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Design research (DR) positions information technology artifacts at the core of the Information Systems discipline. However, dominant DR thinking takes a technological view of the IT artifact, paying scant attention to its shaping by the organizational context. Consequently, existing DR methods focus on building the artifact and relegate evaluation to a subsequent and separate phase. They value technological rigor at the cost of organizational relevance, and fail to recognize that the artifact emerges from interaction with the organizational context even when its initial design is guided by the researchers' intent. We propose action design research (ADR) as a new DR method to address this problem. ADR reflects the premise that IT artifacts are ensembles shaped by the organizational context during development and use. The method conceptualizes the research process as containing the inseparable and inherently interwoven activities of building the IT artifact, intervening in the organization, and evaluating it concurrently. The essay describes the stages of ADR and associated principles that encapsulate its underlying beliefs and values. We illustrate ADR through a case of competence management at Volvo IT.

Critical realism is emerging as a viable philosophical paradigm for conducting social science research, and has been proposed as an alternative to the more prevalent paradigms of positivism and interpretivism. Few papers, however, have offered clear guidance for applying this philosophy to actual research methodologies. Under critical realism, a causal explanation for a given phenomenon is inferred by explicitly identifying the means by which structural entities and contextual conditions interact to generate a given set of events. Consistent with this view of causality, we propose a set of methodological principles for conducting and evaluating critical realism-based explanatory case study research within the information systems field. The principles are derived directly from the ontological and epistemological assumptions of critical realism. We demonstrate the utility of each of the principles through examples drawn from existing critical realist case studies. The article concludes by discussing the implications ofcritical realism based research for IS research and practice.

While criteria or principles for conducting positivist and interpretive research have been widely discussed in the IS research literature, criteria or principles for critical research are lacking. Therefore, the purpose of this paper is to propose a set of principles for the conduct of critical research in information systems. We examine the nature of the critical research perspective, clarify its significance, and review its major discourses, recognizing that its mission and methods cannot be captured by a fixed set of criteria once and for all, particularly as multiple approaches are still in the process of defining their identity. However, we suggest it is possible to formulate a set of principles capturing some of the commonalities of those approaches that have so far become most visible in the IS research literature. The usefulness of the principles is illustrated by analyzing three critical field studies in information systems. We hope that this paper will further reflection and debate on the important subject of grounding critical research methodology.

This paper resumes the discussion in information systems research on the use of partial least squares (PLS) path modeling and shows that the inconsistency of PLS path coefficient estimates in the case of reflective measurement can have adverse consequences for hypothesis testing. To remedy this, the study introduces a vital extension of PLS: consistent PLS (PLSc). PLSc provides a correction for estimates when PLS is applied to reflective constructs: The path coefficients, inter-construct correlations, and indicator loadings become consistent. The outcome of a Monte Carlo simulation reveals that the bias of PLSc parameter estimates is comparable to that of covariance-based structural equation modeling. Moreover, the outcome shows that PLSc has advantages when using non-normally distributed data. We discuss the implications for IS research and provide guidelines for choosing among structural equation modeling techniques.

Online crowdsourcing markets (OCM) are becoming more popular as a source for data collection. In this paper, we examine the consistency of survey results across student samples, consumer panels, and online crowdsourcing markets (specifically Amazon’s Mechanical Turk) both within the United States and outside. We conduct two studies examining the technology acceptance model (TAM) and the expectation–disconfirmation theory (EDT) to explore potential differences in demographics, psychometrics, structural model estimates, and measurement invariances. Our findings indicate that (1) U.S.-based OCM samples provide demographics much more similar to our student and consumer panel samples than the non-U.S.-based OCM samples; (2) both U.S. and non-U.S. OCM samples provide initial psychometric properties (reliability, convergent, and divergent validity) that are similar to those of both student and consumer panels; (3) non-U.S. OCM samples generally provide differences in scale means compared to those of our students, consumer panels, and U.S. OCM samples; and (4) one of the non-U.S. OCM samples refuted the highly replicated and validated TAM model in the relationship of perceived usefulness to behavioral intentions. Although our post hoc analyses isolated some cultural and demographic effects with regard to the non-U.S. samples in Study 1, they did not address the model differences found in Study 2. Specifically, the inclusion of non-U.S. OCM respondents led to statistically significant differences in parameter estimates, and hence to different statistical conclusions. Due to these unexplained differences that exist within the non-U.S. OCM samples, we caution that the inclusion of non-U.S. OCM participants may lead to different conclusions than studies with only U.S. OCM participants. We are unable to conclude whether this is due to of cultural differences, differences in the demographic profiles of non-U.S. OCM participants, or some unexplored factors within the models. Therefore, until further research is conducted to explore these differences in detail, we urge researchers utilizing OCMs with the intention to generalize to U.S. populations focus on U.S.-based participants and exercise caution in using non-U.S. participants. We further recommend that researchers should clearly describe their OCM usage and design (e.g., demographics, participant filters, etc.) procedures. Overall, we find that U.S. OCM samples produced models that lead to similar statistical conclusions as both U.S. students and U.S. consumer panels at a considerably reduced cost.

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.

After accounting for wrath in Purgatory 16, Dante deals with how wrath relates to the poetic imagination in the first 45 lines of the next canto. His examples are the story of Procne and Philomela from Ovid's Metamorphosis, the story of Esther and Haman from the Old Testament, as retold by Brunetto Latini, and the story of Amata, the Queen of the Latins, from Virgil's Aeneid. At issue is how these stories, but Virgil's in particular, make use of the imagination to represent wrath. As I show in the paper, this is Dante's way of condemning the poetic imagination which, instead of resolving the problem of wrath, as Dante does in Purgatory 16, displaces it or misrepresents it.

Among La Marchesa Colombi's admirable accomplishments in Un matrimonio in provincia is her ability to evoke a convincing experience of female adolescence when the significance of this developmental stage was not yet completely understood. In fact, Colombi offers the seeds of what Sibilla Aleramo would call for some twenty years later in her landmark Una donna — modern women's stories in their own voices rather than imitations of men's texts. This article studies the female protagonists in Un matrimonio in provincia and Una donna in the context of their adolesce development. It posits that Denza is believable in part because of her youthful willfulness. In this way, Colombi not only anticipates Aleramo's plea for more authentic female writing but also foresees a character like Aleramo's protagonist whose early resolve resurfaces in adulthood allowing her to live with integrity. Through their resilient characters, these authors strive for new definitions of womanhood.

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.

Within the Information Systems literature, there has been an emerging interest in the use of formative measurement in structural equation modeling (SEM). This interest is exemplified by descriptions of the nature of formative measurement (e.g., Chin 1998a), and more recently the proper specification of formatively measured constructs (Petter et al. 2007) as well as application of such constructs (e.g., Barki et al. 2007). Formative measurement is a useful alternative to reflective measurement. However, there has been little guidance on interpreting the results when formative measures are employed. Our goal is to provide guidance relevant to the interpretation of formative measurement results through the examination of the following six issues: multicollinearity; the number of indicators specified for a formatively measured construct; the possible co-occurrence of negative and positive indicator weights; the absolute versus relative contributions made by a formative indicator; nomological network effects; and the possible effects of using partial least squares (PLS) versus covariance-based SEM techniques. We provide prescriptions for researchers to consider when interpreting the results of formative measures as well as an example to illustrate these prescriptions.