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The purpose of this paper is to analyze two case studies with a trust matrix tool, to identify trust issues related to electronic health records.

A qualitative research approach is applied using two case studies. The data analysis of these studies generated a problem list, which was mapped to a trust matrix.

Results demonstrate flaws in current practices and point to achieving balance between organizational, person and technology trust perspectives. The analysis revealed three challenge areas, to: achieve higher trust in patient-focussed healthcare; improve communication between patients and healthcare professionals; and establish clear terminology. By taking trust into account, a more holistic perspective on healthcare can be achieved, where trust can be obtained and optimized.

A trust matrix is tested and shown to identify trust problems on different levels and relating to trusting beliefs. Future research should elaborate and more fully address issues within three identified challenge areas.

The trust matrix’s usefulness as a tool for organizations to analyze trust problems and issues is demonstrated.

Healthcare trust issues are captured to a greater extent and from previously unchartered perspectives.

The present paper aims to explore how to measure trust as a receptivity force in an intra-organisational knowledge-sharing network with the help of self-developed algorithms of modelling percolations.

In this paper, a completely new methodology is applied by using a sample study of an international company’s financial centre as an example. Computer software has been developed to simulate the network and calculate the percolation thresholds by combining its characteristics, thereby revealing what and to what extent connectivity and trust, respectively, influence knowledge sharing.

The application of computer modelling to build up a percolation network is useful for answering questions about the determinants of knowledge sharing. Arguably, the authors demonstrate how the applied new methodology is superior in addressing how to measure the critical values of trust, connectivity and interaction issues, as well as leading to better insights about how these can be managed. The present paper confirms that trust is an essential factor influencing knowledge sharing and that there is a reciprocal effect between social interaction and trust.

The model provides a useful tool for assessing features of the intra-organisational knowledge-sharing network and thus an important foundation for implementing actions in practice. The findings of this study imply that managers should consider the important role of task-related trust between actors and in general for knowledge sharing. With the help of percolation modelling, the degree of trust in an organisation can be computed, and this provides managers with an approach for managing trust.

The topic of “how can trust be measured” is very important and is becoming even more important now because the financial crisis and other issues are raising questions about trust and moral compass rather than financial data. A percolation-based approach to studying knowledge sharing has not been researched in depth before now, and this study attempts to fill that gap. Fundamentally, this multidisciplinary research adds value to the theoretical foundation of the percolation network and research methodology to be used in social sciences and gives an example of their potential practical implications.

This paper aims to propose a trust-based ant-colony recommender system. It achieves high accuracy and coverage by integrating the importance level of friends. This paper has two main contributions, namely, selecting higher-quality raters and improving the prediction step. From these two contributions, the proposed trust-based ant-colony recommender system could provide more accurate and wider-coverage prediction than existing systems.

To obtain higher-quality raters, the data set was preprocessed, and then, trust values were calculated. The depth of search was increased to obtain higher coverage levels. This work also focuses on the importance level of friends in the system. Because the levels of influence on the active user of all friends are not equal, the importance level of friends is integrated into the system by transposing rater’s rating to the active user’s perspective and then assigning a weight to each rater.

The experimental evaluation clearly demonstrates that the proposed method achieves better results in terms of both accuracy and coverage than existing trust-based recommender systems. It was found that integrating the importance level of friends into the system, which transposes ratings and assigns weight to each user, can increase accuracy and coverage.

Existing trust-based ant-colony recommender systems do not consider the importance level of friends in the prediction step. Most of them only focus on finding raters and then using the rater’s real ratings in the prediction step. A new method is proposed that integrates the importance level of friends into the system by transposing a rater’s rating to match the active user’s perspective and assigning a weight for each rater. The experimental evaluation demonstrates that the proposed method achieves better accuracy and coverage than existing systems.

Green technology adoption (GTA) in small and micro enterprises (SMEs) is a complex multi-attribute group decision-making issue. Conflicts of opinions can hamper the achievement of group coherence. The purpose of this paper is to solve the conflict decision-making problem in SMEs.

This paper reconstructs the GTA evaluation index system for SMEs and proposes an intragroup coordination relationship model to mitigate group decision-making conflicts based on the cloud model and social networks (GCS). The numerical characteristics of these GTA indicators are determined using fuzzy theory and cloud model.

It was found that establishing a trust relationship network and a conflict coordination relationship could improve the consistency of group decision-making. The degree of coordination has subtle differences with the trust relationship. Particularly, the coordination relationship has obvious influences on the decision-making results among small groups in SMEs.

The conflict coordination model can significantly improve the consistency of group decision-making in SMEs. At the same time, it discusses the differences between the level of conflict coordination and the trust propagation in the process of group decision-making.

This study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts.

The trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment.

The results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus.

The authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.

Purpose – The purpose of this conceptual chapter is to discuss the limitations of the network organization in multinational corporations (MNCs). Since many IB/IM publications concentrate on the advantages of this organizational form, the focus of the chapter is on identifying the limitations that MNCs need to be aware of when they use network organizations.

Methodology – The analysis is based on a sound review of the literature that refers to the network organization in general and its application in MNCs.

Findings – The chapter shows that MNCs present a context that can aggravate the problems of a network organization. Four types of problems are identified: (1) knowledge transfer between MNCs’ subunits, (2) trust-building and corporate culture within MNCs, (3) subsidiary development and subsidiary managers’ stress, and (4) additional problems of a more general nature.

Practical implications – As a result of these problems, it is expected that the formal, hierarchical structure will remain an important organizational instrument for MNCs. The chapter specifies in which ways the formal organizational structure can help to reduce the limitations of the network organization. Finally, the chapter argues that, among the formal organizational models, the matrix structure should be considered more intensively in the future.

Originality/value of chapter – Since existing discussion of the network organization in MNCs tends to ignore the limitations and downsides of this organizational form, the chapter contributes to a more balanced understanding of the network organization.

This study aims to construct a Weber point-based model to complete the visualization of preference aggregation in group decision-making problem, in which decision-makers are associated with trust relationship.

This study mainly comprises four parts: trust propagation, preference aggregation, opinion adjustment and alternative selection. Firstly, the incomplete trust between decision-makers is completed with trust transfer operators and propagation probability in trust propagation process. Secondly, a preference aggregation model based on Weber point is proposed to aggregate the group preference visually. Thirdly, opinions are adjusted to reach a consensus. Finally, the ranking of alternatives is determined by the correlation coefficient with the group preference as a reference.

The Weber point-based model proposed in this study can minimize the gap in the preference of alternatives between the group and all decision-makers, and realize the visualization of aggregation result. A case of plan selection is introduced to illustrate the feasibility and effectiveness of the proposed model.

By comparing the result with the weighted average-based preference aggregation method, the Weber point-based model proposed in this study can show the result of preference aggregation intuitively and improve group consensus.

Organizational trust is an important construct for organizational and public relations scholars and practitioners for its influence on key organizational outcomes, yet the convergent validity of organizational trust instruments has not been investigated by any study. The purpose of this paper is to address an important gap in the literature by reporting the results of a systematic investigation of the convergent validity of three organizational trust measures, taking an interpersonal, public relations, and organizational approach to trust in organizations respectively.

IRB approval was obtained for a cross‐sectional study design gathering self‐reports from participants through an online data gathering system of a large Midwestern university in the USA. Correlational matrices, along with exploratory and confirmatory factor analyses adapting the multitrait‐multimethod matrix, were employed for data analysis.

The three trust measures demonstrate mixed evidence of convergent validity with each other and with a theoretically correlated construct, organizational identification, demonstrating that while the three measures share the same conceptual ground, there is a need to clarify their underlying theoretical specification, especially with respect to organizational identification.

Future large‐scale studies can extend the findings based on a student population by employing multiple and diverse data sets, as well as investigate method variance.

Recommendations to improve convergent validity include: increasing item parsimony to decrease redundancy; revise item construction; and improved theoretical specification investigating the conceptual boundaries organizational identification with trust.

The study provides empirical evidence of the need for greater conceptual and operational clarification of the theoretical bases of trust. It is the first to advance research on trust in organizations by providing empirical support for the convergent validity of organizational trust measures assessed from organizational, interpersonal, and public relations perspectives.

The existing consensus reaching mechanisms ignore the influence of social triangle structure on the decision-makers’ (DMs') weights, and the consensus reaching process (CRP) fails to fully reflect the DMs' subjectivity and can be time consuming and costly. To solve these issues, a novel CRP for multi-criteria group decision-making (MCGDM) problems with intuitionistic grey linguistic numbers (IGLNs) is proposed in this paper.

First, a weight calculation method is proposed by analysing the triangle structure of DMs' social network and scale of adjacent nodes. Then, a consensus degree index based on three-level polygon area is defined and applied to identify the inconsistent DMs. Finally, the feedback mechanism based on particle swarm optimisation (PSO) algorithm under grey linguistic environment is developed, where subjective trust relationships in social network is utilised to determine the adjustment coefficient.

The advantages of the proposed method are highlighted by two practical applications of the evaluation of tunnel construction method and the selection of a hotel for the centralised isolation. Comparision analysis and numerical simulation are performed to reveal the effectiveness and applicability of the method.

The proposed model can not only reflect the effect of triangle structure in social network on DMs' weights, but also reduce the time and cost of decision-making.

The main contribution of this paper is to propose a new MCGDM model based on intuitionistic grey linguistic numbers, which can handle the problem of inconsistency of information more effectively.

Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.

This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.

The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.

The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.