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PurposeThis study was developed in response to the need to develop age-friendly smart living environments due to the complex demands placed on society by the ageing of the population. This study aims to analyse the potential that knowledge visualisation offers collaborative decision-making applied to the development of a multiple criteria framework supporting knowledge management (KM) through knowledge collaboration (KC) and knowledge sharing (KS) in the context of regional innovation systems (RIS).Design/methodology/approachUsing a socio-technical approach, knowledgeable and experienced representatives of RIS innovation actors were brought together to develop a constructivist multiple criteria framework that integrates knowledge visualisation and collaborative decision-making techniques (i.e. cognitive mapping and system dynamics).FindingsThe study introduces a multiple criteria model supporting KM encompassing conditions and practices of RIS innovation actors facilitating and encouraging KC and KS. The potential for knowledge visualisation in collaborative decision-making is explored in great depth and illustrated in a case study setting.Practical implicationsThe panel members who participated in this study consider our methodological proposal to be extremely versatile and see great potential for further applications in RIS contexts.Originality/valueThe combined use of cognitive mapping and system dynamics according to the strategic options development and analysis (SODA) approach offers a holistic and well-informed perspective on the issue in question. The literature reports no prior work of this methodological combination in the same research context.

The purpose of this paper is to delineate a green supply-chain performance measurement framework using an intra-organisational Collaborative Decision-Making (CDM) approach. A fuzzy-Analytic Network Process (ANP) based Green Balanced Scorecard (GrBSc) has been used within the CDM approach. CDM aids in arriving at a consistent, accurate and timely data flow across all cross-functional areas of a business thereby providing real-time information for the evaluation, control and improvement of processes, products and services so as to meet both business objectives and rapidly changing customer needs. A green causal relationship is established and linked to the fuzzy-ANP approach. The causal relationship involves organisational commitment, eco-design, green supply-chain process, social performance and sustainable performance constructs. Sub-constructs and sub-sub-constructs are also identified and linked to the causal relationship to form a network. The fuzzy-ANP approach suitably handles the vagueness of the linguistics information of the CDM approach. The CDM approach is implemented in a UK-based carpet manufacturing firm. The performance measurement approach, in addition to the traditional financial performance and accounting measures, aids in making decisions of the firm in regard to the overall organisational goals. The implemented approach assists the firm in identifying further requirements of the collaborative data across the supply-chain and information about customers and markets. Overall, the CDM-based GrBSc approach assists managers in deciding if the suppliers’ performances meet the industry and environment standards and the human resource is effective.

Collaborative initiatives such as collaborative design, collaborative planning and forecasting, and open collective innovation are increasingly accepted as approaches that can effectively support decision-making (DM) processes in a range of different industries. However, justifying and demonstrating the benefits of collaborative solutions remains a challenge and has been under-researched. Demonstrating the feasibility of implementing collaborative solutions as opposed to traditional, linear and transactional solutions is even less evident. The purpose of this paper is to conceive a collaborative solution that supports the multi-level DM process in a real, tree-based automotive supply chain environment. The hypothesis presented posits that by sharing information collaboratively, improvements in terms of the profit and service levels will be found within the supply chain and at every supply chain node., The authors thanks the support from the project 'Operations Design and Management in Global Supply Chains (GLOBOP)' (Ref. DPI2012-38061-C02-01), funded by the Ministry of Science and Education of Spain, for the supply chain environment research contribution. In addition, we thank the EWG-DSS and their four expert anonymous referees as well as the guest editorial board for their useful suggestions and criticism on earlier versions of this paper.

This paper studies information exchange in collaborative group activities involving mixed networks of people and computer agents. It introduces the concept of “nearly decomposable” decision-making problems to address the complexity of information exchange decisions in such multi-agent settings. This class of decision-making problems arise in settings which have an action structure that requires agents to reason about only a subset of their partnersʼ actions – but otherwise allows them to act independently. The paper presents a formal model of nearly decomposable decision-making problems, NED-MDPs, and defines an approximation algorithm, NED-DECOP that computes efficient information exchange strategies. The paper shows that NED-DECOP is more efficient than prior collaborative planning algorithms for this class of problem. It presents an empirical study of the information exchange decisions made by the algorithm that investigates the extent to which people accept interruption requests from a computer agent. The context for the study is a game in which the agent can ask people for information that may benefit its individual performance and thus the groupʼs collaboration. This study revealed the key factors affecting peopleʼs perception of the benefit of interruptions in this setting. The paper also describes the use of machine learning to predict the situations in which people deviate from the strategies generated by the algorithm, using a combination of domain features and features informed by the algorithm. The methodology followed in this work could form the basis for designing agents that effectively exchange information in collaborations with people.

The paper aims at the development of a Collaborative Multi-Criteria Spatial Decision Support System (C-MC SDSS) for the evaluation of multifunctional landscape according to a human smart perspective. The C-MC SDSS addresses landscape transformation and preservation processes, leading to consistent choices with the principles of local self-sustainable development, considering the decision-making environment as a heterogeneous field, where conflicting interests interplay. The methodological approach, implemented in the case study of the National Park of Cilento, Vallo di Diano and Alburni (Southern Italy) demonstrates that the inclusion of web-based data and common knowledge in MC-SDSSs is a major challenge. The multidimensional nature of the landscape evaluation requires a mutual understanding, communication and collaboration, allowing an expansion of the knowledge-base to sharpen questions and improving the quality of the decision process. The paper investigates potentials of a C-MC SDSS for supporting transparent and democratic decision processes, helpful in selecting landscape enhancement policies.

International audience; The notion of context seems to be a growing subject in the engineering sciences. Several studies have pointed out the necessity to address the contextual limitations in collaborative design projects. It has been identified as one of the major factors that contribute to inadequate knowledge reuse in design projects. Nevertheless, the definition of the context depends on the subject and the field of application. In this paper, we point out different definitions of the context in artificial intelligence, engineering and cognitive sciences. In this research study we present a proposition for context modelling representing only one part of collaborative decision-making support. The research study presented in this paper is a result of collaboration with one of the French car manufacturers. Within the conceptual model of collaborative decision-making, we propose to integrate the information concerning three different contexts: decision, project and enterprise context. The aim of this model is to help and support the project team in project management.