Purpose The purpose of this paper is to investigate and quantify the effects of personality traits, as defined by the five-factor model (FFM) on an individual’s ability to detect fake news. The findings of this study are increasingly important because of the proliferation of social media news stories and the exposure of organizational stakeholders and business decision makers to a tremendous amount of information, including information that is not correct (a.k.a. disinformation). Design/methodology/approach The data were collected utilizing the snowball sampling methodology. Students in an Management Information Systems course completed the survey. Since a diverse sample was sought, survey participants were instructed to recruit another individual from a different generation. The survey questions of the FFM identify particular personality traits in respondents. Survey respondents were given a collection of nine news stories, five of which were false and four that were true. The number of correctly identified stories was recorded, and the effect of personality traits on the ability of survey respondents to identify fake news was calculated using eta-squared and the effect size index. Findings Each of the five factors in the FFM demonstrated an effect on an individual’s ability to detect disinformation. In fact, every single variable studied had at least a small effect size index, with one exception: gender, which had basically no effect. Therefore, each variable studied (with the exception of gender) explained a portion of the variability in the number of correctly identified false news stories. Specifically, this quantitative research demonstrates that individuals with the following personality traits are better able to identify disinformation: closed to experience or cautious, introverted, disagreeable or unsympathetic, unconscientious or undirected and emotionally stable. Originality/value There is scant research on an individual’s ability to detect false news stories, although some research has been conducted on the ability to detect phishing (a type of social engineering attack to obtain funds or personal information from the person being deceived). The results of this study enable corporations to determine which of their customers, investors and other stakeholders are most likely to be deceived by disinformation. With this information, they can better prepare for and combat the impacts of misinformation on their organization, and thereby avoid the negative financial impacts that result.

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As countless regional, national, and international accrediting bodies continue to employ student engagement measures as mechanisms for quality assurance, universities become more intent on achieving this important gauge of student success. Specifically, the growth in enrollment in distance learning programs adds a unique level of complexity leading researchers to search for ways to increase engagement in the online course environment. Organizations continue to value teamwork and many instructors have incorporated group work into their online courses to teach students this important skill. The present study examines the impact of student engagement on group satisfaction. Furthermore, this research places student engagement at the center of a structural equation model to determine both predictors and outcomes of this important element of student learning. Specifically, this analysis examines whether students’ perceptions of computer self‑efficacy impact student engagement and group satisfaction in online business courses. Our findings indicate that computer self‑efficacy leads to student engagement and, further, that student engagement influences group satisfaction. Importantly, the relationship between student engagement and group satisfaction is mediated by group expectations. Discussions of findings can be utilized to understand the factors that lead to student engagement and its outcomes in online courses.

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While there has been a substantial amount of attention within the information systems research community towards understanding the phenomenon of adoption, much less is known about non-adoption. This study examines the factors surrounding the decision to not adopt a technology and whether certain factors exert differing effects on individuals in particular ways such that concurrent factors could be identified to develop a classification of the specific types of non-adoption behavior. Utilizing inhibitor theory and the symbolic adoption model as a foundational framework for the different types of non-adoption, we posit that different types of non-adoption exist which is demonstrated by determining the perceptions towards technology that coalesce around different types of non-adoption. We conducted a two-phase investigation into nonadoption with two goals in mind: 1) identify and explore specific factors of the IT that are associated with the rejection decision and are distinct from the adoption decision; and 2) determine the extent to which these factors (along with traditional enablers) differentiate between different types of non-adoption. The results from a discriminant function analysis (DFA) indicate the coalescence of specific perceptual variables according to the types of non-adoption behavior, specifically, the discriminatory power of differing perceptions of IT between trial rejecters, symbolic rejecters, trial accepters, symbolic adopters, and adopters. The implications for research and implications for practice are discussed.