Future of Business Administration

Adoption of Third-Party E-Commerce Platforms by Small Retail Businesses in Nigeria: Benefits, Barriers, and Business Implications

2025-07-28T12:35:38+00:00

This empirical paper investigates the adoption of third-party electronic commerce (e-commerce) platforms by small and medium-sized enterprises (SMEs) in Nigeria. It examines the motivations for adoption, perceived benefits, and barriers faced by retail businesses in the e-commerce landscape. A qualitative research design was employed, guided by an abductive approach, which allowed for iterative movement between theory and empirical findings. Data were collected through structured questionnaires administered to merchants with storefronts on Nigeria’s major third-party e-commerce platforms. Findings reveal that e-commerce adoption among Nigerian SMEs is relatively low compared to adoption rates in Asia and Western countries. The decision to adopt e-commerce platforms is largely influenced by the digital literacy and awareness level of business owners and managers. Key benefits include increased market access, improved operational efficiency, and enhanced information exchange. The most significant barriers are lack of awareness and limited technical know-how. A notable advantage of using third-party platforms is the relief it provides merchants from direct marketing burdens, allowing them to concentrate on sourcing and managing products. This study contributes original insights into e-commerce adoption in a developing economy context and provides practical implications for SME operators, platform developers, and policymakers. It also lays a foundation for future comparative studies on digital commerce adoption.

Cyber Risk and Banks Profitability: A Machine Learning Approach

2025-07-30T12:45:11+00:00

Researchers assessed the cyber risk effect on banking profitability through financial metrics obtained from multiple banks enduring several years. This research investigates how the "Cyber Risk Ratio" indicates cyber risk to affect major profitability measurements including Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin (NPM), Operating Profit Margin (OPM), Gross Profit Margin (GPM), Bank Size and Capital Adequacy Ratio (CAR). The researchers used Linear Regression and XGBoost machine learning models to first estimate profitability measures depending on cyber risk levels. With its R-squared value, computed at 0.984, the Linear Regression model generated rather good forecasts since its proposed cyber risk ratio accounted for 98.4% of data changes in the anticipations. The model performed well in terms of accuracy through RMSE measurement of 0.019 and MAE measurement of 0.016. The Linear Regression model shows remarkable success as a technique for this dataset because it establishes a clear linear relationship between bank profitability and cyber risk. Excellent predictions were achieved through XGBoost which identified non-linear patterns while demonstrating R-squared of 0.914 together with RMSE value of 0.283. The specific application demonstrated XGBoost achieved slightly less precision than Linear Regression but added the capability to detect complex correlations in addition to resisting outliers. The XGBoost model proved able to make accurate predictions for non-linear patterns by reaching 85% success during binary classification evaluations. This research reveals that cyber risk introduces substantial effects on bank financial performance and Operating Profit Margin demonstrates the most intense relationship between these variables.

Strategic Group Analysis in the Tourism Industry: A Destination’s perspective

2025-09-08T07:11:14+00:00

Strategic group analysis is one of the most important methodological tools a decision-maker can use to explore the external environment of a business. In a constantly evolving sector like tourism, this analysis becomes even more significant, especially at the destination level, where there is a limited number of studies in the literature. To address this, the present research first provides an extensive literature review of the applications of strategic groups at the level of tourism businesses and destinations. Subsequently, the model is applied to cases of competitive destinations in Greece, distinguishing them based on their completeness, carrying capacity, and attractiveness in terms of overnight stays. This form of analysis can be useful for both Destination Management Organizations (DMOs) in the strategy formulation process and potential investors, as it offers a combined approach to attractiveness, tourism demand, and sustainability in terms of the destination’s carrying capacity relative to its competitors.

Augmented Reality in Fashion Retail: Platform-dependent Acceptance by Generation Z Customers

2025-09-08T13:36:57+00:00

Using the technology acceptance model, this article provides a comparison between the acceptance of augmented reality applications in stationary and mobile fashion retail. A quantitative online survey was conducted of a predominantly younger user group. Specifically, two fictitious scenarios were posed to the participants to investigate their opinions on the use of an augmented reality app. For the estimation of the model, a PLS-SEM approach has been implemented. The study reports on changes between a stationary and a mobile scenario and establishes that, in particular, the ease of use does not universally impact the attitude towards the technology. Additionally, it has been shown that the customer experience uniformly matters, i.e., in particular in the stationary scenario.