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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 attempts to investigate the relationship between emotional biases (loss aversion bias, overconfidence bias, and regret aversion bias) and investment decisions through a meta-analysis approach.

A meta-correlation analysis was done using sample size and correlation (r) data from several relevant studies that look at how emotional biases (loss aversion bias, regret aversion bias, and overconfidence bias) affect investment decisions. Additionally, beta coefficients (ß) were also converted to correlation coefficients (r) from six studies.

This study analysed 31 empirical studies and found a significant positive correlation between emotional biases and investment decisions [loss aversion bias (r = 0.492), regret aversion bias (r = 0.401), and overconfidence bias (r = 0.346)]. We set the statistical significance threshold at 0.05.

The review covered 31 online research publications that showed significant heterogeneity, possibly influenced by various methodological, population, or other factors. Furthermore, the use of correlational data restricts the ability to establish causation.

This is a novel attempt to integrate the results of various studies through meta-analysis on the relation between these emotional biases (loss aversion, overconfidence, and regret aversion) and investment decisions.

This paper aims to proffer a broad overview of publications in the International Journal of Contemporary Hospitality Management (IJCHM) by conducting bibliometric analyses for the duration ranging from 1989 to 2022.

The research approach analyses the top authors, publications, most collaborative countries and top co-occurring keywords and significant themes published in IJCHM with the help of the Scopus database. The study entails performance analyses on IJCHM. A de-duplicating process was used to study the evolution of themes, so that the keywords identified from co-occurrences of authors’ keywords and thematic evolution map were refined to first- and second-order themes, further leading to the development of inductive analysis proposing aggregate themes.

The findings of this study not only help paint a comprehensive picture of the customer experience, but also illustrate how topics have evolved in the literature and reveal the most relevant upcoming fields of research. The thematic evolution map reveals thematic areas. There is evidence of contributions by authors across the world and spanning a multitude of themes such as business ethics, corporate and firm performance, stakeholders and avenues for the management of disruption, specifically in times of the COVID-19 pandemic outbreak.

Significant trends in authors, publications, nations, authors’ keywords and themes as uncovered by this study can greatly help budding authors understand the expectations and emerging research themes that define the IJCHM.

Through extensive bibliometric analyses, this study has created a historical log of the publications in IJCHM. It has identified the key research trends for future research and presented a conceptual framework based on the keyword analysis map and thematic evolution.

This paper aims to identify the impacts of sociodemographic covariates on behavioural biases (BB) scores; the psychometric evidence of the BB measurement instruments; and the main BB that influences the decision-making of individual investors.

Papers were retrieved through search using keywords in ten databases. This systematic review is based on 69 peer-reviewed papers, most of which were published between 2017 and 2021. The relevance of the included papers was assessed through the analysis of statistical/psychometric methods used, and content analysis of the BB literature and its sociodemographic correlations.

Overconfidence is higher in men and not related to age. There was no consensus regarding the relationship between BB and other sociodemographic variables. Most measuring instruments are ad hoc, showing ≤ 4 types of psychometric evidence and assessing ≤ 9 BB. Therefore, the findings demonstrate that there is no gold standard instrument for measuring investors’ BB. Furthermore, 37 BB were cited as influencers of individual investors’ decision-making and overconfidence, herding, anchoring, representativeness and loss aversion were the most prevalent.

Considering that very few systematic reviews have been published in the behavioural finance area, this paper highlights the current state-of-the-art and identifies significant gaps in the literature that can be explored by further research.

To the best of the authors’ knowledge, this is the first systematic review that analyses the psychometric properties of instruments used for individual investors BB assessment.

This study aims to systematically review various behavioral biases that impact an investor’s decision-making process. The prime objective of this paper is to thematically explore the behavioral bias literature and propose a comprehensive framework that can elucidate a more reasonable explanation of changes in financial markets and investors’ behavior.

Systematic literature review (SLR) methodology is applied to a portfolio of 71 peer-reviewed articles collected from different electronic databases between 2007 and 2021. Content analysis of the extant literature is performed to identify the research themes and existing gaps in the literature.

This research identifies publication trends of the behavioral biases literature and uncovers 24 different biases that impact individual investors’ decision-making. Through thematic analysis, an attribute–consequence–impact framework is proposed that explains different biases leading to individual investors’ irrationality. The study further proposes directions for future research by applying the theory–characteristics–context–methodology framework.

The results of this research will help scholars and practitioners in understanding the existence of various behavioral biases and assist them in identifying potential strategies which can evade the negative effects of these biases. The findings will further help the financial service providers to understand these biases and improve the landscape of financial services.

The essence of the current paper is the application of the SLR method on 24 biases in the area of behavioral finance. To the best of the authors’ knowledge, this study is the first attempt of its kind which provides a methodical and comprehensive compilation of both cognitive and emotional behavioral biases that affect the individual investor’s decision-making.