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In an era where customer expectations are rapidly evolving, enhancing the efficiency of hotel reception services in Iran is crucial for the growth of the hospitality sector. Recent research highlights the importance of digital transformation in improving service delivery and operational efficiency in the hospitality industry. These studies indicate that technological advancements can significantly streamline operational processes, improve customer satisfaction, and foster a competitive advantage in the hospitality industry. This research presents a technological innovation model aimed at modernizing reception desk services, addressing the pressing need for improvement in this area. Using an interpretive paradigm and an inductive approach, we conducted a qualitative study that incorporated a systematic review. Subsequently, the structures and components were extracted from the studies through qualitative coding. Our findings, derived from a review of 54 studies, revealed 295 open codes distilled into 15 constructs and four main components. This study highlights the significant impact of technological innovation on reception services, emphasizing the roles of ease of use and perceived usefulness in the technology adoption process. These insights provide essential guidelines for advancing reception desk technologies within the Iranian hotel industry, ultimately contributing to enhanced service quality.

In the Industry 4.0 era, many pioneering industries are leveraging emerging technologies such as the Internet of Things (IoT) as solutions in the digital age. One of the largest and most active industries in Iran is the food industry, which stands to benefit significantly from these advancements. Achieving a sustainable competitive advantage is often possible at the level of the supply chain, where companies use information and communication technologies, such as IoT, to coordinate information, finances, and materials among supply chain actors. This research aimed to identify the key success factors (KSFs) for implementing IoT in the food supply chain. Firstly, through a systematic literature review, the KSFs for IoT implementation in the food supply chain were identified. To develop a measurement model, confirmatory factor analysis using structural equation modeling was employed, making the research applied-descriptive. A questionnaire was designed and completed by 142 members of the "Amadeh Laziz" supply chain (a case study), who were selected using a stratified random sampling method. Confirmatory factor analysis and LISREL 8.83 were then used to validate the proposed model. Finally, the cause-and-effect relationship between KSFs in IoT implementation in the food supply chain was analyzed using Grey DEMATEL. Based on the confirmatory factor analysis findings, the KSFs in implementing IoT in the food supply chain were identified as technical, economic, legal, cultural and social, security, applicability of IoT throughout the supply chain, and implementation of IoT applications. Thus, the measurement model included eight factors and 27 measures. According to the cause-and-effect relationship findings, "Implementation of IoT applications" and "Economic" factors were found to be mostly influenced, while "Applicability of IoT throughout the supply chain" and "Technical" factors were recognized as the most influential. The results of this research can guide food producers and technology policymakers in their supply chains and help avoid trial and error in IoT implementation by leveraging global and national experiences.

The positive effect of social networking, particularly social networking sites (SNSs), on improving the process of learning has been acknowledged by many recent types of research. The relationship between features and characteristics of SNSs and the development of students' social networking was of interest to past researchers. As social networking is primarily perceived as intelligent thought and action in both real and virtual environments, there seems to be a need for a qualitative exploration of the influential factors of students' social networking. The study has been conducted using the case study method to look at the identified factors retrieved from previous research. A semi-structured in-depth interview was used to investigate the viewpoints and experiences of socially proactive and successful students at Iranian universities. Findings explain students' social networking due to three factors categorized as central, causal, and contextual. The personal learning system has a critical position among the various factors affecting students' social networking. Therefore, despite the facilitating role of social networking in promoting the learning process, students' social networking would be useless without utilizing a personal learning system. We can see a dynamic and interactive cycle of learning and social networking in the university context. The research has been founded on critical consideration of previously studied factors affecting social networking that were mainly limited to online technologies according to qualitative exploration. As a result of this research, different learning and social networking levels regarding diverse meaning, function, and complexity were identified.

With the advent of user-generated text information on the Internet, text sentiment analysis plays an essential role in online business transactions. The expression of feelings and opinions depends on the domains, which have different distributions. In addition, each of these domains or so-called product groups has its vocabulary and peculiarities that make analysis difficult. Therefore, different methods and approaches have been developed in this area. However, most of the analysis involved a single-domain and few studies on cross-domain mood classification using deep neural networks have been performed. The aim of this study was therefore to examine the accuracy and transferability of deep learning frameworks for the cross-domain sentiment analysis of customer ratings for different product groups as well as the cross-domain sentiment classification in five categories “very positive”, “positive”, “neutral”, “negative” and “very negative”. Labels were extracted and weighted using the Long Short-Term Memory (LSTM) Recurrent Neural Network. In this study, the RNN LSTM network was used to implement a deep transfer learning framework because of its significant results in sentiment analysis. In addition, two different methods of text representation, BOW and CBOW were used. Based on the results, using deep learning models and transferring weights from the source domain to the target domain can be effective in cross-domain sentiment analysis.

The adoption of E-learning in academic environments, particularly in Iran, has accelerated over the past decades. However, the lack of established guidelines for instructional design seems to have hindered the creation of effective online educational environments. Therefore, this study aimed to conduct a comprehensive qualitative research synthesis (QRS) to identify the criteria for an optimal online learning environment for Iranian university students. The study followed Major and Savin-Baden's three-phase QRS model and included 12 studies with 252 participants and 42 researchers. The synthesis integrated findings from multiple studies to gain insights into experts’ opinions and students’ perceptions, preferences, and experiences within online learning environments. The research identified four overarching themes: targeted learning, effective teaching management, socio-affective engagement, and learner empowerment. Targeted learning focuses on problem-oriented and need-oriented teaching; effective teaching management emphasizes balanced content and time management, clear objectives, and diverse presentation and evaluation methods; socio-affective engagement involves interactive feedback, social presence, and emotional communication; learner empowerment stresses autonomy, agency and active learning, including experiential and discovery learning. Applying these findings seems to offer a genuine contribution, leading to the development of culturally relevant and high-quality E-learning experiences and addressing the challenges posed by the nascent E-learning system in Iran.

The eXtensible Business Reporting Language (XBRL) functions as an independent, open platform that facilitates efficient information transmission over the Internet, improving business information utilization. Despite its widespread adoption and numerous benefits, unresolved assurance issues undermine its effectiveness, revealing a significant research gap. This study explores the complex landscape of XBRL data assurance challenges within the Malaysian Business Reporting System (MBRS). Utilizing a qualitative case study methodology, the research highlights key challenges in XBRL data assurance and presents strategic, innovative solutions. Through semi-structured interviews and document analysis, insights from diverse stakeholders are captured, revealing the development of artificial intelligence-enhanced audit software aimed at improving the quality of XBRL filings in Malaysia. Despite its potential, awareness of this advanced software among preparers remains disappointingly low. This research serves as a valuable resource for practitioners and researchers, offering an in-depth analysis of XBRL data assurance challenges and pioneering solutions, thereby making a significant contribution to this critical field.

The unpredictability of the stock market makes it a serious area of study and analysis. With the help of the accumulated information available in the current digital age and the power of high-performance computing machines, there is a great focus on using these capabilities to design algorithms that can learn stock market trends and successfully predict stock prices. The main goal is to create an intelligent system that provides these features for predicting short-term stock price trends to facilitate the investment decision process. To increase the accuracy and productivity of these systems and facilitate the routine of using common-sense knowledge in machine learning systems, developing or enriching knowledge bases and ontology for market modeling will be one of the effective measures in this field. In this research, an attempt has been made to strengthen and enrich the basic ontology created by the authors by using other global ontologies related to the subject of the stock market, and parts of the target space that were not addressed have been added to the ontology. By combining reference ontologies, a level of standardization is also created for the ontology and stability in the representation of concepts and relationships is ensured. In the next step, it has been tried to test the impact of the concepts and relations of the ontology in predicting stock price movements. For this purpose, news in the field of economy is considered as input and a model is created that first filters the textual inputs related to the desired stock symbol and then observes their effect on the price changes of the related stock. After improving the performance and comprehensiveness of the ontology, the study conducted in this report presented a model to measure and prove the effect of the relationships in this ontology on price changes. In practice, according to human limitations and the tools used, this effect was observed and confirmed with a proper level of certainty by checking the economic news.

For investors, predicting stock market changes has always been attractive and challenging because it helps them accurately identify profits and reduce potential risks. Deep learning-based models, as a subset of machine learning, receive attention in the field of price prediction through the improvement of traditional neural network models. In this paper, we propose a model for predicting stock prices of Tehran Stock Exchange companies using a long-short-term memory (LSTM) deep neural network. The model consists of two LSTM layers, one Dense layer, and two DropOut layers. In this study, using our studies and evaluations, the adjusted stock price with 12 technical index variables was taken as an input for the model. In assessing the model's predictive outcomes, we considered RMSE, MAE, and MAPE as criteria. According to the results, integrating technical indicators increases the model's accuracy in predicting the stock price, with the LSTM model outperforming the RNN model in this task.

This paper aims to conceptualize the success factors of a digital transformation (DT) strategy and analyze its impact on a company's economic performance. We explore the concepts that affect the field of DT definition and the key drivers that lead to successful DT. Through these key drivers considered as success factors, we propose a research framework linking these drivers to the DT strategy and then corporate economic performance in emerging markets. To test the research model empirically and provide a contextualized interpretation of the results, we adopted a sequential explanatory design. Initially, we performed a quantitative study through a survey among companies listed on the Casablanca Stock Exchange in Morocco. We then analyzed the collected data using the structural equation method. Next, to explain the results, we conducted a qualitative analysis through interviews with semi-structured questions. The findings show that in an emerging economy context such as Morocco, placing the customer at the core of the DT strategy, aligning the organization with the DT strategy, adopting a value system imbued with DT values, and establishing an operational roadmap to drive the change can enhance the company’s digital transformation. These drivers contribute to 59.5% of the implementation of the DT strategy. Driving a DT strategy has a significant impact on companies' economic performance, contributing to 21.5% of their commercial and financial outcomes. This study highlights that the maintenance of a "phygital" business model, which mixes digital and physical business models, and the lack of human resources involvement in the DT process are specific to the emerging market context studied.

Leveraging supplementary technology such as Blockchain has the potential to alter the stakeholders involved in a system. Paying attention to stakeholders is one of the main pillars of developing a system. Evidence has shown that Blockchain can solve existing challenges and add new capabilities. These actions will change the stakeholders of PHR. If a value is different for everyone, at the first stage, stakeholders should be identified, and that is our goal in this study. The research adhered to the guidelines outlined in the PRISMA statement. To this end, the study utilized databases including MEDLINE, ScienceDirect, and Google Scholar for English language articles, while the "iranjournals.nlai.ir" database was accessed for Persian language articles. Finally, 35 articles were chosen from searching databases, and six extra articles were selected from reviewing the final articles' references. Stakeholders were categorized into 15 groups. The patient (individual) was identified as the most frequent stakeholder (41 times), and infrastructure providers and the token exchange market were mentioned once each. The usage type is categorized into four groups: direct user interaction, data user, impact user, and financial beneficiaries, comprising six, eight, four, and four stakeholders, respectively. Patients (individuals) use the four groups, and health care providers, policymakers, hospitals, and the government each use two groups. Intelligent contracts are neglected in PHR, which can significantly impact the motivation and creation of incentives for using different stakeholders. The grouping presented here can be used in the preparation of the business model of PHR based on Blockchain. Data has the most usage for stakeholders and strengthens and supports investments in technologies such as Blockchain as an infrastructure for creating data markets, new business models, and creating value.

This paper provides a systematic review of the literature on big data capabilities, business model innovation, firm performance, and environmental uncertainty. It aims to establish a foundation for theoretical modeling, research proposition refinement, and the overall research framework by meticulously examining the theoretical backgrounds of existing studies and identifying research gaps. An initial search yielded 1,360 articles, which were filtered to remove duplicates and irrelevant studies, resulting in 475 articles for final analysis. These articles were classified into three main categories: the relationship between big data capabilities and business model innovation, the impact of business model innovation on firm performance, and the integrated relationship involving environmental uncertainty. Additionally, it examines the mediating role of business model innovation on firm performance as well as the moderating effect of environmental uncertainty on these relationships. Finally, the paper formulates research hypotheses and discusses identified research gaps, establishing a solid groundwork for methodological discussions in future research and contributing to the advancement of knowledge in the field.

This study aims to develop an adoption model tailored for service-oriented organizations and then evaluate its effectiveness within the specific context of Tehran Municipality, Iran's foremost service-oriented institution. Utilizing a mixed-method research approach integrating qualitative and quantitative methodologies, this study delineated the dimensions, categories, and indicators pertinent to the adoption of Electronic Human Resource Management (EHRM) systems in service-oriented organizations. Qualitative methodologies were employed to identify and develop the adoption model, which was subsequently evaluated within Tehran Municipality using a quantitative approach. In the qualitative segment of this study, in-depth interviews were conducted using a snowball sampling technique until theoretical saturation was achieved. For the quantitative phase, a sample of 310 experts affiliated with Tehran Municipality's EHRM system was surveyed. Structural equation modeling and Smart PLS 4.0 software were employed for data analysis. Ultimately, this research extracted five dimensions, 14 categories, and 94 indicators for the proposed adoption model. Notably, experts accorded the highest priority to the technological dimension in the adoption model, with specific emphasis on “adaptive architecture, security and privacy of employees, trialability and reliability, organizational citizenship behavior, organizational dynamic capabilities, digital Leadership Policy and Actions, cloud computing, etc…”, as pivotal factors in EHRM adoption. The organizational dimension assumed the second-highest priority, while the individual dimension was assigned a third-place ranking. Micro and macro-environmental factors followed in subsequent priority order.

Green information technology is in the spotlight for organizations, helping them save money by using information technology (IT) to achieve the highest efficiency and thus reduce environmental impacts. One of the ways that can help organizations planning for deploying green IT is to evaluate green information technology maturity (GITM). Previous studies have referred to various criteria for green IT evaluation, most of which are qualitative criteria that are difficult to measure and evaluate in ambiguous conditions. The main objective of this study is to identify crucial criteria that affect the GITM level and to design a fuzzy inference system to assess the GITM level in any organization. While using a Mamdani Inference system, inputs can be verbal expressions or crisp values, and the output shows the level of maturity of green information technology. Since green IT knowledge is not modeled in previous studies, modeling it in the current study is a valuable step for organizations confused about various factors they should consider for going green. The main system criteria are the conditions of the data center, office environment, work practice, procurement, and corporate citizenship. Due to the generality of the model used for the knowledge base system development, organizations can use this system for the green IT maturity level determination. The presented inference system helps organizations understand their status of being IT green and plan for the following steps to accomplish their desired maturity level. The proposed inference system has been tested, validated, and used to determine the maturity level of Tehran municipality.

This study investigates Ghanaian students' adoption of Mobile Learning (ML) by extending the technology acceptance model with a subjective norm variable. Specifically, this study focuses on the moderating effect of gender using the Measurement Invariance of Composite Models for the analysis. The study used a purposive sampling technique to collect the data for the study from sec-ond-year diploma students at the University of Professional Studies in Accra. SmartPLS 3.3.3 was used to analyze the data from 330 respondents. The findings of the study suggest that perceived ease of use, perceived usefulness, and subjective norm have a significant influence on the behavior-al intention to adopt mobile learning for the complete data set. In addition, the results suggest that the impact of the subjective norm was not significant for female students but for male students. Also, the impact of perceived ease of use and perceived usefulness on behavioral intention were insignificant. Furthermore, the findings suggest that behavioral intention influences students' actual use of mobile devices to access learning materials. Finally, gender moderates the relationship be-tween subjective norms and behavioral intention. The findings demonstrate group heterogeneity, therefore, investigations on technology adoption must always incorporate group dynamics to un-derstand how different groups respond to its adoption. The findings of the study hold significance for both policy and research implications.

Information technology (IT) governance has become a crucial area of study because of the growing corpus of research in the area. This study aims to conduct both a literature review and bibliometric analysis focusing on three specific research questions: (RQ1) examining the trends in publications and citations, (RQ2) recognizing the most impactful countries, journals, and authors, and (RQ3) investigating popular research areas and trending topics in IT governance. Data surrounding IT governance research were obtained through the Scopus database, with a total of 1,510 documents published from 1995 to February 2023. The extracted documents were analysed using Harzing’s Publish or Perish, VOSviewer, and Bibliometrics sub-tool Biblioshiny software. Publications have continued to rise since 2004 when the United States has had the greatest influence over IT governance publication and research collaboration, while Indonesia is the most prolific. Primary sources on IT governance research were taken from conference proceedings related to IT topics. As for authors, Pereira R from Portugal is at the top of the list while Van Grembergen W from Belgium is the most influential. This study is the first to identify and propose themes and current hotspots in IT governance research which serves as a guide for future researchers and professionals.

This research delves into the socio-economic aspects of online gambling in Iran, exploring the underlying cultural drivers that have contributed to its rapid growth in recent years. Online gambling's prevalence is intricately linked to cultural norms and values, both overt and covert, which have shaped its emergence in the country. Despite the religious, legal, and cultural taboos associated with gambling in Iran, this study investigates the cultural motifs that have challenged these obstacles, particularly among the youth. To unravel the psychological mechanisms at play among young online gamblers, this study adopts a qualitative approach, employing in-depth interviews with 17 male online gamblers aged between 20 and 40, alongside participatory observations. The data is analyzed thematically, with a focus on cultural motifs that reinforce mental accounting biases, drawing from the principles of behavioral economics. Notably, the central theme that emerges from this analysis is the "psychology of success", a worldview that influences the mindset and actions of individuals engaged in online gambling. The findings of this study reveal a significant thematic alignment between the teachings of the psychology of success and the cultural elements that bolster cognitive biases in the speech and conduct of online gamblers. The psychology of success, which emphasizes a sense of agency and empowerment, appears to drive cultural change and contributes to the widespread acceptance of online gambling despite its illegitimate status. Furthermore, the study highlights a connection between the prevalence of online gambling and the devaluation of the national currency.

Today, gamification is being used in various areas such as education, health, and business to enhance engagement and increase the system's efficiency. Despite significant scholarly interest, in many cases, undesirable results have been achieved using gamified solutions. This highlights the need for further research to explore these challenges through innovative methodologies and to devise new solutions. Addressing this gap, we conducted a systematic review of the literature on the emerging and growing subject of gamification using the PRISMA methodology and proposed a novel model for the strategic development of future gamification studies. The research led to the identification of 48 qualified empirical studies which have been analyzed to outline the existing views, gaps, and consequently the implications for future research. Through the analysis, we delineate the impact and effectiveness of gamification, highlighting its potential to transform user experience positively when implemented with strategic finesse. Consequently, we propose a novel model for the strategic development of future gamification studies, presenting it in three main dimensions: Contexts, Users, and Elements, and for each dimension, significant and less-paid topics are discussed. In addition, we represent six main suggestions for the design of the entire gamified system: Decision-Making Methods, Success Factors, Validation Methods, Dynamic Design Approach, Timeframe, and Modern Technology. Our proposed model not only facilitates a deeper understanding of gamified systems but also offers actionable insights and guidelines for both academics and practitioners. It is meticulously designed to assist researchers and practitioners in crafting more effective gamified systems that are customized to meet specific user needs and environmental contexts. By doing so, it aims to maximize the sustainable benefits of gamification, ensuring that these systems deliver significant and lasting impacts. This strategic approach integrates the latest advancements in technology and dynamic design principles, establishing a robust framework for the future of gamification research and application.

Cloud computing technology could bring about cost-effectiveness, scalability, usage easiness, and flexibility against environmental changes. Despite several advantages of this technology, the acceptance and movement to the cloud computing environment occurs slowly, particularly in several organizations in developing countries. Cloud computing allows an individual to share distributed sources and services. This study aims to identify the optimal security framework in cloud computing, focusing on the Infrastructure Communications Company as a case study. The method used in this study is descriptive (non-test) and the correlative research scheme is PLS (partial least squares). The participants of the current study were selected among the Tehran Communication Infrastructure Company employees. Based on the Cochran formula and the research population which was 600, 234 were selected using the random sampling method. To measure the security framework in cloud computing, the questionnaires related to Joils and Opria (2017), Tari (2014), Qio and Gayi (2017), and Sing et al. (2016) were used. Findings indicate that the impacts of technological and technical factors, experienced human resources, financial resource provision and secure infrastructures on information technology security were significant (0.01). Moreover, information technology management and supporting regulations on security risk assessment have significant effects. The assessment of security risk has a significant effect on information security. Additionally, information security management and supporting regulations have a significant effect on Goal setting (0.01). Goal setting has a significant effect on environmental and physical security. Based on the results, the evaluated model of the research could explain 0.53 of information security management, 0.50 of security evaluation, 0.36 of goal setting, 0.55 of information security, and 0.35 of environmental security.