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The Information Systems Journal (ISJ) published its first issue in 1991, and in 2015, the journal celebrated its 25th anniversary. This study presents an overview of the leading research trends in the papers that the journal has published during its first quarter of a century via a bibliometric and ontological analysis. From a bibliometric perspective, the analysis considers the publication and citation structure of the journal. The study then develops a graphical analysis of the bibliographic material by using visualization of similarities software that employs bibliographic coupling and cocitation analysis. The work produces an ontological framework of impact and analyses the journal papers to assess qualitatively ISJ's impact. The results indicate that the journal has grown significantly over time and is now recognized as one of the leading journals in information systems. Yet challenges remain if the journal is to meet its aims in impacting and setting the agenda for the development of the Information Systems field.

PurposeThe role of emerging digital technologies is of growing strategic importance as it provides significant competitive advantage to organisations. The chief information officer (CIO) plays a pivotal role in facilitating the process of digital transformation. Whilst demand continues to increase, the supply of suitably qualified applicants is lacking, with many companies forced to choose information technology (IT) or marketing specialists instead. This research seeks to analyse the organisational capabilities required and the level of fit within the industry between CIO requirements and appointments via the resource-based view.Design/methodology/approachJob postings and CIO curriculum vitae were collected and analysed through the lens of organisational capability theory using the machine learning method of Latent Dirichlet Allocation (LDA).FindingsThis research identifies gaps between the capabilities demanded by organisations and supplied by CIOs. In particular, soft, general, non-specific capabilities are over-supplied, while rarer specific skills, qualifications and experience are under-supplied.Practical implicationsThe research is useful for practitioners (e.g. potential CIO candidates) to understand current market requirements and for companies aiming to develop internal training that meet present and future skill gaps. It also could be useful for professional organisations (e.g. CIO Forum) to validate the need to develop mentoring schemes that help meet such high demand and relative undersupply of qualified CIOs.Originality/valueBy applying LDA, the paper provides a new research method and process for identifying competence requirements and gaps as well as ascertaining job fit. This approach may be helpful to other domains of research in the process of identifying specific competences required by organisations for particular roles as well as to understand the level of fit between such requirements and a potential pool of applicants. Further, the study provides unique insight into the current supply and demand for the role of CIO through the lens of resource-based view (RBV). This provides a contribution to the stream of information systems (IS) research focused on understanding CIO archetypes and how individual capabilities provide value to companies.

To determine the relationship between per capita income and COVID-19 cases in Broward and Miami-Dade Counties of Florida, USA. BackgroundLow socioeconomic status predisposes individuals to worse health outcomes. For example, during the 2003 SARS-CoV pandemic and the 2009 H1N1 influenza pandemic disadvantaged individuals were more likely to become infected. More recently, a study found that deaths due to COVID-19 were associated with disadvantaged areas across the United States. South Florida, in particular Broward and Miami-Dade Counties, has experienced a significant burden of coronavirus cases. Investigating the association of income on coronavirus cases in Broward and Miami-Dade Counties may aid in identifying and treating those individuals at increased risk. MethodsThis retrospective cross-sectional study used data gathered by the Florida Department of Health and 2018 U.S. Census. COVID-19 cases from March 2 -November 1, 2020 were tallied by ZIP code in Florida's Broward and Miami-Dade Counties and scaled per housing unit. An exhaustive regression analysis using County "Miami-Dade" or "Broward," sex, race, ethnicity, median age, and estimated per capita income was performed for each combination of independent variables in MATLAB (MathWorks, Natick, USA). Regression models were evaluated using both adjusted R-squared and the Akaike Information Criterion, along with the number of significant predictors. The most optimal model with the highest number of significant predictors was selected. ResultsAmong all other variables, sex, race, and ethnicity as the variables that best predicted COVID-19 cases per housing unit within a certain ZIP code. The adjusted R-squared of this optimal model was 0.5062, indicating that within each ZIP code in Broward and Miami-Dade Counties 50.62% of the variance in COVID-19 cases per housing unit can be explained by these variables. A significant relationship was found between the number of COVID-19 cases and individuals who were Black or African American (p < 0.001), individuals who were Hispanic or Latino (p < 0.001), and male to female ratio (p = 0.016). Per capita income, age, and county were not statistically significant predictors in any model tested. ConclusionsRacial and gender disparities may be more significant contributors to COVID-19 cases than per capita income in housing units. Based on the results of this study, investigators may consider applying this model to similar variables in order to inform the management and prevention of cases in the present and future pandemics.