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This study aims to present a bibliometric analysis of research on pensions and retirement systems over the past 100 years. The study examines the intellectual structure and mapping in the field of pension and retirement; uncovers growth and publication patterns; identifies thematic areas in the pension domain; provides analysis of gaps; and recommends direction for future research.

The study sourced data from the Scopus database between 1910 and 2022 covering a 112-year period. Employing bibliometric techniques, a total of 6,661 papers were selected and analyzed using SPSS and VOSviewer software.

Results from the cluster analysis suggest research in this domain has focused on five thematic areas namely pension plans, retirement systems, pension schemes, demographic, and socio-economic determinants of pension and retirement decisions. The authors show from the overlay visualization output how these themes have evolved within the period under review. The study further presents major developments, conclusions and suggestions for future research directions based on insights obtained from the research themes to enrich the field of pension and retirement planning.

The study is useful for informing researchers and practitioners on the state of the pension domain, and findings are useful avenues in developing the research field.

The study adds to existing literature on pension and retirement by offering an analysis of the state of pension research over a century and highlighting areas for future research.

The authors examine whether or not applicants and recipients of federal disability insurance (DI) inflate their self-assessed health (SAH) problems relative to others. To do this, the authors employ a technique which uses anchoring vignettes. This approach allows them to examine how various cohorts of the population interpret survey questions associated with subjective self-assessments of health. The results of the analysis suggest that DI participants do inflate the severity of a given health problem, but by a small but significant degree. This tendency to exaggerate the severity of disability problems is much more apparent among those with more education (especially those with a college degree). In contrast, racial minorities tend to underestimate severity ratings for a given disability vignette when compared to their white peers.

This article presents fieldwork perspectives and research reflexivity gained from the cross-national research team, with the aim of promoting better qualitative research practices in transnational research. It focuses on how the team incorporates diverse cultural perspectives and insider and outsider roles to enhance the research in the data collection process.

This article is drawn from the authors' qualitative research with 25 Japanese retirees in Thailand, addressing cultural challenges encountered by researchers in the cross-national team when conducting field research.

Our findings indicate that researchers with an emic view in the cross-national team who shared nationality and cultural background as the participants facilitated an effective recruitment process and productive collaboration in data gathering. They also served as cultural brokers, tailoring smooth communication during interviews on certain cultures, participant traits and sensitive issues. On the other hand, the outsiders helped the team uncover more transnational issues that the insiders had overlooked. Additionally, combining emic and etic perspectives helps to avoid ethnocentric narratives or purely etic and emic conclusions.

This article addresses a gap in the methodological reflections in transnational research that remains largely overlooked. Our reflection highlights the advantages of cross-national teams, which include researchers from emigration and immigration countries. Their status and roles as insiders and outsiders significantly facilitate a positive impact on the research process and increase the extent of investigating the complex cultural dynamics of transnational practices. The incorporation of emic and etic perspectives is suggested in the methodological approach for transnational migration research.

This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.

The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.

Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.

The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.

Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

The purpose of this study is to identify the drivers and future scenarios of Iran’s Social Security Organization.

The research is applied in terms of orientation and mixed in terms of methodology. In this research, the methods of theme analysis, root definitions, fuzzy Delphi and Cocoso were used. The theoretical population is the managers and senior experts of the social security organization, and the sampling method was done in a judgmental way. The tools of data collection were interviews and questionnaires. The interview tool was used to extract the main and subdrivers of the research and develop the scenarios.

Through theme analysis, 35 subdrivers were extracted in the form of economic, sociocultural, financial and investment, policy, marketing, environmental and legal themes. Due to the large number of subdrivers, these factors were screened with fuzzy Delphi. Eleven drivers had defuzzied coefficient higher than 0.7 and were selected for final prioritization. The final drivers were prioritized with the CoCoSo technique, and the two drivers of social security holdings governance and state of government revenues had the highest priority. Based on these two drivers, four scenarios of prosperity, resilient social security, unstable development and collapse have been developed.

Some of the suggestions of the research are: using the capacity of FinTechs and financial startups to invest the government revenues of the organization, using digital technologies such as business intelligence for more efficient decisions and developing corporate governance in the organization.

This study aims to investigate the effects of financial literacy, financial behaviour and financial stress on awareness of investment scams among retirees.

Using a questionnaire survey, data was distributed to 200 retirees. A total of 53 responses were obtained. The data was subsequently analysed using PLS-SEM version 3 software.

Findings indicated that while financial literacy has a significant influence on awareness, there is no conclusive evidence to support the relationship between financial behaviour and financial stress on awareness. These results highlighted the critical need to strengthen financial literacy among retirees as a prevention mechanism for them to avoid from being scammed.

The finding from this study is relevant to regulators and law enforcement agencies to aid potential and actual retirees by educating them on the danger of investment scams.

As there are relatively few studies conducted on investment scams specifically among retirees, this study extends the investment scam literature by examining the underlying factors that affect their awareness towards the fraudulent activities.

This study aims to reflect on how the COVID-19 pandemic has intensified the social and economic vulnerability of Indian elderly women, thereby making them prone to varied forms of abuse and denying them of the basic rights of secured existence.

This study was conducted by analyzing primary data from government sources that dealt with the aging Indian population and the common predicaments that elderly women experienced during the pandemic. A qualitative interview was conducted in three old-age homes in India where the experiences of 26 elderly female residents were documented for understanding their experiences during the pandemic. The secondary data collected from different newspaper articles and online resources also enabled in perusing the difficulties that they faced both at home and the caregiving space at the critical juncture of COVID-19.

Nearly 73% of the elderly population in the country has faced an incidence in different forms of abuse and exploitation during the subsequent waves of the pandemic. Disrespect and neglect were the most common type of mistreatment and around 23.1% reported physical assault. The elderly women were victimized further on socioeconomic grounds and their rights of living a secured and dignified life were significantly neglected.

The basic premise of this paper operates on the ground that the family as an institution has shunned taking care of the responsibilities vis-a-vis the elderly and therefore formal institutions have been introduced to aid in the conventional caretaking responsibilities in the Indian societal structure. This situation became all the more grave during the pandemic and therefore needed much intervention. This paper follows the theoretical lens of gender theory and case study method to analyze the data.

The HelpAge India report findings entail that elderly women/widows are doubly marginalized in the Indian society, and the COVID-19 pandemic has escalated the caregiver stress on manifold levels, thus exacerbating the problem. As most of the female senior citizens are economically dependent on their children and relatives, financial exploitation became one of the important premise that deprived them of a healthy living both at home and elderly caregiving institution. Despite the prevalence of certain elderly assistance schemes in the country, the older women’s needs and well-being got heavily impacted and their voice gets hardly recognized in the wider spectrum of sociopolitical events. To extend the requisite help and assistance to this socially vulnerable section, the government on September, 2021, launched a pan-India, toll-free helpline number “Elder Line” to provide relevant information on elderly legal and medical aid and guidance on procuring pension.

According to the secondary findings, a significant percentage of elderly women have been susceptible to physical and emotional abuse and factors such as widowhood, economic dependency, physical infirmity, cognitive impairment along with other stressors have aggravated their exposure to ill-treatment during the pandemic span. Thus, to recognize and mitigate the existing problems affecting the elderly subjects, the government should devise the necessary protocols and adopt essential measures to ensure the welfare of the marginalized section and protect their basic rights of a holistic existence.

The low level of financial literacy among young adults is a pressing concern at both individual and country levels. Therefore, there is a dire need to understand the best-worst antecedents of financial literacy and how they influence each other.

A two-phased multicriteria decision-making (MCDM) technique consisting of best-worst method and interpretive structural modeling (BWM-ISM) was employed for pair-wise comparison, assigning weights, ranking and establishing the relationship among antecedents of financial literacy.

Results suggest that use of Internet (SF1), role of financial advisors (SF3) and education level of individuals (DS7) are top ranked antecedents, whereas masculinity/feminity, language and power distance in society are the least ranked antecedents of financial literacy. Findings will help both academicians and practitioners focus on the key factors and make efforts to increase financial literacy by minimizing resource usage.

The current study provides clarity among antecedents of financial literacy by following BWM-ISM approach for the first time in the financial literacy context.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0746

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.