Journal of Economics, Management and Trade

Analyzing the Impact of AfCFTA on Product Diversification: The Mediating Role of Non-tariff Measures

Ebenezer Amonoo — 2026-03-21

This study investigates the causal impact of the African Continental Free Trade Area (AfCFTA) on export product diversification, with particular attention to the role of non-tariff measures (NTMs). While tariff reductions under AfCFTA are expected to stimulate intra-African trade, the persistence of NTMs raises important questions about the extent to which the agreement can drive structural transformation. Using a panel of 36 African countries from 2015 to 2023, we apply the Synthetic Control Method (SCM) to estimate counterfactual outcomes for treated units. The analysis incorporates NTMs and export volumes as predictors, ensuring a comprehensive assessment of both trade barriers and trade intensity.

Results show that AfCFTA membership increased product diversification by an estimated 9.84 additional products relative to synthetic controls, with placebo tests confirming the robustness of this effect. However, the results also reveal that NTMs act as a mediating factor, dampening but not eliminating the gains from AfCFTA.

The findings highlight that AfCFTA can substantially promote diversification, but its success depends on complementary policies to streamline NTMs. These results provide important policy insights for African trade policymakers seeking to enhance regional trade integration and support sustainable economic transformation.

The Effect of Digitalization and Ownership Structure on The Financial Constraints of Indonesian Non-Financial Companies

Yulyanah Yulyanah — 2026-03-30

Aims: This study aims to examine the influence of digitalization, managerial ownership, and family ownership on the financial constraints of non-financial companies in Indonesia, while contributing to the literature on digital transformation and corporate governance in emerging markets.

Study Design: This study adopts a quantitative research design using panel data regression analysis.

Place and Duration of Study: This study uses secondary data from non-financial companies listed on the Indonesia Stock Exchange (IDX) during the period 2020–2024.

Methodology: A total of 41 companies were selected as research samples using purposive sampling, resulting in 205 firm-year observations. The analysis was conducted using panel data regression with the Fixed Effects Model (FEM), selected based on model suitability tests. Digitalization, managerial ownership, and family ownership are used as independent variables, while financial constraints are used as the dependent variable.

Results: The results show that digitalization has a positive and significant effect on financial constraints. Managerial ownership also has a positive and significant effect on financial constraints, while family ownership has a positive effect on financial constraints. The independent variables explain approximately 97.15% of the variation in financial constraints.

Conclusion: The findings indicate that digitalization, managerial ownership, and family ownership tend to increase financial constraints. This study provides important managerial implications, suggesting that firms need to carefully manage digital investment decisions alongside sound cash flow and financing strategies. Furthermore, higher managerial and family ownership should be balanced with improved transparency and disclosure quality to reduce concerns from creditors and investors.

Value Relevance of Earnings and Book Value: Evidence from Listed Companies in India

Priyanka Bhattacharya — 2026-03-31

Aims: This study aims to examine the value relevance of accounting information by analysing the extent to which earnings per share (EPS) and book value per share (BVPS) explain stock price variations of selected companies listed in the Nifty-50 index in India.

Study Design: The study adopts an empirical research design using cross-sectional regression and pooled panel data analysis based on the Ohlson (1995) price valuation model.

Place and Duration of Study: The study focuses on Nifty-50 indexed companies from 2016-2017 to 2024-2025, which corresponds to the post-adoption period of Indian Accounting Standards (Ind AS).

Methodology: The sample consists of 24 non-financial companies selected from the Nifty-50 index after excluding financial sector firms and companies with missing data. A total of 216 firm-year observations were analysed. The study employs Ordinary Least Squares (OLS) regression to estimate three models in order to examine the individual and combined explanatory power of EPS and BVPS in determining stock prices. The explanatory power of the models is evaluated using adjusted R² values.

Results: The empirical findings reveal that both EPS and BVPS have a statistically significant relationship with stock prices. The results indicate that BVPS demonstrates relatively stronger and more consistent explanatory power across most of the years, while EPS exhibits a higher marginal impact on stock price when statistically significant. Furthermore, the combined regression model including both EPS and BVPS provides greater explanatory power compared to the individual models.

Conclusion: The findings suggest that accounting information continues to play a meaningful role in stock price determination in the Indian capital market. This research contributes to the value relevance literature as it presents recent evidence from the Indian capital market, and provides some insights for investors, regulators and standard-setters regarding the continued importance of traditional accounting metrics in equity valuation processes.

Artificial Intelligence-Driven Recruitment as Business Capability: Evidence on Hiring Accuracy from Firms Operating in Dar es Salaam

Mwinula A. Lumelezi — 2026-03-30

Aims: This study examined whether AI-based recruitment systems serve only administrative efficiency or also function as a strategic Business Capability through improved recruitment accuracy, employee performance, and employee retention.

Study Design: The study was conducted using an ex-post facto design of the quantitative research type.

Place and Duration of Study: The study was conducted among technology and financial service firms in Dar es Salaam, Tanzania.

Methodology: The research was conducted using archival data collected from 400 middle-level employees of technology and financial service firms. The data were collected from two sets of 200 employees each who were recruited using AI-based recruitment systems and those recruited using traditional manual screening methods. The accuracy of the recruitment was compared using standardized performance appraisal ratings. Employee retention was also compared based on the outcomes of the 18-month retention period. Additionally, the AI-generated match scores were compared using the 0-100 scale to evaluate the association between the match scores and the subsequent employee performance outcomes for the various job roles.

Results: The research findings indicated that the performance ratings of the employees recruited using AI-based recruitment systems were significantly higher compared to those recruited using the traditional recruitment method (p < 0.01). Additionally, the employees recruited using AI-based recruitment systems also showed higher retention outcomes compared to those recruited using the traditional method (p < 0.05). The AI-generated match scores were also strongly associated with the subsequent employee performance outcomes (r = .68). The predictive validity of the match score was higher for technical roles (r = .75) compared to creative roles (r = .42).

Conclusion: The findings suggest that AI-based recruitment systems may not only be used for administrative efficiency purposes but could potentially be used as strategic tools to improve accuracy in recruitment processes, quality of the workforce, and stability in emerging markets.

This paper offers empirical findings on the efficacy of AI-based recruitment systems in terms of accuracy, employee performance, and retention outcomes. This paper contributes to the literature by offering quantitative findings from the emerging market context, where such evidence is scarce. By incorporating the constructs of Task-Technology Fit (TTF) and Resource-Based View (RBV), this paper extends the literature on AI-based recruitment as an operational and Business Capability.