While information technology benefits society in numerous ways, it unfortunately also has potential to create new vulnerabilities. This special issue intends to stimulate thought and research into understanding and mitigating these vulnerabilities. We identify four mechanisms by which ubiquitous computing makes various entities (people, devices, organizations, societies, etc.) more vulnerable, including: increased visibility, enhanced cloaking, increased interconnectedness, and decreased costs. We use the papers in the special issue to explain these mechanisms, and then outline a research agenda for future work on digital vulnerabilities spanning four areas that are, or could become, significant societal problems with implications at multiple levels of analysis: Online harassment and incivility, technology-driven economic inequality, industrial Internet of Things, and algorithmic ethics and bias.
The 50-year march of Moore’s Law has led to the creation of a relatively cheap and increasingly easy-to-use world-wide digital infrastructure of computers, mobile devices, broadband network connections, and advanced application platforms. This digital infrastructure has, in turn, accelerated the emergence of new technologies that enable transformations in how we live and work, how companies organize, and the structure of entire industries.
Information systems have great potential to reduce healthcare costs and improve outcomes. The purpose of this special issue is to offer a forum for theory-driven research that explores the role of IS in the delivery of healthcare in its diverse organizational and regulatory settings. We identify six theoretically distinctive elements of the healthcare context and discuss how these elements increase the motivation for, and the salience of, the research results reported in the nine papers comprising this special issue. We also provide recommendations for future IS research focusing on the implications of technology-driven advances in three areas: social media, evidence-based medicine, and personalized medicine.
This paper argues that Web 2.0 tools, specifically wikis, have begun to influence business and knowledge sharing practices in many organizations. Information Systems researchers have spent considerable time exploring the impact and implications of these tools in organizations, but those same researchers have not spent sufficient time considering whether and how these new technologies may provide opportunities for us to reform our core practices of research, review, and teaching. To this end, this paper calls for the IS discipline to engage in two actions related to wikis and other Web 2.0 tools. First, the IS discipline ought to engage in critical reflection about how wikis and other Web 2.0 tools could allow us to conduct our core processes differently. Our existing practices were formulated during an era of paper-based exchange; wikis and other Web 2.0 tools may enable processes that could be substantively better. Nevertheless, users can appropriate information technology tools in unexpected ways, and even when tools are appropriated as expected there can be unintended negative consequences. Any potential changes to our core processes should, therefore, be considered critically and carefully, leading to our second recommended action. We advocate and describe a series of controlled experiments that will help assess the impact of these technologies on our core processes and the associated changes that would be necessary to use them. We argue that these experiments can provide needed information regarding Web 2.0 tools and related practice changes that could help the discipline better assess whether or not new practices would be superior to existing ones and under which circumstances.
The decision processes surrounding investments in innovative information technology (IT) platforms are complicated by uncertainty about expected payoffs and irreversibilities in the costs of implementation. When uncertainty and irreversibility are high, concepts from real options should be used to properly structure the evaluation and management of investment opportunities, and thereby capture the value of managerial flexibility. However, while innovation researchers have posited that option value can influence the motivations of early adopters, and options researchers have identified emerging IT as a promising area for application of options valuation techniques, there has yet to be a systematic theoretical integration of work on IT innovation and real options. This paper seeks to all this gap by developing a model of the determinants of option value associated with investments in innovative IT platforms. In so doing, the model addresses a central question in the innovation field: When should a firm take a lead role in innovation with emerging technologies? The analysis begins with an explanation of real options analysis and how it differs from conventional approaches for evaluating new technologies. Then a set of 12 factors--drawn from 4 complementary perspectives on organizational innovation (technology strategy, organizational learning, innovation bandwagons, and technology adaptation)--is synthesized into a model of the option value of IT platform investments. Rationales are provided to explain the direct effects of these factors on option value, and selected interactions among the factors are also considered. Finally, the implications of the model are presented in three areas: predicting IT platform initiation and adoption, valuing IT platform options, and managing IT platform implementation.
The extent of organizational innovation with information technology, an important construct in the IT innovation literature, has been measured in many different ways. Some measures have a narrow focus while others aggregate innovative behaviors across a set of innovations or stages in the assimilation lifecycle. There appear to be some significant tradeoffs involving aggregation: more aggregated measures can be more robust and generalizable and can promote stronger predictive validity, while less aggregated measures allow more context-specific investigations and can preserve clearer theoretical interpretations. This article begins with a conceptual analysis that identifies the circumstances when these tradeoffs are most likely to favor aggregated measures. It is found that aggregation should be favorable when: (1) the researcher's interest is in general innovation or a model that generalizes to a class of innovations, (2) antecedents have effects in the same direction in all assimilation stages, (3) characteristics of organizations can be treated as constant across the innovations in the study, (4) characteristics of innovations can not be treated as constant across organizations in the study, (5) the set of innovations being aggregated includes substitutes or moderate complements, and (6) sources of noise in the measurement of innovation may be present. The article then presents an empirical study using data on the adoption of software process technologies by 608 U.S. based corporations. This study--which had circumstances quite favorable to aggregation--found that aggregating across three innovations within a technology class more than doubled the variance explained compared to single innovation models. Aggregating across assimilation stages also had a slight positive effect on predictive validity. Taken together, these results provide initial confirmation of the conclusions from the conceptual analysis regarding the circumstances favoring aggregation.
Innovation researchers have known for some time that a new information technology may be widely acquired, but then only sparsely deployed among acquiring firms. When this happens, the observed pattern of cumulative adoptions will vary depending on which event in the assimilation process (i.e., acquisition or deployment) is treated as the adoption event. Instead of mirroring one another, a widening gap--termed here an assimilation gap--will exist between the cumulative adoption curves associated with the alternatively conceived adoption events. When a pronounced assimilation gap exists, the common practice of using cumulative purchases or acquisitions as the basis for diffusion modeling can present an illusory picture of the diffusion process--leading to potentially erroneous judgments about the robustness of the diffusion process already observed, and of the technology's future prospects. Researchers may draw inappropriate theoretical inferences about the forces driving diffusion. Practitioners may commit to a technology based on a belief that pervasive adoption is inevitable, when it is not.This study introduces the assimilation gap concept, and develops a general operational measure derived from the difference between the cumulative acquisition and deployment patterns. It describes how two characteristics--increasing returns to adoption and knowledge barriers impeding adoption--separately and in combination may serve to predispose a technology to exhibit a pronounced gap. It develops techniques for measuring assimilation gaps, for establishing whether two gaps are significantly different from each other, and for establishing whether a particular gap is absolutely large enough to be of substantive interest. Finally, it demonstrates these techniques in an analysis of adoption data for three prominent innovations in software process technology--relational database management systems (RDBs), general purpose fourth generation languages (4GLs), and computer aided so...