Information Technology Innovativeness and Data-Breach Risk: A Longitudinal Study

2023 | Journal of Management Information Systems | Citations: 0

Authors: Wang, Qian; Ngai, Eric W. T.; Pienta, Daniel; Thatcher, Jason Bennett

Abstract: The adoption of new Information Technology (IT) innovations has led to increased ... Expand

Abstract: The adoption of new Information Technology (IT) innovations has led to increased uncertainty among employees, a greater demand for security measures, and more entry points for cyber-attacks, which all increase the risk of data breaches for firms. Despite the prevalence of discussions around this issue, there has been a lack of empirical research examining the data breach risk associated with IT innovations. To address this gap, we have developed arguments based on an organizational learning theoretical framework that explains how IT innovativeness can exacerbate data breach risk. Through our analysis of a sample of data breaches that occurred between 2013 and 2021, we have discovered that there is a positive association between firm IT innovativeness and the risk of data breaches. We also find that the effects of IT innovativeness can vary under certain conditions. For example, we find that the positive relationship between IT innovativeness and data breach risk is mitigated when managers possess IT expertise or when firms have established extensive board connections with cybersecurity managers. Moreover, we find that the relationship between IT innovativeness and data breach risk is amplified in complex environments but not in dynamic or munificent ones. This study takes the lead in advancing the theoretical understanding and empirical validation of security-related risks associated with IT innovations. Moreover, our findings serve as a timely reminder for research and practice to carefully consider the implications of introducing novel technologies into firms and the potential dark side consequences that may arise. Additionally, this study underscores the importance of understanding organizational learning in risk assessment and change management, as well as the critical role of contextual factors in moderating the unintended security-related consequences linked to IT innovations. Collapse

Topics: data breach digital innovation IT security data security organization learning

Methods: robustness check descriptive statistic propensity score method cluster analysis two sample t-test with equal variance

Theories: organizational learning theory

Show Me The Money, Sooner! How Faster Payments Boost Gig Workers' Efforts and Productivity

2023 | International Conference on Information Systems | Citations: 0

Authors: Wang, Shiyi; Tong, Jack; Jia, Nan

Abstract: Despite their astounding growth in recent years, online gig platforms face key ... Expand

Abstract: Despite their astounding growth in recent years, online gig platforms face key challenges to increase gig workers' working commitment. This study aims to examine the impact of a fundamental organizational design element payout frequency, which refers to the intervals at which workers access the funds they have earned. Drawing on expectancy theory, we argue that a higher payout frequency enhances both the quantity and quality of gig work. To investigate this, we analyze proprietary data from a quasi-natural experiment that involved an unexpected reduction in the payout cycle for gig workers in a specific geographic region. By employing propensity score matching and a difference-in-differences approach, we demonstrate that a shorter payout cycle led to an increase in the effort among the impacted gig workers and also resulted in improved work quality. These findings contribute to our understanding of effectively motivating and managing gig workers, ultimately influencing customer engagement on platforms. Collapse

Topics: platform worker mobile application chat room personality human resource management

Methods: experimental group longitudinal research difference in differences propensity score matching pilot experiment

Theories: expectancy theory of motivation

Social Media Use Purposes and Psychological Wellbeing in Times of Crises

2023 | HICSS | Citations: 0

Authors: Wang, Tawei (David); Deng, Xuefei

Abstract: This study investigates the effect of social media (SM) use purposes and user ch ... Expand

Abstract: This study investigates the effect of social media (SM) use purposes and user characteristics on individual psychological wellbeing (PWB) during the coronavirus pandemic (COVID-19). Informed by the uses and gratifications theory and PWB research, this study analyzed survey data collected from 282 SM users aged 18 through 59 from a minority-serving university in the United States in March-April 2020. Our quantitative data analysis showed that social media can be used to improve the quality of personal experiences during the COVID-19 crisis through three mechanisms—connectedness (i.e., social), engagement (i.e., collaborative), and entertainment (i.e., hedonic). However, the effect varied by gender, SM usage level, and individual concern about COVID-19 risk. The findings contribute to the literature and offer implications in technology use for enhancing public mental health during crises. Collapse

Topics: social media pandemic covid 19 social network information exchange

Methods: survey descriptive statistic factor analysis ordinary least square conceptual modelling

Theories: uses and gratifications theory

Augmented Reality in Sports Event Videos: A Qualitative Study on Viewer Experience

2023 | HICSS | Citations: 0

Authors: Wang, Tianjiao; Du, Zhao; Wang, Fang; Wang, Shan

Abstract: Augmented reality (AR) has been widely used in sports broadcasting. However, lit ... Expand

Abstract: Augmented reality (AR) has been widely used in sports broadcasting. However, little is known about viewer experience with AR in sports event videos. To identify key AR features as well as its advantages and drawbacks in sports event videos, this research conducted a qualitative study through a semi-structured interview with 30 participants. Content analysis on the interview transcript identified four salient features of AR in the sports event video context, i.e., informativeness, novelty, vividness, and telepresence. It also revealed three key advantages of AR to sports audiences, including game comprehension, enjoyment, and fan socialization, as well as two drawbacks, including distraction and inauthenticity. The qualitative study provides a theory-building process and results in a conceptual model, which, based on the net valence approach, postulates the relationships between AR features and viewers’ behavioral intentions through the mediation of perceived advantages and drawbacks. Collapse

Topics: video conference behavioral intention word of mouth user experience

Methods: qualitative interview conceptual modelling personal interview qualitative content analysis theory development

Enabling Privacy-Preserving Prediction for Length of Stay in ICU - A Multimodal Federated-Learning-based Approach

2023 | European Conference On Information Systems | Citations: 0

Authors: Wang, Tongnian; Guo, Yuanxiong; Choo, Kim-Kwang

Abstract: While the proliferation of data-driven machine learning approaches has resulted ... Expand

Abstract: While the proliferation of data-driven machine learning approaches has resulted in new opportunities for precision healthcare, there are a number of challenges associated with fully utilizing medical data, for example partly due to the heterogeneity of data modalities in electronic health records. Moreover, medical data often sits in data silos due to various regulatory, privacy, ethical, and legal considerations, which complicates efforts to fully utilize machine learning. Motivated by these challenges, we focus on clinical care-length of stay prediction and propose a Multimodal Federated Learning approach. The latter is designed to leverage both privacy-preserving federated learning and multimodal data to facilitate length of stay prediction. By applying this approach to a real-world medical dataset, we demonstrate the predictive power of our approach as well as how it can address the earlier discussed challenges. The findings also suggest the potential of the proposed multimodal federated learning approach for other similar healthcare settings. Collapse

Topics: privacy health information system healthcare data data governance artificial intelligence

Methods: longitudinal research federated learning time series analysis design science computational algorithm

Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care

2023 | Information Systems Frontiers | Citations: 2

Authors: Wang, Weisha; Chen, Long; Xiong, Mengran; Wang, Yichuan

Abstract: Artificial Intelligence (AI) technology is transforming the healthcare sector. H ... Expand

Abstract: Artificial Intelligence (AI) technology is transforming the healthcare sector. However, despite this, the associated ethical implications remain open to debate. This research investigates how signals of AI responsibility impact healthcare practitioners’ attitudes toward AI, satisfaction with AI, AI usage intentions, including the underlying mechanisms. Our research outlines autonomy, beneficence, explainability, justice, and non-maleficence as the five key signals of AI responsibility for healthcare practitioners. The findings reveal that these five signals significantly increase healthcare practitioners’ engagement, which subsequently leads to more favourable attitudes, greater satisfaction, and higher usage intentions with AI technology. Moreover, ‘techno-overload’ as a primary ‘techno-stressor’ moderates the mediating effect of engagement on the relationship between AI justice and behavioural and attitudinal outcomes. When healthcare practitioners perceive AI technology as adding extra workload, such techno-overload will undermine the importance of the justice signal and subsequently affect their attitudes, satisfaction, and usage intentions with AI technology. Collapse

Topics: artificial intelligence responsible artificial intelligence usage intention WeChat behavioral intention

Methods: survey mediation analysis partial least squares path modeling partial least squares regression PLS tool

Learning Personalized Privacy Preference From Public Data

2023 | International Conference on Information Systems | Citations: 0

Authors: Wang, Wen; Li, Beibei

Abstract: Understanding consumers' privacy preferences is crucial for firms and policymak ... Expand

Abstract: Understanding consumers' privacy preferences is crucial for firms and policymakers to establish trust and encourage innovation and competition. With the widespread use of digital technologies, individuals generate and share vast amounts of data about them selves in the public domain. Even without knowing a person's private information, the psychosocial traits revealed in public data can provide valuable insights into their pri vacy preferences. In this study, we aim to predict personalized privacy preferences us ing social media posts. Our prediction model shows that psychosocial traits such as per sonality, lifestyles, riskpreference, economic thinking, emotions, etc extracted from posts provide significantly greater predictive power than demographic characteristics. Fur thermore, we demonstrate the practical value and impact of our model for business and society through a simulation analysis. Our tool can help platforms and policymakers es timate the impact of privacy policies and prevent potential harms such as discrimination. Collapse

Topics: privacy social media Twitter shapley additive explanation information privacy concern

Methods: BERT machine learning experiment computational algorithm topic model

Theories: game theory

Exploring the effects of relationship quality and c-commerce behavior on firms' dynamic capability and c-commerce performance in the supply chain management context

2023 | Decision Support Systems | Citations: 0

Authors: Wang, Wei-Tsong; Lin, Ying-Lien; Chen, Ting-Jun

Abstract: Although previous studies indicate the critical role of collaborative commerce ( ... Expand

Abstract: Although previous studies indicate the critical role of collaborative commerce (c-commerce) adoption and dy­ namic capability in the supply chain process, they have not addressed the relationship between c-commerce behavior and dynamic capability. By adopting the commitment-trust theory and the dynamic capability view, this study empirically examines the effects of relationship quality, c-commerce behavior, and dynamic capability on c-commerce performance. Survey data collected from 257 professionals in various manufacturing industries were analyzed using the partial least squares technique. The findings suggest that c-commerce behavior and dynamic capability are positively associated with c-commerce performance. However, negative calculative commitment does not positively affect c-commerce behavior. Moreover, institution-based trust does not nega­ tively affect negative calculative commitment. This study is one of few studies investigating the relationships among the variables that contribute to c-commerce performance. The findings can serve as references for practitioners in supply chain practices. Collapse

Topics: supply chain management interorganizational relationship marketing management innovation management digital innovation

Methods: survey statistical hypothesis test descriptive statistic data transformation factor analysis

Theories: dynamic capabilities theory commitment-trust theory

Does regulation help? The impact of California‘s AB5 on Gig Workers

2023 | HICSS | Citations: 0

Authors: Wang, Xunyi; Lin, Yu-Wei; Han, Wencui

Abstract: There is a widespread debate over how gig workers should be classified. The pass ... Expand

Abstract: There is a widespread debate over how gig workers should be classified. The passage of California Assembly Bill 5 (AB5) - a landmark legislation that aims to correct the misclassification of gig workers, has significant implications for workers, platforms, regulators, and the economy. In this research, leveraging the passage of AB5 as a shock for a natural experiment, we empirically investigate the impact of AB5 on gig workers in California to provide insights. With the data collected from a leading online labor market that connects clients and gig workers, we applied a Difference in Difference approach, and we found that the monthly earnings of gig workers in California, compared to those in other states, have a significantly higher increase after AB5 was signed into law. This effect stems from both increased daily earnings and increased working days. We discuss the implications for policymakers and platforms. Collapse

Topics: platform worker covid 19 legal context online labor market platform work site

Methods: experimental group natural experiment propensity score method robustness check synthetic control method

Novel role-wise attention mechanism for predicting purchases made through chat-based online customer services

2023 | Decision Support Systems | Citations: 0

Authors: Wang, Yajing; Chen, Gang; Xu, Yunjie (Calvin); Lu, Xianghua

Abstract: Online chat has become a key channel for firms to provide pre-purchase customer ... Expand

Abstract: Online chat has become a key channel for firms to provide pre-purchase customer services and generate pur­ chases. Firms must deploy their service personnel based on the customers' purchase probability. Conversational contingency is indicative of purchase probability. However, the unstructured nature of textual data hinders the use of interaction information. We develop a novel role-wise attention mechanism to capture the conversational contingency among customers and service agents. Our results reveal that the role-wise attention mechanism is effective for measuring service interaction quality. When the mechanism is combined with a deep-learning model, it outperforms traditional self-attention mechanisms for predicting customer purchases. The area under the curve of the model is 0.85. Ablation studies indicate that external attention, a core element of the role-wise attention mechanism, substantially improves prediction performance by capturing the conversational contin­ gency between communicators. Collapse

Topics: customer service social media online chat Python service-support information system

Methods: machine learning BERT deep learning design artifact ablation analysis