This study examined socio-economic factors affecting Micro, Small, and Medium Enterprises (MSME) e-commerce adoption, focusing on gender, income, and education. Using the 2022 National Socio-Economic Survey (Susenas) data, a logistic regression model was employed to analyze key determinants of e-commerce utilization. Additionally, an online survey of 550 MSMEs across 29 provinces was conducted to assess the impact of digitalization on business performance. In comparison, an offline study of 42 MSMEs with low digital adoption provided insights into the barriers hindering digital transformation. A natural experiment was conducted to evaluate the effectiveness of behavioral interventions in promoting the adoption of e-payments and e-commerce. The main contribution of this study lies in integrating large-scale national survey data with experimental approaches to provide a deeper understanding of digital adoption among MSMEs. Unlike previous studies focusing solely on socio-economic determinants, this research incorporated a digital nudging experiment to examine how targeted incentives influenced e-commerce participation. The findings revealed that digital transformation significantly enhanced MSME performance, particularly in turnover, product volume, customer base, and worker productivity. Socio-economic factors such as gender, household head status, and social media access significantly influenced digital adoption decisions. Behavioral nudging proved effective in increasing MSME participation in e-commerce. Although this study was limited to Susenas 2022 data and survey responses, it bridges a critical research gap by linking socio-economic factors with behavioral interventions in MSME digitalization. The findings offer key insights for policymakers in formulating evidence-based strategies to drive MSME digital transformation and e-commerce growth in Indonesia.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.

Finding the right technique to optimize a complex problem is not an easy task. There are hundreds of methods, especially in the field of metaheuristics suitable for solving NP-hard problems. Most metaheuristic research is characterized by developing a new algorithm for a task, modifying or improving an existing technique. The overall rate of reuse of metaheuristics is small. Many problems in the field of logistics are complex and NP-hard, so metaheuristics can adequately solve them. The purpose of this paper is to promote more frequent reuse of algorithms in the field of logistics. For this, a framework is presented, where tasks are analyzed and categorized in a new way in terms of variables or based on the type of task. A lot of emphasis is placed on whether the nature of a task is discrete or continuous. Metaheuristics are also analyzed from a new approach: the focus of the study is that, based on literature, an algorithm has already effectively solved mostly discrete or continuous problems. An algorithm is not modified and adapted to a problem, but methods that provide a possible good solution for a task type are collected. A kind of reverse optimization is presented, which can help the reuse and industrial application of metaheuristics. The paper also contributes to providing proof of the difficulties in the applicability of metaheuristics. The revealed research difficulties can help improve the quality of the field and, by initiating many additional research questions, it can improve the real application of metaheuristic algorithms to specific problems. The paper helps with decision support in logistics in the selection of applied optimization methods. We tested the effectiveness of the selection method on a specific task, and it was proven that the functional structure can help the decision when choosing the appropriate algorithm.

This study examines the determinants of inflation in Tunisia from 1998 to 2023, with a particular focus on the role of fiscal policy. The study analyzes the long-run and short-run relationships between inflation and key macroeconomic variables, including government expenditure, government revenue, money supply, balance of trade, and budget deficits using ARDL model. The empirical findings reveal that budget deficits have a significant and positive impact on inflation, underscoring the critical role of fiscal imbalances in driving price instability. In contrast, government expenditure, government revenue, money supply, and balance of trade do not exhibit statistically significant long-term effects on inflation. The results highlight the importance of fiscal discipline and effective coordination between fiscal and monetary policies to achieve price stability. These findings provide valuable insights for policymakers in Tunisia and other developing economies facing similar inflationary pressures, emphasizing the need for prudent fiscal management and structural reforms to mitigate inflation volatility and ensure macroeconomic stability.