favor of earlier preventative procedures. We test our hypotheses using four years of data on 45,000 cardio-metabolic patients from the U.S. state of Vermont, which implemented a Patient-Centered Medical Home (PCMH) program. Our study includes four cohorts with increasing levels of IT and analytics use: (1) non-PCMH practices, (2) PCMH practices with basic IT systems installed, (3) practices that completed data quality sprints (DQS) to increase use of IT systems, and (4) practices that use analytics through the Vermont Healthcare Information Exchange (VHIE). Our results provide insights into how TDC effects develop over time. In Year 1 after implementation, the DQS cohort demonstrates a marked increase in the use of preventative procedures such as eye exams and neuropathy screenings, the increase becomes more pronounced in Years 2 and 3, and the increase is even greater for the VHIE cohort. As the use of preventative procedures increases, emergency department utilization decreases, with a more pronounced decrease for the VHIE cohort than the DQS cohort. By Year 2, the DQS and VHIE cohorts experience a decrease in total healthcare costs, with a greater decrease for the VHIE cohort than the DQS cohort. By Year 3, the healthcare outcomes indicator of Hemoglobin A1c (HbA1c) level is statistically significantly lower, with a greater decrease for the VHIE cohort than the DQS cohort. The increased use of low-intervention healthcare treatments earlier in the process leads to a decrease in overall healthcare costs, which then leads to an improvement in healthcare indicators. [ABSTRACT FROM AUTHOR]