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This paper illuminates the distinction between individual and organizational actors in business-to-business markets as well as the coexistence of formal and informal mechanisms of coordination in multinational corporations. The main questions addressed include the following. (1) What factors influence the occurrence of personal contacts of foreign subsidiary managers in industrial multinational corporations? (2) How such personal contacts enable coordination in industrial markets and within multinational firms? The theoretical context of the paper is based on: (1) the interaction approach to industrial markets, (2) the network approach to industrial markets, and (3) the process approach to multinational management. The unit of analysis is the foreign subsidiary manager as the focal actor of a contact network. The paper is empirically focused on Portuguese sales subsidiaries of Finnish multinational corporations, which are managed by either a parent country national (Finnish), a host country national (Portuguese) or a third country national. The paper suggests eight scenarios of individual dependence and uncertainty, which are determined by individual, organizational, and/or market factors. Such scenarios are, in turn, thought to require personal contacts with specific functions. The paper suggests eight interpersonal roles of foreign subsidiary managers, by which the functions of their personal contacts enable inter-firm coordination in industrial markets. In addition, the paper suggests eight propositions on how the functions of their personal contacts enable centralization, formalization, socialization and horizontal communication in multinational corporations.

The purpose of this paper is to understand the formation of knowledge management (KM) decisions, including intention for knowledge contribution and knowledge exploration. The authors build on the goal-directed model and a trust-based lens to develop a belief-trust-decision framework. The authors theorize belief as individual factors (one’s virtual skill) and environmental factors (cooperative norms, familiarity), and trust as emotional trust and cognitive trust. Individual factors represent one’s virtual skill to control knowledge exchange, while environmental factors reflect the level of support/control for this exchange by the context.

This study uses a survey method to collect data and partial least squares to analyze them.

The authors found that KM decision is affected by two types of trust, directly or indirectly. They are, in turn, influenced by individual factors and environmental factors.

Generalizability of the findings to virtual communities with different collaboration protocol deserves further investigation. This study contributes to the research on KM and social behavior by providing a comprehensive explanation on KM decision through one’s goal achievement in knowledge exchange behavior, in terms of trust development. Besides, the authors theorize one’s belief on knowledge exchange as skill-control and context-control to represent the drivers for trust.

The results provide suggestion for managers regarding how skill-control and context-control should be managed to improve trust development, which serves as goal achievement for KM decisions.

The authors extend prior work by yielding a new insight into how and why one’s beliefs on skill-control and context-control for knowledge exchange are transferred into KM decision through one’s goal achievement, characterized as trust development at both emotional and cognitive levels.

This paper proposes an integrated knowledge visualization and digital twin system for supporting strategic management decisions. The concepts and applications of strategic architecture have been illustrated with a concrete real-world case study and decision rules of using the strategic digital twin management decision system (SDMDS) as a more visualized, adaptive and effective model for decision-making.

This paper integrates the concepts of mental and computer models and examines a real case's business operations by applying system dynamics modelling and digital technologies. The enterprise digital twin system with displaying real-world data and simulations for future scenarios demonstrates an improved process of strategic decision-making in the digital age.

The findings reveal that data analytics and the visualized enterprise digital twin system offer better practices for strategic management decisions in the dynamic and constantly changing business world by providing a constant and frequent adjustment on every decision that affects how the business performs over both operational and strategic timescales.

In the digital age and dynamic business environment, the proposed strategic architecture and managerial digital twin system converts the existing conceptual models into an advanced operational model. It can facilitate the development of knowledge visualization and become a more adaptive and effective model for supporting real-time management decision-making by dealing with the complicated dependence of constant flow of data input, output and the feedback loop across business units and boundaries.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

The purpose of this paper is to develop a knowledge elicitation strategy to elicit and compile home energy retrofit knowledge that can be incorporated into the development of an intelligent decision support system to help increase the uptake of home energy retrofits. Major problems accounting for low adoption rates despite well-established benefits are: lack of information or information in unsuitable and usable format for decision making by homeowners. Despite the important role of expert knowledge in developing such systems, its elicitation has been fraught with challenges.

Using extensive literature review and a Delphi-dominated data collection technique, the relevant knowledge of 19 industry experts, selected based on previously developed determinants of expert knowledge and suitable for decision making was elicited and compiled. Boolean logic was used to model and represent such knowledge for use as an intelligent decision support system.

A combination of comprehensive knowledge elicitor training, Delphi technique, semi-structured interview, and job shadowing is a good elicitation strategy. It encourages experts to describe their knowledge in a natural way, relate to specific problems, and reduces bias. Relevant and consensus-based expert knowledge can be incorporated into the development of an intelligent decision support system.

The consensus-based and relevant expert knowledge can assist homeowners with decision making and industry practitioners and academia with corroboration and enhancement of existing knowledge. The strategy contributes to solving the knowledge elicitation challenge.

No previous study regarding a knowledge elicitation strategy for developing an intelligent decision support system for the energy retrofit industry exists.

The purpose of this paper is to provide a theoretical framework of how knowledge management (KM) systems can facilitate the incorporation of big data into strategic decisions. Advanced analytics are becoming increasingly critical in making strategic decisions in any organization from the private to public sectors and from for-profit companies to not-for-profit organizations. Despite the growing importance of capturing, sharing and implementing people’s knowledge in organizations, it is still unclear how big data and the need for advanced analytics can inform and, if necessary, reform the design and implementation of KM systems.

To address this gap, a combined approach has been applied. The KM and data analysis systems implemented by companies were analyzed, and the analysis was complemented by a review of the extant literature.

Four types of data-based decisions and a set of ground rules are identified toward enabling KM systems to handle big data and advanced analytics.

The paper proposes a practical framework that takes into account the diverse combinations of data-based decisions. Suggestions are provided about how KM systems can be reformed to facilitate the incorporation of big data and advanced analytics into organizations’ strategic decision-making.

This is the first typology of data-based decision-making considering advanced analytics.

The knowledge of artefact design in design science research can have an important application in the improvement of decision support systems (DSS) development research. Recent DSS literature has identified a significant need to develop user-centric DSS method for greater relevance with respect to context of use. The purpose of this paper is to develop a collective DSS design artefact as method in a practical industry context.

Under the influence of goal-directed interaction design principles the study outlines the innovative DSS artefact based on design science methodology to deliver a cutting-edge decision support solution, which provides user-centric provisions through the use of design environment and ontology techniques.

The DSS artefact as collective information technology applications through the application of design science knowledge can effectively be designed to meet decision makers’ contextual needs in an agricultural industry context.

The study has limitations in that it was developed in a case study context and remains to be fully tested in a real business context. It is also assumed that the domain decisions can be parameterised and represented using a constraint programming language.

The paper concludes that the DSS artefact design and this development successfully overcomes some of the limitations of traditional DSS such as low-user uptake, system obsolescence, low returns on investment and a requirement for continual re-engineering effort.

The design artefact has the potential of increasing user uptake in an industry that has had relevancy problems with past DSS implementation and has experienced associated poor uptake.

The design science paradigm provides structural guidance throughout the defined process, helping ensure fidelity both to best industry knowledge and to changing user contexts.

Global competition has pushed firms to improve and upgrade their manufacturing operations continuously. Explores the role of knowledge base and learning to facilitate this phenomenon. Developing a knowledge base requires organising knowledge and expertise for a field of inquiry and making it available in formats suitable for users to support and aid various operational, developmental, and organisational functions. Classification and coding form the basis for organising knowledge bases. Classification implies grouping objects into similar classes on the basis of some similarity criteria pertinent to one or more attributes. Learning in the context of classification implies discovering new attributes, bases for grouping and requires frequent updating of the knowledge base. A formal knowledge base makes a firm’s knowledge cumulative and serves an important integrating and coordinating role for the organisation. Presents an example application utilizing classification as a tool for knowledge acquisition in design support activities.

The Equal Pay Act 1970 (which came into operation on 29 December 1975) provides for an “equality clause” to be written into all contracts of employment. S.1(2) (a) of the 1970 Act (which has been amended by the Sex Discrimination Act 1975) provides:

The dynamics of the physician knowledge system in the Southern California Region of Kaiser Permanente are explored. The framing and analysis use concepts from the knowledge management literature and network theory. The criticality of this issue to the establishment of sustainable healthcare relates to the lynchpin nature of embedding evidence-based knowledge in healthcare practice and the simultaneous challenge of combining this with clinical knowledge that derives from practice.

The case study is compiled from longitudinal interviews with over 40 physicians and other stakeholders and an examination of archival information including published articles generated by the learning system.

The socio-technical approach to building this learning system was critical given the expectations of physicians for autonomy in making clinical decisions with respect to their patients. This robust learning system builds on rich professional and organizational networks, is led by physicians, and builds on and extends the foundation of evidence relating to quality and value. The goals of the physician practice and a robust measurement and feedback system provide focus for the learning system.

Accelerating the incorporation of evidence-based practice and increasing the scope and reach of the learning system entails building physician networks, having a robust system for critically examining and extending evidence, and a clear linkage to valued outcomes.

This detailed examination of the dynamics of knowledge absorption extends understanding of the capacity of medical care systems to absorb evidence-based knowledge.