Explore the vast range of titles available.

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.

Design science research (DSR) has staked its rightful ground as an important and legitimate Information Systems (IS) research paradigm. We contend that DSR has yet to attain its full potential impact on the development and use of information systems due to gaps in the understanding and application of DSR concepts and methods. This essay aims to help researchers (1) appreciate the levels of artifact abstractions that may be DSR contributions, (2) identify appropriate ways of consuming and producing knowledge when they are preparing journal articles or other scholarly works, (3) understand and position the knowledge contributions of their research projects, and (4) structure a DSR article so that it emphasizes significant contributions to the knowledge base. Our focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, we propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. We evaluate the DSR contribution framework and the DSR communication schema via examinations of DSR exemplar publications.

Despite the fact that validating the measures of constructs is critical to building cumulative knowledge in MIS and the behavioral sciences, the process of scale development and validation continues to be a challenging activity. Undoubtedly, part of the problem is that many of the scale development procedures advocated in the literature are limited by the fact that they (1) fail to adequately discuss how to develop appropriate conceptual definitions of the focal construct, (2) often fail to properly specify the measurement model that relates the latent construct to its indicators, and (3) underutilize techniques that provide evidence that the set of items used to represent the focal construct actually measures what it purports to measure. Therefore, the purpose of the present paper is to integrate new and existing techniques into a comprehensive set of recommendations that can be used to give researchers in MIS and the behavioral sciences a framework for developing valid measures. First, we briefly elaborate upon some of the limitations of current scale development practices. Following this, we discuss each of the steps in the scale development process while paying particular attention to the differences that are required when one is attempting to develop scales for constructs with formative indicators as opposed to constructs with reflective indicators. Finally, we discuss several things that should be done after the initial development of a scale to examine its generalizability and to enhance its usefulness.

This research essay highlights the need to integrate predictive analytics into information systems research and shows several concrete ways in which this goal can be accomplished. Predictive analytics include empirical methods (statistical and other) that generate data predictions as well as methods for assessing predictive power. Predictive analytics not only assist in creating practically useful models, they also play an important role alongside explanatory modeling in theory building and theory testing. We describe six roles for predictive analytics: new theory generation, measurement development, comparison of competing theories, improvement of existing models, relevance assessment, and assessment of the predictability of empirical phenomena. Despite the importance of predictive analytics, we find that they are rare in the empirical IS literature. Extant IS literature relies nearly exclusively on explanatory statistical modeling, where statistical inference is used to test and evaluate the explanatory power of underlying causal models, and predictive power is assumed to follow automatically from the explanatory model. However, explanatory power does not imply predictive power and thus predictive analytics are necessary for assessing predictive power and for building empirical models that predict well. To show that predictive analytics and explanatory statistical modeling are fundamentally disparate, we show that they are different in each step of the modeling process. These differences translate into different final models, so that a pure explanatory statistical model is best tuned for testing causal hypotheses and a pure predictive model is best in terms of predictive power. We convert a well-known explanatory paper on TAM to a predictive context to illustrate these differences and show how predictive analytics can add theoretical and practical value to IS research.

The dominant way of producing knowledge in information systems (IS) seeks to domesticate high-level reference theory in the form of mid-level abstractions involving generic and atheoretical information technology (IT) components. Enacting such epistemic scripts squeezes IS theory to the middle range, where abstract reference theory concepts are directly instantiated or slightly modified to the IS context, whereas IT remains exogenous to theory by being treated as an independent variable, mediator, or moderator. In this design, IT is often operationalized using proxies that detect the presence of IT or its variation in use or cost. Our analysis of 143 articles published in MIS Quarterly and Information Systems Research over the past 15 years demonstrates that over 70 percent of published theory conforms to this mode of producing IS knowledge. This state of play has resulted in two negative consequences: the field (1) agonizes over the dearth of original and bold theorizing over IT and (2) satisfices when integrating theory with empirics by creating incommensurate mid-range models that are difficult to consolidate. We propose that one way to overcome these challenges is to critically examine and debate the negative impacts of the field’s dominant epistemic scripts and relax them by permitting IS scholarship that more fluidly accommodates alternative forms of knowledge production. This will push IS inquiry to the “edges” and emphasize, on the one hand, inductive, rich inquiries using innovative and extensive data sets and, on the other hand, novel, genuine, high-level theorizing around germane conceptual relationships between IT, information and its (semiotic) representations, and social behaviors. We offer several exemplars of such inquiries and their results. To promote this push, we invite alternative institutionalized forms of publishing and reviewing. We conclude by inviting individual scholars to be more open to practices that permit richer theorizing. These recommendations will broaden the field’s knowledge ecology and permit the creation of good IS knowledge over just getting “hits.” We surmise that, if such changes are carried out, the field can look confidently toward its future as one of the epicenters of organizational inquiry that deal with the central forces shaping human enterprise in the 21st century.

In this essay, we argue that pervasive digitization gives birth to a new type of product architecture: the layered modular architecture. The layered modular architecture extends the modular architecture of physical products by incorporating four loosely coupled layers of devices, networks, services, and contents created by digital technology. We posit that this new architecture instigates profound changes in the ways that firms organize for innovation in the future. We develop (1) a conceptual framework to describe the emerging organizing logic of digital innovation and (2) an information systems research agenda for digital strategy and the creation and management of corporate information technology infrastructures. [PUBLICATION ABSTRACT]