Decision Support Systems最新文献

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 market. We employ multivariate time series autoregressive models to test the relationship between emotion polarity and stock market returns or trading volumes. The results reveal that societal sentiment toward the pandemic has a significant effect on the returns of the Dow Jones Industrial Average and S&P 500. In contrast, the macro-level emotion does not significantly affect the return for NASDAQ 100. The findings also suggest a significant association between incidental emotion and trading volumes. We conduct a battery of sensitivity tests that further support our conjecture. The study underscores the robust role of incidental emotion in investment decision-making, highlighting its significance as a distinctive feature that should be incorporated into financial decision support systems.

Sectorization problems refer to dividing a large set, area or network into smaller parts concerning one or more objectives. A decision support system (DSS) is a relevant tool for solving these problems, improving optimisation procedures, and finding feasible solutions more efficiently. This paper presents a new web-based Decision Support System for Sectorization (D3S). D3S is designed to solve sectorization problems in various areas, such as school and health districting,planning sales territories and maintenance operations zones, or political districting. Due to its generic design, D3S bridges the gap between sectorization problems and a state-of-the-art decision support tool. The paper aims to present the generic and technical attributes of D3S by providing detailed information regarding the problem-solution approach (based on Evolutionary Algorithms), objectives (most common in sectorization), constraints, structure and performance.

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 types of ESM usage—work-related and social-related—and examines their impact on employee improvisation ability. Through the analysis of 307 paired data collected from multi-wave and multi-source questionnaires using Smart-PLS software, the results show that both work-related and social-related ESM use can promote employees' psychological meaningfulness, availability, and safety, thus further stimulating employees' improvisation ability. ESM policies only significantly moderated the effects of work-related ESM use on the three psychological conditions of employees. Moreover, there are significant differences in the intensity of the influence of the two types of ESM uses on the psychological conditions of employees. This study not only enriches and promotes the existing research on ESM usage, psychological conditions, and employee improvisation ability but also helps enterprise management effectively guide employees to use ESM to promote their improvisation ability.

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, thus hindering effective decision-making. To fill this gap, uses and gratification theory is applied to build a model for explaining the mechanism underlying the influence of game escapism and telepresence on game experience and game use. We collect 1347 online gamer responses with which to test the model. The results indicate that game escapism improves all game experiences, while only enjoyment and concentration increase game use. Moreover, telepresence strengthens the impact of game escapism on enjoyment, concentration, and fantasy. Our findings offer insights for decision-makers, enabling them to leverage game mechanisms to either provide or negate the impact of game escapism, thus changing game use.

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 on contrastive disentanglement have proven highly effective. In these methods, PLMs can disentangle sensitive information from non-sensitive information in sentence embedding, and then adapts non-sensitive information only for downstream tasks. Such approaches hinge on having good sentence embedding as input. However, recent research found that most non-fine-tuned PLMs such as BERT produce poor sentence embedding. Disentangling based on these embedding will lead to unsatisfactory debiasing results. Taking a finer-grained perspective, we propose PCFR (Prompt and Contrastive-based Fair Representation), a novel disentanglement method integrating prompt and contrastive learning to debias PLMs. We employ prompt learning to represent information as sensitive embedding and subsequently apply contrastive learning to contrast these information embedding rather than the sentence embedding. PCFR encourages similarity among different non-sensitive information embedding and dissimilarity between sensitive and non-sensitive information embedding. We mitigate gender and religion biases in two prominent PLMs, namely BERT and GPT-2. To comprehensively assess debiasing efficacy of PCFR, we employ multiple fairness metrics. Experimental results consistently demonstrate the superior performance of PCFR compared to representative baseline methods. Additionally, when applied to specific downstream decision tasks, PCFR not only shows strong de-biasing capability but also significantly preserves task performance.

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 (honest description and positive description) using machine learning and rule-based text analysis methods. Then we verified the different effects of two host description strategies on each of consumer behaviors based on a panel dataset from Airbnb. Positive description and honest description have a positive impact on room booking and consumer satisfaction respectively. Room price moderates the relationship between host descriptions and consumer behaviors. A highly positive description strategy can promote bookings for high-priced listings but decrease satisfaction. The honest description strategy has a positive effect on the bookings of low-priced listings. This study contributes to tourism literature and property hosts in practice.

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 product differentiations: search and experience differentiation, as well as vertical and horizontal differentiation. Our findings reveal that self-selection bias can have varying positive and negative effects on the usefulness of online reviews in suggesting product quality (i.e., review utility) to buyers, thus affecting buyer satisfaction. While self-selection bias tends to decrease review utility in most scenarios, interestingly, it can also increase review utility by enabling a “screening” function of online reviews in addition to its normal “measuring” function. We also find that the varying effects of self-selection bias on buyer satisfaction are contingent upon the type of products under scrutiny and the interaction of different types of self-selection bias. This research makes valuable contributions to the existing literature on online reviews by introducing a novel theory to explain the effects of self-selection bias on buyer satisfaction.

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 solutions exhibit limited capabilities for capturing and modeling users' needs to execute on-demand data integration. This study, underpinned by affordance theory and the goal definition principles from the Goal-Question-Metric approach, designs and instantiates a goal-driven data integration framework for data analytics. The proposed innovative design automates data integration for non-technical data users. Specifically, it demonstrates how to elicit and ontologize users' data-analytic goals and addresses semantic heterogeneity, thereby recognizing goal-relevant datasets. In a structured evaluation using the context of counter-terrorism analytics, our design artifact shows promising performance in capturing diverse and dynamic user goals for data analytics and in generating integrated data tailored to these goals. Our research establishes a theoretical framework to guide future scholars and practitioners in building smart, goal-driven data integration.

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 using modern machine learning techniques. In view of the recent Responsible Artificial Intelligence Strategy published by the United States Department of Defense, this research leverages interpretable and explainable machine learning methods to create traceable and equitable models that may be responsibly and reliably governed. These models are used to regress candidates’ average merit assignment selection system scores based on information available for selection and prior to training. More specifically, using data provided by the USAF from 2010 to 2018, this paper develops and analyzes multiple interpretable models based on Gaussian Bayesian networks, as well as multiple black-box models rendered explainable by SHAP values and conformal prediction. A preferred pair of interpretable and explainable models is selected and embedded within a DSS for USAF pilot candidate selection boards: the Air Force Pilot Applicant Selection System. The utilization of this DSS is explored, the analyses it enables are discussed, and relevant USAF policymaking issues are examined.

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) but also without user prompts (i.e., IS-invoked delegation). Yet, little is known about whether, how, and why users agree to delegation when they are asked by the IS in contrast to when they self-initiate the delegation. Drawing on self-affirmation theory, we investigate user acceptance of IS- versus user-invoked delegation in two complementary online experiments in software development. Our core findings reveal that IS-invoked (vs. user-invoked) delegation increases users' perceived self-threat and thus decreases their willingness to accept delegation. This threatening effect is larger the less (vs. more) the user perceives control after the potential delegation. Taken together, we uncover defensive user responses to IS-invoked delegation. Furthermore, we shed light on the underlying and moderating mechanisms representing the reasons and contextual features that explain and mitigate these defensive measures. These findings have significant implications for IS designers seeking to improve user-IS collaboration and outcomes by employing IS-invoked delegation.