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 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 During the last decade, several retailers have started to combine traditional store deliveries with the fulfillment of online sales to consumers from omni‐channel warehouses, which are increasingly being automated. A popular option is to use autonomous mobile robots (AMRs) in collaboration with human pickers. In this approach, the pickers' unproductive walking time can be reduced even further by zoning the storage system, where the pickers stay within their zone periphery and robots transport order totes between the zones. However, the robotic systems' optimal zoning strategy is unclear: few zones are particularly good for large store orders, while many zones are particularly good for small online orders. We study the effect of no zoning (NZ) and progressive zoning strategies on throughput capacity for balanced zone configurations with both fixed and dynamic order profiles. We first develop queuing network models to estimate pick throughput capacity that correspond to a given number of AMRs and picking with a fixed number of zones. We demonstrate that the throughput capacity is dependent on the chosen zoning strategy. However, the magnitude of the gains achieved is influenced by the size of the orders being processed. We also show that using a dynamic switching strategy has little effect on throughput performance. In contrast, a fixed switching strategy benefiting from changes in the order profile has the potential to increase throughput performance by 17% compared to the NZ strategy, albeit at a higher robot cost.

Kaveh Azadeh

Debjit Roy

René de Koster

Seyyed Mahdi Ghorashi Khalilabadi

Zoning strategies for human–robot collaborative picking

人机协作拣选的分区策略

ABSTRACT Building on recent unique, yet potentially complementary, approaches to understanding the formation of user perceptions about technology (Venkatesh, 1999; Venkatesh &amp; Speier, 1999), the present work reanalyzes the data from both studies to develop an integrated model of technology acceptance. The integrated model specifically examines the influence of pre‐training and training environment interventions (termed user acceptance enablers) to understand how user perceptions are formed prior to system implementation. The model is then further extended and tested using longitudinal data in a field setting. The results indicate that the integrated model emerged as a better predictor of user behavior when compared to the existing models.

Cheri Speier

Michael G. Morris

User Acceptance Enablers in Individual Decision Making About Technology: Toward an Integrated Model

个体技术决策中用户接受促进因素：迈向综合模型

ABSTRACT Probabilistic selling has recently been introduced to facilitate consumer segmentation. It allows the retailer to mix products from multiple competing suppliers to generate a probabilistic good. The probabilistic good effectively creates consumer differentiation, and its presence invites extensive interactions among channel participants. In this article, we show that the equilibrium channel structure may be asymmetric: one supplier retains his brand‐name product and the other one delegates to the retailer. We further show that this asymmetric equilibrium can be mutually beneficial for all firms compared with other equilibria. In addition, the introduction of the probabilistic good is beneficial to the channel members.

Gangshu (George) Cai

Ying‐Ju Chen

Chi‐Cheng Wu

Lu Hsiao

Probabilistic Selling, Channel Structure, and Supplier Competition*

概率性销售、渠道结构与供应商竞争*

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.

Anfei Xia

Sandun C. Perera

Muhammad U. Ahmed

Jianying Tang

Jian‐Jun Wang

Voice or text? The role of physician media choice on patient experience in online medical communities

语音还是文本？医生媒体选择在在线医疗社区中对患者体验的影响

Abstract We examine the impact of interdependencies in business process outsourcing (BPO) relationships on client and vendor firm values both in the short term and over a longer time horizon. Drawing from recent research incorporating the resource dependence theory, which appertains the interdependence to both mutual dependence and power imbalance in interfirm relationships, we posit the value proposition of BPO to be greater for mutually dependent engagements, and more so for the vendor than for the client. In addition, we posit the power imbalance in BPO relationships to be detrimental for clients and vendors, and thus, the value proposition of such BPO is expected to be lower for both. We empirically examine these issues through an event study of 285 publicly announced BPO engagements and find that mutually dependent BPO relationships, which were difficult to decouple, are value enhancing for both clients and vendors, and more so for vendors. In addition, although the power imbalances were expected to be detrimental to both, we find this power imbalance to negatively affect only the vendors but had a positive effect on clients.

Sukruth Suresh

T. Ravichandran

Dependence, power imbalance, and value gains in business process outsourcing

依赖性、权力失衡与业务流程外包中的价值获取

ABSTRACT To ensure high service quality, managers need to personalize treatment options and meet their customer demands. Our research is motivated by the need to better anticipate and prepare for that. We develop a generalizable framework that is the first to address two healthcare risk management goals: (1) identifying high risk and stable‐demand customers and (2) predicting the medium‐term demand for services of stable‐demand customers. We also design a model‐agnostic method for variable evaluation. It can rank predictors based on their global impact, and highlight their effect on a model's local accuracy. In this research, we leverage a large electronic medical records' data set, which comprised of 48,344 chronic kidney disease patients treated across geographically diverse Veterans Affairs regions. Our framework indicates that although only 1.3% of the examined individuals are high‐risk patients, it can correctly identify 35% of them and highlight an additional 8.9% as having important demand implications. Identifying high‐risk individuals can be used in (1) monitoring prioritization, (2) patients' motivation, and (3) patients' stabilization. Furthermore, our model accurately predicts the monthly need for care of stable‐demand individuals up to 3 years into the future and outperforms popular statistical and data mining models. This information is especially critical for hospital management in identifying future hiring needs.

短名	Decis. Sci.
Journal Impact	2.76
国际分区	MANAGEMENT(Q2)
期刊索引	SCI Q2中科院 4 区

ISSN	0011-7315, 1540-5915
h-index	123
国内分区	管理学(4区)管理学管理学(4区)

涉及主题	计算机科学经济数学业务工程类运筹学政治学法学统计营销哲学微观经济学人工智能管理物理生物运营管理
出版信息	出版商: Wiley-Blackwell Publishing Ltd，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 1970，原创研究文献占比: 100.00%，自引率:3.60%， Gold OA占比: 11.70%

Decision Sciences

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Technology Acceptance Model 3 and a Research Agenda on Interventions

Zoning strategies for human–robot collaborative picking

User Acceptance Enablers in Individual Decision Making About Technology: Toward an Integrated Model

Probabilistic Selling, Channel Structure, and Supplier Competition*

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

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

AI in business research

Voice or text? The role of physician media choice on patient experience in online medical communities

Dependence, power imbalance, and value gains in business process outsourcing

Identifying influential individuals and predicting future demand of chronic kidney disease patients