信息共享对供应链合作广告影响的博弈分析

本文研究需求不确定环境下合作广告供应链中的信息共享对供应链上下游企业决策及绩效的影响。在此供应链中,上游制造商决定产品批发价格及地方性广告费用分担比例,下游零售商决定地方性广告费用的投入及产品售价。运用斯坦伯格博弈模型研究了不确定性对上下游定价和广告投入决策的影响,分析比较了有信息共享和无信息共享两种情况下供应链各成员及供应链利润。结果表明,对零售商而言,其广告水平,产品售价,及期望利润都随着广告效应的增大而增加;对制造商而言,当广告费用分担率较大时,较强的广告效应会导致零售商对广告的过度投入,从而损害制造商的利润。给定制造商广告分担率,信息共享可以提高制造商期望利润,却减少了零售商期望利润,并可能减少供应链的总体利润。给定批发价格的情况下,信息共享会提高制造商及供应链期望利润,而对零售商来讲无差别。     

Reexamining a Model for Evaluating Information Center Success Using a Structural Equation Modeling Approach

Structural equation modeling is a technique that has been widely used for instrument validation and model testing in research in marketing and organizational behavior. The technique has also been introduced to MIS researchers and used in several studies recently reported in the literature. This article offers an example of how the technique can be used for instrument validation and model testing. The illustration is made through a reexamination of a model for evaluating information center (IC) success. With the growth of end-user computing, the success of an IC is more important than ever. Obtaining a valid model for measuring IC success is thus crucial to organizations with ICs. The results of this study highlight the importance of a strong theoretical base in developing such a valid model, and management should be cautious when using these models to assess the performance of their ICs.     

The Effects of Interruptions,Task Complexity,and Information Presentation on Computer‐Supported Decision‐Making Performance

Interruptions are a frequent occurrence in the work life of most decision makers. This paper investigated the influence of interruptions on different types of decision‐making tasks and the ability of information presentation formats, an aspect of information systems design, to alleviate them. Results from the experimental study indicate that interruptions facilitate performance on simple tasks, while inhibiting performance on more complex tasks. Interruptions also influenced the relationship between information presentation format and the type of task performed: spatial presentation formats were able to mitigate the effects of interruptions while symbolic formats were not. The paper presents a broad conceptualization of interruptions and interprets the ramifications of the experimental findings within this conceptualization to develop a program for future research.     

Using Real‐Time Decision Tools to Improve Distributed Decision‐Making Capabilities in High‐Magnitude Crisis Situations*

Multi‐organizational collaborative decision making in high‐magnitude crisis situations requires real‐time information sharing and dynamic modeling for effective response. Information technology (IT) based decision support tools can play a key role in facilitating such effective response. We explore one promising class of decision support tools based on machine learning, known as support vector machines (SVM), which have the capability to dynamically model and analyze decision processes. To examine this capability, we use a case study with a design science approach to evaluate improved decision‐making effectiveness of an SVM algorithm in an agent‐based simulation experimental environment. Testing and evaluation of real‐time decision support tools in simulated environments provides an opportunity to assess their value under various dynamic conditions. Decision making in high‐magnitude crisis situations involves multiple different patterns of behavior, requiring the development, application, and evaluation of different models. Therefore, we employ a multistage linear support vector machine (MLSVM) algorithm that permits partitioning decision maker response into behavioral subsets, which can then individually model and examine their diverse patterns of response behavior. The results of our case study indicate that our MLSVM is clearly superior to both single stage SVMs and traditional approaches such as linear and quadratic discriminant analysis for understanding and predicting behavior. We conclude that machine learning algorithms show promise for quickly assessing response strategy behavior and for providing the capability to share information with decision makers in multi‐organizational collaborative environments, thus supporting more effective decision making in such contexts.     

Knowledge Management Using Information Technology: Determinants of Short‐Term Impact on Firm Value*

The importance of knowledge management (KM) processes for organizational performance is now well recognized. Seeking to better understand the short‐term impact of KM on firm value, this article focuses on public announcements of information technology (IT)‐based KM efforts, and uses cumulative abnormal return (CAR) associated with an announcement as the dependent variable. This article employs a contingency approach, arguing that the KM announcement would have a positive short‐term impact on firm value in some conditions but not in others. Thus, it pursues the following research question: What are the effects of contextual factors on the CAR associated with the announcement of an IT‐based KM effort? Specific hypotheses are proposed based on information‐processing theory, organizational learning theory, the knowledge‐based theory of the firm, and the theory of knowledge creation. These hypotheses link CARs to alignment between industry innovativeness and the KM process, alignment between firm efficiency and the KM process, firm‐specific instability, and firm diversification. The empirical study utilizes secondary data on 89 KM announcements from 1995 to 2002. The results largely support the hypotheses. Overall, this article provides empirical support for the theory‐based arguments, and helps develop a contingency framework of the effectiveness of KM efforts.     

Sense and Avoid Requirements for Unmanned Aircraft Systems Using a Target Level of Safety Approach

One of the most critical challenges to full integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS) is the requirement to comply with CFR 14 Part 91.113 to “see and avoid” other aircraft. Various attempts have been made to develop systems to “sense and avoid” other aircraft so UAS can comply with the intent of the regulation. This article proposes a framework to develop effectiveness requirements for any SAA system by linking UAS characteristics and operating environments to midair collision risk quantified by a fatality rate. The framework consists of a target level of safety (TLS) approach using an event tree format. Safety has been identified as the most important consideration in the UAS integration process. While safety can be defined in many ways, the authors propose using a fatality rate metric that follows other statistics used in the industry. This metric allows for the use of a TLS approach to the development of SAA requirements for system certification. Failure to adequately link system requirements to safety could result in the implementation of SAA systems that either do not adequately mitigate the risk associated with UAS operations or are overdesigned, resulting in increased cost and complexity. This article demonstrates the use of the proposed framework to develop specific SAA effectiveness standards based on UAS weight and airspace class combinations.     

Path to “Stardom” in Globally Distributed Hybrid Teams: An Examination of a Knowledge‐Centered Perspective using Social Network Analysis

Although distributed teams have been researched extensively in information systems and decision science disciplines, a review of the literature suggests that the dominant focus has been on understanding the factors affecting performance at the team level. There has however been an increasing recognition that specific individuals within such teams are often critical to the team's performance. Consequently, existing knowledge about such teams may be enhanced by examining the factors that affect the performance of individual team members. This study attempts to address this need by identifying individuals who emerge as “stars” in globally distributed teams involved in knowledge work such as information systems development (ISD). Specifically, the study takes a knowledge‐centered view in explaining which factors lead to “stardom” in such teams. Further, it adopts a social network approach consistent with the core principles of structural/relational analysis in developing and empirically validating the research model. Data from U.S.–Scandinavia self‐managed “hybrid” teams engaged in systems development were used to deductively test the proposed model. The overall study has several implications for group decision making: (i) the study focuses on stars within distributed teams, who play an important role in shaping group decision making, and emerge as a result of a negotiated/consensual decision making within egalitarian teams; (ii) an examination of emergent stars from the team members’ point of view reflects the collective acceptance and support dimension decision‐making contexts identified in prior literature; (iii) finally, the study suggests that the social network analysis technique using relational data can be a tool for a democratic decision‐making technique within groups.     

Impacts of Information and Communication Technology Implementations on Employees' Jobs in Service Organizations in India: A Multi‐Method Longitudinal Field Study

India is an important frontier for economic growth, investments, and development. The service sector, like the manufacturing industry, in India is booming. Following the trend of their western counterparts, service organizations in India are implementing enterprise‐level information and communication technologies (ICTs) to support service processes. In this paper, we used socio‐technical systems theory to develop hypotheses about the effects of ICTs on the five job characteristics, i.e., skill variety, task identity, task significance, autonomy, and feedback, in the job characteristics model (JCM) in a service organization (a bank) in India. We also tested the entire JCM that relates job characteristics to job satisfaction and job performance via various mediators and moderators. In a 32‐month longitudinal field study of 1743 employees, we gathered one wave of data before an ICT implementation and two waves after the implementation. We found that, although the ICT enriched employees' job characteristics, employees reported significantly lower job satisfaction and job performance. To understand this puzzling finding, we conducted a qualitative study and identified four contextual forces that contribute to these results and hinder successful implementation of ICTs in the service sector in India and, possibly, other developing countries: environmental barriers, learning difficulty, culture shock, and employee valuation.     

A Tiered Framework for Risk‐Relevant Characterization and Ranking of Chemical Exposures: Applications to the National Children's Study (NCS)

A challenge for large‐scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co‐occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure‐relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new “Tiered Exposure Ranking” (TiER) framework, developed to support various aspects of risk‐relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both “discovery‐driven” and “hypothesis‐driven” analyses. “Tier 1” applications focus on “exposomic” pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk‐relevant exposure metrics for populations and individuals. In this article, “tier 1” applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A “tier 2” application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk‐relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.