Search Results (210)

The weights and structure determination (WASD) neuronet (or neural network) is a single-hidden-layer feedforward neuronet that exhibits an excellent approximation ability, despite its simple structure. Thanks to its strong generalization, fast speed, and ease of implementation, the WASD neuronet has been the subject of many modifications, including metaheuristics, and applications in a wide range of scientific fields. As it has garnered significant attention in the last decade, the aim of this study is to provide an extensive overview of the WASD framework. Furthermore, the WASD has been effectively used in numerous real-time learning tasks like regression, multiclass classification, and binary classification due to its exceptional performance. In addition, we present WASD’s applications in social science, business, engineering, economics, and medicine. We aim to report these developments and provide some avenues for further research. Full article

This study investigates the relationship between the business environment and the financial performance of companies engaged in mergers and acquisitions (M&As), with a particular emphasis on how “ease of doing business” (EDB) indicators affect post-merger outcomes, as measured by return on assets (ROA), return on equity (ROE), and profit margin (PM). We consider a sample of 230 firms from fifteen European countries (Bulgaria, Cyprus, Denmark, France, Greece, Hungary, Italy, Germany, Lithuania, The Netherlands, Norway, Poland, Romania, Slovakia, and Spain) grouped according to COFACE criteria for five years (2015–2019). By applying panel data methods, the research highlights that, three years after the merger in low-risk countries, there is an increase in return on equity, better asset recovery, and economies of scale, largely due to effective government policies. The study highlights the differentiated effects of specific EDB sub-indicators, providing insight into how tailored regulatory frameworks can enhance M&A success across varying economic contexts. The business environment can stimulate the performance of firms after mergers and acquisitions if the regulations are friendly to the firms and are adapted to the state of the country’s economy. Full article

Developing countries face a disproportionate degree of threat from climate change. As such, they require and receive significant financial support to address the menace. However, little is known about the potential externalities of this form of external liquidity for the business sector. This paper evaluates the impact of climate finance on the ease of doing business (EODB). On the one hand, climate finance might lead to an improved business environment as the funds facilitate infrastructure provision, technological innovation, and international collaboration for recipient countries. On the other hand, however, the business environment might be negatively impacted by complex new regulations, disruptive technological transitions, market distortions, and resource diversions. Countries receiving climate funds may also introduce new environmental and business regulations, implement new technologies, and divert resources to new programs to justify the receipt of aid or demonstrate a commitment to balancing economic development with environmental objectives. We theorize that given the expected disruptions to business, climate finance should negatively impact the EODB. We also argue that this negative impact will be more severe for resource-rich countries than for their resource-poor peers. Countries rich in natural resources might experience higher disruptions to business operations as they attempt to balance resource-dependent economic operations with environmental objectives mandated by climate finance. Utilizing panel data for 86 recipient countries for the 2002–2021 period, we test our hypotheses using the Generalized Methods of Moments (GMM) technique. The baseline results suggest that climate finance has a weak positive impact on the EODB. However, as argued, resource-dependence heterogeneity analysis reveals that climate finance significantly negatively disrupts the EODB in resource-rich countries. Furthermore, a sectoral comparative analysis shows that while climate finance has a significant positive impact on the growth of the service sector, it significantly slows the growth of the resource sector, affirming the argument that climate finance might attract higher disruptions to resource-dependent business operations. By implication, lowly diversified economies might realize more negative than positive effects of climate finance, and investors should consider providing support to ease the pains of transitioning from resource-intensive growth to clean energy-driven development strategies. Full article

Mobile payment systems have rapidly expanded globally, especially in developing countries like Thailand, Malaysia, and Indonesia. Technological advances, public acceptance, and increased adoption during the COVID-19 pandemic drive this growth. Mobile payments involve key stakeholders: technology providers, end-users, government regulators, and merchants, each contributing to the adoption ecosystem. Users prefer mobile payments for their speed and convenience over traditional cash transactions. This study explores the driver and barrier factors influencing mobile payment QR adoption among merchants, particularly from the MSME perspective, using existing frameworks based on previous research adapted to MSME conditions. Conducted in Indonesia with 418 MSME business respondents, this study employs a quantitative, cross-sectional methodology with a 95% confidence level and an SEM analysis. The findings reveal that perceived ease of use does not significantly impact perceived experience, while perceived usefulness does. Perceived risk, convenience, experience, and word-of-mouth learning statistically significantly influence merchants’ intention to use mobile payments. However, customer engagement, cost, trust, and complexity appear less influential. Overall, this research advances understanding of the key factors affecting merchants’ adoption of mobile payment and provides insights relevant to MSME economic growth. Full article

The growing use of Instagram and its influencers in marketing demonstrates their substantial influence on customer behavior intentions. Despite this, further research into the effects of Instagram mega-influencers on purchase intention is needed among Generation Z (born between 1997 and 2012) consumers. The main aim of the study was to assess the influence of attractiveness, expertise, perceived ease of use, and perceived usefulness on intention to purchase due to Instagram mega-influencers among Generation Z in South Africa. This study combined purposive and snowball non-probability sampling methods using a quantitative approach. An online questionnaire was used to assess the effect of Instagram mega-influencers on young consumers’ purchase intention, which resulted in 497 Generation Z respondents. Positive associations were found between attractiveness, expertise, perceived ease of use, and perceived usefulness on intention to purchase. Perceived ease of use was also found to mediate the positive effects of attractiveness and expertise on intention to purchase. This analysis provides businesses with practical information about the use of Instagram mega-influencers in digital marketing to connect with Generation Z consumers and improve profitability. The research contributes to the theoretical development of constructs in the technology acceptance and source credibility models and generation cohort theory. Full article

Adopting Industry 4.0 technologies across sectors is critical for enhancing operational efficiency and competitiveness. However, empirical studies on the determinants of such adoption have yielded inconsistent results. This study conducted a systematic review and meta-analysis based on the Technology Acceptance Model and its extensions. A total of 47 empirical studies were extracted from five academic databases and included in the meta-analysis. The findings confirmed that perceived usefulness (PU), perceived ease of use (PEOU), and social influence (SI) significantly and positively influenced behavioral intention (BI) toward adopting Industry 4.0 technologies. Among them, PU exhibits the strongest correlation with BI (r = 0.528), followed by PEOU (r = 0.469) and SI (r = 0.487). Subgroup analyses based on geographical region, organization size, and sector showed consistent significance in effect sizes, although moderating effects across subgroups were not statistically significant. The findings of this study contributed to the literature with an in-depth understanding of the acceptance of Industry 4.0 technologies in various sectors and how moderators influence the acceptance. Practically, the findings provided evidence-based guidance for policymakers, technology developers, and business leaders to tailor adoption strategies and foster digital transformation across sectors. Full article

In the wine production chain, climate change affects the yields and quality of the wine. Several studies have found that high temperatures have a significant impact on the value of wine production. In this context, adaptation measures represent a good practice to face the damage caused by negative climatic events. Since these practices have a cost, this study aims to provide an estimate of economic convenience for their adoption, using the methodology developed within the LIFE Adaptation in Agriculture project. In this paper, four measures used in winemaking processes and useful for reducing damage induced from climatic events were analyzed. The findings show that the adaptation measures considered in the wine sector usually have an initial investment cost of medium/high. Large farms, due to their size and resources, can opt for one or more measures with a greater ease of cost depreciation. Overall, the analysis shows that although the implementation of measures presents medium/high investment costs, it is always appropriate to assess the specific business case, even where the results indicate a lower degree of cost-effectiveness. When deciding on the adoption of measures, it should be kept in mind that costs depend not only on farm characteristics but also on the choice of implementation method and staff training and specific public subsidies. Full article

As climate change has become of eminent importance in the last two decades, so has interest in industry-wide carbon emissions and policies promoting a low-carbon economy. Investors and policymakers could improve their decision-making by producing accurate forecasts of relevant green finance market indices: carbon efficiency, clean energy, and sustainability. The purpose of this paper is to compare the performance of single-step univariate forecasts produced by a set of selected statistical and regression-tree-based predictive models, using large datasets of over 2500 daily records of green market indices gathered in a ten-year timespan. The statistical models include simple exponential smoothing, Holt’s method, the ETS version of the exponential model, linear regression, weighted moving average, and autoregressive moving average (ARMA). In addition, the decision tree-based machine learning (ML) methods include the standard regression trees and two ensemble methods, namely the random forests and extreme gradient boosting (XGBoost). The forecasting results show that (i) exponential smoothing models achieve the best performance, and (ii) ensemble methods, namely XGBoost and random forests, perform better than the standard regression trees. The findings of this study will be valuable to both policymakers and investors. Policymakers can leverage these predictive models to design balanced policy interventions that support environmentally sustainable businesses while fostering continued economic growth. In parallel, investors and traders will benefit from an ease of adaptability to rapid market changes thanks to the computationally cost-effective model attributes found in this study to generate profits. Full article

With the rapid advancement of artificial intelligence (AI), chatbots represent a transformative tool in digital customer engagement, reshaping customer–brand relationships. This paper explores AI chatbots on customer–brand interactions by analyzing key features, such as interaction, perceived enjoyment, customization, and problem-solving. Based on the Technology Acceptance Model (TAM), the research investigates how these attributes influence perceived ease of use, perceived usefulness, customer attitudes, and ultimately, customer–brand relationships. Adopting a mixed-methods approach, this study begins with qualitative interviews to identify key engagement factors, which then inform the design of a structured quantitative survey. The findings reveal that AI chatbot features significantly enhance customer perceptions, with ease of use and usefulness in shaping positive attitudes and strengthening brand connections. The research further underscores the role of AI-driven personalization in delivering sustainable customer engagement by optimizing digital interactions, reducing resource-intensive human support, and promoting long-term brand loyalty. By integrating TAM with customer–brand relationship theories, this study contributes to AI and sustainability research by highlighting how intelligent chatbots can facilitate responsible business practices, enhance operational efficiency, and promote digital sustainability through automation and resource optimization. The findings provide strategic insights for businesses seeking to design AI-driven chatbot systems that improve customer experience and align with sustainable digital transformation efforts. Full article

Entrepreneurship has been critical in fostering economic growth. The technological innovations and quality of institutions are crucial in promoting entrepreneurship and promoting an environment conducive to entrepreneurial activities. This study investigated the effect of technological innovations and institutional quality on entrepreneurial development with annual data from 2014 to 2021 across Southeastern European countries. The cross-sectional auto-regressive regressive distributed lag model (C-S ARDL), quantile regression and Granger causality were employed to achieve the objectives of this study. A dynamic panel generalized method of moments (GMM) estimator was also applied to perform a robust analysis. The findings revealed a significant long-term relationship between technological innovations and entrepreneurial development, with a coefficient of 0.088. There also exists a significant and positive impact on institutional quality and entrepreneurial development in the long run, with a coefficient of 5.912. Furthermore, the outcome revealed that the exchange rate negatively influences entrepreneurial development in Southeast Europe. The Granger causality reports a bi-directional relationship between technological innovations and entrepreneurial development in Southeastern Europe. The study concluded that a significant relationship exists between technological innovations, institutional quality, and entrepreneurial development in Southeastern Europe. The study recommends that governments of Southeastern European countries strengthen their regulatory structures and institutions to improve the welfare of society through a reduction in political, social, and economic unpredictability while boosting trust and investment from entrepreneurs. Full article

Background and Methods: A primary survey of a multi-national sample of higher institutional educators has been conducted to investigate the determinants of the adoption of management simulation games. The research model is developed based on the technology acceptance model (TAM) and technology–organisation–environment (TOE). Structural equation modelling has been used to test the research model. The paper focuses on the use of management simulation games among educators in higher educational institutions (HEIs). Its purpose is to determine the factors influencing educators’ use of these games from both individual and institutional perspectives. The TAM captures the individual perspective, while the TOE framework addresses the institutional perspective. The structural equation model confirmed most of the TAM hypotheses. Results: However, the model does not support the hypotheses regarding the relationship between perceived ease of use and attitude toward usage or between perceived ease of use and perceived usefulness. The technological factors within the TOE framework did not significantly impact perceived usefulness, only perceived ease of use. Conclusion: The combined TAM-TOE model has demonstrated valid representativeness. Previous research on the usage of management simulation games has primarily focused on students, neglecting the broader perspective of educators in HEIs in business and economics within both the TAM and TOE frameworks. Full article

Heightened interest in providing transparency and security within business systems and processes has triggered a multitude of studies on the adoption of emergent technologies. Blockchain technology (BCT) has emerged to address issues of transparency, efficiency, and security in manufacturing, supply chain, enterprise, and e-commerce systems. Given the enormous success of BCT in financial applications, there is keenness to explore other application areas. This research explores the integration of BCT in enterprise systems to enhance the engineering change management (ECM) process. Employing the technology acceptance model (TAM) to assess adoption intentions from the perspective of users, two concepts were examined: perceived usefulness (PU) and perceived ease of use (PEU). A quantitative survey method collected and analysed the perception data. The findings revealed a positive perception of both usefulness and ease of use from the users in adopting a blockchain-enabled enterprise platform to support the ECM process. More detailed findings uncovered the workflow management function as an area that may require further technical exploration. These findings provide valuable insights on the benefits of blockchain in optimizing business processes and contribute to the growing literature about perceptions of integrating BCT in enterprise platforms. Full article

Today, businesses are adopting digital transformation strategies to make their production processes more agile, efficient, and sustainable. At the same time, lean manufacturing principles aim to create value by reducing waste in production processes. In this context, it is important that the machine to be selected for inventory tracking can meet both the technological features suitable for digital transformation goals and the operational efficiency criteria required by lean manufacturing. In this study, multi-criteria decision-making methods were used to select the most suitable machine for inventory tracking based on digital transformation and lean manufacturing perspectives. This study applies a framework that integrates the Continuous Intuitionistic Fuzzy Analytic Hierarchy Process (CINFU AHP) and the Continuous Intuitionistic Fuzzy Combinative Distance-Based Assessment (CINFU CODAS) methods to select the most suitable machine for inventory tracking. The framework contributes to lean manufacturing by providing actionable insights and robust sensitivity analyses, ensuring decision-making reliability under fluctuating conditions. The CINFU AHP method determines the relative importance of each criterion by incorporating expert opinions. Six criteria, Speed (C1), Setup Time (C2), Ease to Operate and Move (C3), Ability to Handle Multiple Operations (C4), Maintenance and Energy Cost (C5), and Lifetime (C6), were considered in the study. The most important criteria were C1 and C4, with scores of 0.25 and 0.23, respectively. Following the criteria weighting, the CINFU CODAS method ranks the alternative machines based on their performance across the weighted criteria. Four alternative machines (High-Speed Automated Scanner (A1), Multi-Functional Robotic Arm (A2), Mobile Inventory Tracker (A3), and Cost-Efficient Fixed Inventory Counter (A4)) are evaluated based on the criteria selected. The results indicate that Alternative A1 ranked first because of its superior speed and operational efficiency, while Alternative A3 ranked last due to its high initial cost despite being cost-effective. Finally, a sensitivity analysis further examines the impact of varying criteria weights on the alternative rankings. Quantitative findings demonstrate how the applied CINFU AHP&CODAS methodology influenced the rankings of alternatives and their sensitivity to criteria weights. The results revealed that C1 and C4 were the most essential criteria, and Machine A2 outperformed others under varying weights. Sensitivity results indicate that the changes in criterion weights may affect the alternative ranking. Full article

Background: Cross-border suppliers have always been points of disruption, further impacting international trade, businesses, and societies along the chain. Understanding the current resilience capabilities of cross-border suppliers is a stepping-stone to implementing resilience initiatives and policies to mitigate disruptions. However, no guidelines or practical tools exist to help cross-border suppliers conduct a deep-dive analysis of their resilience. Therefore, this paper proposes an assessment tool to guide cross-border suppliers in assessing their resilience capabilities. Methods: The supplier-focused resilience assessment approach was adapted from the Logistics Performance Index concept. The questionnaire and its resilience assessment dimensions were established through a literature review with the support of experienced cross-border professionals. Case study validation was further conducted to demonstrate the tool’s applicability. Results: The assessment evaluation through the Cross-Border Resilience Performance Index facilitates detailed analysis and benchmarking, enabling recommendations for necessary resilience initiatives and policies. Conclusions: This study contributes to the supply chain literature by adding a more practical resilience assessment approach focusing on cross-border suppliers. The Cross-Border Resilience Performance Index is the study’s primary contribution and is novel to the literature. The tool’s advantages include ease of use, replication potential, and its ability to glean comprehensive insights, ultimately improving supplier resilience and supply chain robustness. By implementing more precise initiatives, the tool increases the chances of cross-border suppliers being selected and maintained by buyers, helping them sustain their businesses and better respond to changing conditions to mitigate disruptions. Full article

Entrepreneurial endeavors are essential for stimulating economic growth and rendering them is a primary concern for policymakers. In recent years, smart city ecosystems have garnered attention for enhancing urban living and tackling contemporary difficulties. The contribution of smart cities in promoting entrepreneurship and improving well-being has received little attention. This study aims at examining the potential of smart city as an ecosystem to promote entrepreneurship and enhance well-being and quality of life (QoL). This study uses a Fuzzy evaluation model and the Analytic Hierarchy Process (AHP) to evaluate essential determinants of smart cities and their significance. Data from sources such as the Smart City Index, Ease of Doing Business Ranking, Global Innovation Index, Sustainable Development Report, and Technological Readiness Ranking are utilized with normalization, guaranteeing a dependable evaluation. The findings underscore the significance of open data efforts and transparent governance in recruiting innovative enterprises and promoting entrepreneurship. The study highlights the necessity of cooperative urban planning and public participation in decision-making. Moreover, the authors propose a new definition of smart cities from citizens’ well-being perspective. This research enhances the comprehension of smart cities’ influence on entrepreneurial endeavors, pinpointing problems and prospects for future investigations focused on improving well-being through smart city advancement. Full article