The ability to manage money has been identified as a vital talent. In this context, financial literacy has a role to play. There are significant gaps, though, such as the financial connection to human behavior. The purpose of this study is to look at the overall effects of financial literacy (FL) on the behavioural aspects of materialism (ML), compulsive buying (CB), and tendency to debt (TD). It attempts to develop and evaluate a model that is based on these variables and to examine the relationships between these research constructs. The study looked at 233 respondents mainly students studying in Omani universities.

Purpose: This study endeavours to (a) develop a comprehensive interpretive structural modelling (ISM) toolkit containing sufficient details about the suitability and procedural aspects of each ISM approach and offer points of reference for budding researchers, (b) highlight the compatibility of ISM approaches with other qualitative and quantitative approaches, and (c) chalk-out an agenda for future research.Design/methodology/approach: This study is based on an extensive review of 74 studies where researchers have used one or more ISM approaches. These studies span across the different industry sectors.Findings: There exists a huge void in terms of the methodological synthesis of ISM approaches. ISM approaches are frequently used in sync with other qualitative and quantitative approaches. Furthermore, it highlights the need of improving the robustness of the proposed ISM models by sharing the critical details of research process.Research limitations/implications: Being a review-based work, it could not illustrate the discussed ISM approaches with real data. However, it offers a research agenda for the prospective researchers.Practical implications: The prerequisites, pitfalls, suitability and the procedural aspects of various ISM approaches contained in this toolkit are equally useful for the academicians as well as practitioners.Originality/value: In the absence of a synthesized framework, this study contributes a comprehensive ISM toolkit which will help the researchers to choose a suitable ISM approach in a given case.

This study examines customer attitudes towards blockchain technology (BCT)-supported banking applications in Oman and explores the relationship between these attitudes and behavioral intentions, highlighting the influencing factors. Blockchain Technology (BCT) has disrupted the banking sector, but understanding customer attitudes towards BCT-supported banking applications and their impact on behavior remains limited. In pursuit of this objective, a quantitative research design was employed, involving the collection of data from retail bank customers in Oman through a structured questionnaire. The collected data underwent rigorous analysis using a robust Structural Equation Model, confirming the constructs' validity, reliability, and distinctiveness. The study uncovered that BCT has a positive impact on various aspects of the banking sector, including customer service quality, regulatory compliance, secured transactions, and cost reduction. By augmenting transparency, efficiency, and trust in banking transactions, BCT significantly improves customer attitudes towards BCT-supported banks. Furthermore, the research reveals that positive attitudes play a pivotal role in influencing customers' intentions to prefer and continue using these blockchain-backed banks. The study also highlights the importance of trust factors and transaction frequency as moderators in shaping customer attitudes and behavioral intentions in this context. The insights gleaned from this research hold practical significance for banks operating in Oman, offering guidance for tailored marketing campaigns, product design, and aligning their offerings with customer expectations in the realm of blockchain-supported banking applications. Additionally, the study provides practical recommendations for the successful implementation and adoption of blockchain-supported financial applications within the banking sector.

This study provides insightful information about personal finance by examining the complex relationship between income and spending patterns in various occupations. It reveals strong relationships between income levels and expected results through regression analysis, providing insight into how different occupational groups are impacted by financial well-being. Building on this basis, a thorough one-way ANOVA and post-hoc tests show that students, academics, labourers, business owners, and government employees have remarkably different spending preferences. These results highlight the significant impact of occupation-specific variables on financial behaviour, including benefits, income stability, and social expectations. This research provides a path corresponding to specific policy interventions and programs aimed at financial literacy. By promoting economic resilience and well-being across a range of demographic and occupational landscapes, the ultimate objective is to cultivate a future in which responsible spending practices are shared realities rather than just ideals.

The valuation of Goodwill (GW) has remained one of the several critical issues in financial analysis. This aspect is particularly important for mergers and acquisitions due to the significance of intangible assets. This study delves into the capitalization method of super profit (CMSP), a prominent technique for GW valuation, enhanced by the integration of statistical tools. Assessing a company’s excess profits over its average return on tangible assets is part of the CMSP. Finding the variables that have a significant impact on GW valuation, such as average profit, capital employed, and rate of return, is the main goal of this research. These issues are thoroughly investigated through statistical analysis to give stakeholders useful information for well-informed decision-making. Additionally, the study seeks to identify the external elements influencing this process as well as the internal aspects influencing GW valuation. Regression analysis, correlation matrices, response analysis and ANOVA are used to improve GW assessment and comprehension of the complex relationships between different factors.