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The literature on formative modelling ("formative measurement") in the information systems discipline claims that measurement model misspecification, where a reflective model is used instead of a more appropriate formative model, is widespread. In this research, we argue that this cannot be true because models misspecified in this way would fail the measurement validation procedures used with reflective models and thus would not be publishable. To support this argument, we present two extensive simulation studies. The simulation results show that in most cases where data originates from a formative model, estimating a reflective model would not produce results that satisfy the commonly used measurement validation guidelines. Based on these results, we conclude that widespread publication of models where the direction of measurement is misspecified is unlikely in IS and other disciplines that use similar measurement validation guidelines. Moreover, building on recent discussions on modelling endogenous formatively specified latent variables, we demonstrate that the effects of misspecification are minor in models that do pass the model quality check. Our results address important issues in the literature on the consequences of measurement model misspecification and provide a starting point for new advances in this area. [ABSTRACT FROM AUTHOR]

The authors begin this brief rejoinder by thanking all the authors who took time to provide comments on their work, which appeared in "Measurement: Interdisciplinary Research and Perspectives" v14 n3 2016. All commentaries appear to suggest that causal indicators cannot be used for measurement but differ in how strongly this conclusion is stated; commentaries challenging our work and its conclusions are notably absent. In this article the authors outline some common themes across the commentaries, provide some thoughts on these themes, and outline areas they believe need further research.

One of the central assumptions of the causal-indicator literature is that all causal indicators must be included in the research model and that the exclusion of one or more relevant causal indicators would have severe negative consequences by altering the meaning of the latent variable. In this research we show that the omission of a relevant causal indicator does not affect downstream estimates relating the focal latent variable to other variables in the model, which challenges the current stance in the literature. Further, we argue that this occurrence presents a fundamental challenge to the causal-indicator literature, in that the lack of negative consequences is not consistent with the tenet that latent variables derive their meaning from the set of causal indicators included in a research model. Rather, though causal indicators help identify the focal latent variable, its meaning is derived from its position as a common factor of other downstream variables--latent or observed--to which it is related.

Information systems researchers have recently begun to propose models that include formatively specified .constructs, and largely rely on partial least squares (PLS) to estimate the parameters of interest in those [...]

As digital technology use becomes widespread, its unintended consequences ranging from personal health to societal righteousness are under more scrutiny. Increasingly, digital designers are accused of not being considerate enough of the depth of their creations, and their impacts on our well-being. In this special issue, we explore an alternative, genuinely human-centered approach to technology design focusing on well-being and making our interactions with digital technology more meaningful, purposeful, and sustainable. To this end, the editorial starts with a brief review of the history of research that led to the growing field of digital well-being. We then introduce the Digital Well-being Design Framework, which goes beyond the ego-centric approach in human-centered design, and is multi-layered with self (intrapersonal), social (interpersonal), and transcendent (extra-personal) levels. Similar topics in related AIS journals are summarized, followed by the application of our framework to introduce and position the papers in this special issue. Our special issue aims to bring the topic of digital well-being to the forefront of the information systems research community and launch a new era of genuinely human-centered design. [ABSTRACT FROM AUTHOR]

Transforming variables before analysis or applying a transformation as a part of a generalized linear model are common practices in organizational research. Several methodological articles addressing the topic, either directly or indirectly, have been published in the recent past. In this article, we point out a few misconceptions about transformations and propose a set of eight simple guidelines for addressing them. Our main argument is that transformations should not be chosen based on the nature or distribution of the individual variables but based on the functional form of the relationship between two or more variables that is expected from theory or discovered empirically. Building on a systematic review of six leading management journals, we point to several ways the specification and interpretation of nonlinear models can be improved. [ABSTRACT FROM AUTHOR]

This study develops a technology category framework to enable the investigation of a possible moderating effect of technology type on adoption behavior by extracting and analyzing the technology descriptions from 950 papers covering over 20 years of technology acceptance research. We utilize both human judgment and statistical techniques by using the results of the manual sorting of technology descriptions by six individuals as input for a multidimensional scaling and cluster analysis to group them into hierarchical cluster structures. One of several potential cluster solutions is selected for further discussion along with its limitations and the future work it suggests. [ABSTRACT FROM AUTHOR]

This research investigated Internet-based knowledge search patterns of engineers and scientists working in R&D for companies in the pharmacological and information technology sectors in Israel. Building on earlier work that considers the multidimensional nature of the relative advantage construct, we examine how perceptions of learning, informational convenience, and trust affected intentions to use the Internet to acquire new knowledge. In particular, these perceptions were studied with regard to both active and passive modes of interaction. We also considered here which types of technological knowledge are acquired by researchers, and how that differs across two professional communities of practice – scientists and engineers. This study sheds light on how R&D workers perceive the relative advantage of acquiring necessary knowledge through passive and active modes of communication with other researchers that are facilitated by the Internet. Findings are of interest to the literature on knowledge spillover because the capability of an organisation to acquire, disseminate, and exploit knowledge is crucial to R&D efforts. [ABSTRACT FROM PUBLISHER]