درخت حوزه‌های تخصصی

This study investigates the constructs and related theories that drive social capital in energy sector from the intention perspectives. This research uses theories of ‘social support’ and ‘planned behaviour’ alongside satisfaction and perceived value to propose a research model that drives social capital for energy sectors in Malaysia. The model reveals that the Theories of Planned Behaviour (TPB) and Social Support Theory (SST) alongside satisfaction and perceived value factors promote social capital development in energy sectors. Using PLS–SEM to analyse data gathered from energy sector employees in Malaysia, this research demonstrates that social capital is present when there is trust and loyalty among the users and positively effects energy sectors in terms of the productivity, effectiveness, efficiency and profitability. The study also contributes to the understanding of individuals' use of social capital in energy sector. A survey is adapted and distributed to 100 respondents as a mean to study on the validity and reliability of the research factors. Results indicate that all seven hypotheses proposed significantly influence social capital.

The purpose of research is focused on the insight into the future of Bitcoin on the financial situation, its implications and challenges. The problem of study is to investigate how to deal with a new type of digital currencies (such Bitcoin) that does not have a physical presence and there is no specific body to issue. Thus, this study aims to identify the nature of Bitcoin currency and what are the challenges associated with it as well as exchange rates with some currencies, as the research hypothesized the main hypothesis that Bitcoin will contribute to financial development in the future, the research also used the analytical rooted approach to present concepts and data, using financial technical analysis to display the results The research also came up with a number of conclusions and recommendations. The results of paper significantly contribute to the literature through providing evidence from financial data of Bitcoin.

Social support is the physical and emotional comfort given to someone in times of need or crises and it is especially important for maintaining good physical and mental health. Despite the contribution of intelligent technological devices, which have led to the availability of various types of social support systems, the latter are still not widely known to many. These support systems are currently available in many versions, such as in tablets and smartphones, where many health information systems have been created specifically to accommodate to the needs of current mobile healthcare consumers. In this paper, we systematically review the role of social support provided via health information systems and report a qualitative study that investigates the perceptions of healthcare professionals and consumers towards incorporating social support in self-care applications. The results obtained through our study reveal that social support is associated with better health management and is a vital component to be incorporated in any novel health information system. Likewise, the results are supported by our qualitative study that indicates healthcare professionals and consumers emphasize the inclusion of the social support component in self-care applications to achieve better health outcomes.

This study examines how gamification mechanics could be used in online retailers' loyalty programs. In other words, this article attempts to create a conceptual model for the relationship between gamification mechanics and customer loyalty elements. We used a field study to conduct our research. In order to validate the survey, 450 customers from one of the greatest online stores in Iran were questioned. The results of this survey were used to validate our 11 phrases on the relationship between gamification mechanics and customer loyalty. The results were analyzed using confirmatory factor analysis, path analysis, and model fitness tests in structural equations modeled in the Lisrel software. According to the research findings, the relationship between variables and the proposed conceptual model was confirmed. Based on the performed analysis, all 11 phrases were verified .

Enterprise risk management represents a process of assessing exposure to risks in an institution. It is a systematic mechanism and a comprehensive tool for predicting events, including unexpected events, and their impacts. This paper is a conceptual study. It aims at designing a model for testing the mediation effect of information technology (IT) quality on the relationship between the enterprise risk management (henceforth, ERM) practice and the financial performance of financial institutions in Iraq. Based on reviewing the literature on ERM, it is found that ERM (leadership roles, risk culture and compliance) has a positive and important effect on the financial performance of financial institution in Iraq. In addition, this paper shows that the IT quality is a good medium for increasing the interaction between practices of ERM and financial performance positively. It provides a practical and theoretical contribution to practices of ERM in listed companies in Iraq. It also has major implications for professional practitioners, regulators and academics in Iraq. Finally, this paper contributes to building relevant knowledge in the field of leadership role, risk culture, compliance and IT quality.

There is a causal relationship between high-quality cost systems and pricing decision makers because pricing decision is in dire need of modern systems that help make rational decisions. The aim of this research is to confirm that quality cost systems affect pricing decisions-making in maintaining the industrial companies. The research results can be utilized by beneficiaries taking into account the effects, obligations and costs involved. This study focused on the importance of the growing interest of companies and individuals interested in quality on the necessity of maintaining the product quality and reaching the high quality of products through strategic decisions-making for the continuity of industrial companies and elimination of future risks. The problem of this study is that quality cost systems are among the most important factors involved in pricing decisions-making of industrial companies. This is attributed to the superiority of foreign companies' interest in high quality production over Arab companies in terms of their interest in product quality in pricing decisions-making within advanced methods used by these companies in maintaining the quality of their products. This study is based on the descriptive approach to form the theoretical base of research by relying on the relevant published books and scientific research. The analytical approach was then adopted through the questionnaire. Among the most important findings is the existence of a positive relationship between quality costs and pricing decisions. Hence, quality costs positively affect the rationalization of pricing decision by providing the necessary data and information about the risk to the organization in increasing the competitive advantage among companies. This competitive increase has led to an increase in seeking more opportunities for companies with quality costs. One of the most important recommendations is to prepare plans to mitigate the competitive risks that have been chosen. The aim of these plans is to provide the necessary information to make strategic long-term decisions, aiming at protecting the rights and interests of shareholders to economic unity and strategic decisions-making that serve interests.

Persian language is one of the most widely used languages in the Web environment. Hence, the Persian Web includes invaluable information that is required to be retrieved effectively. Similar to other languages, ranking algorithms for the Persian Web content, deal with different challenges, such as applicability issues in real-world situations as well as the lack of user modeling. CF-Rank, as a recently proposed learning to rank data, aims to deal with such issues by the classifier fusion idea. CF-Rank generates a few click-through features, which provide a compact representation of a given primitive dataset. By constructing the primitive classifiers on each category of click-through features and aggregating their decisions by the use of information fusion techniques, CF-Rank has become a successful ranking algorithm in English datasets. In this paper, CF-Rank is customized for the Persian Web content. Evaluation results of this algorithm on the dotIR dataset indicate that the customized CF-Rank outperforms baseline rankings. Especially, the improvement is more noticeable at the top of ranked lists, which are observed most of the time by the Web users. According to the NDCG@1 and MAP evaluation criteria, comparing the CF-Rank with the preeminent baseline algorithm on the dotIR dataset indicates an improvement of 30 percent and 16.5 percent, respectively.

Given the complexities of supply chain networks, the firms consider modern technologies as a potential factor to improve their supply chain performances. One of these technologies is the Internet of Things (IoT). Hence, the main purpose of this study has been to achieve the conceptual model of the IoT implementation in hospital supply chains. Considering the qualitative nature of the study, relevant articles were specified through library research and collecting the related literature. The outputs obtained were analyzed using the meta-synthesis method and the grounded theory. Then the selected articles were extracted and the grounded theory was used to obtain the conceptual model of the IoT implementation in hospital supply chains. The research results indicated that the model featured 7 main categories, 19 subcategories and 86 codes. The results present a conceptual model for the implementation of the IoT in hospital supply chains including an explanation of the main research category, drivers, prerequisites and enablers, environmental and contextual conditions, challenges, technology implementation strategies, and results and outcomes.

Recommender systems are important tools for users to identify their preferred items and for businesses to improve their products and services. In recent years, the use of online services for selection and reservation of hotels have witnessed a booming growth. Customer’ reviews have replaced the word of mouth marketing, but searching hotels based on user priorities is more time-consuming. This study is aimed at designing a recommender system based on the explicit and implicit preferences of the customers in order to increase prediction’s accuracy. In this study, we have combined sentiment analysis with the Collaborative Filtering (CF) based on deep learning for user groups in order to increase system accuracy. The proposed system uses Natural Language Processing (NLP) and supervised classification approach to analyze sentiments and extract implicit features. In order to design the recommender system, the Singular Value Decomposition (SVD) was used to improve scalability. The results show that our proposed method improves CF performance.