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This paper aims to explore how the maieutic role of management accounting (MA) can be sustained in the context of MA digitalization.

The paper begins with practitioners’ descriptions of the context that makes the MA support of non-routine decisions maieutic. To understand how the maieutic characteristics can be sustained in future MA digitalization, the authors then analyze the discourses these practitioners have about artificial intelligence (AI) in providing MA support.

As a basis, the authors’ data show various maieutic characteristics within the use of MA answers in decision-making as well as within the MA process of generating such answers. The paper then identifies three MA digitalization discourses, namely, “computation,” “judgment” and human-AI “interaction” discourse, each with their unique agendas on how AI should be used.

The paper is based on the premises that AI and digitalization are often discussed without sufficient understanding about the context being digitalized. The authors’ data suggest that MA support in non-routine decision-making is fundamentally maieutic, and AI – as it currently stands – is not expected to change this by providing perfect answers. The authors provide novel insights about maieutic MA support and the current discourses on using AI in MA support, and how digitalization does not necessarily compromise maieutic MA support but instead has the potential to sustain or even enhance it.

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.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

Examines some principles of the design and implementation of decision support systems in the context of statistical quality control. These principles are a function both of the level within the organization at which the decision is made and of the type of decision that is made. The principles are well known in the field of decision support systems and have proved effective. Argues that these principles are of great value to researchers, managers and quality specialists in the field of statistical quality control as well. The argument is supported by a number of actual examples in the application of statistical quality control where knowledge of the principles proved of value in improving quality.

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.

This paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Two fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.

The findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.

The developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.

The support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.

There is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

The aim of the DECIMAL Project is to produce an integrated Decision Support Module for library management systems. This is being developed from an assessment of the needs and practices of library managers in small to medium size libraries in the UK, Spain and Italy. The Project, which commenced in February 1995, comprises four Phases — Management, Research, Technical Development and Evaluation. The objectives and structure of the Project are described, with an account of progress in the technical development to date and a summary of the findings of the Research Phase. The Module is being developed to incorporate both textual and numeric information to support the decision process.

Decision support (DS), as a traditional management concept, have had a remarkable role in competitiveness or survival of organizations and nowadays, business intelligence (BI), as a brand modern impression, has various contributions in supporting decision-making process. Although, a variety of benefits are expected to arise from BI functions, researches, and models that determining the effect of BI functions on the decisional and organizational benefits are rare. The purpose of this paper is to study the relationship between BI functions, DS benefits, and organizational benefits in context of decision environment.

This research conducts a quantitative survey-based study to represent the relationship between BI capabilities, decision support benefits, and organizational benefits in context of decision environment. On this basis, the partial least squares (PLS) technique employs a sample of 228 firms from different industries located in Middle-East countries.

The findings confirm the existence of meaningful relationship between BI functions, DS benefits, and organizational benefits by supporting 15 out of 16 main hypotheses. Essentially, this research provides an insightful understanding about which capabilities of BI have strongest impact on the outcome benefits.

The results can provide effective and useful insights for investors and business owners to utilize more appropriate BI tools and functions to reach more idealistic organizational advantages. Also it enables managers to better understand the application of BI functions in the process of achieving the specified managerial support benefits.

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.

IT support for marketing planning can aid in the use of marketing tools, facilitate group planning, and support moves towards continuous planning based on a live marketing model of the business. But, amongst other factors, achieving these benefits depends on the style of support provided by the system. After a review of relevant decision support system (DSS) literature, describes here the findings relating to support style from a qualitative evaluation of a system named EXMAR. The findings support Little’s classic rules of “decision calculus”, such as the importance of ensuring that managers understand and can control the system, rather than the objective influenced by management science of prescribing an optimal recommendation. Also emphasises the role of systems in enhancing mutual understanding in a cross‐functional planning team, and hence in building commitment to the resulting plan.