Knowledge that Transforms

Self-monitoring is a strategy that patients use to manage their chronic disease and chronic disease risk factors. Technological advances such as mobile apps, web-based tracking programs, sensing devices, wearable technologies, and insideable devices enable IT-based self-monitoring (ITSM) for chronic disease management. Since ITSM is multidisciplinary in nature and our understanding is fragmented, a systematic examination of the literature is performed to build a holistic understanding of the phenomenon. We review 159 studies published in 108 journals and conferences between 2006 and 2017. By adapting affordance actualization theory, we develop an overarching framework to organize the existing literature on ITSM for chronic disease management. Four themes emerge: key ITSM functionalities that enable affordances; effects on ITSM system use; effects on the achievement of chronic care goals; and the role of intermediary outcomes. For each theme, we identify what is known, what is unknown, and opportunities for future research. We also discuss cross-theme opportunities for future research where more diverse theoretical perspectives can contribute to our understanding of the phenomenon. This work provides research directions for IS researchers studying ITSM for chronic disease self-management.

Faced with the challenge of multifaceted digital phenomena, researchers in IS and related fields have increasingly adopted qualitative comparative analysis (QCA). However, in the absence of explicit guidelines for how to use QCA for theory development, the popularity and proliferation of QCA possibly amplifies the risk of using QCA in an atheoretical manner, hindering theoretical advancement. In this paper, we offer a conceptual framework and prescriptive guidelines for applying QCA to develop causal recipes that account for complex digital phenomena marked by theoretical and configurational multiplicity. Causal recipes are formal statements explaining how causally relevant elements combine into configurations associated with outcomes of interest. We describe these causal recipes in terms of which causes matter (i.e., factorial logic) and how these causes combine into configurations (i.e., combinatorial logic) to produce target outcomes, and propose an ecology of configurations that elucidates the explanatory power of multiple configurations as well as their explanatory overlap. Further, we offer two illustrative empirical examples to demonstrate the usefulness of our framework and step-by-step guidelines for applying QCA to deductive theory testing as well as inductive theory development on phenomena marked by multiplicity.

Gamification has been shown to encourage contributions of user-generated reviews (word-of-mouth: WOM) in various domains, including travel and leisure related platforms (Foursquare, TripAdvisor), e-commerce (Amazon), and auctions (eBay). WOM contributors write reviews about products/services provided by business venues and WOM consumers read reviews and use them to form attitudes and make purchase decisions. Gamification elements such as points and badges, awarded to WOM contributors for various reasons, and displayed to WOM consumers, have a dual role in WOM context. First, points awarded for user contributions help motivate WOM contributors to increase their participation. Second, badges awarded to users for visiting business venues signal prior experience or competence, and they help determine how WOM consumers perceive WOM contributors and form their judgments based on the reviews. While the first role of gamification (i.e., motivating users) has been widely studied, the impact of WOM presented along with gamification elements on the perceptions and behavior of the target audience, WOM consumers, has not been examined. This is important to businesses that are looking to attract customers. Drawing on social psychology literature, we show that gamification symbols signaling experience that accompany WOM leads to perceptions of positive WOM contributors as more competent. This leads to important changes in behavioral outcomes such as willingness to visit/buy and willingness to recommend the reviewed outlets.

This research examines physician-driven online health communities (OHC), a social media application in healthcare that engages both patients and physicians. Drawing on the “patient–physician partnership” paradigm in managing chronic disease (Bodenheimer et al. 2002), we argue that physician-driven OHC facilitates patient–physician collaborative care and self-management support, which may improve patient well-being and patient–physician relationships. We test the mutual impact between patients’ and physicians’ participation in physician-driven OHC and the impact of patients’ and physicians’ participation on patient well-being and the patient–physician relationship in the context of managing diabetes and depression. We collect data from a leading Chinese online consultation platform. To make credible causal inference, we exploit two events that separately create plausibly exogenous variations in patients’ and physicians’ participation. We find that physicians’ participation significantly increases patients’ participation for both diabetes and depression, but patients’ participation only increases physicians’ participation for depression. Although both patients’ and physicians’ participation significantly improve patient well-being and the patient–physician relationship, there are interesting nuances in these effects over time. These findings have important implications for self-managing chronic diseases and healthcare policy making.

How should firms configure organizational capabilities to achieve competitive advantage in complex digital environments? To answer this question, we investigate parsimonious configurations for high firm performance in digital environments characterized by organized complexity. We adopt a configurational perspective accompanied by a fuzzy-set qualitative comparative analysis (fsQCA) to explicate complex nonlinear relationships among key digital and non-digital capabilities in the form of conjunction, equifinality, and asymmetry in producing the outcome. With this approach, we shift attention from individual capabilities to configurations of capabilities to develop a better understanding of the complex role of IT in the digital world. Our analyses, using a rare and unique dataset of 376 observations for organizations in healthcare, education, manufacturing, and service sectors in the United States, reveal three key findings. First, IT-enabled information analytics capability alone is neither necessary nor sufficient in any configuration for high performance; however, it is an important component of the configurations in which it plays multifaceted roles varying from an enabling role in some contexts, to no role or a counterproductive role in other contexts. Second, we document a few parsimonious configurations emergent from complex nonlinear interactions among six organizational capabilities. Interestingly, these configurations often have an isomorphic structure that produces both high financial performance and high customer performance simultaneously. Third, the structures of configurations for high performance differ from those of not-high performance, suggesting an asymmetric view of causality that underpins organizational performance. Together, the findings provide implications for further research on complexity theory in digital business strategy, and for managers to view and redesign digital business strategy as configurations of IT and organizational capabilities.

This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model’s power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research.

This paper draws on complex adaptive systems (CAS) theory to explore the transformation of an analog automation product platform as it was infused with extensive and deepening digital capacities over a 40-year period. Our case demonstrates how the deepening digitization of components and functions drives complexity by connecting the platform to multiple social and technical settings and producing new interactions and information exchanges. The increased connectivity and dynamism invited unexpected and significant architectural and organizational shifts that moved the platform toward an ecosystem-centered organizing logic. CAS theory and its notion of constrained generating procedures (CGPs) are used to analyze how new connections and interactions produced a multilevel and nonlinear change in the platform organization. We offer two main contributions. First, we provide a novel empirical analysis of how product platform digitization leads to phase transitions and show the mediating role of three mechanisms in this process treated as CGPs: interaction rules, design control, and stimuli-response variety. Second, we demonstrate the multilevel and recursive nature of digitally driven growth in physical product platforms.

The notions of significance and relevance have provoked much controversy and confusion among those who conduct and those who are intended to be informed by quantitative research in the information systems (IS) field. The history of quantitative research in the IS field and beyond reveals not only disputes over the adequacy of statistical significance to warrant the scientific merits of research, but also pleas for drawing attention to practical significance, as well as a lack of distinction between relevance and practical significance. This essay offers a remedial, overarching account. We establish the position that statistical significance, practical significance, and relevance are distinct qualities, where the latter two transcend mere statistical concerns and respectively refer to the distinct matters of research impressiveness and real-world usefulness. Furthermore, we draw attention to the importance of proper communication of quantitative/statistical analyses through a detailed examination of published IS research. Our examination gives rise to three major issues. The three issues are concerned with the proper communication of (1) research rigor, (2) practical significance, and (3) research relevance. We express our opinions with respect to the three issues and provide a number of recommendations.

Complexity is all around us in this increasingly digital world. Global digital infrastructure, social media, Internet of Things, robotic process automation, digital business platforms, algorithmic decision making, and other digitally enabled networks and ecosystems fuel complexity by fostering hyper-connections and mutual dependencies among human actors, technical artifacts, processes, organizations, and institutions. Complexity affects human agencies and experiences in all dimensions. Individuals and organizations turn to digitally enabled solutions to cope with the wicked problems arising out of digitalization. In the digital world, complexity and digital solutions present new opportunities and challenges for information systems (IS) research. The purpose of this special issue is to foster the development of new IS theories on the causes, dynamics, and consequences of complexity in increasing digital sociotechnical systems. In this essay, we discuss the key theories and methods of complexity science, and illustrate emerging new IS research challenges and opportunities in complex sociotechnical systems. We also provide an overview of the five articles included in the special issue. These articles illustrate how IS researchers build on theories and methods from complexity science to study wicked problems in the emerging digital world. They also illustrate how IS researchers leverage the uniqueness of the IS context to generate new insights to contribute back to complexity science.

Today’s healthcare workers, specifically nurses, are experiencing technostress associated with the use of healthcare information technology (HIT). Technostress is often characterized by IS researchers as negative, or as being on the “dark side” of technology. However, a broader reading of the stress literature suggests that technostress may be both positive and negative, and can therefore have a “bright side” in addition to a dark side. The objective of this study is to conceptualize a holistic technostress process that includes positive and negative components of technostress embedded in two subprocesses: the techno-eustress subprocess and the techno-distress subprocess, respectively. The study instantiates this holistic technostress model through a sequential mixed-methods research design in the context of HIT. Phase 1 of the design is a qualitative, interpretive case study involving interviews with 32 nurses. Based on the findings from the case study, the paper builds a research model that operationalizes the concepts embedded in the holistic technostress model and identifies contextually relevant challenge and hindrance technostressors and outcomes. In Phase 2, the research model is empirically validated by analyzing survey data collected from 402 nurses employed in the United States. Results reveal that several challenge and hindrance technostressors are related to positive and negative psychological responses, respectively, and that such responses are related to job satisfaction and attrition, which impact turnover intention. Contributions to theory and practice are also discussed.