This study tackles the challenge of identifying optimal investment sectors amid the growing importance of environmental, social, and governance (ESG) factors, which are often complex and conflicting. This research aims to effectively evaluate and prioritize ten investment sectors based on twelve ESG criteria by integrating expert evaluations with two advanced multi-criteria decision-making (MCDM) methods. Three expert teams assessed each sector’s performance based on these criteria using fuzzy logic to manage uncertainties in expert judgments. The MEREC (MEthod based on the Removal Effects of Criteria) identified biodiversity and land use as the most critical factor, while transparency and disclosure was least significant. The AROMAN (Alternative Ranking Order Method Accounting for two-step Normalization) method was further used to rank the ten alternative sectors, with impact investing funds emerging as the top choice, followed by renewable energy and sustainable responsible investment funds. Conversely, ESG-compliant stocks, ESG-focused exchange-traded funds, and ESG-focused real estate investment trusts ranked the lowest. The study’s findings were validated through comparisons with other MCDM tools and sensitivity analysis, confirming the robustness of the proposed model. This research offers a valuable framework for investors looking to incorporate ESG considerations into their decision-making, promoting sustainable and responsible investing practices.

This study presents an in-depth analysis of the selection process for primary sectors impacting the economic activity in Romania, employing an interval-valued fuzzy (IVF) approach combined with multi-criteria decision-making (MCDM) methodologies. This research aims to identify eight key criteria influencing the selection of Romanian primary sectors, including technology adaptation, infrastructure development and investment, gross domestic product (GDP), sustainability, employment generation, market demand, risk management and government policies. The current analysis evaluates eight primary sector performances against these eight criteria through the application of three MCDM methods, namely, Simple Additive Weighting (SAW), Weighted Product Model (WPM), and Weighted Aggregated Sum Product Assessment (WASPAS). Ten economic experts comprising a committee have been invited to provide their views on the criteria’s importance and the alternatives’ performance. Based on the decision-maker’s qualitative judgement, GDP acquires the highest weightage, followed by environmental impact and sustainability, thus indicating the most critical factors among the group. The IVF-MCDM hybrid model indicates the energy sector as Romanian primary sector with the most potential, followed by the agriculture and forestry sector among the list of eight alternatives. It also explores the robustness of results by considering sensitivity analysis and the potential impacts of political and international factors, such as pandemics or armed conflicts, on sector selection. The findings indicate consistency in sector rankings across the different methodologies employed, underscoring the importance of methodological choice and criteria weighting. Additionally, this study sheds light on the potential influence of political and international dynamics on sector prioritization, emphasizing the need for comprehensive decision-making frameworks in economic planning processes.

The rapid digitalization of banking services has transformed consumer interactions, necessitating a deeper understanding of the factors influencing online banking adoption. This research investigates the factors influencing consumer adoption in a country undergoing rapid digital transformation but still facing gaps in digital skills and infrastructure—Romania. The objective of the study is to analyze how key variables such as ease of use, security, speed, usefulness, and social pressure influence online banking behavior of Romanian consumers, especially the most digitally engaged ones. The study utilizes a multi-method empirical approach, hypothesis testing, binary logistic regression for prediction modeling, and segmentation analysis to offer a comprehensive view of customer behavior. The findings identify essential adoption drivers and separate customer profiles, providing useful information for financial organizations aiming to enhance their digital strategy. Perceived ease of use and perceived security are primary factors influencing adoption; nevertheless, decision tree analysis indicates that speed and usefulness have a more significant impact than logistic regression implies, but social pressure unexpectedly serves as an impediment. These results highlight the necessity for banks to customize their digital services, harmonizing security and user-friendliness with improved efficiency and usefulness to promote broader adoption in emerging digital economies like Romania.