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We investigate whether information technology (IT) can help physicians more efficiently acquire new knowledge in a clinical environment characterized by information overload. We combine analysis of data from a randomized trial with a theoretical model of the influence that IT has on the acquisition of new medical knowledge. Although the theoretical framework we develop is conventionally microeconomic, the model highlights the non-market and non-pecuniary influence activities that have been emphasized in the sociological literature on technology diffusion. We report three findings. First, empirical evidence and theoretical reasoning suggests that computer-based decision support will speed the diffusion of new medical knowledge when physicians are coping with information overload. Second, spillover effects will likely lead to “underinvestment” in this decision support technology. Third, alternative financing strategies common to new IT, such as the use of marketing dollars to pay for the decision support systems, may lead to undesirable outcomes if physician information overload is sufficiently severe and if there is significant ambiguity in how best to respond to the clinical issues identified by the computer. This is the first paper to analyze empirically and theoretically how computer-based decision support influences the acquisition of new knowledge by physicians.

Traditional definitions of decision support systems emphasise their support role in individual decision making and utilise notions of rational choice. By considering decisions as an organisational activity, the interpretation of decision support systems use in organisations can move beyond this technical rational understanding, to include potential political and legitimating roles for these systems. These three possible interpretations are discussed in relation to the implementation of a large decision support system in a local government context described by Dutton (1981). In its technical role, the system was used as part of a rational planning agenda. However, the system was clearly also used politically, to promote particular interests and as a lever in negotiations between various groups. Part of the appeal of the decision support system was the appearance of rationality and technical neutrality that it gave to the planning and decision making process, and the legitimation it provided with external constituents. The paper concludes that an unquestioning acceptance of the technical received view of decision support system use is limiting, and that a more reflective approach to their development, implementation and use is required.

With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels.

This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation.

Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper.

The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.

This chapter focuses on techniques and technologies to aid groups in making decisions, with an emphasis on computer-based support. Many office workers regularly meet colleagues and clients in virtual meetings using videoconferencing platforms, which enable participants to carry out tasks in a manner similar to a face-to-face meeting. The development of computer-based platforms to facilitate group tasks can be traced back to the 1960s, and while they support group communication, they do not directly support group decision making. In this chapter we distinguish four technologies developed to provide support to group decisions, clustered into two main traditions. Technologies in the task-oriented tradition are mainly concerned with enabling participants to complete tasks to solve the group's decision problem via computer-supported communications. Group Decision Support Systems and social software technologies comprise the task-oriented tradition. Alternately, in the model-driven tradition, participants use computers to build and use a model that acts as a referent to communicate, mostly verbally, about the group's decision problem. System modeling and decision-modeling technologies constitute the model-driven tradition. This chapter sketches the history and guiding ideas of both traditions, and describes their associated technologies. The chapter concludes with questioning if increased availability of online tools will lead to increased use of group decision support technologies, and the differential impact of communication support versus decision support.

Digital transformations of business processes are on the rise and the result is a need for a better understanding of how the elements of intellectual capital (IC) play a role in achieving successful digital project outcomes. New structural capital in the form of digital technologies must be identified and understood. Evolving skills of human capital in assimilating digital elements must also be considered, while collaboration within the development process involving relational capital provides a critical integration among these IC elements. This study illustrates the importance of identifying and managing the integration of IC components within an agile project management framework that are essential to achieving success for a digital initiative. More specifically, this study describes the process by which a multinational technology-based products company successfully developed a dynamic decision support platform utilizing an agile approach to guide a project management team to better manage the company's operations.

This study focuses on a case analysis approach of a multinational commercial and consumer products company. The paper presents existing research on the evolving state of project management for digital initiatives and focuses on agile methods. This study then delves into the case analysis that illustrates how IC played an integral role in the company successfully developing effective decision support involving an interactive dashboard using agile Project Management (PM), which enabled the project management team to better manage resources.

An examination at the case level illustrates that effective management and integration of IC has positive effects on project outcomes. While a balanced approach is evident as a requirement, the unique characteristics of the agile project management approach entails greater emphasis on select elements to adapt to a more dynamic development process.

This work depicts the complexities in providing analytic-based decision support in an agile/flexible project management scenario. This work adds to existing research by illustrating elements within IC categories and the elements' interdependencies that play an essential role in achieving success in this more flexible project environment.

The purpose of this paper is to identify the most appropriate multidisciplinary data sources related with energy optimization decision support as well as the related methodologies, tools and techniques for data capturing and processing for each of them.

A review is conducted on the state-of-play of decision support systems for energy optimization, focussing on the municipal sector, followed by an identification of the most appropriate multidisciplinary data sources related with energy optimization decision support. An innovative methodology is outlined to integrate semantically modeled data from multiple sources, to assist city authorities in energy management.

City authorities need to lead relevant actions toward energy-efficient neighborhoods. Although there are more and more energy and other related data available at the city level, there are no established methods and tools integrating and analyzing them in a smart way, with the purpose to support the decision-making process on energy use optimization.

A novel multidimensional approach is proposed, using semantic technologies to integrate data from multiple sources, to assist city authorities to produce short-term energy plans in an integrated, transparent and comprehensive way.

Management information for decision making is an essential tool for library managers. This paper outlines the European Commission Libraries Programme decision support projects that were brought together under the CAMILE concerted action. The three common themes to emerge from the CAMILE projects are then discussed. The themes are: information for decision making, systems for decision support, and the future challenges of measuring impact and developing performance measures for the elec‐tronic library. To conclude, recommendations for future research in the field of decision support in European libraries are considered.

The application of theories on group decision support is yet to be explored extensively in supplier selection literature, although the literature in both domains is extremely rich, in isolation. The purpose of this paper is to explore the application of group decision support theories for supplier selection.

The row geometric mean method (RGMM) of the analytic hierarchy process (AHP) has been used in this study for the prioritization of group preferences under consensus. A case study was conducted to test the theories of consensual group decision making and compare it with other approaches based on AHP.

The study establishes that the application of decision support theories for group decision making can improve the supplier selection process. Findings further imply that RGMM is more effective than eigen value method, for group decision making under consensus.

Methodologically, the study highlights the greater regularity in outcome of group decision making, vis-à-vis individual decision making, for the same decision-making context. Also, it highlights how RGMM is more effective since it preserves reciprocal properties and diversity in preferences better.

The study establishes that firms can improve supplier selection processes by leveraging on the collective expertise of a group rather than depending on individual decision-making expertise.

This study explores the application of different theories based on AHP for consensual group decision making. It compares different approaches based on AHP and establishes that RGMM is a superior approach for supplier selection.

The purpose of this study is to identify and prioritize 30 decision support capabilities. Not limited to quantitative analysis techniques, the highest priority capabilities included explicit knowledge access and reliability, timely risk management, ability to communicate decisions and learn from their implementation.

To meet the needs of AKEs, it is critical developers implement the right capabilities. This requires a unified approach that identifies a complementary decision support set of capabilities. Using a triangulation approach of qualitative architecting and quantitative survey processes, this study evaluates enterprise‐specific operating environment and business models. It expands the integrated informed decision cycle model to identify decision making, decision implementation, and knowledge management‐related capabilities.

Agile knowledge‐based enterprises (AKEs) drive a need for a more aware, inclusive, and responsive decision support system. Goldman et al. observe that organizations are responding to these needs in a piecemeal fashion. Although significant research exists on knowledge management, decision analysis, and other decision support applications, Vahidov and Kersten assert that researchers and developers lack a unified decision‐support approach. Without such an overarching framework for identification and evaluation of needed capabilities, investment choices can provide incompatible or wrong capabilities with substantial chance of missing needed capabilities. All of these situations lessen the return on investment expected from a unified approach.

The combined capability set informs organization infrastructure investment for future evaluation and development of decision support.

The marriage of new scanner‐type data sources and new computing and analysis methods is allowing a new approach to the development and use of models for decision support and product line management. Data‐driven modeling describes a process of model‐building wherein models are created that fit the dynamics of the data rather than assuming a priori relationships among brands and their marketing mix elements. Based on a combination of time‐series and econometric modeling methods, these models can significantly improve a modeler’s ability to capture marketplace structure and dynamics. Although more complex than their predecessors, the capabilities of these new data‐driven decision support models make them potentially very powerful tools, improving intuition and managerial understanding while suggesting improved decision alternatives. Develops such a model using detailed multiproduct retail data and demonstrates its capabilities.