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The strength of weak ties and fake news believability Decis. Support Syst. (IF 6.7) Pub Date : 2024-07-10 Babajide Osatuyi, Alan R. Dennis
Are we more likely to believe a social media news story shared by someone with whom we have a strong or weak tie? We tend to trust close ties more than weak ties, but weak ties are sources of new information more often than strong ones. We conducted an online experiment to examine the effect of tie strength (strong ties vs. weak ties) on the decision to believe or not believe fake news stories. Participants
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Social contagions in business resilience: Evidence from the U.S. restaurant industry in the COVID-19 pandemic Decis. Support Syst. (IF 6.7) Pub Date : 2024-07-09 Long Xia, Christopher Lee
The unprecedented COVID-19 has led to the collapse of numerous businesses, notably within the tourism and hospitality sectors. Despite the burgeoning research on resilience, few studies have embraced a theoretical lens, particularly from a social network perspective. In addition, most extant resilience studies have not explicitly considered the geographic accessibility prerequisite inherent to tourism
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The effect of different types of comparative reviews on product sales Decis. Support Syst. (IF 6.7) Pub Date : 2024-07-06 Yuzhuo Li, Min Zhang, G. Alan Wang, Ning Zhang
Comparative online reviews have evolved into a vital instrument for consumers in decision-making, offering valuable comparisons and available options. Drawing on the insights from the linguistic category model (LCM) and elaboration likelihood model (ELM), we propose that different types (attribute-based and experience-based) of comparative reviews can affect consumers' perceived credibility of online
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MEMF: Multi-entity multimodal fusion framework for sales prediction in live streaming commerce Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-29 Guang Xu, Ming Ren, Zhenhua Wang, Guozhi Li
Live streaming commerce thrives with a rich tapestry of multimodal information that intertwines with various entities, including the anchor, the commodities, and the live streaming environment. Despite the wealth of data at hand, the synthesis and analysis of this information to predict sales remains a significant challenge. This study introduces a framework for multi-entity multimodal fusion, which
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Explainable AI for enhanced decision-making Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-28 Kristof Coussement, Mohammad Zoynul Abedin, Mathias Kraus, Sebastián Maldonado, Kazim Topuz
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The faster or richer the response, the better performance? An empirical analysis of online healthcare platforms from a competitive perspective Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-25 Haoyu Ren, Liuan Wang, Junjie Wu
The emergence of online healthcare platforms has changed the competitive environment among physicians. However, little is known about how physicians can improve their performance in this new environment. Platforms also face challenges in comprehending the competitive mechanisms among physicians, which might hinder them from formulating strategic managerial decisions that foster sustained growth. In
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How transparency affects algorithmic advice utilization: The mediating roles of trusting beliefs Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-22 Xianzhang Ning, Yaobin Lu, Weimo Li, Sumeet Gupta
Although algorithms are increasingly used to support professional tasks and routine decision-making, their opaque nature invites resistance and results in suboptimal use of their advice. Scholars argue for transparency to enhance the acceptability of algorithmic advice. However, current research is limited in understanding how improved transparency enhances the use of algorithmic advice, such as the
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Understand your shady neighborhood: An approach for detecting and investigating hacker communities Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-21 Dalyapraz Manatova, Charles DeVries, Sagar Samtani
Cyber threat intelligence (CTI) researchers strive to uncover collaborations and emerging techniques within hacker networks. This study proposes an empirical approach to detect communities within hacker forums for CTI purposes. Eighteen algorithms are systematically evaluated, including state-of-the-art and benchmark methods for identifying overlapping and disjoint groups. Using discussions from five
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An adaptive simulation based decision support approach to respond risk propagation in new product development projects Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-16 Shanshan Liu, Ronggui Ding, Lei Wang
Developing new products by multiple stakeholders is inclined to project delays and even failures due to complex risk propagation, calling for accurate predictions of varying risk states and stakeholders' potential response actions. This study proposes an adaptive simulation-based decision support approach, starting with an adaptive simulation model capable of generating future intervention actions
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Predicting digital product performance with team composition features derived from a graph network Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-12 Houping Xiao, Yusen Xia, Aaron Baird
This paper examines video games, a form of digital innovation, and seeks to predict a successful game based on the composition of game development team members. Team composition is measured with observable features generated from a graph network based on development team information derived from individual team member work on previous games. Features include network features, such as team member closeness
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Selecting textual analysis tools to classify sustainability information in corporate reporting Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-11 Frederik Maibaum, Johannes Kriebel, Johann Nils Foege
Information on firms' sustainability often partly resides in unstructured data published, for instance, in annual reports, news, and transcripts of earnings calls. In recent years, researchers and practitioners have started to extract information from these data sources using a broad range of natural language processing (NLP) methods. While there is much to be gained from these endeavors, studies that
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Dynamic product competitive analysis based on online reviews Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-10 Lu Zheng, Lin Sun, Zhen He, Shuguang He
Competitive intelligence is vital for enterprises to survive in the market. Recently, online reviews have gained popularity among enterprises and researchers as a means to acquire timely and precise competitive insights. However, extant studies overlook the evolution of competitive information because they do not account for the variation of online reviews and products. In this research, we propose
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Unraveling juxtaposed effects of biometric characteristics on user security behaviors: A controversial information technology perspective Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-10 Jing Zhang, Zilong Liu, Xin (Robert) Luo
Biometric authentication has become ubiquitous and profoundly impacts decision-making for both individuals and firms. Despite its extensive implementation, there is a discernible knowledge gap in understanding the nuanced influence of biometric characteristics on user security behaviors. To advance this line of research, we embrace the controversial information technology framework to delve into the
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From engagement to retention: Unveiling factors driving user engagement and continued usage of mobile trading apps Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-10 Sajani Thapa, Swati Panda, Ashish Ghimire, Dan J. Kim
The popularity of online mobile trading has led to an increase in the development of mobile stock trading applications. Despite this increase in popularity, there is a dearth of empirical studies that examine the factors influencing the continued usage intention of these applications (hereafter, apps). Drawing on stimulus-organism-response (S-O-R) theory, this paper investigates the features of stock
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Decomposing the hazard function into interpretable readmission risk components Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-08 James Todd, Steven E. Stern
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Influentials, early adopters, or random targets? Optimal seeding strategies under vertical differentiations Decis. Support Syst. (IF 6.7) Pub Date : 2024-06-05 Fang Cui, Le Wang, Xin (Robert) Luo, Xueying Cui
Product seeding, defined as the act by which firms send products to selected customers and encourage them to spread word of mouth, is a critical decision support strategy for the success of new products. Using multiple agent-based simulation techniques, we investigated the relative importance of three widely adopted seeding strategies (seeding influentials, early adopters, and random targets) in a
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Cyber resilience framework for online retail using explainable deep learning approaches and blockchain-based consensus protocol Decis. Support Syst. (IF 6.7) Pub Date : 2024-05-24 Karim Zkik, Amine Belhadi, Sachin Kamble, Mani Venkatesh, Mustapha Oudani, Anass Sebbar
Online retail platforms encounter numerous challenges, such as cyber-attacks, data breaches, device failures, and operational disruptions. These challenges have intensified in recent years, underscoring the importance of prioritizing resilience for businesses. Unfortunately, conventional cybersecurity methods have proven insufficient in thwarting sophisticated cybercrime tactics. This paper proposes
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Supporting organizational decisions on How to improve customer repurchase using multi-instance counterfactual explanations Decis. Support Syst. (IF 6.7) Pub Date : 2024-05-24 André Artelt, Andreas Gregoriades
Improving customer repurchase intention constitutes a key activity for maintaining sustainable business performance. Returning customers provide many economic and other benefits to businesses. In contrast, attracting new customers is a process that is associated with high costs. This work proposes a novel counterfactual explanations methodology that utilizes textual data from electronic word of mouth
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Focusing on the fundamentals? An investigation of the relationship between corporate social irresponsibility and data breach risk Decis. Support Syst. (IF 6.7) Pub Date : 2024-05-23 Junmin Xu, Wei Thoo Yue, Alvin Chung Man Leung, Qin Su
In an era of growing social activism, companies engaged in socially irresponsible practices are increasingly vulnerable to data breaches, resulting in substantial reputational and financial losses. This study examines how corporate social irresponsibility (CSI) influences a company's data breach risk. We argue that CSI has an impact on data breach risk by influencing the intentional behaviors of both
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Blockchain as a trust machine: From disillusionment to enlightenment in the era of generative AI Decis. Support Syst. (IF 6.7) Pub Date : 2024-05-22 Shaokun Fan, Noyan Ilk, Akhil Kumar, Ruiyun Xu, J. Leon Zhao
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The power of choice: Examining how selection mechanisms shape decision-making in online community engagement Decis. Support Syst. (IF 6.7) Pub Date : 2024-05-21 Jung-Kuei Hsieh, Yu-Hui Fang, Chien Hsiang Liao
The significance of online communities in our lives is indisputable. These communities take various forms, including social networking sites, brand communities, and virtual platforms, where individuals digitally connect and interact. This article suggests that users' perceptions and beliefs about online communities are shaped by multiple selection mechanisms, which significantly influence decision-making
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Comparing expert systems and their explainability through similarity Decis. Support Syst. (IF 6.7) Pub Date : 2024-05-14 Fabian Gwinner, Christoph Tomitza, Axel Winkelmann
In our work, we propose the use of Representational Similarity Analysis (RSA) for explainable AI (XAI) approaches to enhance the reliability of XAI-based decision support systems. To demonstrate how similarity analysis of explanations can assess the output stability of post-hoc explainers, we conducted a computational evaluative study. This study addresses how our approach can be leveraged to analyze
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When ownership and copyright are separated: Economics of non-fungible token marketplaces with secondary markets Decis. Support Syst. (IF 6.7) Pub Date : 2024-05-11 Dongchen Zou, Meilin Gu, Dengpan Liu
Creators have long strived to secure royalties for their works but with little success. In the digital realm, monetization presents an even greater challenge, as traditional digital assets frequently suffer from piracy issues, primarily due to the lack of verifiable ownership. Recently, non-fungible token (NFT), a blockchain-enabled tradable digital asset, has aroused great public attention for its
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Shopping trip recommendations: A novel deep learning-enhanced global planning approach Decis. Support Syst. (IF 6.7) Pub Date : 2024-05-11 Jiayi Guo, Jiangning He, Xinran Wu
Brick-and-mortar shopping malls are embracing Artificial Intelligence (AI) technology and recommender systems to enhance the shopping experience and boost mall revenue. Echoing this trend, we formulate a new shopping trip recommendation problem, which aims to recommend a shopping trip (i.e., a list of stores) that matches customer preferences and has appropriate trip lengths. To solve this problem
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Decision support system for policy-making: Quantifying skill and chance in daily fantasy sports Decis. Support Syst. (IF 6.7) Pub Date : 2024-05-07 Aishvarya, Tirthatanmoy Das, U. Dinesh Kumar
We explore the question of skill versus chance dominance in Daily Fantasy Sports (DFS), which has been the subject of numerous legal disputes around the world. Our study examines whether a contestant's winnability in DFS is influenced by factors reflecting skills using cricket-based daily fantasy contest data and a true fixed effects stochastic frontier model. We find that skill contributes significantly
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The impact of doctors' facial attractiveness on users' choices in online health communities: A stereotype content and social role perspective Decis. Support Syst. (IF 6.7) Pub Date : 2024-05-06 Xing Zhang, Yuanyuan Wang, Quan Xiao, Jingguo Wang
This study examines the impact of doctors' facial attractiveness on users' choices in online health communities (OHCs). We conducted a field study using a sample of 14,897 doctors registered on a Chinese OHC. The results indicate a significant negative relationship between the facial attractiveness of doctors and the number of visits to their homepage by users. However, this relationship only holds
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Avatars and organizational knowledge sharing Decis. Support Syst. (IF 6.7) Pub Date : 2024-05-04 Dennis D. Fehrenbacher, Martin Weisner
We study how organizational knowledge sharing behavior is affected by avatar use during computer-mediated communication (CMC) with an unknown co-worker. Experimental results from two ethnically different samples provide theory-consistent evidence that outgroup discrimination—manifested as refusal to share knowledge—can get magnified in the ‘virtual world’ when avatars are used for self-representation
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Transparency in design science research Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-30 Alan R. Hevner, Jeffrey Parsons, Alfred Benedikt Brendel, Roman Lukyanenko, Verena Tiefenbeck, Monica Chiarini Tremblay, Jan vom Brocke
Research transparency promotes openness and trust in the process, evidence, contributions, and implications of scientific inquiry. Information Systems (IS), as a pluralistic research community, must address transparency in relation to its use of multiple research methods appropriate to complex socio-technical contexts and challenging research questions. This commentary presents a set of important transparency
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When do consumers buy during online promotions? A theoretical and empirical investigation Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-28 Tao Zhu, Cheng Nie, Zhengrui Jiang, Xiangpei Hu
An increasing number of merchants are using online platforms to promote their products; however, much is still unknown about how consumers behave in response to online promotions. This study investigates factors affecting consumers' purchase intentions and purchase behaviors during online promotions. We classify consumers into two categories, one mainly affected by the time pressure of promotion and
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Freedom of speech or freedom of reach? Strategies for mitigating malicious content in social networks Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-27 Saurav Chakraborty, Sandeep Goyal, Annamina Rieder, Agnieszka Onuchowska, Donald J. Berndt
Malicious content threatens the integrity and quality of content in social networks. Research and practice have experimented with network intervention strategies to curb malicious content propagation. These strategies lack efficiency, target malicious content propagators, and abridge freedom of speech. We draw upon the preferential attachment literature and cognitive load theory to employ the mechanisms
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Explaining the model and feature dependencies by decomposition of the Shapley value Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-27 Joran Michiels, Johan Suykens, Maarten De Vos
Shapley values have become one of the go-to methods to explain complex models to end-users. They provide a model agnostic post-hoc explanation with foundations in game theory: what is the worth of a player (in machine learning, a feature value) in the objective function (the output of the complex machine learning model). One downside is that they always require outputs of the model when some features
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The information content of financial statement fraud risk: An ensemble learning approach Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-27 Wei Duan, Nan Hu, Fujing Xue
This study aims to assess the financial statement fraud risk ex ante and empirically explore its information content to help improve decision-making and daily operations. We propose an ex-ante fraud risk index by adopting an ensemble learning approach and a theoretically grounded framework. Our ensemble learning model systematically examines the fraud process and deals effectively with the unique challenges
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Explainable Learning Analytics: Assessing the stability of student success prediction models by means of explainable AI Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-26 Elena Tiukhova, Pavani Vemuri, Nidia López Flores, Anna Sigridur Islind, María Óskarsdóttir, Stephan Poelmans, Bart Baesens, Monique Snoeck
Beyond managing student dropout, higher education stakeholders need decision support to consistently influence the student learning process to keep students motivated, engaged, and successful. At the course level, the combination of predictive analytics and self-regulation theory can help instructors determine the best study advice and allow learners to better self-regulate and determine how they want
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The design of human-artificial intelligence systems in decision sciences: A look back and directions forward Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-24 Veda C. Storey, Alan R. Hevner, Victoria Y. Yoon
The field of decision sciences is undergoing significant disruption and reinvention because of rapid advances in artificial intelligence (AI) technologies and the design of complex human-artificial intelligence systems (HAIS). The integration of human decision behaviors with cutting-edge AI capabilities is transforming business and society in irreversible ways. In this paper, we examine prior research
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Modeling the evolution of collective overreaction in dynamic online product diffusion networks Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-24 Xiaochao Wei, Yanfei Zhang, Xin (Robert) Luo
With the development of e-commerce, collective overreactions such as buying frenzy have become prominent. However, studies have rarely investigated the mechanism of irrational consumer behavior at the group level. To investigate the evolution of collective overreaction in dynamic online product diffusion networks, we employed a sequential multiple-methods approach. A conceptual model is constructed
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Strategic team design for sustainable effectiveness: A data-driven analytical perspective and its implications Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-21 Teng Huang, Qin Su, Chuling Yu, Zheng Zhang, Fei Liu
Teams are building blocks of organizations and essential inputs of organizational success. This article studies a data-driven analytical approach that exploits the rich data accumulated in organizations in the digital era to design teams, including prescribing team composition and formation decisions. We propose to evaluate a team regarding its performance and temporal stability, referred to as (SE)
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Enhancing healthcare decision support through explainable AI models for risk prediction Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-18 Shuai Niu, Qing Yin, Jing Ma, Yunya Song, Yida Xu, Liang Bai, Wei Pan, Xian Yang
Electronic health records (EHRs) are a valuable source of information that can aid in understanding a patient’s health condition and making informed healthcare decisions. However, modelling longitudinal EHRs with heterogeneous information is a challenging task. Although recurrent neural networks (RNNs) are frequently utilized in artificial intelligence (AI) models for capturing longitudinal data, their
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Hybrid black-box classification for customer churn prediction with segmented interpretability analysis Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-06 Arno De Caigny, Koen W. De Bock, Sam Verboven
Customer retention management relies on advanced analytics for decision making. Decision makers in this area require methods that are capable of accurately predicting which customers are likely to churn and that allow to discover drivers of customer churn. As a result, customer churn prediction models are frequently evaluated based on both their predictive performance and their capacity to extract
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A meta-path, attention-based deep learning method to support hepatitis carcinoma predictions for improved cirrhosis patient management Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-04 Zejian (Eric) Wu, Da Xu, Paul Jen-Hwa Hu, Liang Li, Ting-Shuo Huang
Hepatitis carcinoma (HCC) accounts for the majority of liver cancer–related deaths globally. Cirrhosis often precedes HCC clinically in a strong, temporal relationship. Therefore, identifying cirrhosis patients at higher risk of HCC is crucial to physicians' clinical decision-making and patient management. Effective estimates of at-risk patients can facilitate timely therapeutic interventions and thereby
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Crowdsourced firm ratings and total factor productivity: An empirical examination Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-04 Zongxi Liu, Donglai Bao, Xiao Xiao, Huimin Zhao
Employees' reviews, feedback, opinions, and experiences shared on crowdsourcing platforms are now widely used by human resource management researchers to analyze a firm's performance, management effectiveness, and culture. The analysis of firm ratings posted by employees on crowdsourcing platforms can not only provide timely feedback and insights into a firm's operations but also inspire managers to
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Towards explainable artificial intelligence through expert-augmented supervised feature selection Decis. Support Syst. (IF 6.7) Pub Date : 2024-04-01 Meysam Rabiee, Mohsen Mirhashemi, Michael S. Pangburn, Saeed Piri, Dursun Delen
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Prioritising national healthcare service issues from free text feedback – A computational text analysis & predictive modelling approach Decis. Support Syst. (IF 6.7) Pub Date : 2024-03-31 Adegboyega Ojo, Nina Rizun, Grace Walsh, Mona Isazad Mashinchi, Maria Venosa, Manohar Narayana Rao
Patient experience surveys have become a key source of evidence for supporting decision-making and continuous quality improvement within healthcare services. To harness free-text feedback collected as part of these surveys for additional insights, text analytics methods are increasingly employed when the data collected is not amenable to traditional qualitative analysis due to volume. However, while
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Real-time decision support for human–machine interaction in digital railway control rooms Decis. Support Syst. (IF 6.7) Pub Date : 2024-03-30 Léon Sobrie, Marijn Verschelde
This study proposes a real-time Decision Support System (DSS) using machine learning to enhance proactive management of Human–Machine Interaction (HMI) in safety–critical digital control rooms. The DSS provides explainable predictions and recommendations regarding near-future automation usage, customized for the railway control room management, who supervise the operations of traffic controllers (TCs)
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Uncovering the relationship between incidental emotion toward a disaster and stock market fluctuations: Evidence from the US market Decis. Support Syst. (IF 6.7) Pub Date : 2024-03-29 Tao Yang, T. Robert Yu, Huimin Zhao
Despite having potentially important implications, there has been little research on the relationship between the public's incidental emotion and the stock market. To that end, we construct a valence-based measure of incidental emotion using BERTweet's sentiment analysis and empirically investigate the association between collective incidental emotion toward the COVID-19 pandemic and the U.S. stock
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D3S: Decision support system for sectorization Decis. Support Syst. (IF 6.7) Pub Date : 2024-03-24 Elif Göksu Öztürk, Pedro Rocha, Ana Maria Rodrigues, José Soeiro Ferreira, Cristina Lopes, Cristina Oliveira, Ana Catarina Nunes
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Effects of enterprise social media use on employee improvisation ability through psychological conditions: The moderating role of enterprise social media policy Decis. Support Syst. (IF 6.7) Pub Date : 2024-03-23 Mengyi Zhu, Yuan Sun, Justin Zuopeng Zhang, Jindi Fu, Bo Yang
The emergence of enterprise social media (ESM) allows enterprises to develop employee improvisation ability for effective decision-making in various emergencies. However, it remains unclear how the use of ESM by employees affects their ability to improvise. Based on the job demands-resources model and Kahn's psychological conditions framework, this study constructs a theoretical model capturing two
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How does escapism foster game experience and game use? Decis. Support Syst. (IF 6.7) Pub Date : 2024-03-08 Tzu-Ling Huang, Jin-Rong Yeh, Gen-Yih Liao, T.C.E. Cheng, Yan-Cheng Chang, Ching-I Teng
Online games represent a rapidly growing and competitive global market for technology firms. Games are viewed as places where people can temporarily escape from reality. However, it is unclear how game escapism fosters game experience and game use, thus indicating a research gap. This gap keeps decision-makers (i.e., firms and policy-makers) in the dark regarding how game escapism affects gameplay
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Towards fair decision: A novel representation method for debiasing pre-trained models Decis. Support Syst. (IF 6.7) Pub Date : 2024-03-06 Junheng He, Nankai Lin, Qifeng Bai, Haoyu Liang, Dong Zhou, Aimin Yang
Pretrained language models (PLMs) are frequently employed in Decision Support Systems (DSSs) due to their strong performance. However, recent studies have revealed that these PLMs can exhibit social biases, leading to unfair decisions that harm vulnerable groups. Sensitive information contained in sentences from training data is the primary source of bias. Previously proposed debiasing methods based
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To be honest or positive? The effect of Airbnb host description on consumer behavior Decis. Support Syst. (IF 6.7) Pub Date : 2024-03-02 Xinyu Sun, Li Gui, Bin Cai
On accommodation-sharing platform, host self-description influence consumer behavior as an important information. Based on the Perceived Value Theory and the Expectation Confirmation Theory, we developed an analytical framework to investigate the relationship between host description strategies and consumer behavior of room booking and satisfaction. We measured host description strategies ( and ) using
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How self-selection Bias in online reviews affects buyer satisfaction: A product type perspective Decis. Support Syst. (IF 6.7) Pub Date : 2024-02-29 Yancong Xie, William Yeoh, Jingguo Wang
Online reviews play a crucial role in shaping buyers' purchase decisions. However, previous research has highlighted the existence of self-selection biases among buyers who contribute to reviews, which in turn leads to biased distributions of review ratings. This research aims to explore the further influences of self-selection bias on buyer satisfaction through agent-based modeling, considering two
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Developing a goal-driven data integration framework for effective data analytics Decis. Support Syst. (IF 6.7) Pub Date : 2024-02-23 Dapeng Liu, Victoria Y. Yoon
Data integration plays a crucial role in business intelligence, aiding decision-makers by consolidating data from heterogeneous sources to provide deep insights into business operations and performance. In the big data era, automated data integration solutions need to process high volumes of disparate data robustly and seamlessly for various analytical needs or operational actions. Existing data integration
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Navigating autonomy and control in human-AI delegation: User responses to technology- versus user-invoked task allocation Decis. Support Syst. (IF 6.7) Pub Date : 2024-02-21 Martin Adam, Christopher Diebel, Marc Goutier, Alexander Benlian
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Responsible machine learning for United States Air Force pilot candidate selection Decis. Support Syst. (IF 6.7) Pub Date : 2024-02-21 Devin Wasilefsky, William N. Caballero, Chancellor Johnstone, Nathan Gaw, Phillip R. Jenkins
The United States Air Force (USAF) continues to be plagued by a chronic pilot shortage, one that could be exacerbated by an accompanying shortfall in the commercial airlines. As a result, efforts have increased to alleviate this shortage by finding methods to reduce pilot training attrition. We contribute to these efforts by setting forth a decision support system (DSS) for pilot candidate selection
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Outlier detection using flexible categorization and interrogative agendas Decis. Support Syst. (IF 6.7) Pub Date : 2024-02-19 Marcel Boersma, Krishna Manoorkar, Alessandra Palmigiano, Mattia Panettiere, Apostolos Tzimoulis, Nachoem Wijnberg
Categorization is one of the basic tasks in machine learning and data analysis. Building on formal concept analysis (FCA), the starting point of the present work is that different ways to categorize a given set of objects exist, which depend on the choice of the sets of features used to classify them, and different such sets of features may yield better or worse categorizations, relative to the task
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Explainable artificial intelligence and agile decision-making in supply chain cyber resilience Decis. Support Syst. (IF 6.7) Pub Date : 2024-02-17 Kiarash Sadeghi R., Divesh Ojha, Puneet Kaur, Raj V. Mahto, Amandeep Dhir
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Assessing financial distress of SMEs through event propagation: An adaptive interpretable graph contrastive learning model Decis. Support Syst. (IF 6.7) Pub Date : 2024-02-17 Jianfei Wang, Cuiqing Jiang, Lina Zhou, Zhao Wang
Accurate assessment of financial distress of SMEs is critical as it has strong implications for various stakeholders to understand the firm's financial health. Recent studies start to leverage network data and suggest the effect of event propagation for predicting financial distress. Yet such methods face methodological challenges in determining and explaining event propagation due to heterogeneous
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Simplicity in joy and detail in anger: Intertwining effect of cognitive and affective review disposition on review helpfulness Decis. Support Syst. (IF 6.7) Pub Date : 2024-02-15 Yicheng Zhang, Xinqi Zhao, Ya Zhou
Review length and readability, are cognitive dispositions of reviews supposed to reflect diagnostic content and lead to favorable evaluation of review helpfulness. However, underlying these two cognitive dispositions of reviews, are discrepancies that make it difficult for a helpful review to simultaneously satisfy both of them. To resolve the discrepancies, this present study, drawn on cognitive tuning
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Improving answer quality using image-text coherence on social Q&A sites Decis. Support Syst. (IF 6.7) Pub Date : 2024-02-09 Yining Song, Xiaoying Xu, Kaushik Dutta, Zhihong Li
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Distilling wisdom of crowds in online communities: A novel prediction market constructed with comment posters Decis. Support Syst. (IF 6.7) Pub Date : 2024-02-09 Li Dong, Haichao Zheng, Liting Li, Chunyu Zhou
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“To share or not to share?” – A hybrid SEM-ANN-NCA study of the enablers and enhancers for mobile sharing economy Decis. Support Syst. (IF 6.7) Pub Date : 2024-02-05 Lai-Ying Leong, Teck-Soon Hew, Keng-Boon Ooi, Patrick Y.K. Chau