Security is Local: The Influence of the Immediate Workgroup on Information Security

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Wang, Dawei; Durcikova, Alexandra; Dennis, Alan

Abstract: Information security is a multilevel phenomenon with employee security decision ... Expand

Abstract: Information security is a multilevel phenomenon with employee security decisions being influenced by macrolevel factors (e.g., organizational policies), mesolevel factors (e.g., one's immediate workgroup-IW), and microlevel factors (e.g., individual personalities). We argue that an employee's local IW (i.e., immediate supervisor and coworkers) has a strong effect on security. This paper focuses on the effects of these mesolevel factors in the presence of macro-and microlevel factors. Drawing on the social structure and social learning framework as well as workgroup research, we hypothesize that the security behavior of an employee's IW supervisor and coworkers moderated by the nature of these relationships influences information security decisions. Our research, based on a sample of 217 full-time employees, reveals that the IW significantly affects security decisions, over and above the micro-and macrolevel factors. These effects are moderated by the nature of the relationship between employees and their IW supervisor (leader-member exchange) and coworkers (team-member exchange). A post hoc analysis shows that the mesolevel factors alone had the same explanatory power as the micro-and macrolevels combined. Our research suggests that future theory and research should include the IW and that organizations should share security responsibilities with line managers and help them understand their substantial impact on information security. Security training programs should ask employees about the behaviors of their IW supervisor and coworkers and, where needed, deliver anti-neutralization training to mitigate the effects of the IW's noncompliance behaviors.Alexandra Durcikova is an associate professor of MIS at the Price College of Business, University of Oklahoma. Her research focuses on knowledge management, knowledge repositories, and human compliance with security policy and characteristics of successful phishing attempts within the area of computer security, and health information systems. Collapse

Topics: data security IT security threat IT security password self efficacy

Methods: survey theory development descriptive statistic data transformation factor analysis

Theories: big five model social learning theory leader–member exchange theory

Paths to open government data reuse: A three-dimensional framework of information need, data and government preparation

2023 | Information & Management | Citations: 0

Authors: Wang, Fang; Zhang, Zhaoqi; Ma, Xin; Zhang, Yichen; Li, Xuguang; Zhang, Xiaofei

Abstract: With the increase of open government data (OGD) supply in many countries, promot ... Expand

Abstract: With the increase of open government data (OGD) supply in many countries, promoting its reuse becomes an important issue to a variety of stakeholders. Considering that most citizens have never used OGD, this study adopted a mixed methods design that includes an experiment and a survey to explore the factors that affect OGD reuse intention. The findings include: (1) user information need, significantly affected by user digital capability, triggers OGD reuse intention. (2) Perceived data value, affected by perceived data quality, information need, and government preparation, is the core influencing factor of OGD reuse intention and user satisfaction. (3) Gov­ ernment preparation has a significant effect on perceived data value, perceived data quality, and user satisfac­ tion. (4) Different from previous studies on other products, perceived data quality and user satisfaction have no direct significant effects on user reuse intention, revealing the dual attributes of OGD as technical products and monopolistic public goods. This study reveals the mechanism of OGD value co-creation by integrating factors related to data, user, and the government, providing novel empirical evidence and theoretical insights to the literature of Customer Satisfaction Index, Technology Acceptance Model, and data reuse. Collapse

Topics: information technology capability data quality user satisfaction public good website

Methods: experiment survey mixed method qualitative coding data transformation

Theories: technology acceptance model

Effect of AI Decision Speed on User Adoption in Human-AI Collaboration: The Moderating Role of Historical Decision Quality

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Wang, Guoxin; Lu, Shouwang; Wang, Kanliang

Abstract: Artificial intelligence (AI) has been widely used in many products and services ... Expand

Abstract: Artificial intelligence (AI) has been widely used in many products and services and has become an important means to assist users in decision-making. In the context of human-AI collaboration, both the quality and speed of AI's decision-making are the two system features that users can readily identify. However, the existing research focuses on decision-making quality and pays little attention to the effect of AI's decision-making speed. Drawing from the theory of cue utilization, this research explored the effect of AI decision speed on user's adoption intention. The results of two experiments show that users have higher AI decision adoption intention at high AI decision speed than at low; the perceived intelligence and perceived risk in decision-making play a mediating role in the above effects. Additionally, historical decision quality moderates the impact of AI decision speed on users' adoption by weakening the above impact in the high-quality condition. The findings enrich the research on AI adoption and have some practical implications for AI service providers and developers. Collapse

Topics: artificial intelligence decision quality decision making user acceptance customer service

Methods: experiment parametric test one-way ANOVA mediation analysis survey

Attentive statement fraud detection: Distinguishing multimodal financial data with fine-grained attention

2023 | Decision Support Systems | Citations: 0

Authors: Wang, Gang; Ma, Jingling; Chen, Gang

Abstract: Financial statement fraud caused by listed companies directly jeopardizes the re ... Expand

Abstract: Financial statement fraud caused by listed companies directly jeopardizes the reliability the financial reporting process. Leveraging multimodal information for financial statement fraud detection (FSFD) has recently become of great interest to academic research and industrial applications. Unfortunately, the predictive ability of multi­ modal information in FSFD remains largely underexplored, particularly the fusion ambiguity embedded in and among multi-modalities. In this study, we propose a novel attention-based multimodal deep learning method, named RCMA, toward an accurate FSFD. RCMA synthesizes a fine-grained attention mechanism including three innovative attention modules, i.e., ratio-aware attention, chapter-aware attention, and modality-aware attention mechanism. The first two attention mechanisms help to liberate the extraordinary predictive power of the financial modality and the textual modality on FSFD, respectively. Moreover, the proposed modality-aware attention mechanism enables better coordination between the two modalities. Furthermore, to ensure effec­ tive learning on the attention-based multimodal embedding, we design a novel loss function named Focal and Consistency Loss, or FCL. It considers class-imbalance and modality-consistency simultaneously, to specialize the optimization of FSFD. The experimental results on the real-world dataset show that the proposed RCMA on FSFD task outperformed the state-of-the-art benchmarks. Furthermore, interpretation analysis visualizes the attention weights of different ratio groups, chapters, and modalities from RCMA, and illustrates how these interpretations influence stakeholders’ decision process for FSFD. Collapse

Topics: accounting fraud detection database system

Methods: machine learning deep learning financial report robustness check experiment

Can AI Chatbots with Anthropomorphic Attributes Enhance User Engagement in Emotional Support Settings? Investigating the Role of Conversational Styles and Avatar Type

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Wang, Guoxin

Abstract: Although AI-based chatbots have become increasingly prevalent in various busine ... Expand

Abstract: Although AI-based chatbots have become increasingly prevalent in various business practices and show promise in providing emotional support, there is still limited understanding of the mechanism that underlies the relationship between the anthropomorphic attributes of AI-based relational chatbots and user engagement. To address this gap, we draw on the social judgment theory and propose that emotional judgment mediates the relationship between conversational styles of chatbots (agentic-style and communal-style) and user engagement. We also examine the potential boundary effect of two types of AI avatars (humanlike vs. cartoonlike) on this relationship. To test our hypotheses, we plan to conduct two studies, which will include online and lab experiments. We aim to extend existing theories involving social judgment theory and human reactivity to AI-based chatbots in emotional support settings and identify how different types of anthropomorphic attributes of AI-based relational chatbots may jointly impact user behaviors. Collapse

Topics: artificial intelligence robotic user behavior

Methods: chatbot experiment survey laboratory experiment statistical hypothesis test

Passing the Torch: How Parental Privacy Concerns Affect Adolescent Self-Disclosure on Social Networking Sites

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Wang, Jingguo; Dong, Meichen; Yang, Zhiyong; Li, Yuan

Abstract: Drawing upon the literatures in information privacy, developmental psychology, ... Expand

Abstract: Drawing upon the literatures in information privacy, developmental psychology, and family science, this research investigates how parental online privacy concerns can be passed on to adolescents and affect their self-disclosure on social networking sites. We propose that parental privacy concerns decrease adolescents' self-disclosure both directly (i.e., compliance) and indirectly through adolescents ' privacy concerns (i.e., internalization) and that such effects are moderated by parent-child privacy dissonance, parental internet evaluative mediation, and adolescents' gender. To test the research model, we collected matched parent-child data from 726 families in China. The results show the indirect effect of parental privacy concerns on adolescents' self-disclosure via their influence on adolescents' privacy concerns. In addition, parent-child privacy dissonance weakens the effect of adolescents' privacy concerns on selfdisclosure. The extent to which parents employ internet evaluative mediation to guide adolescents' online activities reinforces the effect of parental privacy concerns on adolescents' privacy concerns. Statistical analyses further revealed that the mediating effect of adolescents' privacy concerns is weakened by parent-child privacy dissonance but strengthened by internet evaluative mediation. We also found that parental privacy concerns affect sons and daughters through different paths, especially when parents employ high internet evaluative mediation. Under high internet evaluative mediation, parental privacy concerns affect sons' self-disclosure primarily through an indirect path (via sons' privacy concerns), but influence daughters' self-disclosure both directly and indirectly via daughters' privacy concerns. We conclude by discussing theoretical contributions and practical implications. Collapse

Topics: information privacy concern internet technology privacy Snapchat Instagram

Methods: chi squared test survey statistical hypothesis test descriptive statistic self reported survey

Theories: privacy calculus theory

How the emoji use in apology messages influences customers’ responses in online service recoveries: The moderating role of communication style

2023 | International Journal of Information Management | Citations: 0

Authors: Wang, Kai-Yu; Chih, Wen-Hai; Honora, Andreawan

Abstract: This research investigates the impact of using emojis (i.e., the pleading-face e ... Expand

Abstract: This research investigates the impact of using emojis (i.e., the pleading-face emoji) on customer forgiveness in relation to handling complaints on social media. Specifically, this research proposes that perceived firm sincerity and perceived firm empathy play mediating roles in the relationship between emoji use and customer forgive­ ness. In addition, the research identifies the moderating role of communication style in the proposed relation­ ship. Results of three experimental studies indicated that the presence of the emoji in online complaint handling leads to an increase in perceived firm sincerity, which in turn increases perceived firm empathy and, subse­ quently, leads to customer forgiveness. The serial mediation effects (use of emojis → perceived firm sincerity → perceived firm empathy → customer forgiveness) were moderated by the service provider’s communication style. Specifically, the serial mediation effect occurs when an informal communication style, but not a formal communication style, is used. Furthermore, no difference exists in the research findings across different service types (hedonic vs. utilitarian). Collapse

Topics: mobile application social media customer service ride sharing platform mobile payment

Methods: business process modeling survey chi squared test experimental design survey design

How Does User Engagement Support Content Moderation? A Deep Learning-based Comparative Study

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Wang, Kanlun; Fu, Zhe; Zhou, Lina; Zhang, Dongsong

Abstract: Content moderation is a common intervention strategy for reviewing user-generat ... Expand

Abstract: Content moderation is a common intervention strategy for reviewing user-generated content on social media platforms. Engaging users in content moderation is promising for making ethical and fair moderation decisions. A few studies that have considered user engagement in content moderation have primarily focused on classifying user-generated comments, rather than leveraging the information of user engagement to make a moderation decision on user-generated posts. Moreover, how to extract information from user engagement to enhance content moderation remains unclear. To address the above-mentioned limitations, this study proposes a framework for user engagement-enhanced moderation of user-generated posts. Specifically, it incorporates the credibility and stance of user-generated content into graph learning. Our empirical evaluation shows that the models based on our proposed framework outperform the stateof-the-art deep learning models in making moderation decisions for user-generated posts. The findings of this study have implications for augmenting the moderation of social media content and for improving the safety and success of online communities. Collapse

Topics: social media Reddit application programming interface online community user-generated content

Methods: RoBERTa deep learning binary classification Student's t-test ablation analysis

Moving Consumers from Free to Fee in Platform-Based Markets: An Empirical Study of Multiplayer Online Battle Arena Games

2023 | Information Systems Research | Citations: 0

Authors: Wang, Le; Lowry, Paul Benjamin; Luo, Xin (Robert); Li, Han

Abstract: Companies in platform-based business markets have widely embraced freemium busin ... Expand

Abstract: Companies in platform-based business markets have widely embraced freemium business models where profit primarily depends on a minority of paying customers. However, the key challenge of these models is transitioning participants from free users to paying consumers. To encourage paid consumption, companies often rely on product differentiation such as providing consumers who pay for products or services with enhanced features. However, limited research has addressed how such product differentiation may convert consumers from “free” to “fee.” Our research examines the multiplayer online battle arena (MOBA) game as a compelling example of a freemium platform-based business model. We contribute to the freemium literature by introducing three new MOBA-specific differentiations—character competency, character variety, and character-appearance differentiation. We also extend consumption values theory into a dual-path model to unveil the underlying mechanisms through which product differentiation influences in-game purchase. We empirically validate our dual-path model using data from a two-wave longitudinal experiment and three cross-sectional experiments. Our findings support opposing mediating paths for product differentiation in character competency and variety and indicate that these two types of differentiation can indeed undermine perceived game fairness. Conversely, character-appearance differentiation exerts only a positive influence on players’ purchasing of in-game items. Consequently, the findings of this study have important potential implications for platform-based companies leveraging freemium business models that seek to increase their share of paying customers.History: Eric Zheng, Senior Editor; Robert Gregory, Associate Editor.Funding: This work was supported by the National Natural Science Foundation of China [Grants 71901172, 72171189, and 72132007], the China Postdoctoral Science Foundation [Grant 2020M673434], and the Social Science Foundation of Shaanxi Province [Grant 2021D002].Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1127. Collapse

Topics: product differentiation free to play game social identity business model electronic mail

Methods: experiment survey experimental group longitudinal experiment longitudinal research

The role of rating feedback and its implications for solver submission behavior in crowdsourcing contests

2023 | Information & Management | Citations: 0

Authors: Wang, Mengmeng; Cui, Xiaocong

Abstract: In crowdsourcing contests, seekers face a decision about how to strategically pr ... Expand

Abstract: In crowdsourcing contests, seekers face a decision about how to strategically provide feedback to solvers, thereby impacting solvers’ submission behavior. Despite considerable interest in seeker feedback, extant literature lacks a thorough understanding of it. This study, in particular, examines how rating feedback can encourage the sub­ mission behavior of solvers from three aspects: the level of rating, the dispersion of rating, and the first rating timing. Based on a sample of 37,881 unique observations from 99design.com, empirical analysis indicates that the level of rating has a curvilinear effect on a solver’s submission behavior, suggesting that seekers can benefit the most from a moderate level of ratings in crowdsourcing contests. This study further finds that solvers who receive a large dispersion of ratings are more likely to contribute submissions in a contest. Finally, results show that providing a lower position of first rating is positively related to submission behavior. These results contribute some implications in both theory and practice. Collapse

Topics: crowdsourcing website

Methods: descriptive statistic cross sectional research robustness check likelihood-ratio test theory development

Theories: lemon market theory