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Spreadsheets are commonly used to inform decision making across many business sectors, despite the fact that research performed in the financial sector has shown that they are quite error-prone. However, few studies have investigated spreadsheet errors and their impact in other domains, like the healthcare sector. This article derives a lifecycle-stage classification scheme of spreadsheet error types based on an aggregation of, and extension of, existing classifications. Based on these classifications, a case study is then presented, performed to investigate the prevalence of these spreadsheet error types in an Irish healthcare setting. Results reveal that more than 90% of the spreadsheets studied contained ‘bottom-line' errors and the average cell-error rate was 13%. There was also a correlation between increased perceived impact of the spreadsheets and the number of errors identified. Recommendations from this research include providing spreadsheet training and guidelines for developers and users, and systematically managing and auditing spreadsheet development and use.

This paper seeks to respond to the research question: How does appropriation take place in the public sector in the development of end user applications by civil servants? Appropriation is defined as taking advantage of opportunities related to the development and use of applications, when the developer has in-depth knowledge of the problem domain and is also the primary user of the application. The author's results showed that public servants who have deep problem domain knowledge can take advantage of end user tools (e.g. spreadsheet programs) in the problem- solving process to solve vaguely defined problems. Appropriation is manifested in the continuous development of various ICT applications. In this paper, the author differentiates between first- and second-order appropriation. First-order appropriation takes place when the potential of the development tool is appropriated by the end user. Second-order appropriation takes place when an application is continuously developed and refined in parallel with the end user's learning process and the development of organizational requirements.

Spreadsheets are used in almost all businesses, for applications ranging from the mundane to the mission-critical. Errors in the data, formulas, or manipulation of spreadsheets could be costly, even devastating. The received wisdom is that about 5% of all formulas in spreadsheets contain errors, and this rate is consistent across spreadsheets. However, this estimate is based on five studies, some of which are quite informal, and a total of only 43 spreadsheets. Our research was designed to deepen our understanding of spreadsheet errors. Specifically, we address three questions about errors in operational spreadsheets: what is the average cell error rate, how does it differ among spreadsheets, and what types of errors are most prevalent? We created a spreadsheet auditing protocol and applied it to 50 diverse operational spreadsheets. We found errors in 0.9% to 1.8% of all formula cells, depending on how errors are defined. We also found that the error rate differed widely from spreadsheet to spreadsheet.

Spreadsheets are widely used in the business, public, and private sectors. However, research and practice has generally shown that spreadsheets frequently contain errors. Several researchers and vendors have proposed the use of spreadsheet static analysis programs (SAPs) as a means to augment or potentially replace the manual inspection of spreadsheets for errors. SAPs automatically search spreadsheets for indications of certain types of errors and present these indications to the inspector. Despite the potential importance of SAPs, their effectiveness has not been examined. This study explores the effectiveness of two widely fielded SAPs in comparison to manual human inspection on a set of naturally generated quantitative errors in a simple, yet realistic, spreadsheet model. The results showed that while manual human inspection results for this study were consistent with previous research in the field, the performance of the static analysis programs at detecting natural errors was very poor for every category of spreadsheet errors.

Alcohol use and misuse can result in substantial disease burden and mortality, with significant public health and social costs. The need for better diagnoses and medications development for all conditions associated with alcohol use emphasizes the need for research into underlying molecular mechanisms. Noncoding ribonucleic acids (ncRNAs) are an explanatory mechanism for transducing environmental effects into cells and tissues. ncRNAs are regulatory RNAs that are diverse in size and function and greatly outnumber protein-coding RNAs in mammals. ncRNAs may play a major role in the pathogenesis and consequences of alcohol use and misuse, and studies in this area could pave the way to developing novel methods of diagnosis and therapy. This scoping review examines the extent, range, and nature of the research linking ncRNAs to alcohol, with a focus on identifying gaps in the existing literature. This scoping review followed the \"Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.\" Peer-reviewed journal articles for all species, including human, animal, or cells, published until December 2023, were included. Publications were retrieved using keyword searches in three online databases: Medline (Ovid), Embase (Ovid), and Academic Search Ultimate (EBSCO). Identified articles were imported in Covidence systematic review software for screening. Each article was evaluated by at least two independent reviewers, and only those receiving votes from both were included in the review. Key findings were then extracted from the included studies, further analyzed, and summarized in a table and figures using Microsoft Excel. Details, including year of publication, species, sex, sample type, and sample processing methods for different types of ncRNAs (i.e., microRNAs [miRNAs], long noncoding RNAs [lncRNAs], circular RNAs [circRNAs]) were also reported. In total, 3,358 studies were identified and imported in Covidence. After removal of duplicates, 1,937 studies were processed for title and abstract screening, and 400 studies were subsequently selected for full-text screening. From these, 338 studies were included in the scoping review. In total, 3,020 initially captured studies were excluded. Among all ncRNAs, miRNAs were the most frequently investigated, followed by lncRNAs and circRNAs. Whereas many studies investigated ncRNA associations with alcohol phenotypes, mechanistic studies were more limited. Studies spanned pathologies related to alcohol use across tissues and organs, including liver, brain, heart, pancreas, placenta, gastrointestinal system, muscle, and bone. However, key variables, including biological sex, age, and genetic variation, were not adequately addressed. The analyses uncovered significant gaps in the research literature, relating primarily to underlying mechanisms. The field of ncRNA research in pathologies associated with alcohol use is still emerging. Given the enormous sizes and species variations of mammalian ncRNA genomes, a significant amount of research is needed to identify relevant ncRNAs in different organs, and at all stages of pathology, and to identify underlying mechanisms. Initial studies show promise that ncRNA research could significantly improve the diagnosis and treatment of alcohol use disorder.

Most organizations use large and complex spreadsheets that are embedded in their mission-critical processes and are used for decision-making. Identification of the various types of errors that can be present in these spreadsheets is, therefore, an important first step to creating controls that organizations can use to govern their spreadsheets. While a considerable amount of research on quantitative error taxonomies exists, there is comparatively little research concerning qualitative error taxonomies. In this paper, we propose a taxonomy for categorizing qualitative errors in spreadsheet models that offers an exploratory framework for evaluating the quality of a spreadsheet model before it is released for use by others in the organization. The classification was developed based on types of qualitative errors identified in the literature and errors committed by end-users in developing a spreadsheet model for Panko's (1996) “Wall Problem.” A principal component analysis of the errors reveals four logical groupings thereby creating four categories of qualitative errors. The usability and limitations of the proposed taxonomy and areas for future research are discussed.

End user computing (EUC) is like dark matter in physics. EUC is enormous in quantity and importance yet has been largely invisible to corporate IT departments, information systems (IS) researchers, and corporate management. EUC applications, especially spreadsheet applications, are also “dark” in the sense that they pose a number of overlooked risks for organizations, including errors, privacy violations, trade secret extrusions, and compliance violations. On the positive side, EUC applications are also like the dark energy of physics. They are supporting critical gains in decision making, computing by scientists and engineers, operational systems, and other important processes in every corner of the firm. It is time to stop ignoring end user computing in general and spreadsheets in particular. The purpose of this paper is to document to the extent possible today then importance of end user computing relative to the concerns of corporate IT departments and IS researchers.

The role of information technology (IT) is critical in many applications, including accounting. However research into spread sheeting errors indicates financial data emanating from computer applications may contain errors. Auditors review financial data and report, among other things, as to its mathematical accuracy. This study evaluates the reliance trainee auditors place upon IT produced financial data and their application of the concept of mathematical accuracy. Data emanating from a supposedly reliable source was intentionally prepared with mathematical errors and given to participants for evaluation. Despite exposure to concepts such as computer error and audit scepticism, a significant proportion either erroneously concluded the data was accurate and/or failed to appropriately apply the concept of mathematical accuracy. The implications for educators are significant. Basic topics such as data integrity and over-reliance upon IT generated data may need further emphasis during training programs across many disciplines, especially accountancy.

Many businesses are heavily reliant on data and many people working in business like to have almost constant access to this data. Mobile phones provide users with access to a wide range of office applications, including word processors, email applications, and spreadsheet applications. However the limitations of the mobile devices can cause a number of usability issues which may limit the productivity of users of these devices. This paper presents the authors’ investigation of the usability of mobile spreadsheet applications. Through a controlled study they examined the performance of users using a mobile spreadsheet application to perform a series of simple tasks, such as finding data and altering values in the spreadsheet. The authors found that although mobile spreadsheet applications can be effective, they are highly inefficient due to a large number of usability issues.