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Expert Cloud as a new class of Cloud computing systems enables its users to request the skill, knowledge and expertise of people by employing internet infrastructures and Cloud computing concepts without any information of their location. Job scheduling is one of the most important issue in Expert Cloud and impacts on its efficiency and customer satisfaction. The purpose of this paper is to propose an applicable method based on genetic algorithm for job scheduling in Expert Cloud.

Because of the nature of the scheduling issue as a NP-Hard problem and the success of genetic algorithm in optimization and NP-Hard problems, the authors used a genetic algorithm to schedule the jobs on human resources in Expert Cloud. In this method, chromosome or candidate solutions are represented by a vector; fitness function is calculated based on response time; one point crossover and swap mutation are also used.

The results indicate that the proposed method can schedule the received jobs in appropriate time with high accuracy in comparison to common methods (First Come First Served, Shortest Process Next and Highest Response Ratio Next). Also the proposed method has better performance in term of total execution time, service+wait time, failure rate and Human Resource utilization rate in comparison to common methods.

In this paper the job scheduling issue in Expert Cloud is pointed out and the approach to resolve the problem is applied into a practical example.

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.

Expert Cloud as a new class of Cloud systems provides the knowledge and skills of human resources (HRs) as a service using Cloud concepts. Task scheduling in the Expert Cloud is a vital part that assigns tasks to suitable resources for execution. The purpose of this paper is to propose a method based on genetic algorithm to consider the priority of arriving tasks and the heterogeneity of HRs. Also, to simulate a real world situation, the authors consider the human-based features of resources like trust, reputation and etc.

As it is NP-Complete to schedule tasks to obtain the minimum makespan and the success of genetic algorithm in optimization and NP-Complete problems, the authors used a genetic algorithm to schedule the tasks on HRs in the Expert Cloud. In this method, chromosome or candidate solutions are represented by a vector; fitness function is calculated based on several factors; one point cross-over and swap mutation are also used.

The obtained results demonstrated the efficiency of the proposed algorithm in terms of time complexity, task fail rate and HRs utilization.

In this paper the task scheduling issue in the Expert Cloud and improving pervious algorithm are pointed out and the approach to resolve the problem is applied into a practical example.

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.

The present investigation goals to empirically test the role of expert cloud on team performance and employee creativity. Here, the expert cloud comprises cloud application, cloud management, cloud infrastructure and cloud resources. The present study aims to identify important and key criteria and examine the relationships among them. In other words, the purpose of this study to find out the impact and relationship between cloud application, cloud management, cloud infrastructure and cloud resources and team performance and employee creativity.

Today, human societies’ rapid growth and the environmental changes that surround us every day are clearly visible. They highly affect our activities. In today’s highly complex organizations, people alone cannot handle all the issues that have arisen. As organizational managers are faced with diverse cultures in the governance of organizations, the need to use work teams with different abilities and specializations to achieve the goals of organizations leads managers to use teamwork and focus on employee creativity. On the other hand, the expert cloud makes it possible for human societies like universities, firms, industries, institutes, businesses and colleges to pool and share their human resources’ skills, knowledge and experiences to meet the competitive era’s demands. Therefore, the current investigation examines the impact of expert cloud on team performance and employee creativity. The research information is collected using an online questionnaire. The data collected is analyzed using AMOS and SMART PLS software.

All of the formulated hypotheses are supported. The results have shown that cloud application, cloud management, cloud infrastructure and cloud resources positively and significantly affect team performance and employee creativity.

Managers must be conscious of the vital role that the professional cloud plays in team performance and the innovation of workers. This paper would also make executives more conscious of the powerful tools in the field of cloud computing. Firms can use the outcomes of this paper investigation to improve team performance and employee creativity.

It is considered one of the initial efforts to demonstrate the impacts of expert cloud on team performance and employee creativity. This study’s value relies on that practitioners and academics may have supporting evidence on the role played by novel technology such as an expert cloud.

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.

The purpose of this article is to identify the critical success factors (CSFs) of information technology (IT) projects using cloud computing. CSFs are variables that can influence the success of projects and therefore need to be identified and managed appropriately.

This is an exploratory qualitative study with 23 experts in cloud computing projects through semi-structured interviews. The data was analyzed using content analysis.

The results present a list of CSFs for projects in a cloud computing environment. The study demonstrated that the CSF with greater relevance to cloud projects is the team’s technical capacity, followed by the support of senior management and the team’s soft skills. In addition, results demonstrated that contract item management is a limiting factor for cloud projects.

The sample comprised only Brazilian experts, so it may not represent the same scenario as in other locations. The CSF ratio for cloud computing projects may vary depending on the company’s maturity in projects of this nature.

The CSF relationship can guide managers in properly conducting cloud computing projects, contributing to minimizing the risks and challenges that may interfere with the project.

The relationship of the CSFs in cloud computing projects proposed fills a gap in studies specifically related to this context and tries to minimize project managers’ stress.

Contract items for the cloud context are added to the CSF literature in IT projects, which have not been addressed so far.

Despite the importance of investigating the impact of cloud computing on the e-learning process, the relationship development between the two is not examined well. Thus, the main goal of this research is to assess how to improve the e-learning process by using cloud services. This paper aims to attempt to investigate the impact of cloud computing on e-learning development.

The paper is based on both quantitative and qualitative methodologies. For review-related work and statistical analysis of questionnaires, the SPSS 22 and SMART-PLS 3.2 software package are used.

The results from the study show that e-learning development is significantly influenced by the quality of services, cloud features, university readiness and users’ readiness.

The adoption of cloud technology within an instructional environment has the capacity of offering new opportunities for improvement and innovation for gaining knowledge of the process.

Nowadays, communications, products, services and costs are customized through the internet technology. The main theory to continue competitiveness in the organizations is customer relationship management (CRM). CRM enables organizations to efficiently interact with customers and gather, store and examine their data for providing a complete view of them. On the other hand, the subject of cloud computing has increasingly become the bridge for the success of the CRM implementation. Therefore, this study aims to investigate the impact of cloud computing (new cloud facility, knowledge of information technology (IT), cloud security and cost) on the success of CRM systems.

The model and the questioners-based data are analyzed using the Smart PLS 3.0. The data were gathered based on 80 employees of three main agricultural companies in Iran.

The obtained results have indicated that all of the considered factors, new cloud facilities, knowledge of IT, cloud security and cost, play an important role in CRM systems’ success. Also, the evaluation and examination of the consistency and validity of the model are performed through the structural equation model.

First, the authors have conducted a study in a single region. It cannot be guaranteed that the results can be generalized to other regions. Second, for this cross-sectional study, the research design was conducted that showed constant relationships between variables. The research done for this study is cross-sectional. Third, because of time and financial restrictions, the authors have gathered data using a sample from a single location.

Proposing a new model for investigating of the impact of cloud computing (new cloud facility, knowledge of Information Technology (IT), cloud security and cost) on the success of CRM systems is the main originality of this paper.

Cloud computing is one of the technologies that improves performance and reduces costs in companies through flexible and easy access to computational resources. Supply chain management is one of the widespread and unlimited computing resources. Applying cloud computing to the green supply chain will eliminate constraints and speed up the implementation of various chain processes. Therefore, this paper aims to present a model and framework for assessing the impact of cloud computing on green supply chain management, which improves the performance of the resulting system in comparison with previous systems.

In this study, a self-made questionnaire containing 38 questions was used to collect data. The questions were compiled based on the variables identified in previous studies. Besides, the answers to the questions were based on the five-point Likert scale (strongly disagree, disagree, no opinion, agree and strongly agree). By reviewing the subject literature, the factors that affect the green supply chain management have been identified, which include cost of services, user-friendly software, energy consumption, subscription and collaboration capabilities and self-service ability.

The findings confirmed the fitness of the proposed model and showed the impact of cloud computing on green supply chain management. In addition, results showed that considered variables (service cost, user-friendly applications, energy consumption, sharing and collaboration and self-service) significantly affect the success management of green supply chain.

The results of this research show that, to help effective management of the complexity of the supply chain, many companies have adopted an advanced IT strategy to increase the flow of information and, in turn, to increase their competitive advantage.

The development of a cloud computing-based green supply chain management system can lead to improved system performance compared to traditional systems, which can revolutionize a wide range of industries and businesses by clarification of businesses and making the processes smart. It can also reduce costs and improve performance of micro- and macro-business firms. Therefore, the purpose of this study is to provide a model for investigating the impact of cloud-based IT systems on green supply chain management.