Without an integrated model of how the human brain works and processes information, artificial intelligence (AI) will remain a mysterious black box that can misfire as circumstances change. An integrated study of the three cognitive computing components (AI, cognitive psychology, and neurobiology) is necessary to create explainable AI findings. This paper introduces cognitive computing systems (CCS) as a domain for information systems (IS) research. It reviews the interdisciplinary implications of CCS concepts by developing a new computational method, knowledge similarity transformation (KST), to improve digital-augmented literature analysis in fragmented knowledge areas. Based on the dual CCS and KST contribution, this article outlines strategic implications for organizational value creation opportunities and future research directions from a technological, psychological, and physiological perspective.

This study explores mechanisms of frugal knowledge sharing for frugal innovation diffusion drawing upon concepts from social capital and diffusion of innovation theories. Three frugal innovations projects from Uganda, Malawi, and Ethiopia are used to undertake a preliminary appraisal of a proposed theoretical model. The study identifies a frugal knowledge sharing mechanism based on “The Church lady model”. From a practice perspective, the study informs how to leverage social capital to facilitate frugal knowledge sharing. Insights drawn from this study cover the surrounding network structure, mode of communication, frugal knowledge sharing modalities, and the opportunity for combination and exchange to spark varied frugal IS innovation projects. From a research point of view, it serves as a basis for frugal knowledge management for frugal innovation diffusion in an African context.

Explaining how, why, and to what extent humans use information systems has been at the heart of the information systems (IS) discipline, and although successful models have emerged, mostly relying on social and cognitive psychology in their theoretical underpinnings, there are still cases that remain unexplainable. In this article, we scrutinize one of these cases where the continued use of technology cannot be explained by one of the most prominent traditional IS models to date. We analyze our qualitative case study by juxtaposing two theoretical lenses: a traditional IS perspective (i.e., the “unified theory of acceptance and use” model) versus an evolutionary psychology perspective (i.e., the four‐drive model). We find that a more comprehensive understanding of continued IS usage is possible when including an evolutionary psychology perspective to the existing models. Specifically, we propose three new concepts, including evolved performance expectancy, evolved effort expectancy, and evolved social influence. We also demonstrate that, in some situations, cognitive and social constructs dominate, whereas in other situations, the evolutionary dependent constructs associated with human nature take over.

Information systems (IS) research has drawn heavily on social and cognitive psychology to explain technology adoption. Indeed, the many variations of the technology acceptance model all share these same theoretical foundations. Focusing exclusively on the socio-cognitive lens can lead to overlooking enhanced explanations of technology acceptance, such that new theoretical perspectives may be warranted. In this qualitative grounded theory study, we discovered how the lens of evolutionary psychology, as embodied in the Four-Drive model, was helpful in understanding technology acceptance across three organizational sites. We contend that evolutionary psychology is an important addition to the theoretical repertoire of IS researchers, and propose including ‘evolved psychological mechanisms’ within traditional models of technology acceptance.

This study investigates the impact of the electronic communication capability of a group support system (GSS) on status differentials in small groups. A laboratory experiment was used to answer the research questions. Three support levels were studied: manual, face-to-face GSS, and dispersed GSS. Two task types were examined: intellective and preference. Five dependent variables reflecting different aspects of status differentials were measured: status influence, sustained influence, residual disagreement, perceived influence, and decision confidence. The results show that manual groups had higher status influence, sustained influence, and decision confidence, but lower residual disagreement than face-to-face GSS and dispersed GSS groups. Preference task groups also produced higher status influence and sustained influence, but lower residual disagreement compared to intellective task groups. In addition, manual groups working on the preference task reported higher perceived influence than face-to-face GSS and dispersed GSS groups working on the same task. These findings suggest that when groups are engaged in activities for which status differentials are undesirable, a GSS can be used in both face-to-face and dispersed settings to dampen status differentials. Moreover, when a task amplifies status differentials, the use of a GSS tends to produce corresponding stronger dampening effects.