CamEd Business School

This study explores how women entrepreneurs in a resource-constrained setting adopt and experience personal technology for business purposes within the broader context of digital transformation. Drawing on the technology acceptance model (TAM) and work-life border theory (WLBT), qualitative data were collected through 32 semi-structured interviews with women entrepreneurs operating micro and small enterprises in Mauritius. Findings reveal that perceived usefulness, ease of use, and institutional support drive the adoption of personal devices, enabling flexibility, cost savings, and improved responsiveness to clients. However, constant connectivity also blurs boundaries between work and family life, heightening stress, emotional fatigue, and security concerns, particularly in the absence of technical support. These dynamics unfold in gendered contexts shaped by cultural expectations and caregiving responsibilities, with technology simultaneously supporting business needs while intensifying work-life conflict. Building on these insights, this study proposes a conceptual model highlighting personal technology's dual impact on business performance and well-being, as well as the moderating and mitigating roles of gender norms, structural constraints, and support systems. The findings contribute to scholarship on gender and digital entrepreneurship, offering implications for gender-sensitive policies that promote equitable and supportive technology adoption in similar Global South (GS) contexts.

This study explores the effect that the adoption of International Financial Reporting Standards (IFRS) 9 has on credit risk in commercial banks in Cambodia, focused primarily on non-performing loans (NPLs) as a significant indicator. In the static and dynamic panel estimations, the analysis shows that the NPL behavior is best characterized using a dynamic specification, which passes relevant diagnostic tests and leads to evidence of persistence and endogeneity, which has not been conducted in Cambodia yet. The study covered the period from 2013 to 2024. During this period, 26 commercial banks had complete datasets. Combining time-series and cross-sectional data, the total sample size was 312 observations. The results show substantial path dependence in NPLs, suggesting credit deterioration is persistent and that early measures are needed. We find evidence that the adoption of IFRS 9 is positively and significantly associated with increased measures of NPLs, though we interpret this as consistent with improved transparency and forward-looking recognition of expected credit losses—and not indicative of deterioration in underlying asset quality. Bank-specific determinants such as profitability, size, leverage, and liquidity emerge as key predictors of credit risk; banks with stronger financial fundamentals experience improved asset quality. Macroeconomic factors like economic growth are key to decreasing NPLs in the dynamic framework as well. The results highlight the need for forward-looking accounting standards, prudent bank-level practices, and macroeconomic stability. Policy issues include increased supervisory vigilance, legal conservatism when assessing IFRS 9-related indicators, a revision of the capital and liquidity regulatory framework in relation to counterparties operating with them, as well as coordinated macroeconomic policies aiming at boosting the financial system—economy arterial connection.

Following the global financial crisis, the transition to IFRS 9’s forward-looking Expected Credit Loss (ECL) model has introduced significant implementation complexity, particularly in emerging markets facing data limitations. This study investigates the heterogeneous ECL compliance strategies adopted within the Cambodian banking sector during a period of heightened credit stress, marked by a system-wide non-performing loan ratio of 8.6%. Utilizing a multiple-case study design and replication logic, a qualitative content analysis was conducted on the 2024 audited financial statements of 13 representative institutions, ranging from market leaders to international subsidiaries. The findings reveal a pronounced technical divide: market leaders utilize advanced internal statistical methods, such as cohort analysis, whereas international subsidiaries rely on top-down parent-group proxy models to bridge local data gaps. A “macro-correlation paradox” was identified, where certain institutions prioritize faithful representation by excluding macroeconomic variables when statistical links to historical defaults remain weak. Furthermore, a significant transparency gap exists, where granular disclosures are consistent with a signaling interpretation regarding institutional safety. These results suggest that ECL compliance in data-limited environments may be interpreted as a strategic management choice rather than a standardized technical exercise, highlighting the need for regulatory standardization of modeling assumptions to improve inter-bank comparability.

The study examines the predictive ability of traditional econometrics and machine-learning models for Cambodian tax revenue estimates using monthly LNTAX from January 2009 to March 2026. Repeated out-of-sample forecasts were then produced through a rolling-origin forecasting design, across two test horizons: 12-months and 24-months. We evaluated different techniques: moving average, exponential smoothing, damped trend, ARIMA, CART, GRNN and KNN. The performance against the moving average benchmark was compared using regression analysis with clustered standard errors, and forecast accuracy measured in terms of mean squared error and symmetric mean absolute percentage error. The results provide evidence that LNTAX is non-stationary in levels, and then stationary at first differencing. The best overall predictive vision is achieved within ARIMA with the smallest errors in most of the evaluations and significantly largest decreases in MSE and sMAPE compared to moving average. Damped-trend exponential smoothing also does quite well, especially for percentage accuracy over the longer horizon. In general, machine-learning models outcomes are mixed: CART shows average performance, whereas KNN in some instances is able to improve sMAPE versus naive-based models but GRNN ranks the weakest. The results indicate that time-series models have a superior forecasting performance than the state-space framework-based univariate tax revenue forecasting model in this context. Hence, it is suggested that ARIMA is used as the main benchmark model and future research should be conducted in hybrid methods, more macroeconomic variables, structural breaks and a policy-oriented forecasting evaluation.

This research investigates the impact of bank-specific and macroeconomic variables on return on equity as a measure of bank profitability in Cambodia during the period 2014–2024. Based on panel estimation methods, including dynamic panel modeling, the findings indicate that profitability is driven by both bank-specific features and country-level economic conditions. The dynamic panel estimates imply positive and statistically significant lagged return on equity, which means moderate profit persistence. Nevertheless, the coefficient is still less than one, indicating that profitability gradually reverts rather than being permanent. In terms of bank-specific factors, non-performing loans have a negative and statistically significant effect on profitability in all models, supporting the view that decline in asset quality leads to deterioration in bank performance via higher provisioning costs, reduced income generation and lower shareholder returns. The size of banks is always positive and statistically significant, meaning larger banks benefit from economies of scale, better market dominance positions, broadening customer bases and greater income diversification. The impact of capital-related variables is also mixed, indicating that the effects of capital strength on profitability depend on model specification. It is only when controlling for bank-specific effects and dynamic adjustment that the loan-to-deposit ratio becomes significant in pooled and random-effects models. Macroeconomic conditions also affect profitability. GDP growth has a positive and significant effect; inflation has a negative and significant effect.