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This paper aims to propose a new method for evaluating the quality and prioritizing of the human resources (HRs) based on trust, reputation, agility, expertise and cost criteria in the expert cloud. To evaluate some quality control (QC) factors, a model based on the SERVQUAL is used.

The aim of this paper is to offer a fast and simple method for selecting the HRs by the customers. To achieve this goal, the ranking diagram of different HRs based on the different criteria of QC is provided. By means of this method, the customer can rapidly decide on the selection of the required HRs. By using the proposed method, the scores for various criteria are evaluated. These criteria are used in the ranking of each HR which is obtained based on the evaluation conducted by previous customers and their colleagues. First, customers were asked to select their needed criteria and then by constructing a hierarchical structure, the ranking diagram of different HRs is achieved. Using a ranking system based on evaluating the quality of the model, satisfy the customer needs to be based on the properties of HRs. Also, an analytical hierarchical process-based ranking mechanism is proposed to solve the problem of assigning weights to features for considering the interdependence between them to rank the HRs in the expert cloud.

The obtained results showed the applicability of the radar graph using a case study and also numerically obtained results showed that a hierarchical structure increases the quality and speed rating of HR ranking than the previous works.

The suggested ranking method in this paper allows the optimal selection due to the special needs of any given customer in the expert cloud.

As social networking services are popular, the need for recognizing reliable people has become the main concern. Quantifying the relationships between the users of social networks is also one of the important challenges with the increasing number of users. So, trust plays an important role in social networks in order to recognize trustworthy people. The purpose of this paper is to propose an approach to recognize trustworthy users and protect users from abuse by untrustable users.

In this paper, the authors suggest a method to measure the trust value between users of social networks based on call log histories such as abundance, novelty, and sincerity and quality of service (QoS) requirements such as accessibility, response ability, tend to respond, and agility. After that, the authors calculate error-hit, precision, and recall to evaluate the trust value.

The results indicate that the proposed approach has better performance in terms of error-hit, precision, and recall than FIFO, combined, QoS-based, and call log-based method.

In this paper, the trust issue in social networks is pointed out and a new approach to evaluate the trust value is proposed based on call log histories and QoS requirements.

The purpose of this paper is to present a formal verification method to prove the correctness of social customer relationship management (CRM)-based service composition approach. The correctness of the proposed approach is analyzed to evaluate the customer behavioral interactions for discovering, selecting and composing social CRM-based services. In addition, a Kripke structure-based verification method is presented for verifying the behavioral models of the proposed approach.

Evaluating the customer behavioral interactions using the social CRM-based service composition approach is an important issue. In addition, formal verification has an important role in assessing the social CRM-based service composition. However, model checking can be efficient as a verification method to evaluate the functional properties of the social CRM-based service composition approach.

The results of model checking satisfied the logical problems in the proposed behavior model analysis. In the statistical testing, the proposed URM mechanism supported the four knowledge creation process conditions. It was also shown that the percentage of state reachability in the URM with KCP conditions is higher than the URM mechanism without supporting KCP conditions.

The comparison of time and memory consumption of the model checking method shows that the social CRM-based service composition approach covers knowledge process features, which makes it an efficient method.

Expert Cloud as a new class of cloud systems enables its users to request and share the skill, knowledge and expertise of people by employing internet infrastructures and cloud concepts. Since offering the most appropriate expertise to the customer is one of the clear objectives in Expert Cloud, colleague recommendation is a necessary part of it. So, the purpose of this paper is to develop a colleague recommender system for the Expert Cloud using features matrices of colleagues.

The new method is described in two phases. In the first phase, all possible colleagues of the user are found through the filtering mechanism and next features of the user and possible colleagues are calculated and collected in matrices. Six potential features of colleagues including reputation, expertise, trust, agility, cost and field of study were proposed. In the second phase, the final score is calculated for every possible colleague and then top-k colleagues are extracted among users. The survey was conducted using a simulation in MATLAB Software. Data were collected from Expert Cloud website. The method was tested using evaluating metrics such as precision, accuracy, incorrect recommendation and runtime.

The results of this study indicate that considering more features of colleagues has a positive impact on increasing the precision and accuracy of recommending new colleagues. Also, the proposed method has a better result in reducing incorrect recommendation.

In this paper, the colleague recommendation issue in the Expert Cloud is pointed out and the solution approach is applied into the Expert Cloud website.

This paper aims to identify, evaluate and integrate the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. To achieve this aim, the authors use systematic literature review (SLR) as a powerful method to collect and critically analyze the research papers. Also, the authors discuss the selected recommender systems and its main techniques, as well as their benefits and drawbacks in general.

In this paper, the SLR method is utilized with the aim of identifying, evaluating and integrating the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. Also, the authors discussed recommender system and its techniques in general without a specific domain.

The major developments in categories of recommender systems are reviewed, and new challenges are outlined. Furthermore, insights on the identification of open issues and guidelines for future research are provided. Also, this paper presents the systematical analysis of the recommender system literature from 2005. The authors identified 536 papers, which were reduced to 51 primary studies through the paper selection process.

This survey will directly support academics and practical professionals in their understanding of developments in recommender systems and its techniques.

The purpose of this paper is to describe how formal verification strategies have been utilized to assess the correctness of Knowledge Creation Process (KCP) in the social systems. This paper analyzes a User Relationship Management (URM) approach in term of human behavior connection in the social systems. A formal framework is displayed for the URM which consolidates behavioral demonstrating strategy.

Evaluating the human behavior interactions is an important matter in the social systems. For this analysis, formal verification is an essential section in the complex information systems development. Model checking results satisfied the logical problems in the proposed behavior model analysis.

Model checking results represent satisfaction of the logical problems in the proposed behavior model analysis. In the statistical testing, the proposed URM mechanism supported KCP conditions. Also, the percentage of state reachability in the URM with KCP conditions is higher than the URM mechanism without supporting KCP conditions.

The model checking results show that the proposed URM mechanism with supporting the KCP conditions satisfies comprehensively behavioral interactions rather than the mechanism without KCP conditions in the social networks.

The aim of this paper is to provide a comprehensive and detailed review of the state-of-the-art mechanisms of knowledge sharing in the education field as well as directions for future research.

In the current study, a systematic literature review until June 2017 is presented, which has been on the education’s mechanisms of knowledge sharing. The authors identified 237 papers, which are reduced to 71 primary studies through the paper selection process.

By providing the state-of-the-art information, the challenges and issues, this survey will directly support academics, researchers and practicing professionals in their understanding of knowledge sharing developments in education.

There are several limitations in this study. First, this study limited the search for articles to Google scholar and four online databases. There might be other academic journals, which may be able to provide a more comprehensive picture of the articles related to the knowledge sharing in education. Second, non-English publications were excluded from this study. The authors believe research regarding the application of knowledge sharing techniques have also been discussed and published in other languages. In addition, more studies need to be carried out using other methodologies such as interviews.

The paper presents a comprehensive structured literature review of the articles’ mechanisms of knowledge sharing in the education field. The paper’s findings can offer insights into future research needs.

Organizations are social entities comprising multiple people that are goal-directed and have coordinated activities that are also linked to the external environment. As information technology improves, the organizational performance is also improved and it results in positive changes and development in the organizations. Near field communication (NFC) is one of such technologies that can be implemented and utilized in an organization. The purpose of this paper is to investigate the important variables impacting the adoption of NFC in organizations and propose an applicable model for it.

In this paper, to have a successful NFC implementation in organizations and analyze main factors impacting the NFC technology adoption in organizations, a technology acceptance model-based approach is used.

The findings show that the main variables impacting NFC adoption are ease of use, potential risk, usefulness and cost. The obtained results indicate that the model has adequate and sufficient reliability, convergent validity and discriminant validity.

In this paper, the factors impacting the NFC adoption in organizations are pointed out, and the proposed model is tested on samples gathered from Azerbaijan railway employees and for statistical analysis of questionnaires, the SMART-PLS 2.0 software package is used.

This study aims to provide an overview of what characterizes the current state of research in the field of cloud computing use in human resource management (HRM) with the identification, analysis and classification of the existing literature and lines of research addressed and to provide guidance for future research.

The systematic literature review (SLR) technique has been used to identify, select, analyze and evaluate the existing publications on cloud computing and HRM. A total of 35 papers published up to December 2020 have been obtained from the Web of Science (WoS) scientific database. The research design has allowed us to determine what characterizes the current state of research on the use of cloud computing in HRM and obtain a novel classification of the literature that identifies four lines of research and the contributions in each line and has allowed us to define the future research agenda.

The four groups into which the papers on the cloud computing-HRM relationship have been classified are: (1) studies focused on the development of cloud platforms for HRM that highlight technical aspects, (2) papers that focus on the concept of human resource elasticity, (3) papers on the adoption and/or implantation of cloud platforms for HRM and (4) studies that highlight the effects or implications of cloud platforms for HRM. This paper proposes some new opportunities for future research and presents some helpful implications from the theoretical and management perspectives.

This study uses only scientific articles in the WoS database with a Journal Citation Report (JCR) or SCImago Journal Rank (SJR) impact.

This paper provides an overview of the knowledge on cloud computing and HRM research and offers recommendations for future research.

This paper follows a systematic literature review (SLR) method covering the published studies until March 2021. The authors have extracted the related studies from different online databases utilizing quality-assessment-criteria. In order to review high-quality studies, 32 papers have been chosen through the paper selection process. The selected papers have been categorized into three main groups, decision-making methods (17 papers), meta-heuristic methods (8 papers) and fuzzy-based methods (7 papers). The existing methods in each group have been examined based on important qualitative parameters, namely, time, cost, scalability, efficiency, availability and reliability.

Cloud computing is known as one of the superior technologies to perform large-scale and complex computing. With the growing tendency of network service users to utilize cloud computing, web service providers are encouraged to provide services with various functional and non-functional features and supply them in a service pool. In this regard, choosing the most appropriate services to fulfill users' requirements becomes a challenging problem. Since the problem of service selection in a cloud environment is known as a nondeterministic polynomial time (NP)-hard problem, many efforts have been made in recent years. Therefore, this paper aims to study and assess the existing service selection approaches in cloud computing.

The obtained results indicate that in decision-making methods, the assignment of proper weights to the criteria has a high impact on service ranking accuracy. Also, since service selection in cloud computing is known as an NP-hard problem, utilizing meta-heuristic algorithms to solve this problem offers interesting advantages compared to other approaches in discovering better solutions with less computational effort and moving quickly toward very good solutions. On the other hand, since fuzzy-based service selection approaches offer search results visually and cover quality of service (QoS) requirements of users, this kind of method is able to facilitate enhanced user experience.

Although the current paper aimed to provide a comprehensive study, there were some limitations. Since the authors have applied some filters to select the studies, some effective works may have been ignored. Generally, this paper has focused on journal papers and some effective works published in conferences. Moreover, the works published in non-English formats have been excluded. To discover relevant studies, the authors have chosen Google Scholar as a popular electronic database. Although Google Scholar can offer the most valid approaches, some suitable papers may not be observed during the process of article selection.

The outcome of the current paper will be useful and valuable for scholars, and it can be a roadmap to help future researchers enrich and improve their innovations. By assessing the recent efforts in service selection in cloud computing and offering an up-to-date comparison of the discussed works, this paper can be a solid foundation for understanding the different aspects of service selection.

Although service selection approaches have essential impacts on cloud computing, there is still a lack of a detailed and comprehensive study about reviewing and assessing existing mechanisms in this field. Therefore, the current paper adopts a systematic method to cover this gap. The obtained results in this paper can help the researchers interested in the field of service selection. Generally, the authors have aimed to specify existing challenges, characterize the efficient efforts and suggest some directions for upcoming studies.