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Journal of Information Technology Management
Number of Followers: 1

This is an Open Access Journal

Open Access journal
ISSN (Print) 2008-5893 - ISSN (Online) 2423-5059
Published by U of Tehran

[9 journals]

An Effective Model for Ontology Relations Efficacy on Stock prices: A Case
Study of the Persian Stock Market
- Abstract: 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.
A Systematic Review of Gamified Systems: A New Model for Strategic
Development in Future Gamification Research
- Abstract: 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.
Networking to learn by learning to network: Social networking among
students
- Abstract: 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.
Developing a Stock Market Prediction Model by Deep Learning Algorithms
- Abstract: 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.
Exploring the Nexus of Big Data Capabilities, Business Model Innovation,
and Firm Performance in Uncertain Environments: A Systematic Review
- Abstract: 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.
Key Success Factors to Implement IoT in the Food Supply Chain
- Abstract: In the Industry 4.0 era, most pioneer industries, are leveraging emerging technologies such as the Internet of Things (IoT), as the development solutions in the digital age. One of the largest active industries in Iran is the food industry, which is bound to benefit from such advantages. Since achieving a sustainable competitive advantage is mostly possible at the level of the supply chain, companies use information and communication technologies such as the IoT to coordinate information, finance, and materials between supply chain actors. This research aimed to identify the key success factors (KSF) in implementing the IoT in the food supply chain. Firstly, through a systematic literature review, the KSFs in the implementation of IoT in the food supply chain were identified. Subsequently to develop a measurement model, confirmatory factor analysis was used by structural equation modeling, and due to this the research could be considered applied-descriptive. To do so, a questionnaire was designed and completed by 142 members of the "Amadeh Laziz" supply chain (as a case study) who were selected using a stratified random sampling method. Then, utilizing confirmatory factor analysis and LISREL 8.83, the proposed model was confirmed. Finally, the cause-and-effect relationship between key success factors (KSFs) in implementing the IoT in the food supply chain was analyzed by Grey DEMATEL. Based on the Confirmatory factor analysis findings, the key success factors (KSFs) in implementing the IoT in the food supply chain were identified as “Technical, Economic, Legal, Cultural and Social, Security, Applicability of IoT throughout the supply chain, Implementation of IoT applications”. According to the cause-and-effect relationship findings, “Implementation of IoT applications” and “Economic” factors were found to be the most influenced factors, while "Applicability of IoT throughout the supply chain" and "Technical" factors were recognized as the most influential factors.