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This study attempts to recognize obstacles and barriers to financial inclusion in the agriculture sector, propose a framework based on the inter-contextual link between the barriers and understand the financial exclusion in the agriculture sector at the grassroots level. Previously published research articles were used to identify the barriers to financial inclusion, followed by informal interviews and collaborative discussions with the local farmers of the Sonipat district of Haryana and expert interviews using a structured questionnaire. TISM and MICMAC analysis are used to decern the nature of the relationship among the barriers discovered. The authors find that inadequate financial literacy, a shortage of financial awareness and the reluctance of various financial institutions are significant linkage barriers to strong driving and dependence power. High transaction costs and poor infrastructural support are the independent barriers. The paper identifies these new barriers to financial inclusion in the Indian agriculture sector and the framework depicting financial exclusion in India. This paper only gives a framework of barriers and does not quantify the effect of any relationship identified, but strongly emphasizes granting the Indian agriculture sector broad and simple financial access to advance and strengthen the nation's sustainable, inclusive economic growth.

In modern societies, consumer well-being is dependent on choices regarding complex services, such as investments, health care, insurance and lending. However, evaluating costs of such services is often difficult for consumers due to a combination of limited cognitive resources and complexity of the service. The purpose of this study is to empirically examine to what extent three specific consequences of complexity influence consumer tendencies to make mistakes when evaluating the costs (or price) of complex services.

Three studies were conducted (survey: n = 153, experiment: n = 332 and conjoint analysis: n = 225), all focusing on how consumers evaluate costs in the complex mutual fund setting.

The authors find that consumers struggle with estimating and using cost information in decision-making in the complex services setting. Consumers of complex services frequently underestimate the costs over the long-term, may see costs as a signal of service quality and are susceptible to influence from presentation formats when evaluating costs.

The study investigates mutual funds, which is one example of a complex service. In order to get a full picture of how consumers deal with costs in complex setting, future research needs to expand this focus to other types of complex services.

The results have implications for both marketers of complex services and policymakers. For marketers, this paper highlights that competing with a low-cost strategy may be difficult in the complex services setting as consumers may lack the ability to actually evaluate what they pay over the long term. For policymakers, increased simplification of prices may be an attractive option. However, it is important that this simplification is done in a way that increases the possibility to compare prices.

As complexity influences several aspects of decision-making, an understanding of how consumers evaluate costs in complex settings is dependent on taking a multidimensional research approach. This paper makes a novel contribution to the literature on pricing by showing that consumers struggle with multiple aspects when evaluating costs in complex contexts. Understanding these effects is important to policy, as well as to research on the cognitive value of simplicity that is currently gaining traction in marketing research.

Fintech has revolutionized personal finance, introducing innovative tools that offer unprecedented access, efficiency, and security in managing finances. This chapter explains fintech's personal finance applications, from intuitive budgeting apps and advanced robo-advisors to peer-to-peer payment platforms. It articulates how these tools have shifted the control and management of finances into the hands of consumers, providing real-time financial data, customized investment strategies, improved credit scores, and streamlined transactions that eliminate the need for traditional intermediaries. Furthermore, this chapter features a select list of FinTech50 firms and highlights how individuals can leverage their services. This comprehensive guide is invaluable for individuals seeking to leverage fintech for personal finance optimization and for professionals keen on understanding and navigating the rapidly evolving fintech landscape.

This study aims to understand the lapse behavior of life insurance policyholders. Despite being accessible for nearly two centuries, only a small fraction of individuals purchase such policies and many of those who do let them lapse. The belief hypothesis model (BHM) is introduced to elucidate the correlation between policyholders' beliefs and their decisions regarding life insurance lapses.

BHM establishes a comprehensive linkage between core beliefs, external data and the lapse behavior exhibited by policyholders. To derive policyholders’ core beliefs about life insurance lapses, the authors conducted a semistructured, in-depth interview with 42 policyholders and 11 insurance advisors, using a grounded theory approach with zero-order, first-order and second-order coding.

The study's findings reveal that policy lapsation is influenced by various factors such as policyholders' beliefs about life insurance, process-related attitudes, trust in insurers and advisors and personal financial viewpoints. Policyholders who consider life insurance unnecessary or misunderstand its purpose are likelier to lapse their policies. Cumbersome documentation processes and technical issues also contribute to policy lapsation, emphasizing the significance of simplified procedures. Trust in insurers and advisors, personal financial literacy and payment preferences influence policy lapsation.

The findings of this research can be practically applied by companies to improve customer retention and by regulatory bodies to encourage policyholders to honor their insurance commitments.

Distinguishing itself from conventional hypotheses-driven and factor-centric models, BHM integrates consumer beliefs, thus enriching comprehension and decision-making insights.

The study aims to identify and understand the underlying behavioral tendencies and motivations influencing investor sentiments and examines the relationship between these underlying factors and investment decisions during the COVID-19-induced financial risks.

The study uses the primary data and information collected from 300 Indian retail equity investors using a nonprobability sampling technique, specifically purposive and snowball sampling. This research uses the insights from Phuoc Luong and Thi Thu Ha (2011) and Shefrin (2002) to delineate behavioral factors influencing investment decisions. Structural equation modeling estimates the causal relationship between underlying behavioral factors and investment decisions during the COVID-19-induced financial risks.

The study establishes that the “Regret Aversion,” “Gambler’s Fallacy” and “Greed” significantly influence investment decisions, and provide a comprehensive understanding of how psychological motivations shape investor behavior. Notably, “Mental Accounting” and “Conservatism” exhibit insignificance, possibly influenced by the unique socioeconomic context of the pandemic. The research contributes to 35% of variance understanding and prompts the researchers and policymakers to tailor investment strategies aligned to these behavioral tendencies.

The findings hold policy implications for investors and policymakers and provide tailored recommendations including investor education programs and regulatory measures to ensure a resilient and informed investment community in the context of India's evolving financial landscapes.

Theoretically, behavior tendencies and motivations have been strongly linked to investment decisions in the stock market. Yet, empirical evidence on this relationship is limited in developing countries where investors focus on risk management. To the best of the authors’ knowledge, this study is among the first to document the influence of underlying behavioral tendencies and motivation factors on investment decisions regarding retail equity in a developing country.

Children are beginning to socialize as consumers earlier than ever, highlighting the importance of their saving behavior as an effective form of consumer protection. The paper explored the influence of parents, peers, attitudes, knowledge, past behavior, allowance and self-efficacy on saving intention.

With the aim to explore a range of determinants of adolescent saving and to specify the potential mechanisms through which different determinants operate, we adopted a multitheoretical approach based on theories of planned behavior, consumer and financial socialization, and self-efficacy. The paper investigates the formation of the saving intentions on a sample of 1,476 children 10–15 years old in Croatia.

The results indicate strong importance of parental influence and self-efficacy, implying that saving intention among tweens requires a supportive family structure as well as beliefs in the tweens themselves that they are able to save money and face difficulties.

This paper investigates the very nature of saving intention formation at a crucial developmental stage; it investigates the interplay of mechanisms through which determinants of savings operate at that developmental stage; and it explores the age-variance of the mechanism and the interplay of relevant variables, shedding light on the nature of the mechanism of development.

As consumer debt can substantially impair subjective well-being, it is crucial for research to gain insights into how consumers can be motivated to improve financial planning. This paper aims to investigate how frontline employees in financial services can help consumers regulate their financial planning behaviors and how financial service providers can effectively support their frontline employees in this effort through leadership and organizational climate.

We incorporate regulatory focus theory and conservation of resource theory to develop a conceptual model that we test in a triadic study with a unique dataset collected from consumers, frontline employees, and managers in the banking sector.

We find that frontline employees must pay attention to the details of consumers’ needs and customize the service to those needs to trigger consumer promotion focus and stimulate consumers’ financial planning behaviors. Moreover, our results emphasize that the organization must act as an integrated entity. Thus, a manager’s servant leadership and an organizational climate of customer stewardship are crucial for frontline employees to transform consumers’ financial planning behaviors.

The study highlights frontline employees’ key role in motivating consumer financial planning behavior, offering a new perspective in transformative service research on enhancing financial well-being.

The findings provide financial service providers with actionable implications for enhancing consumers’ financial planning. This benefits both consumers and financial institutions, as customers with greater spending power can buy more financial products.

This study advances transformative service research on consumer financial planning behavior, which has largely focused on consumer-related or society-level variables, by exploring the role of frontline employees and organizational support in terms of leadership and climate.

This study aims to draw lessons from the financial technology (FinTech) ecosystem literature through a systematic literature review.

This study systematically studied a sample of 134 articles from the Scopus database to assess the pattern of research development within the scope of the FinTech ecosystem over the last 15 years (2008–2023).

The results obtained indicated that the current research focus leads to several aspects: digital technology and financial inclusion, FinTech and customer behavior, FinTech ecosystem, business model, as well as aspects of governance and regulation. In the effort to develop Islamic FinTech, some aspects that might be targeted include aspects of business development and the Islamic FinTech ecosystem in general, extending financial inclusion to governance and managerial implementation of Islamic FinTech itself.

This research has limitations because it did not focus on the study of more specialized sectors, such as insurance or microfinance institutions, in adopting FinTech, requiring the use of other specifications of institutions in addition to Islamic banking.

This research has substantial theoretical implications in mapping the intellectual structure of Islamic FinTech research, which has been underexplored by previous researchers, as well as providing essential information about which sectors should be prioritized to encourage inclusiveness and overall performance of financial institutions.

This research explores more deeply with a comprehensive approach so that it becomes a pioneer in the study of FinTech ecosystem literature for the development of Islamic FinTech.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.