While networking among small and medium-sized manufacturing firms (SMEs) is a growing phenomenon, there has been little empirical study of the factors that lead to the success of these networks. This study identifies the following eight success factors for manufacturing networks of SMEs, which are ranked by perceived degree of importance to the success of the network: (1) participant character; (2) chief executive officer (CEO) support; (3) confidence; (4) dedication; (5) capabilities; (6) external relationships; (7) intermediary; and (8) information technology. Four success factors were perceived to be significantly more important in joint production and marketing networks compared to joint learning and resource sharing networks: (1) participant character; (2) confidence; (3) external relationships; and (4) information technology.

This article is the second of two whose goal is to advance the discussion of IS risk by addressing limitations of the current IS risk literature. The first article [Alter and Sherer, 2004] presented a general, but broadly adaptable model of system-related risk that addressed the limited usefulness of existing IS risk models for business managers. In this article, we focus on organizing risk factors to make them more useful and meaningful for business managers. This article shows how the nine elements of the work system framework can be used to organize the hundreds of risk factors in the IS risk literature. It also shows that many of the most important and most commonly cited risk factors for IS in operation and IS projects are actually risk factors for work systems in general. Furthermore, risk factors initially associated with one type of system (e.g. ERP implementation) are often equally relevant at other levels (e.g., information systems projects or work systems in general). Over half of the risk factors in a representative sample of the IS risk literature are valid for work systems in general. This conclusion is a step toward useful risk diagnostic tools based on an organized set of risk factors that are meaningful to business managers and IT professionals.

This article is the first of two whose goal is to advance the discussion of IS risk by addressing limitations of the current IS risk literature. These limitations include: • inconsistent or unclear definitions of risk, • limited applicability of risk models, • frequent omission of the temporal nature of risk, and • lack of an easily communicated organizing framework for risk factors. This article presents a general, but broadly adaptable model of system-related risk. The companion article, CAIS Volume 14, Article 2 [Sherer and Alter, 2004] focuses on IS risk factors and how these factors can be organized. This article starts by identifying criteria for a general, but broadly applicable risk model. It compares alternative conceptualizations of risk and provides clarifications of the definitions of risk and of different treatments of goals, expectations, and baselines for assessing risk. It presents several of the risk models in the IS literature and discusses the temporal nature of risk.

No abstract

a b s t r a c tWhile Collaborative Planning, Forecasting, and Replenishment (CPFR) information systems have been increasingly deployed to improve supply chain operations in a cross section of industries, the extant literature has largely overlooked the learning effects within organizations, thereby resulting in incomplete assessment of their business value. Using an operational-level panel data for nine product lines over 2.5 years, we empirically examine the learning curves in CPFR between Motorola, a mobile phone manufacturer, and one of its U.S.-based national retail partners. We found that the two key components of CPFR, collaborative forecasting (CF) and collaborative replenishment (CR), exhibit distinct learning curves. Forecast accuracy improves immediately following CPFR implementation but the rate of improvement slows over time, whereas inventory levels increase at first and begin decreasing after a period. Further, we found different learning effects in terms of inventory levels when products are later replaced with new form factors. Product replacements have lower inventory levels than their antecedents, at least for lowend products. We discuss important implications for theory and practice at the interface of information systems and operations management.