Modes of Theory Integration

IS, among other social sciences, have moved from a relative paucity of theories about social phenomenon to a a state of multiple, overlapping, and overly narrow theories. We offer three Modes for theory Integration that will enable researchers to better integrate theories and processes into internally coherent models within theories, across theories and between fields. The basis for integration are semantic similarity, nomological congruence and physical/functional/causal overlap. We develop a framework that will justify propositions for theory integration that can subsequently be tested for correspondence to real world phenomenon

Visualizing the core-periphery distinction in theory domains

As specific parts of a theory are refined over time, the aggregated set of variables and associations of multiple theory instances provide the identity of a theory domain. This research applies a meta-theoretical analysis to the problem of theory identity and the core-periphery distinction. The theoretico-empirical network for quantitative publications over a 20 year span of two top Information Systems journals is analysed and visualized to illustrate these aspects of theory. The analysis provides insight into the density of research in specific theory domains, the verisimilitude and explanatory ubiquity of core versus peripheral postulates, and suggests opportunities for increasing explanatory depth and integration in select theory domains.<br /

ReImagining Individuals’ Digital Mindset: Toward A Theoretical Synthesis

The idea that individuals can have a digital mindset has gained popularity against the backdrop of radical shifts toward digital transformation and the future of work. Despite the burgeoning scholarly interest across disciplines, efforts to conceptualize digital mindset remain fragmented so far. This paper starts a discourse about unresolved ontological assumptions and theoretical inconsistencies. We address the prevailing knowledge fragmentation by synthesizing three research streams on individuals’ affect, behaviors, and cognitions in the context of digital transformation and revealing their underlying commonalities. We propose that two beliefs jointly form the integrative foundation of individuals’ digital mindset: how individuals think about and perceive (a) digital technologies (as opportunity or threat) and (b) their own abilities (as malleable or fixed) in the context of digital transformation. Our theoretical synthesis lays the groundwork for future research to work toward an inter-nomological network and a more holistic understanding of individuals’ digital mindset

Validating the South African Personality Inventory (SAPI) : Examining green behavior and job crafting within a nomological network of personality

Abstract: Personality assessment in organizations has mostly served as a tool for decision-making regarding selection and job performance. In this article the focus is shifted towards understanding the role of personality in individuals’ propensity to exhibit contemporary work-related behaviors, such as employee green behavior (EGB) and job crafting (JC), through a nomological network. From an indigenous perspective, the cultural applicability of EGB and JC was estab-lished prior to investigating the external validity of the South African Personality Inventory (SAPI). The unidimensional EGB-framework developed by Ones and Dilchert (2009) was found to have a covert and an overt component in the South African context, while the JC-model developed by Tims, Bakker, and Derks (2012) was unchanged. Within the nomological network, Positive Social-Relational Disposition did not display any predictive qualities. Conscientious-ness and Negative Social-Relational Disposition were found to predict both EGB (covert) and JC. Extraversion, Open-ness, and Neuroticism displayed predictive qualities only within the JC-model. Further investigation of these relation-ships is suggested, using quantile regression

Improving Usability of Social and Behavioral Sciences’ Evidence: A Call to Action for a National Infrastructure Project for Mining Our Knowledge

Over the last century, the social and behavioral sciences have accumulated a vast storehouse of knowledge with the potential to transform society and all its constituents. Unfortunately, this knowledge has accumulated in a form (e.g., journal papers) and scale that makes it extremely difficult to search, categorize, analyze, and integrate across studies. In this commentary based on a National Science Foundation-funded workshop, we describe the social and behavioral sciences’ knowledge-management problem. We discuss the knowledge-scale problem and how we lack a common language, a common format to represent knowledge, a means to analyze and summarize in an automated way, and approaches to visualize knowledge at a large scale. We then describe that we need a collaborative research program between information systems, information science, and computer science (IICS) researchers and social and behavioral science (SBS) researchers to develop information system artifacts to address the problem that many scientific disciplines share but that the social and behavioral sciences have uniquely not addressed

Artifact Sampling: Using Multiple Information Technology Artifacts to Increase Research Rigor

Researchers in many scientific disciplines routinely conceptualize information technologies (IT) as antecedents or outcomes in theoretical models. The ongoing theorizing of IT leads to a novel methodological challenge termed instantiation validity (IV). In this paper, we contribute to research on remediating IV challenges by proposing and advocating the methodological practice of artifact sampling, whereby multiple artifacts are sampled from the population of all possible artifacts (the instantiation space). Artifact sampling extends the practice of employing multiple research subjects or survey respondents, routinely used in social sciences, into the IT artifact design space. Artifact sampling is an important methodological practice that stands to increase the rigor of research dealing with software artifacts. As it is currently not being adequately undertaken in the aforementioned research, many studies may result in biased or unjustified conclusions

Cognitive Mechanisms and Computational Models: Explanation in Cognitive Neuroscience

Cognitive Neuroscience seeks to integrate cognitive psychology and neuroscience. I critique existing analyses of this integration project, and offer my own account of how it ought to be understood given the practices of researchers in these fields. A recent proposal suggests that integration between cognitive psychology and neuroscience can be achieved `seamlessly' via mechanistic explanation. Cognitive models are elliptical mechanism sketches, according to this proposal. This proposal glosses over several difficulties concerning the practice of cognitive psychology and the nature of cognitive models, however. Although psychology's information-processing models superficially resemble mechanism sketches, they in fact systematically include and exclude different kinds of information. I distinguish two kinds of information-processing model, neither of which specifies the entities and activities characteristic of mechanistic models, even sketchily. Furthermore, theory development in psychology does not involve the filling in of these missing details, but rather refinement of the sorts of models they start out as. I contrast the development of psychology's attention filter models with the development of neurobiology's models of sodium channel filtering. I argue that extending the account of mechanisms to include what I define as generic mechanisms provides a more promising route towards integration. Generic mechanisms are the in-the-world counterparts to abstract types. They thus have causal-explanatory powers which are shared by all the tokens that instantiate that type. This not only provides a way for generalizations to factor into mechanistic explanations, which allows for the `upward-looking' explanations needed for integrating cognitive models, but also solves some internal problems in the mechanism literature concerning schemas and explanatory relevance. I illustrate how generic mechanisms are discovered and used with examples from computational cognitive neuroscience. I argue that connectionist models can be understood as approximations to generic brain mechanisms, which resolves a longstanding philosophical puzzle as to their role. Furthermore, I argue that understanding scientific models in general in terms of generic mechanisms allows for a unified account of the types of inferences made in modeling and in experiment

Research Perspectives: Improving Action Research by Integrating Methods

Action research (AR) has developed extensively since the 1970s. We reviewed the AR literature within the information systems (IS) discipline and found 16 different methods, which constitutes a problematic situation for researchers. We describe and critique those methods before integrating their strengths to improve the AR method that is most frequently practiced in IS: canonical action research (CAR). The existing set of principles and criteria for CAR is modified and elaborated to enhance the foundation for undertaking AR consistently. We discuss the general implications of this improved form of the method, which we name integrated action research (IAR). We specifically suggest how IAR can be used to investigate the application of disruptive technologies, including those that embody artificial intelligence and enable more flexible and socially distanced work

Data Management for Meta-Analyses of Causal Models and Measurements in Survey Research

Crucial to social research is the sustainable design and repeated usage of measurement instruments. In addition, it is important that researchers are able to find and compare relevant models and scales in order to make appropriate decisions. Therefore we propose a database that allows to create a joint collection of (causal) models and (multi-item) scales in order to enable a more extensive data usage for automation of scientific workflows, for the generation of recommendations and for meta-analyses. This includes finding appropriate constructs and scales, comparisons of items and quality measures as well as the detection of undocumented links between different topics and disciplines. In order to illustrate the potential, we refer to several approaches of measuring Technology Acceptance