Decision Support Systems最新文献

Since the Economist magazine heralded blockchain as “the trust machine” in 2015, the blockchain paradigm has experienced crests and falls, including a recent phase of disillusionment due to its failure to meet the high expectations, e.g., to revolutionize record keeping, data management, and workflow, envisioned during its early history. However, despite the waning interest in this technology in some quarters, its deployment has become ever more essential in areas such as decentralized finance (DeFi), Non-fungible Tokens (NFTs), and other application domains beyond cryptocurrencies. In particular, recent advancements in Artificial Intelligence (AI) surrounding Large Language Models (LLM) offer new opportunities for blockchain adoption where trust and reliability become critical. As the blockchain technology transitions from a stage of disillusionment to one of enlightenment, anticipation is building for its mainstream adoption, with focused endeavors towards removing adoption barriers across diverse business contexts, exemplified by studies included in this special issue on Blockchain Technology and Applications. In this paper, we first survey the current state of the blockchain technology and then highlight its potential for enhancing trust and accountability in emerging phenomena such as AI generated content (AIGC). We conclude by introducing the papers included in the special issue.

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 processes related to community participation. This article is supported by two studies, with the second study building upon the first. Study 1 retrospectively explores selection mechanisms by drawing from network theory, social capital theory, and motivation theory. Through principal component analysis, these mechanisms are identified and categorized as community selection mechanisms. In Study 2, the focus shifts to examining whether these mechanisms lead to differences in community engagement behaviors. These behaviors encompass intentions to continue participating, knowledge sharing, and electronic word-of-mouth (e-WOM). By comparing various communities based on their characteristics, the results reveal that each selection mechanism holds varying degrees of importance in influencing community engagement. For instance, content gratification is a key mechanism for the selection of professional and travel communities, but it lacks significance as a predictor for the game community. These findings not only advance our understanding of community selection mechanisms but also provides valuable insights for businesses looking to optimize their decision-making processes.

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 the stability of explanations amidst various changes in the ML pipeline. Our results show that modifications such as altered preprocessing or different ML models lead to changes in the explanations and illustrate the extent to which stability can suffer. Explanation similarity analysis enables practitioners to compare different explanation outcomes, thus monitoring stability in explanations. Alongside discussing the results and practical applications in operationalized ML, including both benefits and limitations, we also delve into insights from computational neuroscience and neural information processing.

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, we develop a novel deep learning-enhanced global planning (DeepGP) approach featuring three methodological novelties. First, we introduce a new shopping intensity term based on deep neural networks to capture the variation of trip lengths specific to different shopping contexts. Second, we innovatively formulate the learning and optimization objectives in a consistent form by balancing the shopping choice likelihood and the shopping intensity likelihood, thus resolving the inconsistency issue encountered by prior global planning methods. Third, to overcome the computational challenge caused by the nonlinear shopping intensity term, we design a new exact and efficient solution technique based on piecewise linear transformations. Using a real-world offline shopping dataset, we empirically demonstrate the superior performances of our approach compared to representative benchmarks in offering more accurate and relevant shopping trip recommendations. Through a simulation, we show the capacity of our approach to attract and balance customer traffic in practical deployments. Overall, our research highlights the efficacy of combining shopping choices and shopping intensity in a consistent learning and optimization framework for offline shopping trip recommendations.

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 potential to address this long-standing issue. Specifically, NFTs empower creators by enabling them to earn resale royalties from post-primary-sale transactions and by providing verifiable ownership that facilitates consumer trading in a secondary market. In this paper, we employ a two-stage game-theoretic model to examine this innovative business model. Here, an NFT creator makes optimal pricing and royalty rate decisions, while consumers make their purchase and resale decisions accordingly. Our study reveals that the creator inevitably reduces the selling price when a secondary market is introduced, even without imposing royalties. Moreover, contrary to conventional wisdom that NFT-enabled royalties always benefit creators, our research uncovers that the introduction of a secondary market leads to unintended revenue loss for the creator, particularly when most consumers only engage in secondary transactions. Furthermore, we find that the platform's profitability diminishes with the introduction of a secondary market, especially when most consumers are uninformed and the platform relies primarily on the primary market for commission collection. Finally, we find that the introduction of a secondary market may leave consumers worse off, despite the resale opportunities it offers. Our findings carry crucial managerial implications for platforms, creators, consumers, and policymakers.

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 towards winnability in five ways. First, contestants performing well in the past do better in the present. Second, gaining more game experiences improves performance. Third, contestants who participated recently, tend to exhibit higher winnability. Fourth, selecting an appropriate contest type enhances winnability. Fifth, the large estimated signal-to-noise ratio indicates that the unobserved skill measured by a non-negative error has a much greater impact on winnability than the regular two-sided random shocks. These results are robust to varying specifications and subsets of data. Decision makers and regulators can use the model presented in the study to distinguish skill-dominant DFS from chance-dominant DFS.

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 true for male surgeons and female internal medicine doctors, not for female surgeons or male internal medicine doctors. These findings suggest the possible presence of gender-specialty bias in the influence of facial attractiveness on patients' decision-making. To further investigate how facial attractiveness influences users' inclination to choose a particular doctor, we develop our research model drawing upon the stereotype content and social role perspective. Through a laboratory experiment, we found that OHC users' perceptions of doctors' warmth and competence act as mediating factors in the relationship between facial attractiveness and user choice. Additionally, this relationship is influenced by stereotypical gender-specialty fit.

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. In supplemental analysis, we use eye-tracking data to provide preliminary evidence for behavioral differences—in terms of gaze fixation—when knowledge sharing requests accompanied by avatar profiles as opposed to photo profiles are processed and further explore how individuals' choice of using avatars vs. photographs for their online profile affects their co-workers' perception. Our study contributes to understanding cooperative organizational behavior in the virtual space. Managing cooperative organizational behavior in the virtual space is becoming increasingly important as digital work further penetrates contemporary work arrangements.

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 challenges and actionable guidance for the Design Science Research (DSR) community. We propose a DSR Transparency Framework containing six forms of transparency: process, problem space, solution space, build, evaluation, and contribution. For each, we discuss challenges with guidance to achieve effective DSR transparency throughout the publication process.

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 the other primarily subject to the effect of memory decay. We then propose an analytical model to capture the market demand during an online promotion. Our analytical result indicates that there exist four types of demand patterns during online promotions, i.e., U-shape, inverted U-shape, monotonically increasing, and monotonically decreasing. We subsequently explore factors that can affect the type of demand patterns, such as the product type (nondurable and durable goods), duration of the promotion, discount level, and product category. Our empirical analyses of real-world promotion and sales data from a B2C e-commerce platform validate the analytical results. The type of demand curves depends on the characteristics of the goods and promotions. For instance, the inverted U-shape demand curve appears only for nondurable consumer goods, whereas the U-shape curve exists only for durable consumer goods. Finally, in a series of counterfactual analyses based on the proposed model, we show how revenues change during an online promotion in response to varying parameters of promotions and derive some interesting observations. These findings provide important insights to online retailers and can help them better understand their consumers and optimize their product promotion strategies.