Decision Sciences, a premier journal of the Decision Sciences Institute, publishes scholarly research about decision making within the boundaries of an organization, as well as decisions involving inter-firm coordination. The journal promotes research advancing decision making at the interfaces of business functions and organizational boundaries. The journal also seeks articles extending established lines of work assuming the results of the research have the potential to substantially impact either decision making theory or industry practice. Ground-breaking research articles that enhance managerial understanding of decision making processes and stimulate further research in multi-disciplinary domains are particularly encouraged.

Decision Sciences is a premier journal of the Decision Sciences Institute that publishes scholarly research on decision making within organizations and decisions involving inter-firm coordination. The journal promotes research that advances decision making at the interfaces of business functions and organizational boundaries. It also seeks articles that extend established lines of work, assuming that the research results have the potential to significantly impact either decision making theory or industry practice. Ground-breaking research articles that enhance managerial understanding of decision making processes and stimulate further research in multi-disciplinary domains are particularly encouraged.

《Decision Sciences》是决策科学学会的顶级期刊，发表关于组织内部决策以及涉及企业间协调的学术研究。该期刊促进在商业职能和组织边界交界处的决策研究。期刊还寻求扩展既有研究方向的文章，前提是研究结果有潜力对决策理论或行业实践产生重大影响。特别鼓励那些能够增强管理者对决策过程理解并激发多学科领域进一步研究的开创性研究文章。

Decision Sciences

ABSTRACT A variety of mathematical forms have been developed to characterize demand functions which depend on a firm's operational and marketing activities. Such demand functions are being increasingly used by researchers in economics and different functional areas of business. We provide a comprehensive survey of commonly used demand models which depend on (i) price, (ii) rebate, (iii) lead time, (iv) space, (v) quality, and (vi) advertising. Our survey includes single firm–demand models in each category, as well as game theoretic multifirm models involving strategic interaction among the firms. We observe that certain types of functional forms, such as linear, power/iso‐elastic, multinomial logit, and multiplicative competitive interaction, have been widely used to construct various demand models in all six categories, but that a large majority of publications deal with categories (i) and (v) of demand models. For each of the six categories, we survey relevant functional forms in the representative papers, and discuss the main properties, the advantages, the disadvantages, and comment on possible future research directions. We also present discussions of the applications of these analytical demand models in empirical studies. The article ends with a summary of our major findings.

Jian Huang

Demand Functions in Decision Modeling: A Comprehensive Survey and Research Directions

决策建模中的需求函数：综合调查与研究方向

ABSTRACT We consider inventory management of a firm that orders periodically from a distantly located supplier and faces random delays in shipment. We analyze various response strategies that the firm may employ to mitigate the risk of demand–supply mismatch resulting from shipment delays. We categorize these strategies along two dimensions: operational flexibility , defined by the flexible resource employed; and lead‐time visibility , defined by the availability of information on shipment delays. For each of the response strategies, we provide analytical characterization of the optimal inventory policy. By comparing the optimal policies across response strategies, we offer insights into the effect of operational flexibility and lead‐time visibility on firm's inventory performance. Using an extensive set of numerically solved examples, we provide cost comparison across these strategies—we show that while the operational flexibility mitigates the adverse effect of long lead‐times, the lead‐time visibility provides protection from lead‐time uncertainty. Further, operational flexibility and lead‐time visibility enhance each other's values, hence must be employed together to gain their full benefits.

Aditya Jain

Responding to Shipment Delays: The Roles of Operational Flexibility &amp; Lead‐Time Visibility

应对发货延迟：运营灵活性与交货时间可见性的作用

ABSTRACT Prior research has provided valuable insights into how and why employees make a decision about the adoption and use of information technologies (ITs) in the workplace. From an organizational point of view, however, the more important issue is how managers make informed decisions about interventions that can lead to greater acceptance and effective utilization of IT. There is limited research in the IT implementation literature that deals with the role of interventions to aid such managerial decision making. Particularly, there is a need to understand how various interventions can influence the known determinants of IT adoption and use. To address this gap in the literature, we draw from the vast body of research on the technology acceptance model (TAM), particularly the work on the determinants of perceived usefulness and perceived ease of use, and: (i) develop a comprehensive nomological network (integrated model) of the determinants of individual level (IT) adoption and use; (ii) empirically test the proposed integrated model; and (iii) present a research agenda focused on potential pre‐ and postimplementation interventions that can enhance employees' adoption and use of IT. Our findings and research agenda have important implications for managerial decision making on IT implementation in organizations.

Viswanath Venkatesh

Hillol Bala

Technology Acceptance Model 3 and a Research Agenda on Interventions

技术接受模型3及干预研究议程

ABSTRACT Persuading users to adopt new information technologies persists as an important problem confronting those responsible for implementing new information systems. In order to better understand and manage the process of new technology adoption, several theoretical models have been proposed, of which the technology acceptance model (TAM) has gained considerable support. Beliefs and attitudes represent significant constructs in TAM. A parallel research stream suggests that individual difference factors are important in information technology acceptance but does not explicate the process by which acceptance is influenced. The objective of this paper is to clarify this process by proposing a theoretical model wherein the relationship between individual differences and IT acceptance is hypothesized to be mediated by the constructs of the technology acceptance model. In essence then, these factors are viewed as influencing an individual's beliefs about an information technology innovation; this relationship is further supported by drawing upon extensive research in learning. The theoretical model was tested in an empirical study of 230 users of an information technology innovation. Results confirm the basic structure of the model, including the mediating role of beliefs. Results also identify several individual difference variables that have significant effects on TAM's beliefs. Theoretical contributions and practical implications that follow are discussed.

Ritu Agarwal

Jayesh Prasad

Are Individual Differences Germane to the Acceptance of New Information Technologies?

个体差异与新信息技术接受度有关吗？

Current concepts in the field of Total Quality Management (TQM) are largely based upon case studies, anecdotal evidence and the prescriptions of leading “gurus.” Consequently, there is little consensus on which factors are critical to the success of the approach. Few attempts have been made to scientifically synthesise frameworks for measuring quality management practices, and a methodology for examining the issue has yet to be established. This has meant that current TQM models, such as the Malcolm Baldrige Award, have not been constructed or validated by empirical means. This paper presents a research methodology that can be used to improve self‐assessment frameworks, such as the Baldrige Award, to better inform organisations in the development of Total Quality Systems. The research extracts a series of items from the Baldrige model and established literature. These items formed the basis of a questionnaire sent to over 200 managers. Data was examined using several well‐established analytical techniques that identified 10 critical factors of TQM. These factors were shown to be reliable and valid, and offer new insights into our understanding of TQM.

Simon A. Black

Leslie J. Porter

Identification of the Critical Factors of TQM*

全面质量管理关键因素的识别

Abstract Hydrogen trucks (HTs) offer promising potential for decarbonizing the transportation sector. Based on current technologies, they have significant advantages over electric trucks (ETs) in terms of range, refueling time, and performance in cold conditions. However, HTs are costly, and there are insufficient hydrogen refueling stations (HRSs). Gradually integrating HTs into the existing diesel truck (DT) fleet is a practical approach for many freight logistics companies. In this article, we formulate a mathematical model to route a mixed fleet of HTs and DTs, and we propose an algorithm called the curve descent search (CDS) to generate the Pareto set based on cost and carbon emissions. We find that CDS can generate better Pareto sets compared to existing algorithms in the literature. We use CDS to comprehensively explore the cost–carbon trade‐off in using a mixed fleet. This question differentiates our study from previous research and is motivated by discussions with one of the largest third‐party logistics companies in North America. Detailed experiments reveal important managerial insights, such as: (1) Achieving a significant reduction in carbon emissions (e.g., a 30% reduction compared to the current diesel fleet) does not need a very dense refueling infrastructure; (2) The cost–carbon trade‐off for mixed fleets is relatively insensitive to variations in customer density and demand, suggesting that HTs can be applicable across a wide range of scenarios (including different sectors or regions); and (3) Although ETs are cheaper to use compared to HTs, their shorter range limits their competitiveness in terms of decarbonization efficiency and customer service.

Siqiang Guo

Erhan Kutanoglu

Shadi Goodarzi

Manjeet Singh

Exploring the cost–carbon trade‐off in using a mixed fleet of hydrogen trucks and diesel trucks

探索使用氢动力卡车与柴油卡车混合车队的成本-碳排放权衡

Abstract 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.

Tracy Jenkin

Stephanie Kelley

Anton Ovchinnikov

Cecilia Ying

Explanation seeking and anomalous recommendation adherence in human‐to‐human versus human‐to‐artificial intelligence interactions

解释寻求与异常推荐依从性：人机交互与人机交互的对比

Abstract 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.

Yue Li

Ruiqing Zhao

Xiang Li

Tsan‐Ming Choi

Fine‐tuning of artificial intelligence managers' logic in a supply chain with competing retailers

供应链中竞争零售商的人工智能管理者逻辑微调

Abstract 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.

Zhi Cao

Meng Li

Paul A Pavlou

AI in business research

商业研究中的AI

Abstract 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.

Abbreviation	Decis. Sci.
Journal Impact	2.76
Quartiles(Global)	MANAGEMENT(Q2)

ISSN	0011-7315, 1540-5915
h-index	123

Publication Information	Publisher: Wiley-Blackwell Publishing Ltd，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1970，Proportion of original research papers: 100.00%，Self Citation Rate:3.60%， Gold OA Rate: 11.70%

Decision Sciences

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