Technological Factors Research Articles

Transforming banking: Examining the role of AI technology innovation in boosting banks financial performance

With the ongoing advancement of technology, artificial intelligence is increasingly being integrated into banking and finance, with the transformative potential to reshape the financial performance of economies worldwide. This research investigates the dynamic relationship between banking and finance artificial intelligence technology innovation and banks' financial performance across 20 countries using the feasible generalized least squares and the generalized method of moments techniques. The results show that banking and finance artificial intelligence technology innovation positively impacts banks' return on assets, highlighting its role in enhancing financial performance. The interaction term between artificial intelligence innovation and economic growth emphasizes their collaborative positive impact on financial performance. Mediation analysis highlights information and communication technology development's role in transforming artificial intelligence innovation into improved financial outcomes. Considering lagged effects, initial innovation surges correlate with improved financial performance, but prolonged exposure leads to diminishing returns. Moreover, the findings indicate that non-performing loans negatively affect financial performance, underscoring the importance of asset quality. Additionally, regulatory capital and economic growth are positively associated with financial performance, while government regulations exhibit a negative impact. This study highlights the essential role of artificial intelligence technology innovation in banking and finance, emphasizing the need to consider economic and technological factors for maximizing its benefits in enhancing financial performance. Policy recommendations include promoting an artificial intelligence innovation ecosystem, adapting regulatory frameworks, and investing in information and communication technology infrastructure to harness artificial intelligence innovation's benefits while addressing associated challenges.

Exploring Innovative Use Cases for Enhancing Digital Wallet Adoption and User Engagement: A Strategic Study for Future Implementation

The advancement of digital payment methods especially the use of the digital wallets has greatly impacted the financial industry as it provides safe means of payment. Nonetheless, while these platforms have become popular, user participation is still low, and current platforms do not offer new functionalities that could improve consumer interaction and address the variety of consumer segments. This research, titled "Exploring Innovative Use Cases for Enhancing Digital Wallet Adoption and User Engagement: The research titled “Digital Wallets: A Strategic Study for Future Implementation,” looks at possible new use cases where digital wallets can be applied to enhance their current roles of facilitating payments. The research aims at establishing how new technologies like; Artificial Intelligence (AI), Blockchain, Augmented Reality (AR) and gamification can improve engagement, the user experience and thus create more adoption. Some of the key areas include Peer-to-Peer (P2P) lending, Cryptocurrency, Voice identity, and transactions that can serve the unbanked population as well as regions that are still to embrace digital wallets. Technological factors, consumer behaviors, and socio- economic conditions that affect the uptake of digital wallets are discussed in this research to guide Fintech firms, banks, and regulators. The study used a systematic literature review which involved using articles from the last four years from academic journals, industry reports, and case studies. This particular secondary research helped to determine the tendencies, problems, and potential of digital wallet usage, including such aspects as simple and intuitive interface for non-technical users, the application of gamified approaches in financial literacy, and the opportunities for the implementation of blockchain technology to provide financial services to unbanked customers. Therefore, the research shows that gamification, P2P lending, and cryptocurrency-enabled wallets will greatly increase user engagement and financial inclusion. In addition, AI-enabled personalization in the context of digital wallets, and AR-based shopping represent a vision of how the future of cross-border payments, sustainability incentives, and UI advancements will shape the user experience. Finally, the research provides a set of tactical recommendations for digital wallet providers on how to bring such innovative use cases into practice to enhance user engagement and to expand market coverage in the developed and developing countries.

Moderating effect of contextual factors on the relationship between travel motivation and destination loyalty among domestic tourists in the Coast region of Kenya

Despite the marginal growth recorded in the tourism sector in Kenya, the domestic visits and estimates fall far below the expectations. The study was guided by the following specific objective; to assess the moderating effect of contextual factors on the relationship between travel motivation and destination loyalty among domestic tourists in the Coast Region of Kenya. The study area comprised frequented attractions in the Coast region of Kenya. The study adopted an embedded mixed approach comprising descriptive survey and explanatory research designs. Simple random sampling and purposive techniques were adopted for domestic tourists, destination managers and experts respectively. Data was collected using questionnaires and interview schedules. Four hundred (400) questionnaires were distributed while 10 destination managers and 5 experts were interviewed. Data analysis was done using multiple regression models, hierarchical multiple regression and descriptive analysis. The multiple regression model results without interaction terms indicated that R-square = 0.442, implying that destination attributes and socio-psychological factors explain 44.2% of destination loyalty. The hierarchical multiple regression demonstrated that contextual factors have a moderating effect on interaction between travel motivation and destination loyalty since the model was significant {R2 = 0.255, F (7, 371) = 12.12, p =0.0001}. The model accounted for 25.5% of variation on destination loyalty. This means that the composite elements of contextual factors namely; political, economic, technological and socio-cultural factors significantly moderates the interaction between travel motivation and loyalty behaviour of domestic tourists. The null hypotheses were tested and rejected. The study recommends need for destinations and service providers to engage more on digital marketing and deploy technology during marketing initiatives and product development to capture niche segments since majority of the respondents were youth travelers

Digital Communication Technology is not the Only Cause of Rapid Spread of Misinformation in Society

While achieving technological development in media communication, it is important to recognize the dual nature of technological advancement. This rapid development has led to a great threat of spreading misinformation, where ideas are no longer geographically restricted and spread globally at an unprecedented rate (Wang et al., 2019). Statistics from the Central Statistical Office (CSO) 2021 show that 62% of Internet users have seen untrue or suspicious content online Georgiev (2024). Many people believe that digital communication technologies are the only reason for the accelerated spread of misinformation. On the contrary, I believe that this view ignores the more complex political motivations, social, cultural, and psychological underpinnings. While technology itself is an important agent in the process of spreading misinformation, other factors are also integral. Therefore, I do not agree that the cause lies solely in digital communication technology, and this paper will illustrate how technological and social factors interact with each other through examples.

An Integrated SIMUS–Game Theory Approach for Sustainable Decision Making—An Application for Route and Transport Operator Selection

The choice of management strategy for companies operating in different sectors of the economy is of great importance for their sustainable development. In many cases, companies are in competition within the scope of the same activities, meaning that the profit of one company is at the expense of the other. The choice of strategies for each of the firms in this case can be optimized using game theory for a non-cooperative game case where the two players have antagonistic interests. The aim of this research is to develop a methodology which, in non-cooperative games, accounts for the benefits of different criteria for each of the strategies of the two participants. In this research a new integrated sequential interactive model for urban systems (SIMUS)–game theory technique for decision making in the case of non-cooperative games is proposed. The methodology includes three steps. The first step consists of a determination of the strategies of both players and the selection of criteria for their assessment. In the second step the SIMUS method for multi-criteria analysis is applied to identify the benefits of the strategies for both players according to the criteria. The model formation in game theory is drawn up in the third step. The payoff matrix of the game is formed based on the benefits obtained from the SIMUS method. The strategies of both players are solved by dual linear programming. Finally, to verify the results of the new approach we apply four criteria to make a decision—Laplace’s criterion, the minimax and maximin criteria, Savage’s criterion and Hurwitz’s criterion. The new integrated SIMUS–game theory approach is applied to a real example in the transport sector. The Bulgarian transport network is investigated regarding route and transport type selection for a carriage of containers between a starting point, Sofia, and a destination, Varna, in the case of competition between railway and road operators. Two strategies for a railway operator and three strategies for a road operator are examined. The benefits of the strategies for both operators are determined using the SIMUS method, based on seven criteria representing environmental, technological, infrastructural, economic, security and safety factors. The optimal strategies for both operators are determined using the game model and dual linear programming. It is discovered that the railway operator will apply their first strategy and that the road operator will also apply their first strategy. Both players will obtain a profit if they implement their optimal strategies. The new integrated SIMUS–game theory approach can be used in different areas of research, when the strategies for both players in non-cooperatives games need to be established.

მიწისქვეშა ნაგებობების საექსპლოატაციო პირობების ზღვრული მდგომარეობები (Springer Nature Switzerland AG-ს მიერ გამოქვეყნებული მონოგრაფიის აბსტრაქტი)

The main topic of this book is the calculation of underground structures at the limit states of serviceability. It considers the main schemes typical for underground structures for various purposes, gives the corresponding mathematical models describing the main geo-mechanical and technological factors in the construction and operation of extended excavations.

Modeling Nuclear-Centric Scenarios for Ukraine’s Low-Carbon Energy Transition Using Diffusion and Regression Techniques

This study presents a mathematical model for forecasting the development of Ukraine’s Integrated Power System (IPS) until 2040, with a specific focus on the expansion of nuclear energy as a cornerstone of the nation’s low-carbon transition. The model is an extension of Frank Bass’s mixed influence diffusion model, incorporating both economic and technological factors. These factors are treated as stochastic variables essential for accurately predicting the evolution of an integrated energy system, particularly in the context of rapid renewable energy sources (RES) growth. The model employs regression techniques using generalized logistic curves, improving forecasting efficiency by aligning modeling parameters with experimental data. The study’s results indicate the potential for optimizing IPS components, including nuclear and thermal power generation, through the model’s application. The model is distinguished by its inclusion of economic and technological impacts, such as state matrices, control actions, and external influence matrices, which enhance the accuracy of simulations and predictions. The validation of the model, based on scenarios of electricity consumption and generation, shows significant alignment with observed trends, confirming the model’s reliability. The findings suggest that this model is an effective tool for developing and refining energy system scenarios, with nuclear energy playing a pivotal role in Ukraine’s sustainable energy future.

A Methodological Approach to Prioritize Digital Twin Development in Manufacturing

ABSTRACTThe digital age has brought about a need for organizations to utilize Digital Twins to improve operational efficiency and decision‐making. However, it is difficult for companies to identify and prioritize Digital Twin initiatives that meet the needs of their stakeholders and align with the capabilities of the company and its strategic plans. This paper proposes a methodology for the systematic identification and prioritization of Digital Twin applications in complex industrial settings. The methodology begins by documenting business requirements, current processes, and challenges, and subsequently identifying areas with potential benefits from Digital Twins through the use of an opportunity scoring system. To refine the portfolio of Digital Twin applications to include only those that are impactful and viable, the feasibility of Digital Twin is quantified by evaluating technological (technical capacity and digital skills), organizational, and project risk factors. To validate the proposed methodology, a case study was conducted in collaboration with an industrial partner specializing in injection molding. This real‐world application demonstrates the effectiveness of our approach in identifying and prioritizing Digital Twin applications in a complex industrial context. This research contributes to the growing body of knowledge surrounding Digital Twins, providing organizations with a structured approach to leverage the potential of this transformative technology.

Determinants of Crypto Investment Decisions: Ra-tional Behavior or Irrational Behavior?

The current phenomenon is that crypto asset investment is considered high risk, yet the number of crypto asset investors is increasing. This research investigates the determinants of crypto investment decisions: driven by rational or irrational behavior, specifically testing the moderating role of social influence on the determinants of investment decisions. The research combines technological factors (crypto platforms) and psychological factors (herding behavior FOMO (Fear of Missing Out)) with external factors of social influence a moderating variable. This study uses quantitative research methods and primary data from respondents who are crypto asset investors domiciled in Indonesia. The sample consisted of 124 respondents consisting of 95 male respondents and 29 female respondents. Statistical analysis including analysis of the Outer Model, Inner Model and direct effect and Moderated Regression Analysis was carried out using SmartPLS 0.3 software. The results showed that crypto platforms, financial literacy, herding behavior, FoMO had an effect on investment decisions, and social influence was able to moderate the influence of crypto platforms, financial literacy, herding behavior, FoMO on investment decisions. Suggestions given for further research are: develop further research models that can represent the determinants of investment decisions in crypto by using other research variables in predicting their influence on investment decisions such as regret experience, attitude, risk tolerance, etc.

Functional characteristics and molecular structural modification of plant proteins. Review

Protein preparations from plant raw materials are widely used in the food industry as improvers, replacers or enrichers for products. However, their functional properties, as a rule, are less pronounced than those of proteins of animal origin. The aim of the review is to analyze and summarize the results of investigations dedicated to studying and characterizing the main functional properties of plant proteins (hydration, solubility, water binding capacity, fat binding capacity and gel-forming capacity, stability of emulsions, foams, rheological properties, texturization) and their modification. The objects of the research were scientific publications, most of which were published in 2017–2024. Functional properties of proteins were characterized; their dependence on the nature and variety of crop, methods of extraction, technological factors of processing and methods of modification was revealed. The search and selection of papers were carried out in the bibliographic databases eLIBRARY.RU, RSCI, Google Scholar, Scopus, Web of Science, Elsevier, and PubMed. Data analysis was performed with their systematization, generalization, intermediate conclusions and general conclusion. The special attention was paid to chemical, physical, physico-chemical and enzymatic methods for modification of protein properties, their advantages and disadvantages, as well as the interrelation of structural and physico-chemical features of proteins with their functional properties. The main regularities were revealed for an effect of modification methods on the secondary and tertiary structures of proteins, surface hydrophobicity, the ratio from fractions, aggregation, denaturation and biological activity obtained by the modern methods of investigations (IR‑spectroscopy, fluorescent microscopy, SDS-PAGE, circular dichroism, spectrophotometry and so on). It has been concluded that it is necessary to carry out further investigations aimed to studying interrelation of molecular structural features of proteins with indicators of functionality and regularities of their behavior in food systems due to an increase in the production of protein preparations from alternative raw materials (pea, chickpea, sunflower, kidney bean, rice and others). Based on the revealed and newly obtained theoretical information, the targeted modification and regulation of properties of protein ingredients for production of high-quality foods are possible.

Internet of Things Adoption in Technology Ecosystems within the Central African Region: The Case of Silicon Mountain

The Internet of Things (IoT) has emerged as a transformative technology with the potential to revolutionize various sectors and industries worldwide. Despite its global significance, the adoption and implementation of IoT technologies in emerging technology ecosystems within the Central African region still need to be studied and explored. This paper presents a case study of the Silicon Mountain technology ecosystem, located in Fako division of the southwest region of Cameroon, focusing on the barriers and challenges to adopting and integrating IoT technologies within this emerging tech ecosystem. Through a survey-based approach, we investigate the factors influencing IoT adoption in the Silicon Mountain tech ecosystem, including technological, economic, social, and regulatory factors. Our study reveals key insights into the current state of IoT adoption, opportunities for growth and innovation, and IoT adoption challenges. Key among the challenges identified for impeding IoT uptake were issues related to standardization and financial resources, labor shortage in the industry, educational and knowledge gaps, market challenges, government policies, security and data privacy concerns, and inadequate power supply. Based on our findings, we provide recommendations for policymakers, industry stakeholders, and academic institutions to promote and facilitate the widespread adoption of IoT technologies in Silicon Mountain and the Central African region at large.

Voice Unbound: The Impact of Localization and Experience on Continuous Personal Voice Assistant Usage and Its Drivers

This research explores the key factors driving the continuous use of Personal Voice Assistants (PVAs), particularly emphasising the roles of localization and experience in shaping these behaviours. Data has been gathered from 1041 PVA users through an online survey. By integrating psychological (Attitude, Enjoyment, and Personal Innovativeness), technological (Perceived Ease of Use and Usefulness), and social (Subjective Social Norms) factors, the study uncovers the complex mechanisms that influence users’ continued usage intentions towards PVAs. It also examines how localization and experience moderate these relationships, offering key insights for developers and marketers to optimise users’ continuous usage and expand market reach by effectively targeting diverse consumer segments. Further, qualitative insights reveal user needs and barriers, offering a deeper understanding of user expectations. The study also highlights several promising avenues for future research to further examine evolving dynamics between users and PVAs.

Research on the Carbon Emission Prediction and Reduction Strategies for the Civil Aviation Industry in China: A System Dynamics Approach

With the continuous growth in the volume of global air transportation, the carbon emissions of the civil aviation industry have received increasing attention. Carbon emission reduction in civil aviation is an inevitable requirement for achieving sustainable social development. This article aims to use system dynamics (SD) methods to establish a carbon emission model for the civil aviation industry that includes economic, demographic, technological, policy, and behavioral factors; analyze the key factors that affect carbon emissions; and explore effective emission reduction strategies. Researchers have found that SD-based carbon emission prediction has a high accuracy and is suitable for predicting carbon emissions in civil aviation. Through different scenario simulations, it has been found that any single emission reduction measure will struggle to effectively contribute to the expected carbon reductions in China’s civil aviation. Simultaneously adopting measures such as improving fuel efficiency, adopting clean energy, and using new-power aircraft is an effective way to reduce carbon emissions from civil aviation. In addition, policy intervention and technological innovation are equally crucial for achieving long-term emission reduction goals. The research results not only provide a scientific basis for the sustainable development of the aviation industry but also provide a reference for policymakers to formulate comprehensive emission reduction strategies.

Open Source Software Adoption in the Financial Services Industry

Although OSS has gained traction across industries worldwide, few studies have been reported on OSS adoption in the financial industry and even less in the context of developing countries. This paper presents an exploratory overview of the state of OSS adoption in the financial industry using the case of South Africa as a country with the largest and most developed financial sector in Africa. To achieve this, we conducted a qualitative study that leveraged the Technology-Organisation-Environment (TOE) framework, and the Diffusion of Innovation (DOI) theory as theoretical references and used thematic analysis to analyse the data collected from a focus group discussion (FGD) of eight experts from four financial services organisations based in South Africa. The study's findings reveal the state of practice of OSS and the technological, organisational, and environmental factors that affect OSS adoption, and diffusion in financial services organisations in South Africa. We also identified seven research themes that should gain the attention of researchers from now on.

Study of the technological factors influence on the process of maturation of muscle tissue of silver carp (Hypophthalmichthys)

Study of the technological factors influence on the process of maturation of muscle tissue of silver carp (Hypophthalmichthys)

Research on Stock Price Prediction Model Based on Sentiment Factor and Multi-Core Bagging Algorithm

Stock price prediction model is one of the key research points in the quantitative financial field. With the rapid development of data acquisition and storage technology, the data of emotional factors based on nonlinear structure is increasing. How to combine this part of information with common European technology factors to increase the ability of the model is a problem that needs to be solved. Based on this, this paper proposes a method of uncertainty quantification that combines European-and non-European data. Specifically, on the one hand, the Gaussian process regression model is used to capture the unknown relationship between predictive variables and price; on the other hand, the multi-kernel learning technology based on European and non- European kernel functions is used to combine the effective information of European and non- European predictive variables. Moreover, the proposed method can weigh the bias of the prediction model against the variance by using the Bagging algorithm. The simulated data analysis shows that the proposed method not only improves the prediction performance of the European Gaussian process, but also works in the presence of irrelevant predictive variables. Finally, the actual data analysis shows that the proposed method has achieved good prediction results in the task of predicting the rise and fall of stock prices.

Protection of the Fulfillment of Rights of Women Victims of Trafficking from a Human Rights Perspective

Human trafficking remains a significant global issue. The UNODC reports 40.3 million annual cases, with 71% involving women. The National Human Rights Commission monitors and addresses these cases. Research, according to Soerjono Soekanto, involves analyzing and constructing methodology. The research aims to enhance understanding of policy implementation in protecting victims' rights through a human rights perspective. In upholding human rights, governments must prioritize protecting victims' rights. Safeguarding individuals, especially women, from crimes like trafficking is essential for human rights protection, highlighting the crucial role of laws in preventing such offenses. The protection of human rights, especially for victims, is crucial for governments to uphold citizens' rights. Human trafficking is driven by economic, educational, and technological factors. Preventive and repressive measures are required to combat trafficking effectively, including law enforcement, intergovernmental cooperation, and addressing root causes. Restitution is a constitutional right for victims, but enforcement challenges remain. Agencies must ensure victims' rights are upheld.

Does county urban shrinkage in China affect the concentration of lung-accessible particulate matter? An analysis from spatial effects and EKC perspectives.

The environmental effects of urban shrinkage have gradually attracted academic attention. Based on spatial effects and EKC perspectives, this study constructs an environmental impact assessment framework and examines the impact of urban shrinkage on the concentration of lung-accessible particulate matter at the county level in China from 2013 to 2020. The results show that, from a two-dimensional perspective in space and time, lung-accessible particulate matter concentrations show a decreasing trend in the time series and an obvious autocorrelation feature in space. Urban shrinkage can affect lung-accessible particulate matter concentrations through spatial effects. Through direct effects, urban shrinkage promotes the increase of lung-accessible particulate matter concentrations. Through spillover effects, urban shrinkage suppresses the growth of lung-accessible particulate matter concentrations. The effect of urban shrinkage on lung-accessible particulate matter concentration is nonlinear (EKC). This may be the result of the "siphon effect." Furthermore, in the extended STIRPAT model, it was found that population and economic and technological factors differentially affect lung-accessible particulate matter concentrations in shrinking cities. This study gives a new idea and insight into the changes in environmental pollution in shrinking cities.

The Future Museum: Integrating Augmented Reality (AR) and Virtual-text with AI-enhanced Information Systems

This study examines how AR, virtual text, and AI in future museums affect visitor engagement, knowledge acquisition, emotional response, and social interaction. The study uses econometrics equations, text analysis, thematic qualitative data analysis, and agent-based modelling. The economic models show that virtual text type, depth of knowledge, personalization, accessibility options, and AR museum experiences increase visitor engagement, knowledge, emotional response, and social interaction. Thematic analysis displays visitor viewpoints such as AR curiosity, personalization, and cultural significance, while correlation matrices indicate their relationships. Bayesian econometrics, text analysis, thematic qualitative data analysis, and agent-based modelling are used to explore museum visitors' interactions with augmented reality, virtual text, and AI-enhanced information systems. These devices improve visitor engagement, social connection, emotional response, and information collection. Self-reported data biases, context biases, and rapid technological advancement limit the study. Museums can benefit from user-centric technology, AR, personalization, and cultural knowledge. The main findings reveal that these technologies significantly increase museum visitors, emphasizing that future museum administration and design must carefully adopt new technology. The findings may affect museum staff, designers, and decision-makers. Learning important technology factors improves AR, virtual text, and AI-enhanced information system design and deployment decisions. UX design prioritizes visitor choices, while immersive and integrated displays connect museum experiences. These practical implications force museums to actively and practically integrate technology to stay relevant. The project's findings, practical insights, and visitor experiences will improve future technological and cultural organisation research. The whole process contributes to the discussion on how emerging technologies affect museum experiences and innovation and decision-making in the ever-changing cultural heritage context.

Project management analysis-based on Shanghai Disney

This paper presents a comprehensive analysis of the project management strategies employed during the development of Shanghai Disneyland. It utilizes three key analytical tools: PESTLE, SWOT, and Fishbone Diagram, to assess various aspects of the project. The PESTLE analysis evaluates the external environment, including political, economic, social, technological, legal, and environmental factors, which influenced the park's successful completion. The SWOT analysis identifies the project's internal strengths and weaknesses, as well as the external opportunities and threats it faced. Additionally, the Fishbone Diagram highlights critical challenges, such as financing, technology, and localization, that contributed to delays in project progress. The findings of this study demonstrate how effective project management, supported by government partnerships and market demand, facilitated Shanghai Disneyland's establishment as a globally significant theme park. However, the paper also underscores the importance of addressing localization and financial sustainability to ensure long-term success. The analysis provides valuable insights into managing large-scale, culturally complex projects.