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Volume 11, Issue 3, 2025

Abstract

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Savings and Credit Cooperative Organizations (SACCOs) play a pivotal role in promoting financial inclusion, reducing poverty, and supporting social welfare especially in rural and underserved areas. However, 21% of DT-SACCOs do not operate with prudent financing decisions exposing themselves to financial stress and economic shocks. Even among the SACCOs that met compliance requirements, a drop in the capital adequacy ratio from 16.4% in year 2022 to 16.1% in year 2023 signaled alarming financial strain posing a threat to the existing SACCOs. Alarmingly, 35% of DT-SACCOs have ceased operations attributable to improper financing decisions with three delicensed in January 2025, raising significant concerns over their long-term financial health. Thus, the current study aimed to assess the moderating effect of SACCO size on the relationship between financing decision practices and the financial sustainability of Deposit-Taking Savings and Credit Cooperative Organizations (DT-SACCOs) in Kenya. Anchored on the pecking order theory, the research adopted a positivist paradigm and a cross-sectional survey design. A total of 176 finance managers representing 176 licensed DT-SACCOs constituted the study population. Data were collected by structured questionnaires with a 98% response rate as a sample of 122 respondents was selected by Yamane’s formula. Results from a binary logistic regression indicated that introducing the moderator led to a slight increase in the Nagelkerke R², while the inclusion of the interaction terms further strengthened the relationship between predictor variables and financial sustainability. The findings confirmed that SACCO size had a statistically significant moderating effect on this relationship. This study recommends integrating scenario-based stress testing into financing decisions to assess their long-term impact on different funding structures, so as to facilitate their confrontation of different economic conditions.

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This case study evaluated the effectiveness of performance measurement framework (PMF) in elevating operational efficiency, with a primary focus on Simbisa Brands, the largest chain in Zimbabwe. The research on the fast food industry in this developing country investigated how the Balanced Scorecard (BSC) could be integrated to monitor the key performance indicators (KPIs) of an organization, in respect of financial performance, customer satisfaction, internal processes, and employee training. Using a mixed method approach, structured questionnaires were distributed to employees, and interviews were conducted with key employees and stakeholders at Simbisa Brands. Results indicated that while the PMF of Simbisa aligned with its strategic objectives, significant challenges and obstacles to operational effectiveness existed in data quality, employee engagement, and customer satisfaction. Moreover, the unstable economic environment in Zimbabwe further complicated financial reporting and cost management. The BSC framework, which aligned KPIs with strategic goals, could effectively track financial performance and customer loyalty in the industry to boost operational excellence and support sustainable growth. Recommendations to stakeholders were proposed to continuously improve data quality, enhance employee involvement, and refine performance metrics to deliver the best purchasing experiences. For Simbisa Brands and other similar organizations, this research offered valuable insights to assist them in gaining competitive advantages and long-term success in the face of challenging business environments.

Open Access
Research article
Materiality Thresholds in Maltese External Auditing: An Analysis
Peter J. Baldacchino ,
matthew pisani ,
lauren ellul ,
Norbert Tabone ,
Simon Grima
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Available online: 09-29-2025

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The objectives of this study are to (i) ascertain the major quantitative and qualitative factors influencing the determination of materiality thresholds in the private sector external audits performed by large and medium-sized Maltese audit firms, (ii) assess the effectiveness of ISA 320 in the determination of such materiality thresholds, as well as the impact of introducing more prescriptive guidelines within the Standard, and (iii) assess the current level of professional judgement and its effectiveness in determining materiality thresholds, as well as ascertain the typical challenges involved in exercising such judgement. A predominantly qualitative mixed-methods approach was adopted. Semi-structured interviews were carried out with twelve audit partners from large and medium-sized Maltese audit firms. The findings indicated that the major quantitative factors influencing overall materiality were 5–10% of profit before tax and 1–3% of total revenue. The major quantitative factor influencing performance materiality and the clearly trivial threshold was 75% and 5% of overall materiality, respectively. Additionally, the major qualitative factors influencing materiality thresholds were fraud and litigation risk, quality of client internal controls, auditor critical thinking skills, client complexity, the client’s sector and a change in auditor. Furthermore, the findings indicated that ISA 320 provided sufficient guidance for determining materiality thresholds. Moreover, the most cited benefit of introducing more prescriptive guidelines within the Standard was greater consistency among auditors, while the most cited drawback was the limitation on professional judgement. The findings also revealed that professional judgement was crucial and generally effective in determining materiality thresholds. However, auditors typically faced a few challenges when exercising such judgement, of which time pressure and the setting of appropriate thresholds are particularly significant.

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This study investigates the influence of lunar phases, behavioral indicators, and technical analysis on cryptocurrency returns, with a focus on Bitcoin (BTC) and Ethereum (ETH). Positioned within the context of behavioral finance and astrofinance, the research aims to explore the role of non-traditional factors in shaping market dynamics. The study employs a combination of event study methodology, regression analysis, and machine learning techniques, particularly XGBoost classification, to examine the impact of lunar phases (such as full and new moons), sentiment measures (derived from Google Trends (GT), the Fear & Greed Index (FGI), and Natural Language Processing (NLP)-based sentiment scores), and technical indicators (such as Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Exponential Moving Average (EMA)) on cryptocurrency price movements. Although no statistically significant direct correlation was found between lunar phases and daily returns, it was observed that these phases are associated with changes in investor sentiment and trading volume. Moreover, behavioral variables, including NLP-derived sentiment scores and the FGI, exhibited interaction effects with market returns, particularly during periods of heightened market sentiment. The XGBoost model demonstrated a predictive accuracy of up to 63% for Ethereum, indicating its effectiveness in capturing complex, non-linear relationships within the data. These findings suggest that integrating astrofinancial timing with behavioral signals can improve the predictive accuracy of short-term cryptocurrency market trends, especially during periods of increased sentiment. This research highlights the potential of incorporating alternative data sources, such as lunar events and sentiment indices, into cryptocurrency trading models. The results also emphasize the effectiveness of machine learning algorithms, like XGBoost, in leveraging such non-traditional indicators to optimize investment strategies.
Open Access
Review article
Big Data Analytics in Auditing: A Review of Current Applications and Future Directions
salsabeel hani almarafi ,
noor afza amran ,
mohd hadzrami harun rasit
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Available online: 09-29-2025

Abstract

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A bibliometric analysis was conducted in this study covering the period between 2015 and 2024 to establish a roadmap for big data analytics in auditing. Excel, RStudio, and R software were employed to analyse the performance, co-occurrences, citations, and authorship of 91 articles selected from the Scopus database. According to the acquired results and pertinent observations, 2022 was the most productive year with 21 publications. The Journal of Emerging Technologies in Accounting was the most prolific journal, with ten publications. Besides, a total of 40 articles originated from the United States, significantly surpassing the number of publications issued by other countries. These findings indicated a growing attention on research related to audit quality and big data analytics in auditing. This thorough review provided insights into the historical background and current status of data analytics and auditing, while identifying gaps that necessitated further academic inquiry. The study provided a performance analysis and described the evolution of a profession, functioning as a vital resource for researchers and professionals who aim to understand emerging research trends in the pursuit of future studies.

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