There is considerable debate in IS-DSR community about what constitutes design knowledge and whether they are best represented as design principles or design theories. While designing an IT artifact to solve a problem addresses the “how” question, we agree that the “why” question (why did this work?) is important to answer. This essay attempts to shed light on two fundamental questions in this domain: (1) What are the various ways that knowledge contribution in DSR can be stated? (2) What pathways exists to create and re(use) knowledge in DSR projects? We present a simple 2 × 2 framework that explains with concrete examples four knowledge types - design principles, design attribute postulates, design theories, and good design practices from DSR projects. We further present a dual-use of design knowledge framework that shows pathways and re(use) of knowledge in DSR. We think that this typology is a useful theoretical contribution that can benefit both academics and practitioners conducting DSR projects.

Mental disorders affect nearly one billion people globally, 94% of whom are undiagnosed and untreated due to an acute shortage of trained clinicians. In response to this crisis, this study introduces mental disorder scan (MDscan), a novel artifact for screening ten mental disorders using data from the SCL-90-R mental disorder screening instrument, an explainable artificial intelligence approach, and our own ShapRadiation algorithm. MDscan converts 90 mental health indicators for each patient into an easily interpretable diagnostic image for mental disorders, similar to radiological images, and explains which indicators contributed to that prediction, increasing clinicians' ability to screen more patients in less time. A field evaluation with clinical data shows that MDscan has high classification accuracy, with average F1 scores between 0.77 and 0.94, compared against prerecorded ground truth. Furthermore, unlike traditional black-box models, MDscan's transparency and explainability can help enhance trust in artificial intelligence (AI) applications for clinical use.

PurposeThe purpose of this research is to evaluate the extent to which credibility of news sources and fact-checkers individually and jointly influence online users' beliefs and intended behaviors regarding online misinformation. The broader goal is to understand why fact-checking seems to have inconsistent effects on the beliefs and behavioral intentions about disinformation. 10;Design/methodology/approachAn online experiment was conducted in a public health (COVID-19) context with 429 validated participants to test three hypotheses linking the main and interaction effects of two independent variables (news source credibility and fact-checker credibility) on three dependent variables (users' believability, reading intention and sharing intention of online news claims). The data was analyzed using multilevel (fixed effects) models controlling for individual differences, claim differences and order effects.FindingsThe author observed a nuanced pattern of effects; news source credibility had a positive main effect on believability but negative effects on reading and sharing intention; fact-checking credibility had a positive main effect on believability, but no effects on reading or sharing intentions, but negatively moderated the effects of source credibility on all three dependent variables.Originality/valueThis paper introduces, conceptualizes and tests whether a more credible fact-checker shapes the beliefs and intentions about online misinformation differently from less credible fact-checkers, especially when examined concurrently with similar effects of the original sources of misinformation claims. Additionally, it suggests that, on average, users have a low perception of credibility for fact-checkers (even reputed ones), which may explain why fact-checking is often ineffective in shaping the beliefs and intended behaviors.

Research indicates that fact-checking has inconsistent effects on our beliefs and behavioral intentions about disinformation. But would it help if we source news from highly credible source and/or fact-check them using highly credible fact-checkers? This study explores this question by postulating the direct and interaction effects of news source credibility and fact-checker credibility on online users’ believability perceptions, reading intention, and sharing intention. These hypotheses are tested using an online experiment in a public health (COVID-19) context. Multi-level analysis of within-subject data suggest a nuanced pattern of effects, in which news source credibility has a positive main effect on believability but negative effects on reading and sharing intention. Fact-checking credibility has a positive main effect on believability, but no effects on reading or sharing intentions, but interestingly, negatively moderates the effects of source credibility on all three dependent variables. The implications of these findings for fact-checking research and practice are discussed.

This study presents an artifact for qualitative coding of large volumes of text data using automated text mining techniques. Coding is a critical component of qualitative research, where the "gold standard" involves human coders manually assigning codes to text fragments based on their subjective judgment. However, human coding is not scalable to large corpora of text with millions of large documents. Our proposed method extends the latest advancements in semantic text similarity using sentence transformers to automate qualitative coding of text for predefined constructs with known operationalizations using cosine similarity scores between individual sentences in the text documents and construct operationalizations. We illustrate our approach by coding corporate 10-K reports from US SEC filings for two organizational innovation processes: exploration and exploitation.

This study will examine the design of speech conversion techniques to create a personalized voice alert system to improve user compliance and reduce wandering among individuals with autistic spectrum disorder (ASD). Using a controlled experiment with ASD patients, we will evaluate our proposed design feature under different conditions of technological familiarity and disease severity.

The introduction of a new information technology (IT) into a workplace often engenders a wide range of responses among users. These responses encompass a variety of emotions, such as excitement, indifference, skepticism, and fear, and behaviors, such as user engagement, avoidance, and workarounds, that are often manifested concurrently in the same work environment. We present a taxonomy of these responses in the context of mandated IT use by classifying user responses as engaged, compliant, reluctant, or deviant. Using a coping theoretic lens, we offer seven propositions to describe the causal factors and processes that drive specific IT user responses and how such responses might change over time. A qualitative analysis of 47 interviews of 42 physicians at a large community hospital over an 8-year period provides support for our taxonomy and propositions. The study’s key contributions are that it conceptualizes different types of user responses that may emerge in mandatory IT use settings, elaborates the key drivers of and processes underlying these diverse responses, and suggests how those behaviors may change over time with changes in the coping process.

This study presents a unified model of information technology (IT) continuance, by drawing upon three alternative influences that are presumed to shape continuance behavior: reasoned action, experiential response, and habitual response. Using a longitudinal survey of workplace IT continuance among insurance agents at a large insurance company in Taiwan, we demonstrate that the above influences are interdependent, complementary, and have crossover effects. This study advances IT continuance research by theorizing and validating a unifying model that extends prior perspectives and by explaining interrelationships between these perspectives.

Expert- and community-governance are becoming popular mechanisms to verify contributions in electronic repositories. This study examines the conditions under which employees make contributions to expert- and community-governed repositories. We conduct a qualitative study using data collected through interviews. Analysis of data using the grounded theory approach reveals that two groups of categories are salient for contribution behaviors: content-based, and need-based. Content-based categories concern the type of content provided for each contribution and involve whether contributions are suggestions/ideas and whether they have sensitive content. Need-based categories concern the different needs of employees and involve whether employees have a need for peer input, expert opinion, or recognition. Findings suggest that employees are more likely to contribute to community-governed repositories if contributions are suggestions/ideas and there is a need for peer input. On the other hand, employees are more likely to contribute to expert-governed repositories if contributions have sensitive content and there is a need for expert opinion and recognition. This study contributes to extant work by distinguishing between two types of governance mechanisms and identifying the salient categories in contributing to repositories that employ these mechanisms.

This study presents and empirically validates a model of end-user migration from client-hosted computing to cloud computing. Synthesizing key findings from IT adoption and post-adoption research, switching research, and cloud computing studies, it builds an integrative framework of cloud migration using migration theory as a theoretical lens, and postulates interdependencies among these predictors. A longitudinal survey of Google Apps adoption among student subjects in South Korea validates our proposed model. This study contributes to our nascent body of knowledge on IT migration by drawing attention to this emerging phenomenon, demonstrating how migration research is different from IT adoption research, identifying salient factors that enable or hinder cloud migration, elaborating interdependencies between these different predictors, and bringing in migration theory as a referent theory to the information systems literature.

Governments are continually looking to save costs in providing services especially with the economic crunch. Studies have shown that there is some form of reluctance to transact with governments over the internet even when it is cost saving and efficient. This is especially true where no policies mandating the utilization of e-government transactional web services (EgovTWS) exist and situations where individuals may show some kind of apathy to anything “government”. Using the elaboration-likelihood model (ELM) and introducing the concept of government approbation, we propose an approach to persuade individuals to accept and utilize EgovTWS.

This paper applies and extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to understand website usage among visually impaired users. We propose two new constructs, web accessibility and vision impairment level, and suggest that these constructs moderate the effects of UTAUT constructs on behavioral intention and actual usage behavior of visually impaired users. We present a plan to empirically test our proposed hypotheses using a field survey of visually impaired users regarding their usage of a website that conforms to accessibility guidelines. This paper contributes to research by drawing attention to the disabled population – an underserved area of information systems research, by identifying relevant constructs that apply in this unique context, and by elucidating how these constructs influence their technology usage.

This study is to empirically test the effect of strategic alignment of information systems on firm performance in the healthcare industry. By analyzing the objectives of healthcare information systems appeared in letters to shareholders, we find that firms implemented information systems with multiple objectives have higher performance than firms implemented information systems with a single objective. We also find that firms have mainly implemented information systems for the objectives of efficiency and satisfaction, rather than for internal or external growth objectives. The results suggest that heath care firms can exploit benefits from information systems with strategic alignment, especially throughout efficiency and satisfaction objectives.

Despite the benefits of using an IT project management methodology, only a handful of organizations are actually able to make their staff use such methodologies. Hence, organizational managers must identify and apply user influence tactics (UIT), to ensure adequate and appropriate use of these methodologies within their organizations. In order to understand the complex nature of UITs and their effects on employees, we use needs theory and develop and test a conceptual model based on a pre-test sample of 65 participants to address the following issues: 1) the need for an abstract taxonomy of UITs to group together similar tactics so as to reduce complexity as well as increase generalizability and results comparability, and 2) the need to examine innate desires of the employee, since the likelihood that a specific UIT will be effective in motivating an individual to use a methodology depends upon the characteristics of the potential adopter as well as that of the methodology.

Information technology (IT) usage within organisations is a multi-level phenomenon that is influenced by individual-level and organisational-level variables. Yet, current theories, such as the unified theory of acceptance and use of technology, describe IT usage as solely an individual-level phenomenon. This article postulates a model of organisational IT usage that integrates salient organisational-level variables such as user training, top management support and technical support within an individual-level model to postulate a multi-level model of IT usage. The multi-level model was then empirically validated using multi-level data collected from 128 end users and 26 managers in 26 firms in China regarding their use of enterprise resource planning systems and analysed using the multi-level structural equation modelling (MSEM) technique. We demonstrate the utility of MSEM analysis of multi-level data relative to the more common structural equation modelling analysis of single-level data and show how single-level data can be aggregated to approximate multi-level analysis when multi-level data collection is not possible. We hope that this article will motivate future scholars to employ multi-level data and multi-level analysis for understanding organisational phenomena that are truly multi-level in nature.