首页期刊导航|Decision sciences: The journal for the American Institute for Decision Sciences
期刊信息/Journal information
Decision sciences: The journal for the American Institute for Decision Sciences
Decision Sciences Institute
Decision Sciences Institute
0011-7315
Decision sciences: The journal for the American Institute for Decision Sciences/Journal Decision sciences: The journal for the American Institute for Decision SciencesSSCIISSHP
查看更多>>摘要：Artificial intelligence (AI) has emerged as a pivotal force in modern business transformation, garnering widespread attention from both practitioners and academics. With a notable exponential increase in AI-related studies, we provide a research framework aiming to synthesize the existing literature on AI in the business field. We conduct a comprehensive review of AI research spanning from 2010 to 2023 in 25 leading business journals according to this review framework. Specifically, we review the literature from three research perspectives: (i) AI applications, (ⅱ) human perceptions of AI, and (ⅲ) AI behavior. We also identify five principal research questions and offer suggestions for future research directions.
查看更多>>摘要：Artificial intelligence (AI) has emerged as a pivotal force in modern business transformation, garnering widespread attention from both practitioners and academics. With a notable exponential increase in AI-related studies, we provide a research framework aiming to synthesize the existing literature on AI in the business field. We conduct a comprehensive review of AI research spanning from 2010 to 2023 in 25 leading business journals according to this review framework. Specifically, we review the literature from three research perspectives: (i) AI applications, (ⅱ) human perceptions of AI, and (ⅲ) AI behavior. We also identify five principal research questions and offer suggestions for future research directions.
查看更多>>摘要：By mitigating supply-demand mismatch through advanced forecast technology, demand learning has attracted widespread attention and is increasingly adopted in conjunction with inventory sharing. However, this combination is not necessarily efficient given the unclear relationship between the two strategies. Therefore, crucially, this article investigates the strategic relationship between inventory sharing and demand learning, that is, when and whether they are substitutes or complements. We develop a theoretical game model consisting of two firms facing uncertain demand, and both of them need to determine their production quantity before demand is realized. Contrary to the intuition that demand learning is a substitute for inventory sharing, we find that these two strategies can be complements when the production cost is relatively low or high. Moreover, when forecast accuracy is relatively low, the substitutability will be weakened while the complementarity will be enhanced as forecast accuracy increases. Additionally, the substitutability first weakly decreases and then weakly increases, while the complementarity first weakly increases and then weakly decreases with the transfer price.
Murtaza NasirNichalin S. SummerfieldSerhat SimsekAsil Oztekin...
549-576页
查看更多>>摘要：Machine learning (ML) models are increasingly being used in decision-making, but they can be difficult to understand because most ML models are black boxes, meaning that their inner workings are not transparent. This can make interpreting the results of ML models and understanding the underlying data-generation process (DGP) challenging. In this article, we propose a novel methodology called Simple Interaction Finding Technique (SIFT) that can help make ML models more interpretable. SIFT is a data- and model-agnostic approach that can be used to identify interaction effects between variables in a dataset. This can help improve our understanding of the DGP and make ML models more transparent and explainable to a wider audience. We test the proposed methodology against various factors (such as ML model complexity, dataset noise, spurious variables, and variable distributions) to assess its effectiveness and weaknesses. We show that the methodology is robust against many potential problems in the underlying dataset as well as ML algorithms.
查看更多>>摘要：Forecasting refined oil sales is essential in energy supply chain management. However, accurate forecasting is limited by several factors, including multiple influences of external features, heterogeneity of different gasoline stations, and difficulty in balancing linear and nonlinear forecasting. To address these issues, we propose a novel variable-weight combined forecasting model. In the first stage, the model incorporates causal analysis and clustering methods to provide a quantitative description of multiple effects of external features and highly correlated aggregation of homogeneous data. Subsequently, based on the patterns of external feature influences learned from historical data, variable-weight combined forecasting is realized to balance linear and nonlinear forecasting dynamically. Experiments based on real sales data procured from several regions demonstrate that the proposed model outperforms other benchmark and widely used models in terms of forecasting accuracy and statistical significance. The ablation experimental results confirm the significance of causal analysis, clustering, and variable-weight combined forecasting in improving the balance between linear and nonlinear forecasting. Moreover, our results indicate that improving the quality of clustering can yield greater benefits than improving the amount of training data. Finally, we also explore whether the forecasting superiority translates into better inventory control, and our results show that the proposed optimization model can effectively balance inventory cost and service level, while also better suppress the bullwhip effect.
Cascha van WanrooijFrans CruijssenJuan Sebastian Olier
605-619页
查看更多>>摘要：This article introduces an artificial intelligence (AI)-based system for forecasting food insecurity in data-limited settings, employing unsupervised neural networks for topic modeling on news data. Unlike traditional methods, our system operates without relying on expert assumptions about food insecurity factors. Through a case study in Somalia, we show that the method can yield competitive performance, even in the absence of traditional food security indicators such as food prices. This system is valuable in supporting expert assessments of food insecurity, unlocking a wealth of untapped information from news outlets, and offering a path toward more frequent and automated food insecurity monitoring for timely crisis intervention.
Anfei XiaSandun C. PereraMuhammad U. AhmedJianying Tang...
620-638页
查看更多>>摘要：Online medical communities (OMCs) are a type of online healthcare, in which physician-patient interaction can be comprised of a variety of media options such as pictures, text, and voice. These media formats are often used to create a personalized patient experience in AI-driven conversational healthcare platforms. To explore how physician media usage affects patient experience, we propose a counterfactual causal inference model using AI-driven data mining methods on 131,083 online consultation records and 7,666,111 messages sent by physicians from one of the largest OMCs in China. Our study reveals the negative impact of physician use of voice on patient experience, compared to text. Drawing upon social support theory, we identify the mechanism by which physician media usage for voice produces a negative effect. The findings indicate that the negative effect of physicians' voice-media usage occurs mainly in low-risk disease conditions, by weakening the role of professional and emotional support. In contrast, in high-risk disease conditions, voice-media usage strengthens the role of professional and emotional support in improving the patient's experience. Our study is one of the first to focus on the social support attributes of the different media formats used in OMCs. We use advanced AI text-analysis algorithms to extract features related to social support in physician-patient conversations, and thus contribute to the use of AI in feature extraction for research.
查看更多>>摘要：Today, with the advance of artificial intelligence, companies in the real world are using AI as managers to make operational decisions, who can respond quickly to market shocks and whose logic can be fine-tuned to programmed pessimism/optimism, that is, underestimating/overestimating the market. The introduction of AI managers poses new challenges to supply chain management, and how to manage AI managers warrants further exploration. We investigate the optimal AI manager fine-tuning strategies in a supply chain consisting of one manufacturer and two competing retailers, each operated by an AI manager in the face of an uncertain market shock. We establish the manufacturer-retailer AI manager fine-tuning game, where the manufacturer and two retailers endogenously decide whether to fine-tune their AI managers' logic. The market may suffer an uncertain shock, and once the shock occurs, the AI managers' logic settings and price decisions can be quickly adjusted. We find that the manufacturer would never fine-tune the AI manager, while the retailers may fine-tune their AI managers to programmed optimism. Notably, AI manager's fine-tunability only benefits the retailers and harms the manufacturer, entire supply chain, consumers, and social welfare. To make AI manager's fine-tunability beneficial to all participants, that is, to reach a win-win-win situation, we design two incentive mechanisms, retailer pessimism incentive mechanism and mutual pessimism incentive mechanism (MPIM), where MPIM can lead to the win-win-win situation. Further, we endogenize the compensation, endogenous retailer pessimism compensation and endogenous mutual pessimism compensation, both achieving the win-win-win outcome. We also make several extensions and provide suggestions for supply chain firms to fine-tune their AI managers' logic.
查看更多>>摘要：The use of artificial intelligence (AI) in operational decision-making is growing, but individuals can display algorithm aversion, preventing adherence to AI system recommendations-even when the system outperforms human decision-makers. Understanding why such algorithm aversion occurs and how to reduce it is important to ensure AI is fully leveraged. While the ability to seek an explanation from an AI may be a promising approach to mitigate this aversion, there is conflicting evidence on their benefits. Based on several behavioral theories, including Bayesian choice, loss aversion, and sunk cost avoidance, we hypothesize that if a recommendation is perceived as an anomalous loss, it will decrease recommendation adherence; however, the effect will be mediated by explanations and differ depending on whether the advisor providing the recommendation and explanation is a human or an AI. We conducted a survey-based lab experiment set in the online rental market space and found that presenting a recommendation as a loss anomaly significantly reduces adherence compared to presenting it as a gain, however, this negative effect can be dampened if the advisor is an AI. We find explanation-seeking has a limited impact on adherence, even after considering the influence of the advisor; we discuss the managerial and theoretical implications of these findings.
NETL
NSTL
Wiley