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This study aims to examine the nexus between digital financial literacy and customers’ perceived financial behavior within the Kuwaiti context. Moreover, it will further explore how digital financial literacy relates to financial behavior dimensions.

Data collection was facilitated by creating a questionnaire derived from multiple literature sources. This study used a cross-sectional, time-based dimension. Data was analyzed using the partial least square (PLS) structural equation modeling approach, using the Smart-PLS 4 software for computation.

Findings demonstrated a significant relationship between digital financial literacy and financial behavior, with a path coefficient of 0.542, a p-value of 0.000 and an R² value of 0.581. The explorative model revealed substantial relationships between many dimensions of digital financial literacy and various dimensions of financial behavior. More precisely, financial knowledge, awareness and decision-making were the factors that had the most significant impact on financial behavior.

Kuwaiti policymakers should consider including digital financial literacy programs in comprehensive financial education programs to improve public understanding of digital financial instruments and their consequences.

As the authors know, this is the initial endeavor to evaluate the relationship between digital financial literacy, financial behavior and their respective dimensions.

This study investigates the relationship between human values and saving behavior, focusing on both personal and cultural values.

The research utilizes data from the seventh wave of the World Values Survey (2017–2020) covering 67,278 respondents across 48 countries and the Hofstede Insights (2024). The study employs principal component analysis to validate the measurement of personal values and multilevel logit regression to explore the associations between personal (individual level) and cultural (country level) values and saving behavior.

The findings, grounded in the functional theory of values, indicate that individuals with personal values oriented toward individual goals and survival needs are more likely to save money, whereas those with values centered on social orientation and thriving needs are less inclined to save. On a cultural level, individualistic societies tend to save more, while countries with high levels of uncertainty avoidance are associated with lower saving behavior.

This study provides further evidence that human values are important components of household savings behavior. Policymakers and stakeholders interested in fostering saving behavior should be aware of the role played by personal and cultural values when designing impactful policies and interventions. This process might involve encouraging survival traits and reducing economic uncertainty.

This study provides a comprehensive analysis of how personal and cultural values shape saving behavior across different societies. It contributes to the literature by highlighting the interplay between individual and societal factors in financial decision-making.

This study aims to examine the influence of digital financial literacy (DFL) on the financial well-being (FWB) of students in Andhra Pradesh, specifically exploring the factors of impulsivity and self-control. Both DFL and FWB are treated as multi-dimensional constructs in the study. The research delves into the impact of DFL dimensions, viz. digital financial knowledge, digital financial experience and digital financial skills, on both impulsivity and self-control. Subsequently, the study assesses the effects of impulsivity and self-control on financial well-being.

To gather data, a questionnaire-based survey method was employed, reaching 475 university students through purposive sampling. The study utilizes confirmatory factor analysis for scale validation and structural equation modeling for hypothesis testing.

The results reveal a significantly negative influence of digital financial knowledge (DFK), digital financial experience (DFE) and digital financial skills (DFS) on impulsivity, while demonstrating a significantly positive impact on self-control. Additionally, the study finds that impulsivity negatively affects financial well-being, whereas self-control has a positive impact. Focusing on higher education institutions in Andhra Pradesh, the research highlights students’ limited concern for long-term financial planning.

This study underscores the relevance of understanding the crucial role of digital financial literacy in enhancing their financial well-being. The implications of these research findings are substantial and can be utilized to shape educational programs for students in higher education institutions. Such programs can guide institutions in imparting knowledge and skills related to personal finance management, particularly in the context of the increasing digitalization of financial transactions.

The purpose of this paper is to investigate network mobilization for collective market shaping. The authors focus on sustainable innovations that require broad cross-sectoral mobilization to create collective action among network actors. By drawing on insights from mobilization in innovation and issue networks, the authors shed light on the role of these two different networks in mobilizing collective action and triggering collective market shaping for sustainable innovations.

This paper presents a qualitative case study on the commercialization of a new low-carbon eco-concrete. The eco-concrete faces significant challenges in entering the market, due to the construction industry’s entrenched practices and institutional arrangements. These challenges emphasize the critical need for mobilizing collective action to generate the momentum for market change.

The findings reveal four types of interplay between mobilization in innovation and issue networks: synergistic and complementary mobilization that can reinforce collective market shaping and conflicting and diluting mobilization that can impede collective action and hinder the market shaping for sustainable innovations.

This paper contributes to a more comprehensive understanding of the role of mobilization in triggering collective market shaping. In particular, this research sheds light on the early stages of market shaping for sustainable innovations where broad mobilization for collective action is critical.

This study aims to systematically review the literature on how various factors influence investor sentiment and affect financial markets. This study also sought to present an overview of explored contexts and research foci, identifying gaps in the literature and setting an agenda for future research.

The systematic literature investigation yielded 555 journal articles, with few other exceptional inclusions. The data have been extracted from the two databases, i.e. Scopus and Web of Science. For bibliometric analysis, VOSviewer and Biblioshiny by R have been used. The period of investigation is from 1985 to July 2023.

This systematic literature review helped us identify factors influencing investor sentiment and financial markets. This study has broadly classified these factors into two categories: rational and irrational. Rational factors include – economics and monetary policy, exchange rate, interest rates, inflation, government mandatory regulations, earning announcements, stock-split, dividend decisions, audit quality, environmental, social and governance aspects and ratings. Irrational factors include – behavioural and psychological factors, social media and online talk, news and entertainment, geopolitical and war events, calendar anomalies, environmental, natural disasters, religious events and festivals, irrationality caused due to government/supervisory body regulations, and corporate events. Using these factors, this study has developed an investor sentiment model. In addition, this review identified research trends, methodology, data and techniques used by researchers.

This review comprehensively explains how various factors affect investor sentiment and the stock market using the investor sentiment model. It further proposes an extensive future research agenda. This study has implications for stock market participants.

Is access to finance a constraint for small and medium enterprises (SMEs) development or a result of SMEs constraint? Considering the demand-side of the credit market, this paper aims to assess the effect of nonfinancial services (NFS) on financial access through demand for financing (direct effect) and access to finance (indirect effect).

Using data from a five-year comprehensive entrepreneurship program on a package of technical assistance, the author uses two impact assessment methods: before/after and propensity score matching approaches.

The author found significant changes in business practices for treated SMEs and entrepreneurs since both the number and frequency of good business practices increased for most of the SMEs in the program with a positive turnover effect. Evidence of the positive effects of NFS on demand for financing is found in SMEs but this does not involve more access to finance. Despite positive changes in business practices, small-size entrepreneurs continue to self-exclude for financing.

Different pass-throughs are operating within this “recycling” of entrepreneurial resources over time. The author shows the effectiveness of the knowledge on financing mechanism, financial conditions and government financial support, even if these mechanisms do not seem to lead to a significant improvement in access to finance.

In recent years, studies have shown that machine learning significantly improves student performance and retention and reduces the risk of student dropout and withdrawal. However, there is a lack of empirical research reviews focusing on the application of machine learning to predict student performance in terms of learning engagement and self-efficacy and exploring their relationships. Hence, this paper conducts a systematic research review on the application of machine learning in higher education from an empirical research perspective.

This systematic review examines the application of machine learning (ML) in higher education, focusing on predicting student performance, engagement and self-efficacy. The review covers empirical studies from 2016 to 2024, utilizing a PRISMA framework to select 67 relevant articles from major databases.

The findings show that ML applications are widely researched and published in high-impact journals. The primary functions of ML in these studies include performance prediction, engagement analysis and self-efficacy assessment, employing various ML algorithms such as decision trees, random forests, support vector machines and neural networks. Ensemble learning algorithms generally outperform single algorithms regarding accuracy and other evaluation metrics. Common model evaluation metrics include accuracy, F1 score, recall and precision, with newer methods also being explored.

First, empirical research literature was selected from only four renowned electronic journal databases, and the literature was limited to journal articles, with the latest review literature and conference papers published in the form of conference papers also excluded, which led to empirical research not obtaining the latest views of researchers in interdisciplinary fields. Second, this review focused mainly on the analysis of student grade prediction, learning engagement and self-efficacy and did not study students’ risk, dropout rates, retention rates or learning behaviors, which limited the scope of the literature review and the application field of machine learning algorithms. Finally, this article only conducted a systematic review of the application of machine learning algorithms in higher education and did not establish a metadata list or carry out metadata analysis.

The review highlights ML’s potential to enhance personalized education, early intervention and identifying at-risk students. Future research should improve prediction accuracy, explore new algorithms and address current study limitations, particularly the narrow focus on specific outcomes and lack of interdisciplinary perspectives.

We aim to examine two issues. First, we intend to identify the best performing expected return proxies. Second, we investigate whether the expected return proxies for individual stocks can track the corresponding realized returns during extremely good or extremely bad times of the economic environment related to business conditions, stock market valuation and broad market performance.

We construct four sets of expected return proxies, including: (1) characteristic-based proxies; (2) standard risk-factor-based proxies; (3) risk-factor-based proxies that allow betas to vary with firm characteristics and (4) macroeconomic-variable-based proxies. First, we estimate expected returns for individual stocks using newly developed methods and evaluate the performance of these expected return proxies based on the minimum variance criterion of Lee et al. (2020). Second, we regress expected return proxies and realized returns on indicator variables that capture the extreme phases of the economic environment. Then we compare the estimated coefficients from these two sets of regressions and see if they are similar in magnitude via formal hypothesis testing.

We find that characteristic-based proxies and risk-factor-based proxies that allow betas to vary with firm characteristics are the two best performing proxies. Therefore, it is important to allow betas to vary with firm characteristics in constructing expected return proxies. We also find that model-based expected return proxies do a reasonably good job capturing actual returns during extremely bad and extremely good phases of business cycles measured by leading economic indicators, consumer confidence and business confidence. However, there is a large gap between the adjustment of model-based expected returns and realized returns during extreme episodes of stock market valuation or broad market performance.

We examine four types of expected return proxies and use the newly developed methodology as in Lee et al. (2020) to see which one is the best. In addition, we document whether model-based expected returns from individual stocks adjust partially or fully to keep pace with actual returns in response to changing economic conditions. No prior studies have examined these two issues.

Branding in the labor market is an important issue due to the growing importance of brand. This study aimed to analyze the term of trust in the employee- and employer-based brand equity for gray-, blue-, and white-collar workers using bibliometric analysis. The study design included the formulation of three research questions. Bibliometric data comprised 205 employee-based brand equity publications and 40 employer-based brand equity publications from the Scopus database. The analysis involved quantitative measures such as the number of publications and citations as well as the frequency of source types, authors, and countries. VOSviewer software mapped the co-occurrence of keywords in employee- and employer-based brand equity publications. These mappings revealed eight clusters related to employee-based brand equity publications and four clusters in terms of employer-based brand equity publications. The issue of trust has been analyzed in publications on employee-based brand equity; no such studies have been noted for employer-based brand equity. Employee- and employer-based brand equity has not been analyzed for various collar workers.

Celebrities communicating about environmental sustainability on social media have the power to inspire young adults to engage in pro-environmental behavior, such as reducing their consumption behavior or only buying local and organic food. However, at the same time, celebrities’ carbon-rich and luxurious lifestyles might generate skepticism when they preach about environmental action. Thus, this study aims to shed light on the effects of celebrity pro-environmental messages on young adults’ perceived authenticity and greenwashing and, subsequently, on young adults’ pro-environmental behavior. Moreover, this study examined the moderation effect of congruent (vs incongruent) messages in the celebrity’s social media profile depicting an environmentally friendly (vs unfriendly) lifestyle.

The authors conducted a 3 (celebrity pro-environmental messages: with concrete action claim vs without vs control group) x 2 (celebrity message-lifestyle congruence: congruent vs incongruent) between-subjects experimental study (N = 400) with young adults (16–26 years old).

Results showed a significant positive effect of celebrity pro-environmental messages with concrete green action claims on authenticity perceptions only when the social media profile depicted a congruent environmentally friendly lifestyle. Moreover, higher perceived authenticity of the celebrity by social media audiences led to a higher likelihood of young adults’ engagement in pro-environmental behavior.

To the best of the authors’ knowledge, this study is the first to consider celebrity message characteristics and young adults’ perceptions of authenticity and greenwashing when investigating the effects of celebrity pro-environmental messages on young adults’ pro-environmental behavior.