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Bridging information systems and marketing: Charting collaborative pathways Decis. Support Syst. (IF 6.7) Pub Date : 2024-09-07 Stephen L. France, Mahyar Sharif Vaghefi, Brett Kazandjian, Merrill Warkentin
Corporate information systems (IS) functions have become ever closer and more intertwined with firms' marketing functions. Marketing technology and e-commerce implementations require synergy between these functions, which has been reflected in the emergence of researchers and practitioners who can work at the intersection of these disciplines. This article utilizes a systematic literature review to
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How credibility assessment technologies affect decision fairness in evidence-based investigations: A Bayesian perspective Decis. Support Syst. (IF 6.7) Pub Date : 2024-09-06 Xinran Wang, Zisu Wang, Mateusz Dolata, Jay F. Nunamaker
Recently, a growing number of credibility assessment technologies (CATs) have been developed to assist human decision-making processes in evidence-based investigations, such as criminal investigations, financial fraud detection, and insurance claim verification. Despite the widespread adoption of CATs, it remains unclear how CAT and human biases interact during the evidence-collection procedure and
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Channel and bundling strategies: Forging a “win-win” paradigm in product and service operations Decis. Support Syst. (IF 6.7) Pub Date : 2024-09-06 Yudi Zhang, Xiaojun Wang, Bangdong Zhi, Jie Sheng
While many companies have benefited from online sales as their sole sales channel with the rapid growth of online retailing, this approach has limitations, especially for products that contain non-digital information and require a complementary service to fully attract customers. Sellers of these types of products are actively considering or have already adopted a multichannel strategy, which includes
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Modeling the co-diffusion of competing memes in online social networks Decis. Support Syst. (IF 6.7) Pub Date : 2024-09-04 Saike He, Weiguang Zhang, Jun Luo, Peijie Zhang, Kang Zhao, Daniel Dajun Zeng
Online social networks have greatly facilitated the spread of information of all sorts. Meanwhile, the abundance of information in today's world also means different pieces of information will increasingly compete for people's finite attention. When different pieces of information spread together in an online social network, why would some become trendy while others fail to emerge? Existing research
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Emotional expressions of care and concern by customer service chatbots: Improved customer attitudes despite perceived inauthenticity Decis. Support Syst. (IF 6.7) Pub Date : 2024-08-30 Junbo Zhang, Jiandong Lu, Xiaolei Wang, Luning Liu, Yuqiang Feng
In customer service, emotional expressions by chatbots are considered a promising direction to improve customer experience. However, there is a lack of comprehensive understanding of how and when chatbots' emotional expressions improve customer attitudes. Although chatbots' emotional expressions of care and concern may feel inauthentic to customers in the inferential path, which can negatively affects
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What can we learn from multimorbidity? A deep dive from its risk patterns to the corresponding patient profiles Decis. Support Syst. (IF 6.7) Pub Date : 2024-08-30 Xiaochen Wang, Runtong Zhang, Xiaomin Zhu
Multimorbidity, the presence of two or more chronic conditions within an individual, represents one of the most intricate challenges for global health systems. Traditional single-disease management often fails to address the multifaceted nature of multimorbidity. Network model emerges as a growing field for elucidating the interconnections among multimorbidity. However, the field lacks a standardized
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Learning-based dynamic pricing strategy with pay-per-chapter mode for online publisher with case study of COL Decis. Support Syst. (IF 6.7) Pub Date : 2024-08-27 Lang Fang, Zhendong Pan, Jiafu Tang
We consider how to make dynamic pricing decision for Chinese Online (COL) at time-points, an online publisher that allow authors to sell their ongoing book projects. Instead of paying for a book, readers pay for each chapter (pay-per-chapter mode) of the ongoing book project. This mode allows readers to pay for as many chapters as they want without taking the risk that the releasing of new chapters
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Approaches to improve preprocessing for Latent Dirichlet Allocation topic modeling Decis. Support Syst. (IF 6.7) Pub Date : 2024-08-27 Jamie Zimmermann, Lance E. Champagne, John M. Dickens, Benjamin T. Hazen
As a part of natural language processing (NLP), the intent of topic modeling is to identify topics in textual corpora with limited human input. Current topic modeling techniques, like Latent Dirichlet Allocation (LDA), are limited in the pre-processing steps and currently require human judgement, increasing analysis time and opportunities for error. The purpose of this research is to allay some of
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Reliability estimation for individual predictions in machine learning systems: A model reliability-based approach Decis. Support Syst. (IF 6.7) Pub Date : 2024-08-22 Xiaoge Zhang, Indranil Bose
The conventional aggregated performance measure (i.e., mean squared error) with respect to the whole dataset would not provide desired safety and quality assurance for each individual prediction made by a machine learning model in risk-sensitive regression problems. In this paper, we propose an informative indicator to quantify model reliability for individual prediction (MRIP) for the purpose of safeguarding
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Generalized visible curvature: An indicator for bubble identification and price trend prediction in cryptocurrencies Decis. Support Syst. (IF 6.7) Pub Date : 2024-08-21 Qun Zhang, Canxuan Xie, Zhaoju Weng, Didier Sornette, Ke Wu
We propose a novel curvature-based indicator constructed on log-price time series that captures an interplay between trend, acceleration, and volatility found relevant to quantify risks and improve trading strategies. We apply it to diagnose explosive bubble-like behaviors in cryptocurrency price time series and provide early warning signals of impending market shifts or increased volatility. This
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Enhanced (cyber) situational awareness: Using interpretable principal component analysis (iPCA) to automate vulnerability severity scoring Decis. Support Syst. (IF 6.7) Pub Date : 2024-08-20 Motahareh Pourbehzadi, Giti Javidi, C. Jordan Howell, Eden Kamar, Ehsan Sheybani
The Common Vulnerability Scoring System (CVSS) is widely used in the cybersecurity industry to assess the severity of vulnerabilities. However, manual assessments and human error can lead to delays and inconsistencies. This study employs situational awareness theory to develop an automated decision support system, integrating perception, comprehension, and projection components to enhance effectiveness
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Analyzing the online word of mouth dynamics: A novel approach Decis. Support Syst. (IF 6.7) Pub Date : 2024-08-12 Xian Cao, Timothy B. Folta, Hongfei Li, Ruoqing Zhu
In today's digital economy, virtually everything from products and services to political debates and cultural phenomena can spark WOM on social media. Analyzing online WOM poses at least three challenges. First, online WOM typically consists of unstructured data that can transform into myriad variables, necessitating effective dimension reduction. Second, online WOM is often continuous and dynamic
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Uplift modeling and its implications for appointment date prediction in attended home delivery Decis. Support Syst. (IF 6.7) Pub Date : 2024-08-03 Dujuan Wang, Qihang Xu, Yi Feng, Joshua Ignatius, Yunqiang Yin, Di Xiao
Successful attended home delivery (AHD) is the most important aspect of e-commerce order fulfillment. Prior literature focuses on incentive scheme development for customers' choices of delivery windows and predictive analytics for delivery results, but it is not clear whether the effect of AHD on the appointment date set by customers increases the success rate of AHD. Therefore, we developed an uplift
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Incentive hierarchies intensify competition for attention: A study of online reviews Decis. Support Syst. (IF 6.7) Pub Date : 2024-07-30 Baojun Zhang, Zili Zhang, Kee-Hung Lai, Ziqiong Zhang
While many online platforms use incentive hierarchies to stimulate consumers to generate more online reviews, the extent to which these hierarchies influence reviewer behavior is not fully understood. This study, drawing on image motivation theory and consumer attention theory, takes a novel approach to investigate whether reviewers strategically adjust their review behavior after reaching higher ranks
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Guiding attention in flow-based conceptual models through consistent flow and pattern visibility Decis. Support Syst. (IF 6.7) Pub Date : 2024-07-28 Kathrin Figl, Pnina Soffer, Barbara Weber
A critical part of flow-based conceptual modeling, such as process modeling, is visualizing the logical and temporal sequence in which activities in a process should be completed. While there are established standards and recommendations, there is limited empirical research examining the influence of process model layout on model comprehension. To address this research gap, we conducted a controlled
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Bridging realities into organizations through innovation and productivity: Exploring the intersection of artificial intelligence, internet of things, and big data analytics in the metaverse environment using a multi-method approach Decis. Support Syst. (IF 6.7) Pub Date : 2024-07-26 Ashutosh Samadhiya, Rohit Agrawal, Anil Kumar, Sunil Luthra
This study investigates how organizations may increase innovation and productivity through the Metaverse environment efficacy (MVEE), Artificial intelligence usage (AIU), Internet of Things usage (IoTU), and Big Data Analytics usage (BDAU). The study gathers responses from the gaming, information technology, and entertainment industries, using a multi-method involving Partial Least Squares Structural
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The value of data, machine learning, and deep learning in restaurant demand forecasting: Insights and lessons learned from a large restaurant chain Decis. Support Syst. (IF 6.7) Pub Date : 2024-07-23 Bongsug (Kevin) Chae, Chwen Sheu, Eunhye Olivia Park
The restaurant industry has been slow to adopt analytics for the supply chain, operations, and demand forecasting, with limited research on this sector. The COVID-19 pandemic's significant impact on the restaurant industry, one of the hardest-hit sectors, has underscored the need for digital technologies and advanced analytics for managing supply chains and making operational decisions. This paper
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From whales to minnows: The impact of crypto-reward fairness on user engagement in social media Decis. Support Syst. (IF 6.7) Pub Date : 2024-07-18 Woojin Yang, Yeongin Kim, Tae Hun Kim, Chul Ho Lee, Yasin Ceran
In an era where user-generated content drives social media growth, effectively incentivizing contributions remains a challenge. This study explores the empirical impact of a crypto-integrated platform, Steemit, focusing on a system transition designed to enhance fairness in reward distribution. We assess how this shift affects user engagement, specifically through the volume of posts. Our findings
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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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Live streaming channel recommendation based on viewers' interaction behavior: A hypergraph approach Decis. Support Syst. (IF 6.7) Pub Date : 2024-07-01 Li Yu, Wei Gong, Dongsong Zhang
Live streaming has become increasingly popular in recent years. Viewers of live streaming channels can interact with live streamers through various behaviors, such as sending virtual gifts and Danmaku. It is very critical to accurately model such viewers' behaviors, which reflect their interest, for recommending live streaming channels. However, existing studies on live streaming channel recommendation
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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