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International Journal of Business, Economics, and Social Development
Published by Research Collaboration Community
ISSN : 27221164     EISSN : 27221156     DOI : https://doi.org/10.46336/ijbesd
Core Subject : Economy, Humanities, Education, Social,
Humanities Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences
International Journal of Business, Economics and Social Development (IJBESD) is published 4 (four) times a year and is the flagship journal of the Research Collaboration Community (RCC). It is the aim of IJBESD to present papers which cover the theory, practice, history or methodology of Business, Economics and Social Development. However, since Business, Economics and Social Development are primarily an applied science, it is a major objective of the journal to attract and publish accounts of good, practical case studies. Consequently, papers illustrating applications of Business, Economics and Social Development to real problems are especially welcome. GENERAL BUSINESS AND MANAGEMENT e-Business International Business Business Strategy Marketing Supply Chain Management Organization Studies Entrepreneurship and Business Development Enterprise Innovation Human Resource Management Business Ethics Business Economics Business Communication Business Finance International Business and Marketing Organizational Development and Challenges Leadership and Corporate Governance Tourism Operations Management Human Resources Economics Regional Economics Industrial Economics Financial Economics Labor Economics Law and Economics Regulatory Economics Economic Growth and Development Policy Technological Change, Innovation Research and Development Economic Systems GENERAL ECONOMICS Economic Methodology Schools of Economics Production and Organizations Market Structure and Pricing Welfare Economics Public Finance & Public Choice Prices, Business Fluctuations Economic Policy International Finance International Economics Institutional & Corporate Finance Accounting Insurance and Risk Management Monetary Banking Marketing Management Issues Innovation and Change Management Banking and Finance Natural Resource Economics Microeconomics Economics in Development and Sustainability Issues Comparative Economic Systems Stock Exchange Business Economics Capital Market Macroeconomics Economics Theory and Policy Issues Energy Economics and Policy Monetary Economics Public Economics Other areas of Economics COMMUNITY DEVELOPMENT Social Work Health and Sport Sciences Human Development Quality of Life Psychology Communication Public Administration Leadership Style Sociology Anthropology Religious Studies Civilizations Social Innovation Other areas of Social Studies and Art & Humanities Political Science Public Policy Political Psychology Protection of Children and Women Political Party System Education Social Sciences Education Science Education Pre-School Education Measurement and Evaluation Talent Development Education Management Education technology Street Children Education Ethnoscience and many more
Arjuna Subject : Ilmu Ekonomi, Ekonometrika dan Keuangan (semua kategori) - Ilmu Ekonomi, Ekonometrika dan Keuangan (Lain-Lain)
Articles 17 Documents
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Search results for , issue "Vol 6, No 1 (2025)" : 17 Documents clear
Empirical Analysis of the AfCFTA in the SADC Region: Evidence using the PPML Estimation Technique Chiranga, Ngonidzashe
International Journal of Business, Economics, and Social Development Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i1.700

Abstract

The challenges that small countries in the Southern African Development Community (SADC) face towards the successful implementation of the Africa Continental Free Trade Agreement (AfCFTA) have not been adequately empirically tested to establish if there is scope for mutually beneficial trade among the countries as advocated by AfCFTA’s intra-regional trade promotion. This study employs the gravity model of trade theory. The main objective of this paper is to empirically test if there are mutual gains from intra-regional trade for small countries in the SADC region that face several political, legal, economic, and institutional challenges toward the successful implementation of AfCFTA. This study utilised a modified structural gravity model estimated using the Poisson Pseudo-Maximum-Likelihood (PPML) approach. A balanced panel data from a set of select nine SADC countries over the period 2010-2022 is used. The study finds that distance negatively and significantly affects bilateral trade. In addition, overlapping Regional Economic Community (REC) membership positively influences bilateral trade for small landlocked SADC countries and island nations that have a high trade presence in the region. However, after considering agricultural-dependent nations, overlapping REC membership has a trade-reducing impact. Furthermore, poor institutional quality at the destination country was found to reduce bilateral trade negatively and significantly which eventually increased the overall trade costs. The study recommends AfCFTA members diversify their exports and add value to their agriculture products to adequately benefit from the agreement.
Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price Prediction Saputra, Moch Panji Agung; Ibrahim, Riza Andrian; Saputra, Renda Sandi
International Journal of Business, Economics, and Social Development Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i1.887

Abstract

The increasing use of cryptocurrencies has changed the dynamics of investment, presenting both opportunities and challenges for investors. Although various studies have compared the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting financial asset prices, there are still differences in results regarding which model is superior. Therefore, this study aims to compare the performance of LSTM and GRU in predicting Ethereum prices using a hyperparameter tuning approach. The data used is historical data of Ethereum (ETH) shares from 2020 to 2025. The research methodology includes data preprocessing using Min-Max scaling, model development with various layer configurations, and comprehensive evaluation using several performance metrics. The results show that the GRU Model provides superior performance with a lower Root Mean Squared Error (RMSE) of 0.0234 and Mean Absolute Error (MAE) of 0.0168, compared to LSTM's RMSE of 0.0265 and MAE of 0.0193. While LSTM exhibits a slightly better Mean Absolute Percentage Error (MAPE) of 18.08% compared to GRU at 18.17%, the GRU model achieves a higher R² Score of 0.9442 compared to LSTM at 0.9282. Visual analysis of the prediction patterns and residual distributions further demonstrates GRU’s more consistent and accurate performance in capturing Ethereum price movements. These findings suggest that while both models are effective for cryptocurrency price prediction, GRU offers slightly better overall performance and stability, especially in maintaining consistent prediction accuracy across different market conditions.
Calculation of Premium Reserve Value in Whole Life Insurance Using the New Jersey Method Farida, Nur Rizky
International Journal of Business, Economics, and Social Development Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i1.874

Abstract

AI Adoption in Business: Opportunities and Challenges for Start-ups Irman, Dede; Putra, Deva
International Journal of Business, Economics, and Social Development Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i1.881

Abstract

Artificial intelligence (AI) has become a technology that plays an important role in business transformation, including in the start-up sector. This study aims to analyze the opportunities and challenges in adopting AI in start-up businesses and its impact on company performance. The research method used is a qualitative and quantitative approach by collecting data through surveys, interviews, and literature studies. The results of the study show that AI provides various benefits for start-ups, such as increasing operational efficiency, optimizing decision-making, personalizing customer service, and reducing labor costs. The fintech and e-commerce sectors are the industries with the highest rates of AI adoption due to the need for automation and data security. However, the implementation of AI also faces various challenges, including high costs, limited expertise, integration with legacy systems, and data security and regulatory issues. Further analysis shows that start-ups that successfully adopt AI have a mature strategy in technology investment and human resource development. In addition, the effective implementation of AI can increase the competitiveness of start-ups and support sustainable business growth. Therefore, a strategic approach is needed in facing the challenges of AI implementation so that the benefits obtained can be optimized. This study is expected to provide insights for business owners, investors, and policy makers in developing more effective AI adoption strategies in the future.
Nature and Extent of COVID-19 Challenges Among Small, Medium, and Micro Enterprises in Botswana. CHAURURA, PEARSON
International Journal of Business, Economics, and Social Development Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i1.741

Abstract

Small, Medium, and Micro Enterprises (SMMEs) make significant contribution to economic growth in both developed and developing economies. In Botswana, the government has made extensive investment in the SMME sector, but the expected returns are compromised by poor performance and business failure among SMMEs. This problem is amplified during crises as they bring various challenges to the sector. This study used the recent COVID-19 pandemic as a proxy to identify and evaluate the nature and extent of challenges faced by SMMEs in Botswana during crisis times. A mixed methods approach was employed to collect data from 250 SMMEs in Gaborone, Francistown and surrounding areas. SPSS and manual coding and thematic analysis were used to analyse the data. It was found that SMMEs in Botswana encountered several challenges the most important being financial (75.4%), technological (74.4%), and human resource (68.8%) challenges. Specifically, these were drop in sales/business (89.2%), need for accelerated digitisation (45.8%), and capacity to ensure the well-being of employees (58.4%). Additionally, there were challenges with accessing and utilising government programmes despite a high level of awareness among SMMEs. Some challenges showed significant association with SMME industry sectors implying their potential importance in some sectors compared to others. The dominance of financial, technological, and human resource challenges may underscore the central role they play in SMME operations. It is not enough for governments to avail resources, programmes, policies and frameworks in support of SMMEs. More needs to be done to promote their access, uptake, and utilisation.
The Impact of the Digital Economy on Employment and Workforce Structure in Indonesia Lianingsih, Nestia; Irman, Dede; Nurnisaa, Nurnisaa
International Journal of Business, Economics, and Social Development Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i1.889

Abstract

This study examines the impact of the digital economy on employment and workforce structure in Indonesia. The digital economy in Indonesia is growing rapidly, driven by e-commerce, digital financial services, app-based transportation, and artificial intelligence-based technology, with a projected contribution of more than USD 130 billion by 2025. Using a mixed-method approach, this study analyzed data from 200 respondents consisting of workers in the digital economy sector and traditional sectors affected by digitalization, as well as in-depth interviews with 15 representatives of workers, business actors, and policy makers. The results showed that 68% of respondents considered the digital economy to increase job opportunities, although 40% of respondents admitted that digitalization also caused job losses in certain sectors. Changes in the workforce structure are seen in the increasing need for technology skills (45%), digital marketing (35%), and digital business management (30%). The main challenges faced by workers include lack of digital skills (50%), uncertain income (40%), and lack of social security (35%). This study recommends strengthening digital skills training programs through collaboration between the government and the private sector, developing regulations for protecting digital workers, and increasing financial inclusion for digital economy workers. This study contributes to a more comprehensive understanding of workforce transformation in the digital era and provides strategic input for policymakers in managing the workforce transition towards an inclusive and sustainable digital economy.
Analysis of Financial Distress in Telecommunication Companies in Indonesia Using the Ohlson O-Score and Zmijewski Methods Bayyinah, Ayyinah Nur
International Journal of Business, Economics, and Social Development Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i1.875

Abstract

Currently, major telecommunication sub-sector companies in Indonesia are experiencing rapid growth and have become dominant players in the market. However, not all telecommunication companies are profitable, as some dominant subsidiaries have experienced declining profits or losses, potentially leading to financial distress. Financial distress is a condition where a company is unable to meet its current obligations, such as trade payables, tax liabilities, and short-term debts. This study aims to analyze and evaluate the accuracy of the Ohlson O-Score and Zmijewski methods in detecting financial distress in telecommunication companies in Indonesia. The data used in this study are historical financial data from several telecommunication companies listed on the Indonesia Stock Exchange. The results show that the Ohlson O-Score is effective in early detection of potential financial distress, while the Zmijewski method is more effective in evaluating companies already in critical financial conditions.

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