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Purpose Companies create brand fan pages (BFPs) on social media platforms to broadcast product information, increase brand awareness and engage customers. A common challenge facing BFPs is how to attract and retain followers effectively. Through the lens of the theory of person–environment fit (TPEF), the purpose of this paper is to develop and validate a theoretical model to explain the role of multidimensional fit perceptions in cultivating BFP users’ continued following intention. Design/methodology/approach Data collected from 193 active followers of BFPs on Sina Weibo, the most prevalent social media platform in China, were used to test the proposed model. The partial least squares method was employed to assess the relationships in the model. Findings The findings reveal that users will continue to follow the BFP if their needs align with what the BFP provides, and if they perceive their values and characteristics to match those of the brand and fellow followers. Originality/value This study is among the first to extend the research context of the TPEF from organizational behaviors to examining how perceived fit influences users’ continued intention to follow in the social media context. In addition to the theoretical contributions, the findings of this study have important implications for practitioners who undertake social media management or user behavior analysis.

Software testing is becoming more critical to ensure that software function properly. As the time, effort, and funds invested in software testing activities have been increased significantly, these resources still cannot meet the increasing demand of software testing. Managers must allocate testing resources to the test cases effectively in uncovering important defects. This study builds a value function that can quantify the relative value of a test case and thus play a significant role in prioritizing test cases, addressing the resource constraint issues in software testing, and serving as a foundation of AI for software testing. The authors conducted a Monte Carlo simulation to exhibit application of the final value function.

In this article, we demonstrate a novel use of case research to generate an empirical function through qualitative generalization. This innovative technique applies interpretive case analysis to the problem of defining and generalizing an empirical cost function for test cases through qualitative interaction with an industry cohort of subject matter experts involved in software testing at leading technology companies. While the technique is fully generalizable, this article demonstrates this technique with an example taken from the important field of software testing. The huge amount of software development conducted in today's world makes taking its cost into account imperative. While software testing is a critical aspect of the software development process, little attention has been paid to the cost of testing code, and specifically to the cost of test cases, in comparison to the cost of developing code. Our research fills the gap by providing a function for estimating the cost of test cases.

As artificial intelligence (AI) continues to shape commercial and governmental innovation, understanding the potential of large language models (LLMs) to improve software testing has become increasingly critical. This paper explores the application of LLMs for generating test cases—an essential yet resource-intensive activity in software quality assurance. Using theoretical lenses of the Design Science Research Evaluation (DSRE) framework, Measurement Theory, and Cognitive Load Theory, the authors systematically design and evaluate procedures for test case generation and execution to benchmark AI-generated test cases against those produced by human testers and traditional pairwise analysis. They develop two custom-built applications seeded with intentional defects and execute test cases generated through each method. This research contributes to the evolving discourse on AI-driven software engineering, offering insights into when LLMs can autonomously generate test cases, when human expertise is indispensable, and when hybrid approaches yield the greatest value.

Data management (DM) has existed in conjunction with software development and the management of the full set of information technology (IT)-related components. However, it has been more than two decades since research into DM as it is practiced has been published. In this paper, the authors compare aspects of DM across a quarter-century timeline, obtaining data using comparable sets of subject matter experts. Using this information to observe the profession’s evolution, the authors have updated the understanding of DM as it is practiced, giving additional insight into DM, including its current responsibilities, reporting structures, and perceptions of success, among other factors. The analysis indicates that successfully investing in DM presents current, real challenges to IT and organizations. Although DM is evolving away from purely operational responsibilities toward higher-level responsibilities, perceptions of success have fallen. This paper details the quarter-century comparison of DM practices, analyzes them, and draws conclusions.

The primary role of testers is to verify and validate the software produced by developers to ensure its quality. Testing is designed to catch problems in the software and report them for correction, so it is a conflict-laden, confrontational, and judgmental process. This “audit” role of testing is inherently adversarial, ensuring the development of components of interpersonal conflict judgments between developers and testers. Prior research indicates that such conflict is likely to be negatively associated with software quality and job satisfaction, producing negative judgments about the artifact production process and about the job itself. This study addresses the question: How do judgments of conflict between developers and testers impact the software development process? The authors develop and empirically test a research model which proposes that the conflict judgment targets of both the tasks and the persons who perform them will have direct impact on both software quality and job satisfaction judgments. Results of testing this model indicate that interpersonal judgments arising from conflict, as well as judgments made by testers and developers about the conflict targets of tasks and persons negatively influence subsequent software quality and job satisfaction judgments. Implications for theory and practice are discussed.

Virtual stores provide great efficiency in the retail value chain, and their existence has tremendously paved the way for the diffusion of electronic commerce. Understanding the determinants of consumer acceptance of virtual stores will provide important theoretical contributions to the area of business-to-consumer (B-to-C) electronic commerce and lead to the development of more effective and meaningful strategies for virtual stores. By expanding the Technology Acceptance Model and the Innovation Diffusion Theory, this study aims to provide an integral theoretical paradigm that can successfully support a wide array of technical, business, and consumer issues involved in online retailing. The results from a Web-based survey of 253 online consumers indicate that the proposed theoretical model is able to explain and predict consumer acceptance of virtual stores substantially well. The resulting theoretical model explains a large portion of the factors that lead to a user's behavioral intention to use and actual use of a virtual store. In addition to providing new theoretical grounds for studying the virtual store phenomenon, this article also supplies virtual stores with a number of operative critical success factors to remain competitive in the volatile electronic marketplace.

As the demand for business intelligence (BI) professionals continues to grow, educators need to calibrate their instruction to accommodate the demand of practitioners for specific technical skills while also providing college students with a broader foundation that includes a general understanding of BI concepts and problem-solving skills that are applicable across disciplines. This paper describes a pedagogical method called report-oriented learning which seeks to combine the established methods of problem-based learning and case-based learning. Report-oriented learning requires students to reflect on the knowledge gained during the conceptual parts of the course and use critical thinking and storytelling skills as they prepare and present several comprehensive reports in class. We applied the report-oriented method in a business intelligence course that consists of four instructional approaches: (1) section preview, (2) lectures and quizzes on basic concepts, (3) application of concepts and development of practical skills with hands-on pro jects, and (4) comprehensive reflection and inquiry in the form of reports. We surveyed students with anonymous questionnaires in the report-oriented BI courses from 2021-2023. The results indicate that the method was effective and perceived by students as having improved their critical thinking and practical skills related to the application of BI techniques and the professional presentation of their findings.

Providers need access to the data in patient healthcare records to make decisions that provide the type of high-quality services, which lead to successful medical treatments. This article summarizes the data accuracy and data availability problems that exist in managing healthcare records, and then describes various technology solutions that could be designed to address specific data problems. [PUBLICATION ABSTRACT]