A Linguistic Cloud-Based Consensus Framework with Three Behavior Classifications Under Trust-Interest Relations

2023 | Group Decision and Negotiation | Citations: 0

Authors: Liu, Weiqiao; Zhu, Jianjun; Liu, Peide; Wang, Peng; Song, Wen

Abstract: In the consensus reaching process (CRP), experts are not infinitely compromising ... Expand

Abstract: In the consensus reaching process (CRP), experts are not infinitely compromising when adjusting opinions, and the “compromise limit” is used to reflect the limited compromise behaviors in the process of expert opinion adjustment. In complex and multiple social relations, experts tend to exhibit different behaviors according to their compromise limits. This paper aims to develop a novel CRP framework to promote a consensus that categorizes and manages experts based on their compromise limits. Firstly, the trust-interest network is defined to represent the multiple relations among experts, and the expert weights are calculated by considering the impact of interest manipulation on trust relations. Secondly, a novel cloud model-based minimum cost consensus model is established, which considers the mutual acceptance between the experts and the group, as well as the changes in the ranges of experts’ hesitation and the collective acceptance. Thirdly, three behavior classifications are defined based on individual compromise limits and group acceptance ranges: cooperative behavior, hesitating non-cooperative behavior, and strong non-cooperative behavior, and a CRP optimization model is constructed to manage the three behaviors. Finally, a numerical example is given to illustrate the validity and superiority of the proposed model. Collapse

Topics: cloud computing social network decision making

Methods: optimization model cross sectional research group interview computational algorithm social network analysis

Theories: optimization theory

Co-creation, Failure Learning, and Relaunch Success: Evidence from Online Crowdfunding Market

2023 | International Conference on Information Systems | Citations: 0

Authors: Liu, Xiaochen; JIN, Yu; Fang, Yulin; Ye, Qiang

Abstract: With intense competition and relatively inexperienced founders, the crowdfundin ... Expand

Abstract: With intense competition and relatively inexperienced founders, the crowdfunding market has reported high failure rates. However, the IT components of the crowdfunding market provide entrepreneurs with more opportunities for experimentation and trial, leading to a new phenomenon of post-failure relaunches. Research into campaign relaunch success is urgently needed but under-researched. By combining failure learning theory with a collective perspective, the present study examines how investors' cocreation, in terms of advocacy and feedback, can benefit crowdfunding relaunch success directly or indirectly (by motivating founders' failure learning). The study tested the proposed mediation model with 1,902 failure-relaunched Kickstarter campaigns, with most hypotheses supported. Furthermore, the study explores the role of the time interval between crowdfunding relaunch and prior release. The findings indicate that an increased time interval enhances the positive effects of founders' learning efforts on relaunch success while attenuating the potential positive effects of investors' advocacy, implying a tradeoff in timing decisions. Collapse

Topics: crowdsourcing crowdfunding participatory design logistics management marketing management

Methods: design methodology regression analysis method computational algorithm logarithmic transformation theory development

Theories: learning theory

The Negative Spillover Effect of Electronic Prescribing for Controlled Substances (EPCS) on Opioid Epidemic

2023 | International Conference on Information Systems | Citations: 0

Authors: Liu, Xiaoyu; Kyung, Nakyung

Abstract: ... Expand

Abstract: Collapse

Topics: electronic prescription pandemic health information system

Methods: experimental group longitudinal research difference in differences quantitative observational study propensity score matching

Quantifying Learning and Competition among Crowdfunding Projects: Metrics and a Predictive Model

2023 | HICSS | Citations: 0

Authors: Liu, Xiexin; Rahmani Moghaddam, Maryam; Fan, Weiguo (Patrick)

Abstract: The performance of a crowdfunding project is highly situational-dependent. In th ... Expand

Abstract: The performance of a crowdfunding project is highly situational-dependent. In this study, we quantify the interactions between crowdfunding projects in order to understand how these interactions can help predict the performance of crowdfunding campaigns. Specifically, we utilize Natural Language Processing (NLP) techniques to create a semi-automated system to label the associated product for each crowdfunding campaign. We also propose three sets of metrics to measure how crowdfunding projects learn from and compete with each other. Finally, we propose a machine learning model and demonstrate that the proposed metrics and the proposed model outperform other combinations when predicting the performance of crowdfunding projects. Collapse

Topics: crowdsourcing crowdfunding research and development mobile system knowledge sharing

Methods: cluster analysis natural language processing machine learning support vector machine term frequency–inverse document frequency

A Group Consensus Model for Multiple Attributes Group Decision Making with Interval Belief Distribution and Interval Distributed Preference Relation

2023 | Group Decision and Negotiation | Citations: 0

Authors: Liu, Yin; Chang, Wenjun; Jia, Xuefei

Abstract: In multiple attributes group decision making (MAGDM), how to generate a consensu ... Expand

Abstract: In multiple attributes group decision making (MAGDM), how to generate a consensus-based solution that is satisfactory to most of decision makers, is always a critical research issue. This paper proposes a group consensus model for MAGDM based on interval belief distribution (IBD) and interval distributed preference relation (IDPR). The transformation function from IBD to IDPR is first designed to unify IBD and IDPR. Two principles are presented to guarantee the satisfaction of internal consistency before and after the transformation. Based on the unification of IBD and IDPR, a consensus reaching process is developed. In the process, the group consensus at the attribute, alternative and global levels based on IDPRs is first measured, and a feedback mechanism consisting of identification rules and suggestion rules is then designed to help accelerate the convergence of group consensus. With the developed group consensus model, a consensus-based solution to the MAGDM problems with IBDs and IDPRs is generated. Finally, the proposed model is applied to a sustainable supplier selection problem of automobile enterprise in Wuhu, Anhui Province, China to demonstrate its correctness and effectiveness. Collapse

Methods: computational algorithm optimization model

A deep multi-embedding model for mobile application recommendation

2023 | Decision Support Systems | Citations: 0

Authors: Liu, Yi-Hung; Chen, Yen-Liang; Chang, Po-Ya

Abstract: With the explosion in the number of mobile applications, it becomes difficult an ... Expand

Abstract: With the explosion in the number of mobile applications, it becomes difficult and time-consuming for users to find the most suitable and interesting application from the millions of applications that exist today. Therefore, mobile app recommendation becomes a top priority to help people solve this problem. In previous studies, there are two common methods to solve this problem, one is to apply matrix factorization to the rating data to predict the target user's ratings for other applications, and the other is to predict ratings based on the user's review text. In addition to combining the above two traditional methods, this study also combines deep learning methods to generate better models. For rating data, we use a deep matrix factorization method to obtain latent factor vectors of users and items from the rating matrix to improve the embedding of ratings. For review text, we improve the text representation by using a multi-embedding approach. Most importantly, our model also incorporates deep neural networks with nonlinear transformation capabilities to extract more representative abstract features. We conduct experiments on datasets collected from the real world. Extensive experimental results show that our proposed method has better performance than other existing methods. Collapse

Topics: mobile application mobile application market recommender system digital distribution mobile system

Methods: BERT deep learning experiment sensitivity analysis artificial neural network

Policy Change, Social and Cultural Capital and Medical Crowdfunding Use: A Quasi-Natural Experiment

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Liu, Yuanyuan; Fan, Rui; Ravichandran, T.

Abstract: This study investigates the impact of health policy, specifically the Individua ... Expand

Abstract: This study investigates the impact of health policy, specifically the Individual Mandate Repeal (IMR), on the use of medical crowdfunding. We employ a difference-in-differences research design and find that IMR has led to an increase in medical crowdfunding usage, with significant heterogeneity among users. Specifically, individuals with high cultural capital and bridging social capital, but low bonding social capital, are more likely to increase their use of medical crowdfunding. These results suggest that the sociotechnology for healthcare financing purpose (i.e., online medical crowdfunding) responds to the gap of the Affordable Care Act (i.e., IMR), and its usage is shaped by individual differences in cultural and social capital. The policy implication is that medical crowdfunding platform provides opportunities to take on the shortcomings of health policy, but the opportunity exploration or its usages are disparate across populations with varying levels of capital. Public health policymakers may develop policies fostering bridging social capital to expand the healthcare affordable opportunities for underserved populations without imposing taxes and mandate regulations. KeywordsMedical Crowdfunding Usage, individual mandate repeal, opportunity, forms of capital, cultural capital, bridging social capital, bonding social capital. Collapse

Topics: crowdsourcing crowdfunding social capital legal context social network

Methods: longitudinal research difference in differences quasi experiment experimental group

Jailbreaking in closed two-sided platforms

2023 | Information & Management | Citations: 0

Authors: Liu, Yunhao; Feng, Gengzhong; Sun, Yangyang; Kong, Xiangyin

Abstract: Jailbreaking, which refers to security-cracking behavior targeted at systems, th ... Expand

Abstract: Jailbreaking, which refers to security-cracking behavior targeted at systems, threatens closed two-sided plat­ forms. By allowing customers to access unauthorised content freely, jailbreaking may hurt developers’ enthu­ siasm and have complicated influences on the platform’s profit. We develop an analytical model to investigate jailbreaking’s influence on closed platforms. The findings reveal that platforms will tolerate jailbreaking only when the customers’ utility suffers a slight decline after jailbreaking. We also prove that jailbreaking hurts the closed platform’s profit. However, when bringing adequate additional benefits for customers beyond free con­ tent, jailbreaking can raise the platform’s profit. Our results are qualitatively tested in several scenarios. Collapse

Topics: network effect price management closed platform decision making price discrimination

Methods: experiment mathematical model optimization model reinforcement learning theoretical contribution

What Other Factors Might Impact Building Trust in Government Decisions Based on Decision Support Systems, Except for Transparency and Explainability?

2023 | HICSS | Citations: 0

Authors: Leewis, S.; Smit, K.

Abstract: Decision Support Systems (DSS) are increasingly being used to support operati ... Expand

Abstract: Decision Support Systems (DSS) are increasingly being used to support operational decision-making using large amounts of data. One key aspect to successful adoption is that the user trusts the DSS. Large contributors to trust often mentioned in literature and practice are transparency and explainability. But what happens when a DSS is transparent and explainable by design? What other contributors to trust are relevant is the main focus of this paper, in the context of Dutch governmental subject-matter experts designing and working with DSSs. We used a Mixed-Method Sequential Explanatory Design in which a survey was conducted to gather empirical data. The findings present 20 focal points contributing toward trust in DSS. These focal points require future research, specifically on considering these for development by the design of a DSS. Ultimately, this could help in increasing the adoption of DSSs in general. Collapse

Topics: decision support system decision making expert system explainable artificial intelligence

Methods: survey qualitative coding focus group grounded theory mixed method

Positive or Negative Reviews? Consumers’ Selective Exposure in Seeking and Evaluating Online Reviews

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

Authors: Lei, Zhanfei; Yin, Dezhi; Zhang, Han

Abstract: How and why positive and negative reviews influence product sales differently h ... Expand

Abstract: How and why positive and negative reviews influence product sales differently has critical implications for both research and businesses. Although earlier online word-of-mouth research empirically documented that negative reviews influence product sales to a greater extent than positive reviews (i.e., a negativity bias), later research has revealed that positive reviews are generally more helpful (i.e., a positivity bias). We propose that an answer to this conundrum may be that negative reviews get more exposure than positive reviews. As consumers are often overwhelmed by the massive number of online reviews, they need to be selective when searching for reviews. This research investigates consumers' preference for positive vs. negative reviews during both the information-seeking and information-evaluation stages of their decision-making process. Drawing on the motivated reasoning literature, we propose that consumers exhibit a negativity bias when they search for reviews to read but manifest a confirmation bias when they evaluate the helpfulness of reviews. We conducted three experiments and found consistent support for these hypotheses. Our findings expand the current understanding of consumers' processing of online reviews to the information-seeking stage, reveal differential biases at different stages, demonstrate a possible explanation for the negativity bias in product sales, and provide important practical implications.Zhanfei Lei is an assistant professor of operations and information management at the Isenberg School of Management, University of Massachusetts Amherst. She received her PhD in information technology management from the Georgia Institute of Technology in 2019. Her research interests focus on user-generated content, biases and heuristics, and electronic commerce. She has published in Information Systems Research and Production and Operations Management.Dezhi Yin is an associate professor of information systems at the Muma College of Business, University of South Florida. He received his PhD in information technology management from the Georgia Institute of Technology in 2012. His research interests include expressed and experienced emotions in online environments, user-generated content and crowds (e.g., online reviews, Q&A sites, crowdfunding), and AI technologies (e.g., virtual AI agents such as chatbots). Collapse

Topics: decision making online review user-generated content information management accounting

Methods: experiment parametric test ANOVA repeated measures ANOVA Student's t-test