Scholarly Works, Business Information Technology

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https://hdl.handle.net/10919/24337

Research articles, presentations, and other scholarship

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Words matter when gangs cyberbang: Predicting imminent urban violence from gang members’ social media posts
Fowler, Sherry L.; Stylianou, Antonis C.; Zhang, Dongsong; Lowry, Paul Benjamin; Mousavi, Reza; Reid, Shannon E. (2026)
The rise in violent crime across major U.S. cities, fueled mainly by gang members using social media to broadcast messages of loss and aggression, poses an urgent challenge. Although prior research has examined gang-affiliated social media content, there remains a crucial gap in identifying which posts serve as credible signals of impending violence. Addressing this gap is essential for enhancing community safety, improving resource allocation, and optimizing law enforcement strategies. This study introduces a novel research model grounded in a contextualized adaptation of signaling theory. The model identifies key indicators of credible signals, such as follower count, specific hashtags, and retweet counts, which correlate with gang-related aggression. Environmental factors, such as temperature, are also examined for their influence on violent crime escalation. Using this contextualized theory, we designed a machine learning model to predict violent crime counts, training it on a dataset of 143,700 gang-affiliated tweets and their accompanying text and metadata. This approach enables automated identification of credible social media signals related to gang violence. The findings contribute to theory and practice by offering new insights into social media credibility and its link to violent crime, and by demonstrating how such signals can be used for prediction. Furthermore, the predictive model provides law enforcement with advanced tools to anticipate crime and inform community-based prevention strategies and policy development.
Fostering Information Disclosure in Telemental Healthcare Settings: How Telehealth Can Mitigate the Deleterious Effects of Stigma
Raimi, Ryan; Lowry, Paul Benjamin; Straub, Detmar (2026)
Insufficient patient disclosure and persistent stigma undermine effective mental health care, a challenge magnified during the COVID-19 pandemic. Telehealth offers a promising avenue to reduce access barriers and improve equity, yet its effectiveness depends on patients’ willingness to disclose sensitive information online. This study develops a middle-range, contextually adapted version of the disclosure processes model (DPM) to explain and predict how stigma and technological features shape online self-disclosure in mental health settings. We conducted a randomized web-based experiment with 309 participants who viewed a video vignette depicting a consultation between a patient and a psychiatrist. The vignette manipulated diagnosis (ADHD vs. schizophrenia) and consultation mode (in-person vs. virtual). Results show that willingness to disclose increases with greater trust in technology, higher perceived social presence, and richer communication media. Initial disclosure goals align with differing levels of technological trust and self-disclosure. However, perceived stigma weakens these positive relationships, reducing patients’ readiness to share sensitive information. The research advances theory by extending the DPM into a context-specific, middle-range information systems framework that integrates stigma and media characteristics in online mental health care. Practically, the findings identify key communication features—such as social presence, richness, and trust in telehealth platforms—that can be calibrated to foster disclosure of stigmatized information. These insights inform the design and implementation of telehealth services that promote open communication and improve treatment engagement in mental health and other stigma-laden domains.
What do we need to know about the Chief Information Security Officer? A literature review and research agenda
Sahin, Zeynep; Vance, Anthony (Elsevier, 2025-01)
Since its establishment in the 1990s, the role of chief information security officer (CISO) has become critical to organizations in managing cybersecurity risks. However, despite widespread recognition of the importance of this role in industry, research about CISOs and the problems they face in protecting organizations is nascent. We review the academic and practitioner literature on CISOs to identify existing themes and highlight a range of challenges related to CISOs in which further research is needed, such as establishing legitimacy within C-suite executive teams, appropriate accountability for cybersecurity incidents, CISO turnover, and promoting security in the face of human factors, business realities, and budget constraints. We also propose a research agenda to address these challenges using potential theoretical lenses. In these ways, this study lays the groundwork for future research on CISOs and their essential role in ensuring the cybersecurity of organizations.
Breached and Denied: The Cost of Data Breaches on Individuals as Mortgage Application Denials
Pang, Min-Seok; Vance, Anthony (Association for Information Systems, 2024-06-07)
While a large body of information systems (IS) literature has investigated the antecedents and consequences of data breaches in organizations, we do not have a good understanding of whether a data breach has a material impact on individuals whose private information is compromised and how much damage it causes. We overcome empirical challenges in investigating the impact of data breaches on individual victims by utilizing a unique natural experimental setting that allows us to credibly identify treated and controlled populations—the breach of South Carolina (SC) taxpayer records in 2012. With residents in SC as the treatment group and those in Georgia and North Carolina as the control group, our difference-in-differences estimations find that after the breach at the SC Department of Revenue, there was a significant increase in denials to SC residents’ residential mortgage applications for refinance and home improvement. We also find that the adverse impact of the breach was more profound for Black and Hispanic residents. Our study provides significant theoretical and policy implications with respect to the harm and costs of a large-scale data breach.
"Complexity Is the Worst Enemy of Security": Studying Cybersecurity Through the Lens of Organizational Complexity
Schneier, Bruce; Vance, Anthony (Association for Information Systems, 2025-03)
Writing about computer systems twenty-five years ago, Schneier wrote that “the worst enemy of security is complexity” (Schneier, 1999), because complex systems are both easier to attack and harder to secure than simpler ones. In this essay, we provide an overview of Schneier’s complexity principle and provide our observations of how two articles in this issue, Liang et al. (2025) and Tanriverdi et al. (2025), employed this principle in their research. We also offer our ideas for why complexity and cybersecurity are especially amenable for study in the field of information systems and where future research can go from here.
The Fog of Warnings: How Non-Security-Related Notifications Diminish the Efficacy of Security Warnings
Vance, Anthony; Eargle, Dave; Kirwan, C. Brock; Anderson, Bonnie Brinton; Jenkins, Jeffrey L. (Association for Information Systems, 2025-12-01)
Users’ disregard of security warnings is a critical problem in cybersecurity. This problem worsens when people confuse security warnings with common, non-security-related notifications, which they learn to routinely disregard. We investigate this problem through the neurobiological phenomenon of generalization of habituation, where habituation to one stimulus transfers to another stimulus that shares similar characteristics. Generalization of habituation suggests that because of habituation to frequent notifications, people may also be deeply habituated to security warnings they have never seen before, leading to warning disregard. Furthermore, because generalization of habituation occurs unconsciously at the neurobiological level, this may occur even though a person can consciously distinguish security warnings from notifications. We address this problem through three experiments—two in the field and one using functional magnetic resonance imaging. These experiments demonstrate how generalization of habituation occurs and can be mitigated by differentiating warnings from notifications in terms of their visual appearance or mode of interaction. These findings provide guidance to software developers for designing warnings that resist generalization of habituation and promote greater warning adherence.
Analyzing Social Vulnerability as a Proxy for Damage in Hurricane Michael
Arnette, Andrew; Zobel, Christopher W. (2026)
Working with the American Red Cross and using the FEMA Individuals and Households Program (IHP) valid registrations data set, we explore the relationship between socially vulnerable populations and the damage that occurred to their homes from Hurricane Michael. We then consider how this information can be used to more effectively target response efforts in these areas, including estimates for sheltering needs and where to focus initial on-the-ground damage assessments.
Signals that Matter: Gender, Content Framing, and Engagement in Online Communities of Practice
Gunarathne, Priyanga; Aljafari, Ruba; Ayabakan, Sezgin; Khader, Samer; Kulaç, İbrahim (2025-12-31)
As social media evolves into a core venue for professional interaction and knowledge exchange, understanding what drives engagement in online communities of practice remains a crucial area of inquiry. This study examines how gender and content framing jointly influence peer engagement in an online physician community (i.e., pathology community) on X. Analyzing 2,467 patient case tweets using matched sampling and negative binomial random-effects models, we find that content authored by female physicians receives more favorable engagement than content authored by male physicians. Engagement also varies by content framing: diagnostic challenges and curbside consultations elicit more replies than mere shares. Female physicians benefit more from sharing less cognitively demanding content, while gender differences diminish for complex cases. These findings highlight the decision dilemma professionals face when framing knowledge contributions and reveal how identity signals and content strategies jointly shape engagement in online communities of practice, offering both theoretical and practical insights.
Examining User Access Options for eGovernment Services During a Crisis from a Digital Inequality Perspective
Zobel, Christopher W.; Pamukcu, Duygu (2023-01-01)
City governments incorporate ICTs into government services to improve citizen participation and access to those services. Too much dependence on technology, however, can lead to concerns about creating a digital divide between different groups of citizens. The potential for digital inequality is a critical issue that can be exacerbated by insufficient attention being paid to vulnerabilities across communities. Given that socio-economically vulnerable populations are the ones who need government services the most, especially during disaster events, it is critical to investigate the extent to which digital inequality is an issue for technology-based government services. With this in mind, this paper analyzes the use of different technology-enabled access options for a representative eGovernment service system, the New York City 311 service system, in the early stages of the COVID-19 pandemic. Two sets of socio-economically distinct locations in New York City are compared, using average income as a proxy for vulnerability, to draw conclusions about potential inequalities in such a system during a crisis.
Proposing a Design Theory for a Human-Learning-Guided Virtual Negotiator for Online Trading Platforms
Cao, Mukun; Wang, G. Alan; Lowry, Paul Benjamin (ACM, 2025-12)
Negotiation-based transactional mechanisms provide flexibility and economic benefits to both sellers and buyers on online trading platforms. Although automated negotiation is a highly desired feature among online platform providers, the complexity and uncertainty of human behavior in human-to-computer (HtC) negotiation make it a problematic solution. This study proposes a design theory for a human-learning guided virtual negotiator (HLG-VN) framework that emulates human learning using multiple machine learning (ML) techniques that collectively mimic four human learning activities: didactic, feedback, observational, and analogical learning. Following the design science research methodology, we built an instantiation system for the proposed design theory and empirically tested it using experiments based on HtC negotiations. The empirical results show that our system outperformed the benchmark system in terms of both economic and some key social-psychological outcomes. Furthermore, the experiment results confirm the effectiveness and correctness of the HLG-VN framework. The proposed design theory provides a theoretical base for using ML techniques to build a virtual negotiator agent for an automated negotiation system. Thus, various agents could be designed and developed based on the theory for online trading platforms, thus improving negotiation efficiency and reducing transaction costs.
A cost-benefit analysis for use of large SNP panels and high throughput typing for forensic investigative genetic genealogy
Budowle, Bruce; Arnette, Andrew; Sajantila, Antti (Springer, 2023-09)
Next-generation sequencing (NGS), also known as massively sequencing, enables large dense SNP panel analyses which generate the genetic component of forensic investigative genetic genealogy (FIGG). While the costs of implementing large SNP panel analyses into the laboratory system may seem high and daunting, the benefits of the technology may more than justify the investment. To determine if an infrastructural investment in public laboratories and using large SNP panel analyses would reap substantial benefits to society, a cost–benefit analysis (CBA) was performed. This CBA applied the logic that an increase of DNA profile uploads to a DNA database due to a sheer increase in number of markers and a greater sensitivity of detection afforded with NGS and a higher hit/association rate due to large SNP/kinship resolution and genealogy will increase investigative leads, will be more effective for identifying recidivists which in turn reduces future victims of crime, and will bring greater safety and security to communities. Analyses were performed for worst case/best case scenarios as well as by simulation sampling the range spaces with multiple input values simultaneously to generate best estimate summary statistics. This study shows that the benefits, both tangible and intangible, over the lifetime of an advanced database system would be huge and can be projected to be for less than $1 billion per year (over a 10-year period) investment can reap on average > $4.8 billion in tangible and intangible cost-benefits per year. More importantly, on average > 50,000 individuals need not become victims if FIGG were employed, assuming investigative associations generated were acted upon. The benefit to society is immense making the laboratory investment a nominal cost. The benefits likely are underestimated herein. There is latitude in the estimated costs, and even if they were doubled or tripled, there would still be substantial benefits gained with a FIGG-based approach. While the data used in this CBA are US centric (primarily because data were readily accessible), the model is generalizable and could be used by other jurisdictions to perform relevant and representative CBAs.
Use of machine learning algorithms to predict optimal hospital length of stay
Abdelhad, Ola; Khansa, Lara Z.; Eminaga, Okyaz; Bagci, Muhammet Isa; Essawi, Adam; Jimoh, Habeeb; Boker, Almuatazbellah; Eldardiry, Hoda (2025-12-09)
Problem: Hospitals often struggle to allocate beds, equipment, and staff efficiently, leading to unnecessary complications. Predicting a patient’s length of stay (LOS) early helps hospitals plan treatment, staffing, and bed availability more effectively. Both extremes of LOS carry risks: discharging too early can result in inadequate care and higher readmissions, while prolonged stays waste resources and increase costs. Solution: Optimizing LOS improves patient outcomes using machine learning, enhances operational efficiency, and reduces overall spending.
Transforming healthcare with artificial intelligence: an integrated approach to patent analysis and strategic commercialization
Aikins, Hudson; Khansa, Lara Z. (2025-11-23)
This paper examines how artificial intelligence (AI) is reshaping healthcare innovation, with a focus on patent trends before and after COVID-19. Key areas include diagnostics, clinical applications, telehealth, and public health surveillance. While diagnostic tools remained central, the pandemic accelerated adoption of telehealth and AI in public health. Post-COVID, hybrid healthcare models and ethical AI governance have gained prominence. The study also highlights the use of large language models like ChatGPT in patent analysis and introduces a new LangGraph-based software architecture to optimize multi-agent AI workflows. Findings offer strategic insights for policymakers, investors, and healthcare leaders navigating AI’s future impact.
Importance of Technology–Job Fit on the Sustained Use of E-Government: Finding the Perfect Fit
Belkhiria, Fares; Thongpapanl, Narongsak; Ashraf, Abdul Rehman; Ferreira, Caitlin; Venkatesh, Viswanath (IGI Global, 2025-11-26)
Success in e-government technology implementation offers many benefits for both governments and citizens; however, the real-world implementation showcases a high failure rate. Such failures are mainly attributed to a lack of use and adequate management expertise in implementation. The authors argue that this deficiency is because of a lack of fit between the technology and the jobs of employees in public institutions. Drawing on foundational work in person-job and person-organization fit, the authors conceptualize technology-job fit (TJF) as a two-dimensional construct: task relevance and workstyle compatibility. Using data from Thai government employees across core administrative functions, they test a moderated model and uncover a quality-fit paradox: high system and information quality only translate into positive outcomes when TJF is perceived as high. When TJF is low, even well-designed systems fail to generate enthusiasm or sustained use. These findings reframe e-government implementation challenges as issues of misalignment rather than technical inadequacy.
How good are large language models at product risk assessment?
Collier, Zachary A.; Gruss, Richard J.; Abrahams, Alan S. (Wiley, 2025-04-01)
Product safety professionals must assess the risks to consumers associated with the foreseeable uses and misuses of products. In this study, we investigate the utility of generative artificial intelligence (AI), specifically large language models (LLMs) such as ChatGPT, across a number of tasks involved in the product risk assessment process. For a set of six consumer products, prompts were developed related to failure mode identification, the construction and population of a failure mode and effects analysis (FMEA) table, risk mitigation identification, and guidance to product designers, users, and regulators. These prompts were input into ChatGPT and the outputs were recorded. A survey was administered to product safety professionals to ascertain the quality of the outputs. We found that ChatGPT generally performed better at divergent thinking tasks such as brainstorming potential failure modes and risk mitigations. However, there were errors and inconsistencies in some of the results, and the guidance provided was perceived as overly generic, occasionally outlandish, and not reflective of the depth of knowledge held by a subject matter expert. When tested against a sample of other LLMs, similar patterns in strengths and weaknesses were demonstrated. Despite these challenges, a role for LLMs may still exist in product risk assessment to assist in ideation, while experts may shift their focus to critical review of AI-generated content.
Workplace nomophobia: a systematic literature review
Hessari, Hassan; Daneshmandi, Fatemeh; Busch, Peter; Smith, Stephen (Springer, 2024-08-01)
Nomophobia, or the fear of being without one's smartphone, is a growing concern in workplaces around the world. This phenomenon affects both employee well-being and organizational productivity. Despite its prevalence, there is a notable lack of systematic reviews investigating nomophobia in workplace, as well as the factors that intensify or inhibit it in workplace settings. This paper bridges this gap by conducting a systematic literature review of workplace nomophobia, drawing insights from 15,009 observations across 36 studies. Our review uncovers the widespread nature of nomophobia, its antecedents, symptoms, and the significant consequences it has in professional settings, such as increased anxiety, work stress, and frequent work interruptions. Demographic factors like age, gender, and education level influence the severity of nomophobia, with younger and more educated employees being especially vulnerable. The findings highlight the urgent need for interventions and organizational strategies to mitigate the negative effects of nomophobia and foster healthier digital habits at work. This study enriches the theoretical understanding of nomophobia and offers practical insights for future research and organizational practice.
Extending the unified theory of acceptance and use of technology for sustainable technologies context
Neves, Catarina; Oliveira, Tiago; Cruz-Jesus, Frederico; Venkatesh, Viswanath (Elsevier, 2025-02-01)
Following the United Nations' Sustainable Development Goals (SDG) recommendations, sustainable technologies are increasingly being introduced as a step toward more sustainable behaviors and efforts against environmental problems. However, a holistic investigation of the main factors influencing its adoption and use is necessary. To this end, this work aims to explain the determinants of sustainable technologies used by consumers. Specifically, we develop a contextualized model that extends the unified theory of acceptance and use of technology 2 (UTAUT2) by leveraging a mixed-methods approach and, therefore, conducting three studies. The so-developed contextualized model of consumer adoption of sustainable technology is tested using 2003 observations from five European countries. Such a sample also provides the opportunity for a cross-country comparison. We found that habit, environmental knowledge, information provision, and innovativeness were significant predictors of sustainable technology use. Additionally, the cross-country comparison revealed that although conclusions are generally consistent across the countries, they differ in some effects, like social influence and price value. Taken together, we thus provide insights into the consumers' motivations to adopt and use sustainable technologies.
Flood-induced mobility in rural and urban coastal jurisdictions: a homeowner's perspective
Bukvic, Anamaria; Zobel, Christopher W. (Springer, 2024-11-01)
Coastal flooding often exceeds homeowners' capacity to cope with repetitive damages and profoundly disrupts their livelihoods. Permanent relocation has been proposed as a solution for some coastal areas experiencing recurrent flooding and anticipating acceleration of impacts. However, it is unclear if homeowners living in such areas would support this strategy, where they would choose to go, and why. This study evaluates the willingness to relocate and the reasoning behind it among rural and urban homeowners residing in coastal high-risk areas. The rural versus urban comparison explores how attitudes toward relocation differ between these settings with distinct sociodemographic, economic, and cultural profiles. A mail survey administered on the Eastern Shore, Maryland, and in the Hampton Roads metropolitan area, Virginia, measured how willingness to relocate differs across the socioeconomic spectrum, prior flood exposure, concerns with flood impacts, and preferences for relocation destination. The survey responses were analyzed using descriptive and inferential statistics. The results show that more than one-third of respondents would consider relocating. The willingness to relocate was marginally influenced by socioeconomic factors and flood experiences and instead was significantly correlated with the risk of disastrous flooding, inadequate insurance compensation, and worsening crime. However, data show a clear shift in relocation support and the distance of the preferred destination from minor to significant flooding. Rural respondents are slightly less likely to relocate than urban ones. Descriptive statistics indicate nuanced differences in flood experiences, reasons to relocate, and preferences for a new destination between rural and urban populations.
Proposing a design theory for a human-learning-guided virtual negotiator for online trading platforms
Cao, Mukun; Wang, G. Alan; Lowry, Paul Benjamin (2025-09)
Negotiation-based transactional mechanisms provide flexibility and economic benefits to both sellers and buyers on online trading platforms. Although automated negotiation is a highly desired feature among online platform providers, the complexity and un- certainty of human behavior in human-to-computer (HtC) negotiation make it a problematic solution. This study proposes a design theory for a human-learning guided virtual negotiator (HLG-VN) framework that emulates human learning using multiple machine learning (ML) techniques that collectively mimic four human learning activities: didactic, feedback, observational, and analogical learning. Fol- lowing the design science research methodology, we built an instantiation system for the proposed design theory and empirically tested it using experiments based on HtC negotiations. The empirical results show that our system outperformed the benchmark system in terms of both economic and some key social-psychological outcomes. Furthermore, the experiment results confirm the effectiveness and cor- rectness of the HLG-VN framework. The proposed design theory provides a theoretical base for using ML techniques to build a virtual negotiator agent for an automated negotiation system. Thus, various agents could be designed and developed based on the theory for online trading platforms, thus improving negotiation efficiency and reducing transaction costs.
Are Negative Reviews Always Helpful? Effects of Emotional Arousal on the Usefulness of Negative Reviews and How Merchants Should Respond
Du, Zhanhe; Chai, Hu; Lu, Haijiao; Li, Lixu; Lowry, Paul Benjamin (2025-09)
In the rapidly evolving digital marketplace, understanding consumer behavior in response to negative reviews is of considerable practical and academic importance. Negative reviews influence consumer decision-making, but not all are accurate or helpful. Moreover, the interplay between emotional arousal and the perceived utility of negative reviews, particularly for high-risk purchases and costly products, remains largely unexplored. Addressing this gap, our study leverages attribution theory to scrutinize the curvilinear relationship between emotional arousal and the usefulness of negative reviews in high-risk purchases. We employed a robust mixed-methods design comprising three different studies aimed to triangulate further and understand these phenomena: Study 1 involved an objective content analysis of 6,147 negative reviews from prominent Chinese e-commerce platforms; Study 2 was a controlled scenario-based experiment with 99 consumers, aimed to test the underlying causal relationships; finally, Study 3 involved in-depth qualitative interviews and coding with 60 consumers. Our findings demonstrate an inverted U-shaped relationship between emotional arousal and the perceived utility of negative reviews in high-risk purchase scenarios. This relationship is mediated by prosocial motives, emphasizing that emotionally aroused consumers will likely find negative reviews helpful only up to a certain point, after which the utility diminishes. Notably, positive merchant responses serve as a crucial moderating variable in the emotional arousal-negative review usefulness relationship and the connection between prosocial motives and review usefulness. Our study advances the online review literature by offering nuanced insights into the complex relationship between emotional arousal and the utility of negative reviews in high-risk purchase scenarios. Our results have immediate implications for e-commerce platforms, enabling them to convert the challenge posed by negative reviews into actionable opportunities through informed response strategies.

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