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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 7.5) 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 7.5) 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 7.5) 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 7.5) 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 7.5) 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 7.5) 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 7.5) Pub Date : 2024-02-21 Martin Adam, Christopher Diebel, Marc Goutier, Alexander Benlian
Users can increasingly delegate to information systems (IS) – that is transferring rights and responsibilities regarding certain tasks – even to the degree that IS can act autonomously (i.e., without the intervention or supervision of users). What is more, IS increasingly offer to assume the rights and responsibilities for a task not only in response to user prompts (i.e., user-invoked delegation)
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Responsible machine learning for United States Air Force pilot candidate selection Decis. Support Syst. (IF 7.5) 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 7.5) 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 7.5) Pub Date : 2024-02-17 Kiarash Sadeghi R., Divesh Ojha, Puneet Kaur, Raj V. Mahto, Amandeep Dhir
Although artificial intelligence can contribute to decision-making processes, many industry players lag behind pioneering companies in utilizing artificial intelligence-driven technologies, which is a significant problem. Explainable artificial intelligence can be a viable solution to mitigate this problem. This paper proposes a research model to address . Using an experimental design, empirical data
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Assessing financial distress of SMEs through event propagation: An adaptive interpretable graph contrastive learning model Decis. Support Syst. (IF 7.5) 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 7.5) 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 7.5) Pub Date : 2024-02-09 Yining Song, Xiaoying Xu, Kaushik Dutta, Zhihong Li
There has been a significant rise in the use of social Q&A to get answers to a variety of queries. One common problem faced by most social Q&A is how to help unskillful answerers construct well-received answers. Prior studies in answer quality assessment usually focus on ranking candidate answers for the sake of askers but show little value for the answerers. Moreover, existing work employs textural
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Distilling wisdom of crowds in online communities: A novel prediction market constructed with comment posters Decis. Support Syst. (IF 7.5) Pub Date : 2024-02-09 Li Dong, Haichao Zheng, Liting Li, Chunyu Zhou
Aggregating the wisdom of crowds from user-generated content in the online community can be valuable for decision-making. However, low-quality comments pose significant challenges for traditional wisdom extraction algorithms, such as prediction polls. Therefore, to extract the wisdom of online crowds effectively, we propose a novel artificial prediction market that can dynamically filter out low-quality
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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 7.5) Pub Date : 2024-02-05 Lai-Ying Leong, Teck-Soon Hew, Keng-Boon Ooi, Patrick Y.K. Chau
The proliferation of mobile technologies have facilitated the popularity of mobile sharing economy (MSE). However, extant literature that used sufficient logic has fallen short in explaining this phenomenon as the findings are insufficient to explain the necessary conditions that activate users' intention and actual usage. The purpose of this study is to identify the enablers (must-have factors) and
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How technical features of virtual live shopping platforms affect purchase intention: Based on the theory of interactive media effects Decis. Support Syst. (IF 7.5) Pub Date : 2024-02-05 Yuan Sun, Yating Zhong, Zuopeng Zhang, Yonggui Wang, Mengyi Zhu
Virtual live shopping platforms (VLSPs) are an innovative form of intelligent shopping DSS that offer brands novel opportunities to interact with customers. However, the impact of VLSPs on purchase intention and underlying mechanisms remains unexplored. Therefore, focusing on VLSPs' technical features is crucial for designing and developing their functionalities. This study addresses the research gaps
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How customized managerial responses influence subsequent consumer ratings: The language style matching perspective Decis. Support Syst. (IF 7.5) Pub Date : 2024-02-04 Xiaojing Ren, Le Wang, Xin (Robert) Luo
In previous studies, customized managerial responses have been viewed as an effective tool for sellers to intervene in online consumer opinions. However, formulating a customized response warrants strategic use of language. To explore this further, this study draws on communication accommodation theory to examine the effect of language style matching between managerial responses and online customer
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Blockchain enabled dynamic trust management method for the internet of medical things Decis. Support Syst. (IF 7.5) Pub Date : 2024-02-02 Xinyin Xiang, Jin Cao, Weiguo Fan, Shousheng Xiang, Gang Wang
The Internet of Things (IoT) connects heterogeneous sensing devices with servers under the support of the network to monitor intelligent communication and real-time data transmission. The system caters well to the needs of medical services and also provides convenience for telemedicine. Many IoT-based solutions have been proposed for medical application scenarios. However, the unpredictable nature
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A decision support framework for integrated lane identification and long-term backhaul collaboration using spatial analytics and optimization Decis. Support Syst. (IF 7.5) Pub Date : 2024-02-02 Mohsen Emadikhiav, Sudip Bhattacharjee, Robert Day, David Bergman
The movement of empty trucks (dead-heading) incurs significant costs and creates greenhouse gas emissions and road congestion. A strategy to tackle the dead-heading problem is to identify long-term backhaul collaboration opportunities, in which shippers and carriers generate frequent movement patterns (lanes) from historical truck movements in stage one, and then use the identified lanes as inputs
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Multi-criteria evaluation of health news stories Decis. Support Syst. (IF 7.5) Pub Date : 2024-02-01 Ermira Zifla, Burcu Eke Rubini
The proliferation of digital and social media technologies has enabled quick and wide dissemination of news stories and press releases about new medical treatments. Evaluating these stories is difficult for two reasons. First, these stories are often not completely true or false. A nuanced approach that considers different aspects of these stories (e.g., the presence of inflated claims, suppression
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FedDQA: A novel regularization-based deep learning method for data quality assessment in federated learning Decis. Support Syst. (IF 7.5) Pub Date : 2024-02-01 Zongxiang Zhang, Gang Chen, Yunjie Xu, Lihua Huang, Chenghong Zhang, Shuaiyong Xiao
Researchers strive to design artificial intelligence (AI) models that can fully utilize the potentials of data while protecting privacy. Federated learning is a promising solution because it utilizes data but shields them from those who do not own them. However, assessing data quality becomes a challenge in federated learning. We propose a data quality assessment method, Federated Data Quality Assessment
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Minimizing block incentive volatility through Verkle tree-based dynamic transaction storage Decis. Support Syst. (IF 7.5) Pub Date : 2024-02-01 Xiongfei Zhao, Gerui Zhang, Hou-Wan Long, Yain-Whar Si
Transaction fees are a crucial revenue source for miners in public and consortium blockchains. However, while public blockchains have additional revenue streams, transaction fees serve as the primary income for miners in consortium blockchains formed by various financial institutions. These miners allocate different levels of computing resources to process transactions and earn corresponding fees.
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Is decentralization sustainable in the bitcoin system? Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-29 Varghese S. Jacob, Sailendra Prasanna Mishra, Suresh Radhakrishnan
The Bitcoin system is a decentralized monetary system in that any participant can potentially verify and record transactions onto a public ledger. Using a simple model, we show that the number of miners could be negatively (positively) related to the expected rewards if miners with low-operating costs also have less (more) severe financing constraints. We characterize the possibility of miners with
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Towards an integrated framework for developing blockchain systems Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-26 Mahdi Fahmideh, Babak Abedin, Jun Shen
Although the expanding applications of blockchain technologies have been widely explored in the IS literature, a noticeable gap exists in understanding information systems development methods (ISDMs) that facilitate the implementation of systems leveraging these technologies. A conceptual foundation that cohesively organizes an ISDM along with its facets associated with the development lifecycle for
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You jump, I jump? Herding behavior in blockchain application platforms Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-23 Jingxuan Cai, Xin (Robert) Luo, Fujun Lai, Peilin Ai, Xi Zhao
Blockchain technology has brought opportunities and challenges to many fields, including operations and supply chain management. Due to the innovative characteristics of decentralization, transparency, immutability, and anonymity, blockchain applications break new ground for users' decision-making in an operations environment. There is a lack of evidence regarding herding in blockchain technology in
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Designing trust-enabling blockchain systems for the inter-organizational exchange of capacity Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-23 Nick Große, Frederik Möller, Thorsten Schoormann, Michael Henke
In times of rapid and unpredictable developments, companies experience significant volatility in capacity utilization. Virtual capacity exchange platforms help to mitigate this challenge by exchanging capacities with anonymous participants in market-like peer-to-peer networks. However, its efficiency is hindered by behavioral uncertainties, including a lack of inter-organizational trust in other participants
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Meme-affordance tourism: The power of imitation and self-presentation Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-20 Yerin Yhee, Jahyun Goo, Chulmo Koo, Namho Chung
To respond to information systems (IS) researchers' on-going call for understanding what happens in new, social media-enabled processes in diverse contexts, this research investigated how Internet memes facilitate the emergence of new online travel activities and influence visit intentions in new forms of meme tourism. Meme tourism involves new forms of visit intention, where individuals are motivated
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Predicting financial distress using current reports: A novel deep learning method based on user-response-guided attention Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-16 Chenyang Wu, Cuiqing Jiang, Zhao Wang, Yong Ding
Effective financial distress prediction (FDP) can discover a company's potential financial risks and support relevant decisions in a timely manner. Previous studies on FDP have mostly focused on using financial indicators and periodic reports. Compared with periodic reports, current reports disclose major events in a timelier manner. But leveraging the information in current reports involves the critical
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A method for the competitiveness estimation of the incremental new product through user-generated content Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-12 Yu-Mei Ma, Xiao-Hu Zhu, Ping-Ping Cao, Ming-Yang Li
The current dynamic market environment challenges successful incremental new product (INP) launches, compelling enterprise managers to promptly recognize and respond to competitive situations. Estimating INP competitiveness before sale helps enterprise managers adjust their strategies effectively and in a timely manner to ensure successful INP launches. However, a lack of historical evaluation information
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Knowledge transfer to aid social coding: The case of Stack Overflow Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-11 Orcun Temizkan, Ram L. Kumar
Focused online question and answer (Q&A) communities aid social coding. Despite the growing importance of social coding, knowledge transfer in this context remains under-researched. Our primary objective is to understand the knowledge transfer process in this context. We conceptualize knowledge transfer as a process that is impacted by the prior knowledge transfer interactions (network) among participants
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Investigating the beneficial impact of segmentation-based modelling for credit scoring Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-08 Khaoula Idbenjra, Kristof Coussement, Arno De Caigny
Due to its vital role in financial risk management, credit scoring has been investigated extensively in extant information systems studies. However, most credit scoring studies rely on one-size-fits-all classifiers with logistic regression (LR) as a popular benchmark. Moreover, extant literature largely focuses on predictive performance as an evaluation criterion. To find a better balance between predictive
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The secret of voice: How acoustic characteristics affect video creators' performance on Bilibili Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-05 Shixuan Fu, Yan Wu, Qianzhou Du, Chenwei Li, Weiguo Fan
The importance of voice has been well acknowledged in sensory decision-making. Yet, past literature on video creators' performance did not shed much light on the impact of video creators' acoustic characteristics. Building on signaling theory of portfolios, we examine how the acoustic characteristics of a video creator and the signals of video quantity affect the number of likes a video creator receives
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Sustainable decision making based on systems integration and decision support system promoting endorheic basin sustainability Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-05 Yingchun Ge, Feng Han, Feng Wu, Yanbo Zhao, Hongyi Li, Yong Tian, Yi Zheng, Wenfei Luan, Ling Zhang, Ximing Cai, Chunfeng Ma, Xin Li
Sustainability has become a target in official policy rhetoric. However, the gap between scientific investigation and practical decision-making poses a significant challenge in achieving sustainability, particularly in endorheic regions. Addressing this challenge requires the translation of scientific outcomes into available decision-making information. In this study, we propose a sustainable decision-making
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Relative effects of the different bundles of web-design features on intentions to purchase experience products online Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-05 Sung Hee (Jodie) Yoo, Muammer Ozer, Jingjun (David) Xu
Selling experience products online is usually more challenging than selling search products. Addressing the calls for future research in the literature, we study how the different combinations of different web-design features can explain people's intentions to purchase experience products online by mitigating three different product uncertainties associated with such products. The results of a detailed
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To alert or alleviate? A natural experiment on the effect of anti-phishing laws on corporate IT and security investments Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-04 Xiaoxiao Wang, Wilson Weixun Li, Alvin Chung Man Leung, Wei Thoo Yue
In the United States, between 2005 and 2017, 23 states enacted anti-phishing laws to prosecute those suspected of phishing. As the primary targets of phishing attacks, firms' interpretations and reactions toward these laws are worth investigating. Utilizing a unique dataset in a natural experimental setting, this study employed the difference-in-differences method to contrast firms' investment decisions
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Entity recognition from colloquial text Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-04 Tamara Babaian, Jennifer Xu
Extraction of concepts and entities of interest from non-formal texts such as social media posts and informal communication is an important capability for decision support systems in many domains, including healthcare, customer relationship management, and others. Despite the recent advances in training large language models for a variety of natural language processing tasks, the developed models and
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Decoding algorithm appreciation: Unveiling the impact of familiarity with algorithms, tasks, and algorithm performance Decis. Support Syst. (IF 7.5) Pub Date : 2024-01-02 Hasan Mahmud, A.K.M. Najmul Islam, Xin (Robert) Luo, Patrick Mikalef
Algorithm appreciation, defined as an individual's reliance or tendency to rely on algorithms in decision-making, has emerged as a subject of growing scholarly interest. Inquiries into this subject are crucial to understanding human decision-making processes as in the era of artificial intelligence, algorithms are increasingly being integrated into decision-making. To contribute to this evolving field
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A new perspective on classification: Optimally allocating limited resources to uncertain tasks Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-30 Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter Verbeke
A central problem in business concerns the optimal allocation of limited resources to a set of available tasks, where the payoff of these tasks is inherently uncertain. Typically, such problems are solved using a classification framework, where task outcomes are predicted given a set of characteristics. Then, resources are allocated to the tasks predicted to be the most likely to succeed. We argue
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Unlocking B2B buyer intentions to purchase: Conceptualizing and validating inside sales purchases Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-29 Migao Wu, Pavel Andreev, Morad Benyoucef, David Hood
This study focuses on understanding the purchase decision-making process of B2B buyers in the context of inside sales. While many studies have explored this topic in a B2C context, there has been little attention given to the unique features of B2B inside sales. To address this gap, we developed and empirically validated a buyer's intention to purchase (BIP) model that integrates the B2B purchase decision-making
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Delegation of purchasing tasks to AI: The role of perceived choice and decision autonomy Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-27 Mariyani Ahmad Husairi, Patricia Rossi
Although artificial intelligence (AI) outperforms humans in many tasks, research suggests some consumers are still averse to having AI perform tasks on their behalf. Informed by the literature of customer decision-making process, we propose and show that consumer autonomy is a significant predictor of customers' decision to adopt AI in the purchasing context. Across three experiments, we found that
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Detecting fake reviewers from the social context with a graph neural network method Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-22 Li-Chen Cheng, Yan Tsang Wu, Cheng-Ting Chao, Jenq-Haur Wang
With the development of mobile Web technologies, people can easily seek advice from social media before making purchases or decisions. Some companies employ expert writers to fabricate reviews or use automated techniques to improve the appeal of their products or services, or to undermine the credibility of their rivals. This obstructs the detection of fake reviews and reviewers. This paper proposes
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Modeling interaction behavior and preference decline for live stream recommendation Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-19 Jiawei Chen, Hongyan Liu
In recent years, live streaming has experienced rapid growth and become an influential way to engage people online. How to recommend live streams to viewers to improve user experience is the core business problem of live streaming platforms. On such platforms, viewers frequently change live streams to watch in each session for the enjoyment of the watching process. Live interaction along with streaming
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A computer vision-based concept model to recommend domestic overseas-like travel experiences: A design science study Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-18 Van-Hau Trieu, Huy Quan Vu, Marta Indulska, Gang Li
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Play it safe or leave the comfort zone? Optimal content strategies for social media influencers on streaming video platforms Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-17 Ling Jiang, Xingyu Chen, Sentao Miao, Cong Shi
Social media influencers (SMIs) endeavor to attract, retain, and, more importantly, influence large audiences through creating videos on streaming video platforms (SVPs). Facing diverse audiences and intense competition, SMIs who differ in their skills and performance levels struggle with two challenging trade-offs in video production decision: (1) trade-off between (i.e., reinforcing a specialized
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Understanding the effect of group emotions on consumer instant order cancellation behavior in livestreaming E-commerce: Empirical evidence from TikTok Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-13 Zeen Wang, Chuan Luo, Xin (Robert) Luo, Xu Xu
Livestreaming e-commerce (LSE) exhibits a noteworthy idiosyncrasy in the form of escalating instant order cancellations, wherein consumers swiftly revoke their purchases during livestreaming before receiving the products. This study leveraged the emotional contagion theory to construct an econometric model and investigate the factors influencing viewers' instant order cancellation behaviors. An extensive
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How profanity in influences perceived authenticity and perceived helpfulness of online reviews: The moderating role of review subjectivity Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-08 David Sugianto Lie, Billy Sung, Michelle Stankovic, Felix Septianto
While profanity may seem offensive, its prevalence within online reviews suggests it might be useful in influencing perceptions of the usefulness of online reviews. The present research investigates whether the presence (vs. absence) of profanity and high (vs. low) levels of review subjectivity jointly influence the perceived helpfulness of online reviews. In Study 1, we mined a large dataset of online
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The effect of review visibility and diagnosticity on review helpfulness – An accessibility-diagnosticity theory perspective Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-07 Kuanchin Chen, Chih-Fong Tsai, Ya-Han Hu, Chen-Wei Hu
Review visibility is the amount of time that a review remains visible to consumers on the first or home page. The Accessibility-Diagnosticity Theory (ADT) suggests that visibility paired with diagnosticity will likely be stronger in affecting user actions. This study explores the effects of various visibility and diagnostic measures on review helpfulness. We also compared machine-learning models to
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Comparison and design of organizational decision mechanisms Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-03 Cui Shang, Runtong Zhang, Xiaomin Zhu
Individuals with bounded rationality may make incorrect decisions regarding innovation projects. Organizational decision mechanisms aim to minimize the impact of individual fallibility and integrate collective wisdom. In recent years, consensus mechanism has posed a challenge to the traditional hierarchy, but its superiority in different decision scenarios is still unknown. We created decision scenarios
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How do system and user characteristics, along with anthropomorphism, impact cognitive absorption of chatbots – Introducing SUCCAST through a mixed methods study Decis. Support Syst. (IF 7.5) Pub Date : 2023-12-03 Shagun Sarraf, Arpan Kumar Kar, Marijn Janssen
Chatbots are radically redefining the customer service landscape. With the advent of AI-enabled chatbots, like ChatGPT, organizations are adopting chatbots to provide better customer services; however, the user experience has been given less attention. Building on IS success model and cognitive absorption theory, we posit that system and user characteristics enhance cognitive absorption amongst users
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The impact of low-immersion virtual reality on product sales: Insights from the real estate industry Decis. Support Syst. (IF 7.5) Pub Date : 2023-11-30 Shih-Hui Hsiao, Yen-Yao Wang, Tony L.J. Lin
The transformation of information flow and shopping experiences on modern digital platforms has significantly shifted. Notably, the integration of low-immersion virtual reality (VR) technology has become a key driver in enhancing the immersive nature of online shopping experiences. The projected exponential growth of the VR market, with anticipated revenue surging from $11.64 billion in 2021 to $227
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Complex business ecosystem intelligence using AI-powered visual analytics Decis. Support Syst. (IF 7.5) Pub Date : 2023-11-30 Rahul C. Basole, Hyunwoo Park, C. David Seuss
Business ecosystems are complex, dynamic systems characterized by a multitude of entities, including companies, ventures, and technologies, as well as activities and trends. Understanding the state of business ecosystems is an increasingly critical strategic imperative for many decision makers, but it is a resource-intensive activity as relevant information sources are dispersed, often highly unstructured
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Online installment payments and price guarantees under randomized pricing Decis. Support Syst. (IF 7.5) Pub Date : 2023-11-28 Chenchen Zhao, Jianghua Wu
Internet technology allows e-tailers to provide installment payment services, allowing consumers to purchase products without immediate payment. In addition, retailers can apply intelligent pricing strategies and provide price-guarantee strategies to encourage consumers to buy in advance. This study uses a dynamic mechanism design to explore the interplay between pricing and financing strategies. Specifically
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Enhancing accuracy and interpretability in EEG-based medical decision making using an explainable ensemble learning framework application for stroke prediction Decis. Support Syst. (IF 7.5) Pub Date : 2023-11-27 Samar Bouazizi, Hela Ltifi
Medical decision making increasingly relies on machine learning algorithms to analyze complex patient data and provide recommendations. However, the lack of interpretability in “black box” models has limited their adoption in clinical practice, which demands transparency and justification. Echo State Networks (ESNs), a recurrent neural network architecture, have shown promise for medical Decision Support
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A decision support system using signals from social media and news to predict cryptocurrency prices Decis. Support Syst. (IF 7.5) Pub Date : 2023-11-27 Hemang Subramanian, Patricia Angle, Florent Rouxelin, Ziyang Zhang
We design, implement, and evaluate a decision support system that combines fine-grained signals derived from news and social media with bitcoin prices using a Long Short-Term Memory (LSTM) neural network. Through this artifact, we construct a portfolio to trade bitcoin in three stages. In the first stage, signals and prices are used as inputs for the LSTM model to predict bitcoin prices. In the second
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Shutdown and compliance decisions in the face of a viral pandemic: A game between governments and citizens Decis. Support Syst. (IF 7.5) Pub Date : 2023-11-27 Puneet Agarwal, Kyle Hunt, Esther Jose, Jun Zhuang
During viral pandemics, governments throughout the world face many complicated and important decisions, such as whether and how to implement a shutdown. In response to shutdown measures that are enacted by governments, citizens are then forced to make decisions regarding whether to comply (and stay home) or violate (and leave their residence for non-essential reasons). To the best of our knowledge
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Decision aggregation with reliability propagation Decis. Support Syst. (IF 7.5) Pub Date : 2023-11-27 Hao Zhong, Yuyue Chen, Chuanren Liu, Hande Benson
People often make decisions differently, even when faced with the same decision-making scenario and objectives, due to their varying abilities to access, process, and comprehend information relevant to the decisions at hand. To reconcile these differing perspectives and arrive at a unified decision, various approaches such as crowd-sourced systems have been developed to tap into the collective intelligence
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More to tip, or tip more? Examining consumers' preservice tipping behavior in the on-demand supermarket delivery context Decis. Support Syst. (IF 7.5) Pub Date : 2023-11-22 Yan Wen, Hongyan Dai, Xun Xu, Tingting Tong
Consumers' preservice tipping behavior serves as a window to reflect their economic and social perception and desire for high service quality but is rarely discussed in tipping literature, compared with postservice tipping. The on-demand feature in the online-to-offline context complicates the fulfillment performance and consumers' preservice tipping behavior, including whether to tip (i.e., tip giving)
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A contrast-composition-distraction framework to understand product photo background's impact on consumer interest in E-commerce Decis. Support Syst. (IF 7.5) Pub Date : 2023-11-15 Mengyue Wang, Xin Li, Yidi Liu, Patrick Chau, Yubo Chen
In e-commerce, product photos are a major component of product presentations that aid consumers' understanding of products. In this study, we investigate the impact of the background of product photos on consumers' interest. Drawing upon the attention theories of visual perception, we propose a contrast-composition-distraction framework to understand the product photo background's impact. We conduct