Authors Statistics from 3 Countries
| Country | Count of Articles |
|---|---|
| Indonesia | 3 |
| Bangladesh | 1 |
| Nigeria | 1 |
Articles
Factors Affecting the Adoption of Artificial Intelligence: A Systematic Literature Review and Policy Perspectives in the Digital Age
Abstract
The adoption of Artificial Intelligence (AI) has become a global strategic agenda, but its acceptance and implementation levels vary greatly between countries, sectors, and populations. This study presents a systematic literature review (SLR) of 41 empi rical studies to map the factors influencing AI adoption, with a particular focus on cognitive, emotional, socio-ethical, and technological dimensions. Using the PRISMA approach, this study identifies cognitive (AI literacy, perceived usefulness) and emotional (trust, anxiety) factors as key determinants, while socio-ethical considerations (fairness, transparency, accountability) are increasingly crucial in the context of public policy. Recent studies in Indonesia confirm this pattern by demonstrating the strong mediating role of emotions, but also reveal independent cognitive and ethical pathways that operate without emotional mediation—findings that challenge the assumptions of traditional technology acceptance models. From a policy perspective, the results of this SLR indicate the need for a multidimensional approach that focuses not only on technological infrastructure, but also on improving AI literacy, managing public emotional responses, and developing a transparent ethical framework. This article contributes to the literature by providing a comprehensive mapping of state-of-the-art AI adoption research and identifying crucial research gaps, particularly regarding affect-independent pathways mechanisms, cultural context variations, and the longitudinal dynamics of AI adoption.
Read full articleDriving Ecotourism Intention in The Era of Sustainable Tourism: A Systematic Literature Review Integrating SOR and Experience Economy Perspective
Abstract
This study aims to identify the main determinants of Ecotourism Intention (EI) by emphasizing the mediating role of Destination Image (DI) and Tourist Satisfaction (TS) through the Stimulus–Organism–Response (SOR) and Experience Economy Theory (EET) approaches. This review uses a Systematic Literature Review (SLR) method following the PRISMA 2020 protocol, with article searches conducted on the Scopus and Google Scholar databases. The inclusion criteria covered publications from the period 2010–2025, written in English, published as peer-reviewed journal articles, and containing at least one variable from Memorable Tourism Experience (MTE), Sustainable Tourism (ST), Destination Quality (DQ), TS, or DI. From an initial 300 articles, 29 relevant articles were selected for thematic and descriptive analysis. The results show that Memorable Tourism Experience (MTE), Sustainable Tourism (ST), and Destination Quality (DQ) are the primary stimuli influencing Ecotourism Intention (EI) both directly and indirectly through Destination Image (DI) and Tourist Satisfaction (TS). Destination Image (DI) acts as a cognitive mediator that shapes positive destination perceptions, while Tourist Satisfaction (TS) serves as an affective mediator that strengthens tourists' behavioral intent. The chained mediating relationship of DI and TS was found to be consistent within the SOR and Experience Economy models. Variations in context were observed, where Destination Image (DI) is more dominant in event/festival destinations, while Tourist Satisfaction (TS) is more influential in service and infrastructure-based destinations. This research extends the theoretical understanding of Ecotourism Intention (EI) through the integration of (SOR) and (EET), and provides practical implications for destination managers in designing strategies to enhance tourist experiences, implement sustainability principles, and strengthen destination image.
Read full articleSocioeconomic Status of Ready-Made Garments Industry Workforce of Bangladesh and Human Assets.
Abstract
The Readymade Garments (RMG) industry has made important contributions to the economic development journey of Bangladesh. This industry has emerged as the largest female-dominant manufacturing sector, employing around 4 million workers with 80% export share and 15% GDP. A positive socioeconomic development trend witnessed in Bangladesh through per capita income growth, reduction of extreme poverty, literacy rate, reduction of malnutrition and child mortality, improved living, female employment, education, and healthcare. Iimprovement in socioeconomic factors indicate socioeconomic development. A composite socioeconomic development index built on human assets of RMG workers was lacking. The studies on the socioeconomic conditions of the RMG sector range from work environment, living environment, income, and wage to deprivation of different work benefits, socioeconomic issues, and other social and economic factors. Conventional human welfare indices like PQLI, HDI, and MPI are unable to find the socioeconomic state of RMG workers. The objective is to construct a composite socioeconomic development Index using core human assets. The quantitative research used a survey research technique with 400 samples of RMG clusters from Dhaka, Savar, Narayanganj, Gazipur, and Savar. The primary data were analysed with the PCA. The Filmer and Prichett 1998 work on household asset index for school enrolment in India was referred to. The index ranked 400 workers- 40% “Poor”, 40% “Middle-Income”, 20% “Rich/Non-poor” as socioeconomic strata
Read full articleImproving Pension Fund Administrators’ Revenue through Efficient Pension Contribution Management
Abstract
The growing volume of unreconciled pension contributions is a concern not only for Pension Fund Administrators (PFAs), who manage these funds, but also for the regulator—the National Pension Commission. The continued existence of unmatched funds undermines the goal of ensuring prompt pension payments to retirees. In addition to lost revenue for PFAs, contributors whose funds are held in the Contribution Reconciliation Account (CRA) experience reduced returns on investment. Thus, this study examined the impact of unreconciled funds on the revenue of PFAs, as they are not permitted to charge management fees on these funds. The study employed an Ex Post Facto research design, as the data were collected after the events had occurred and could not be manipulated (Nworie & Orji-Okafor, 2024). The target population consisted of 19 Pension Fund Administrators (PFAs) in Nigeria, but data from 17 were used due to availability constraints. Covering a six-year period from 2018 to 2023, the analysis used multiple regression in Microsoft Excel to assess the impact of four independent variables—Fund Under Management (FUM), Annual Contributions (CON), Number of Retirement Savings Accounts (RSA), and Contribution Reconciliation Account balances (CRA)—on the dependent variable, Revenue (REV). The findings indicate that CRA balances have a significant negative effect on PFA revenue, evidenced by a negative coefficient and a p-value of 0.01. We recommend that PFAs leverage information technology to identify the owners of these funds, working in collaboration with employers to reduce the volume of unreconciled contributions. Additionally, PFAs should adopt a uniform naming convention for unreconciled funds in their balance sheets to enhance clarity. Lastly, Section 11(6) of the Pension Reform Act (PRA) 2014 should be amended to include penalties for employers who submit contribution schedules with incorrect or incomplete information.
Read full articleDeterminants of Financial Distress of Property and Real Estate Companies
Abstract
This study was conducted with the aim of determining the influence of interest rates, inflation, exchange rates, profitability, liquidity, activity, leverage, and company size on the financial distress of property and real estate companies. The population in this study is 35 property and real estate companies listed on the Indonesia Stock Exchange. The sampling method in this study used full sampling, the period of 2019-2023, and a total of 135 observations. The data analysis technique uses the panel data regression method processed using Eviews 13. The results of the study are as follows: (1) Interest rates have a positive effect on financial distress, (2) Inflation has no effect on financial distress, (3) Exchange rate has a negative effect on financial distress, (4) Profitability has a positive effect on financial distress, (5) Liquidity has a positive effect on financial distress, (6) Activity has no effect on financial distress, (7) Leverage has no effect on financial distress, (8) The size of the company has no effect on financial distress.
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