
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13532-3
10.1016/j.heliyon.2024.e37501
e37501
Research Article
A scoping review of the impact of ageing on individual consumers' insurance purchase intentions
Zheng Zhangwei P118284@siswa.ukm.edu.my
⁎
B.A.M Hafizuddin-Syah m_hafiz@ukm.edu.my

Omar Zaki Hafizah hafizah.omar@ukm.edu.my

Tan Qin Lingda P132640@siswa.ukm.edu.my

Faculty of Economics and Management, Universiti Kebangsaan Malaysia, Malaysia
⁎ Corresponding author. UKM Bangi, Selangor, 43600, Malaysia. P118284@siswa.ukm.edu.my
07 9 2024
30 9 2024
07 9 2024
10 18 e375017 2 2024
22 7 2024
4 9 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Recently, the phenomenon of population ageing and its impact on the insurance industry has garnered increasing global attention. However, a notable gap in scholarly research persists in understanding the nuanced effects of ageing on consumer behaviour and insurance purchase intentions. This study maps the current academic evidence on how ageing influences individual consumers' insurance decisions. Using a scoping review methodology aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews and Joanna Briggs Institute guidelines, 44 articles out of 1082 from four databases—Web of Science, Scopus, ScienceDirect, and Emerald Insight—are reviewed. The results reveal a rising interest in this research area, with China emerging as a significant contributor. The focus is predominantly on Theory of Planned Behavior, quantitative methods, questionnaire survey, regression analysis, older population, and general health insurance. Variables capturing the impact of ageing, beyond demographic information, include family-related, risk-related, and expectation-related factors. This study highlights the current state of research on ageing's effect on insurance purchase intentions and offers valuable insights and directions for future research.

Keywords

Ageing
Insurance
Purchase intention
Young-old
Scoping review
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pmc1 Introduction

In recent years, population ageing has become a major concern for policymakers, insurers, and academics due to declining birth rates and increased life expectancy. According to definitions from the United Nations (UN) [1], the US Population Reference Bureau (PRB) [2], and the National Bureau of Statistics (NBS) of China [3], ageing is a dynamic process where the average age of a nation rises due to a growing proportion of older individuals or a shrinking proportion of younger people. According to UN standards, a society is classified as ageing, deep ageing, or super ageing when the percentage of individuals aged 65 or above is 7–14 %, 14–20 %, or above 20 %, respectively [1]. Currently, major global economies face ageing issues. For instance, China is becoming a deep ageing society, with 14.2 % of its population aged 65 and above in 2021 [3], equating to over 200 million people. This is a significant increase from just 7 % in 2000 [3]. Hence, China's rapid ageing highlights the importance of researching ageing and its impacts.

In consumer behaviour research, the impact of ageing has been extensively studied [[4], [5], [6], [7], [8], [9]]. However, there is limited research on how ageing affects insurance purchase intentions. As ageing progresses, consumers' purchasing behaviours and willingness to make purchases, especially regarding insurance, change significantly. Ageing influences not just individuals but also has broader societal implications, such as changes in labour markets, healthcare systems, intergenerational relationships, and social welfare [[10], [11], [12]]. For instance, high levels of ageing can lead to inadequate social benefits like pensions or delayed retirement [13], while low birth rates may impose future burdens on younger generations [14]. Ageing often alters risk perception, financial priorities, and outlook on the future [[15], [16], [17]], which can significantly affect how individuals engage with insurance services. Insurance, as a financial tool for security and risk mitigation, is crucial for protecting against unforeseen adversities [18]. Thus, exploring the impact of ageing on insurance purchase intentions is vital for both academic research and industry practice.

A search using relevant keywords reveals that no existing literature reviews have conducted a retrospective on this theme. Current reviews mainly focus on elderly consumer decision-making [[19], [20], [21]] or specific types of insurance [22], which do not align with this scoping review's scope. As the impact of ageing on insurance purchase intentions is emerging, this study uses a scoping review methodology to systematically assess the literature on this topic. The aim is to identify existing knowledge on how ageing influences insurance purchase intentions, map and summarise existing evidence, and highlight knowledge gaps to inform future research [23]. The central question is: How does ageing affect individual consumers' insurance purchase intentions? The specific sub-questions are: (1) What are the trends in publication years and study regions? (2) What are the theories and research methods used? (3) What types of insurance are covered? (4) What are the age characteristics of the subjects? (5) What variables and factors are used to measure the impact of ageing?

The subsequent structure of this article includes an introduction to the methods, a presentation of results based on the research questions, followed by a corresponding analysis and discussion. The final section provides conclusions and recommendations for future research.

2 Methods

2.1 Design

To understand the impact of ageing on insurance purchase intentions, this study employed a scoping review approach, which examines a wide range of literature sources and research designs to explore existing knowledge [23]. Scoping reviews are valuable for quickly identifying research trends and key concepts across diverse fields [24]. Following the guidelines of Norsworthy et al. [25] and Pham et al. [26], this review adhered to two main frameworks: the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR) [27] and the Joanna Briggs Institute (JBI) guidelines [28]. The aim was to systematically and transparently explore how ageing affects insurance purchase intentions by gathering, reviewing, and synthesising relevant literature. Based on Arksey and O'Malley [24] and Peters et al. [28], the review followed key stages: defining research questions and objectives, searching for relevant studies, selecting studies according to inclusion and exclusion criteria, charting data (data extraction), and reporting results with conclusions and implications.

2.2 Search strategy

This study accessed existing literature using four online databases: Web of Science (WOS), Scopus, ScienceDirect, and Emerald Insight. The search strings (Table 1), derived from the research questions, included 'ageing', 'insurance', 'purchase', 'intention', and their synonyms. Given the novelty of the research topic and the lack of extensive prior studies, the search strategy imposed no time restrictions and did not limit results by language, ensuring a comprehensive and extensive literature compilation.Table 1 Search strategies used in the scoping review.

Table 1Database	Search Strategy	Search results	
Web of Science (WOS)	(((TS=(aging OR ageing OR senescence)) AND TS=(insurance)) AND TS=(purchas* OR buy* OR acquire OR obtain OR shopping OR procure OR decision-making)) AND TS=(intent* OR desire OR willingness OR motivation)	319	
Scopus	(TITLE-ABS-KEY(aging OR ageing OR senescence) AND (insurance) AND (purchas* OR buy* OR acquire OR obtain OR shopping OR procure OR decision-making) AND (intent* OR desire OR willingness OR motivation))	512	
ScienceDirect	(aging OR ageing) AND (insurance) AND (purchase OR buy OR decision-making) AND (motivation OR intention OR willingness)	34	
Emerald Insight	abstract:'insurance' AND (abstract:'aging' OR 'ageing' OR 'senescence') AND (abstract:'purchase' OR 'purchasing' OR 'buying' OR 'buy' OR 'obtain' OR 'shopping' OR 'acquire' OR 'procure' OR 'decision-making') AND (abstract:'intention' OR 'intent' OR 'motivation' OR 'willingness' OR 'desire')
+ title:'insurance' AND (title:'aging' OR 'ageing' OR 'senescence') AND (title:'purchase' OR 'purchasing' OR 'buying' OR 'buy' OR 'obtain' OR 'shopping' OR 'acquire' OR 'procure' OR 'decision-making') AND (title:'intention' OR 'intent' OR 'motivation' OR 'willingness' OR 'desire')	217	
Total	–	1082	

2.3 Study selection

This review applied four inclusion and five exclusion criteria (Table 2). As detailed in Table 1, a total of 1082 papers were extracted from the four databases. After removing 71 duplicates using EndNote 20's de-duplication function, 1011 articles remained. Following the inclusion and exclusion criteria in Table 2, articles were screened by title and abstract, resulting in 927 irrelevant articles focusing on ageing economics, medical treatment of ageing-related diseases, and healthcare resource preferences. The full texts of 84 articles were reviewed, leading to the exclusion of 40 articles for failing to meet the criteria. Specifically, 10 articles were excluded due to unavailable original literature, one for being medical rather than business-related, and another for using only macro data. Additionally, 28 articles were excluded for not addressing both 'ageing' and 'insurance purchase intentions' together. Among these, 12 articles covered only ageing and not insurance purchasing intentions, focusing on topics such as retail banking, financial planning, and long-term care services. The remaining 16 articles addressed insurance purchase intentions but did not consider ageing, covering topics like agriculture insurance, flood insurance, and smart car insurance. It should be noted that the high number of excluded articles is typical due to the broad search scope, which includes titles, abstracts, and keywords, and requires manually removing many irrelevant articles due to the interdisciplinary nature of the research. Additionally, given that global ageing reached 7 % in 2007 (the threshold for an ageing society) [29], and that China—a major economic power and key contributor to ageing research—entered a deeply ageing society with over 14 % in 2021 [3], it is reasonable that this emerging topic has limited historical research.Table 2 Inclusion and exclusion criteria for study selection.

Table 2Inclusion criteria	Exclusion criteria	
1. Articles discussing the factors influencing 'insurance purchase intentions', which may involve age or ageing factors.
2. Articles discussing 'insurance purchase intentions' related to 'ageing', like 'ageing-related insurance', 'elderly consumers' insurance purchase intentions', or 'the impact of ageing on insurance consumption'.
3. Articles using microdata.
4. Articles focusing on individual consumers, rather than business consumers.	1. Articles for which the original full text cannot be found.
2. Articles that only focus on 'ageing' or 'insurance purchase intentions,' without any connection to the other topic.
3. Articles utilising only macrodata.
4. Articles that are conference abstracts, letters, research proposals, book chapters, or any other non-research paper.
5. Articles outside the field of business (e.g., management, marketing, finance), especially in the biological or medical fields.	

During the selection process, four authors collectively established the inclusion and exclusion criteria. Two authors independently screened titles and abstracts based on these criteria. Discrepancies were resolved through discussions with the other two authors, preventing individual biases and errors. As this scoping review aims to map a broad spectrum of current evidence rather than critically appraise specific research contents, a quality assessment was not conducted. Hence, a total of 44 articles were finally selected for review. Fig. 1 illustrates the flow diagram of entire search strategy.Fig. 1 PRISMA-ScR flow diagram of studies search and selection.

Fig. 1

2.4 Data extraction and synthesis

Arksey and O'Malley [24] noted that a scoping review should determine the outcome of the research topic by extracting multiple key items. As a scoping review, it aims to map existing evidence on an emerging area. Therefore, four researchers developed a data-charting form based on PRISMA-ScR [27] and JBI [28] guidelines to determine the extraction items, facilitating data organisation from the articles. Specifically, items included the author, publication year, countries selected for research, types of insurance, research design, data collect and analysis method, number and characteristics of subjects (age groups), ageing-related factors, and findings. Two authors independently extracted relevant information from each article, while the other two reviewed the articles to verify data completeness and accuracy. The authors summarised findings aligned with the research question, using them as the foundation for the narrative synthesis.

3 Results

The search was conducted in September 2023. As shown in Fig. 1, a total of 1082 articles were obtained from four databases. After several rounds of screening, 44 articles were included in the review.

3.1 Publication years and countries

Table 3 presents the publication years and countries of the 44 studies. The earliest articles on the impact of ageing on insurance purchase intentions were from 2001, with no time limit set for this scoping review. The distribution is as follows: 2 articles (5 %) were published between 2001 and 2004, 5 (11 %) between 2005 and 2008, 3 (7 %) between 2009 and 2012, 12 (27 %) between 2013 and 2016, 9 (20 %) between 2017 and 2020, and 13 (30 %) between 2021 and 2023.Table 3 Publication years and countries of the reviewed studies (n = 44).

Table 3Category	Description	N	%	
Publication year	2001–2004	2	5 %	
2005–2008	5	11 %	
2009–2012	3	7 %	
2013–2016	12	27 %	
2017–2020	9	20 %	
2021–2023	13	30 %	
Research countries	USA	13	30 %	
China	12	27 %	
Europe	2	5 %	
Spain	2	5 %	
Australia	2	5 %	
UAE	2	5 %	
Burkina Faso	1	2 %	
UK	1	2 %	
Italy	1	2 %	
Malaysia	1	2 %	
Iran	1	2 %	
Ghana	1	2 %	
Korea	1	2 %	
Pakistan	1	2 %	
Romania	1	2 %	
Slovak	1	2 %	
Multi-country	1	2 %	

In terms of countries, the US and China (including Hong Kong, Macao, and Taiwan) were the most studied, representing 30 % and 27 % of the research, respectively. Europe, Spain, Australia, and the UAE followed, each with 5 %. Other countries, including Malaysia, the UK, Italy, Korea, Iran, Ghana, Pakistan, Romania, and Slovakia, were each represented once (2 %).

3.2 Theories and methods used

Table 4 outlines the theories and methods used in the 44 reviewed studies. The Theory of Planned Behaviour (TPB) was the most common theoretical framework, featured in 9 % of the studies. Utility Maximization Theory and the Theory of Intrafamily Moral Hazard each appeared in 7 % of the studies. Theories such as the Theory of Bounded Rationality, the Theory of Reasoned Action, and Insurance Demand Theory were used in 5 % of the studies, while Socioemotional Selectivity Theory and Rational Economic Theory were utilised in 2 % of the studies.Table 4 Theories and methods used in the reviewed studies (n=44).

Table 4Discription	No.	%	Discription	No.	%	
Theory			Data analysis method			
Theory of Planned Behaviour	4	9 %	Regression analysis (29)			
Utility maximization theory	3	7 %	Logistic regression	7	16 %	
Theory of intrafamily moral hazard	2	5 %	Multinomial logit regression	3	7 %	
Theory of bounded rationality	2	5 %	Linear probability regression	3	7 %	
Theory of Reasoned Action	2	5 %	Random-effects logistic regression	2	5 %	
Insurance demand theory	2	5 %	Weighted multinomial regression	1	2 %	
Prospect theory	2	5 %	Weighted logistic regression	1	2 %	
Socioemotional Selectivity Theory	1	2 %	Two-limit tobit model	1	2 %	
Rational economic theory	1	2 %	Single-equation probit regression	1	2 %	
State-dependent utility framework	1	2 %	Probit regression	1	2 %	
Life course theory	1	2 %	Ordered probit regression	1	2 %	
Commitment-trust theory	1	2 %	Multivariate logistic regression	1	2 %	
Cue utilization theory	1	2 %	Multinomial logistic regression	1	2 %	
Situational Theory of Problem Solving	1	2 %	Linearized logistic regression	1	2 %	
Random Utility Theory	1	2 %	Linear regression	1	2 %	
Theory of peer effects	1	2 %	Conditional logit regression	1	2 %	
Expected utility theory	1	2 %	Bivariate probit regression	1	2 %	
Trust transfer theory	1	2 %	Binary logit regression	1	2 %	
Fuzzy Trace Theory	1	2 %	Binary logistic regression	1	2 %	
Unified Theory of Acceptance and Use of Technology (UTAUT)	1	2 %	Structural equation modeling (6)			
			SEM	3	7 %	
Research design			PLS-SEM	3	7 %	
quantitative	38	86 %	Contingent Valuation Method (6)			
qualitative	4	9 %	CVM (bidding game)	3	7 %	
mix	2	5 %	CVM (double-bounded dichotomous choice)	1	2 %	
			CVM (take-it-or-leave-it)	1	2 %	
Data collection method			CVM (single-bounded discrete referendum)	1	2 %	
Questionnaire	23	52 %	Correlation analysis (3)			
Secondary data	17	39 %	Correlation analysis	2	5 %	
Focus group	3	7 %	Spearman's correlation test	1	2 %	
Experiment	2	5 %	Discrete choice experiment	3	7 %	
Systematic review	1	2 %	Content analysis	3	7 %	
			Difference-in-Differences analysis	1	2 %	
			Double hurdle model	1	2 %	
			Neural network	1	2 %	
			Microsimulation approach	1	2 %	
			Repeated measures analysis	1	2 %	

For research design, quantitative methods were dominant, employed in 86 % of the studies, with qualitative methods used in 9 % and mixed methods in 5 %. Data collection methods predominantly involved questionnaires (52 %) and secondary data (39 %). Focus groups and experiments were less common, used in 7 % and 5 % of the studies, respectively, while systematic reviews appeared in 2 % of the studies. Regarding data analysis methods, regression analysis was the most prevalent data analysis method, used in 29 studies. Logistic regression, the most frequently applied regression technique, appeared in 16 % of the studies. Multinomial logit regression and linear probability regression were used in 7 % of the studies each. Other regression techniques included random-effects logistic regression and weighted multinomial regression. Structural equation modelling (SEM and PLS-SEM) was used in 7 % of the studies, and the contingent valuation method (CVM) in various forms was also employed in 7 % of the studies.

3.3 Types of insurance covered

This scoping review extracted insurance types directly from the original articles with minor adjustments for categorisation and analysis. For instance, 'private medical insurance' and 'private medical plans' were combined as 'medical insurance,' while 'longevity insurance,' 'annuity insurance,' and 'longevity annuities' were classified as 'longevity annuity insurance.' 'Takaful' and 'Islamic insurance' were standardised as 'Islamic insurance (Takaful).'

Table 5 lists the insurance types covered in the 44 reviewed articles. The total number of insurance types exceeds 44 (46) because two articles addressed multiple types. Health insurance (30 %) and long-term care insurance (28 %) were the most frequently mentioned, followed by life insurance and medical insurance (11 % each), Islamic insurance (Takaful) and longevity annuity insurance (7 % each), travel insurance (4 %), and social pension insurance (2 %).Table 5 Types of insurance covered in the review (n=46).

Table 5Insurance	N	%	
health insurance	14	30 %	
long-term care insurance	13	28 %	
life insurance	5	11 %	
medical insurance	5	11 %	
Islamic insurance (Takaful)	3	7 %	
longevity annuity insurance	3	7 %	
travel insurance	2	4 %	
social old-age insurance	1	2 %	

3.4 Age characteristics of respondents/participants

Given that ageing is most directly reflected in the increasing proportion of the older population, this study extracted age characteristics of respondents/participants from the articles to map the target groups. Articles not specifying the age profile were assumed to cover an age range from 18 to 100 years, excluding minors. Of the 44 articles, 43 considered age characteristics, as one systematic review did not address the study population.

Table 6 displays the age characteristics of the study populations in the 43 articles. This study uses the UN classification to define individuals aged 65 and above as the older population and those below 65 as the young-old population. Further categorisation follows the Organisation for Economic Co-operation and Development (OECD) standards [30]: individuals aged 16–24 are young adults, 25–54 are prime working-age adults, and 55–64 are older adults. This dual reference to UN and OECD criteria enhances the detail and scope of demographic analysis. Table 6 shows that 50 % of the studies focused on the older population, followed by prime working-age adults (32 %), older adults (13 %), and young adults (5 %).Table 6 Age characteristics of respondents/participants in the review (n = 43).

Table 6Age	Description	%	
16–24	Young adults (young-old)	5 %	
25–54	Prime working-age adults (young-old)	32 %	
55–64	Older adults (young-old)	13 %	
65–100	Oldest old	50 %	

Fig. 2 shows the specific age groups covered in each study and their overlap [[31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73]]. The green square represents age groups addressed in the article (denoted by 1), while the white areas indicate those not covered (denoted by 0). This figure supports the findings in Table 6, highlighting that most studies focused on the older population. Among the young-old population, studies on prime working-age adults were most common, followed by those on older adults and young adults.Fig. 2 Age distribution of respondents/participants in the review.

Fig. 2

3.5 Influencing factors related to ageing

This study extracted variables capturing the impact of ageing from the 44 articles and classified them into five categories: demographic, family-related, risk-related, cognitive-related, and expectation-related. Table 7 presents these results. To streamline categorisation and analysis, similar variables were combined. For instance, 'family size,' 'number of children,' and 'number of older individuals' were grouped as 'number of dependents.' Concepts related to 'perceived likelihood of needing care in the future' or 'perceived inability to live independently' were categorised as 'anticipated dependence.' 'Cognitive decline' and 'cognitive ability (numeracy and cognitive reflection)' were combined into 'cognitive abilities.' Variables such as 'presence of a chronic disease,' 'number of chronic diseases,' and 'perceived health conditions' were merged as 'health status.' 'Risk aversion' and 'risk attitudes' were classified as 'risk propensity.' Additionally, 'homeownership (which leads to a tendency to self-insure)' and 'accessibility of family care' were grouped as 'family insurance expectations (self-insurance).'Table 7 Influencing factors related to ageing in the review (n = 44).

Table 7Category	Ageing related variables	N	%	
Demographic information	Age	44	100 %	
Marital status	23	52 %	
Health status	23	52 %	
Number of dependents	17	39 %	
Saving behaviour	4	9 %	
Presence of private insurance coverage	2	5 %	
Family-related	Interpersonal influence	5	11 %	
Caregiving experience	2	5 %	
Bequest motive	2	5 %	
Risk-related	Risk perception	7	16 %	
Risk propensity	3	7 %	
Cognitive-related	Cognitive abilities	5	11 %	
Cognitive age	1	2 %	
Expectation-related	Family insurance expectations(self-insurance)	5	11 %	
Anticipated dependence	4	9 %	
Life expectancy expectations	2	5 %	
Public insurance expectations	2	5 %	
Perceived economic instability	1	2 %	

Table 7 reveals that most studies used demographic information to assess the impact of ageing. Specifically, age was used in all studies, while 52 % included marital status and health status. The number of dependents was considered in 39 % of studies, savings behaviour in 9 %, and private insurance coverage in 5 %. Family-related variables were less common, with 11 % of studies examining interpersonal influence, and 5 % each investigating caregiving experience and bequest motives. Risk-related factors were used by 16 % of studies for risk perception and 7 % for risk propensity. Cognitive-related factors were discussed in 11 % of studies for cognitive ability and 2 % for cognitive age. Expectation-related factors included family insurance expectations (self-insurance) in 11 % of studies, anticipated dependence in 9 %, and life expectancy and public insurance expectations in 5 % each. An additional 2 % referenced perceived economic instability, reflecting concerns about the future economic security of respondents' adult children.

4 Discussion

The limited number of publications (44 articles over 23 years) suggests that this research topic is a niche within the broader field of ageing. Despite fluctuations, Fig. 3 shows a consistent increase in studies on the impact of ageing on consumers' insurance purchase intentions. The peak in publications occurred between 2021 and October 2023, indicating a rapidly growing trend, with future publications expected to rise further. This rise aligns with the global trend of population ageing. The UN [29] reports that 10 % of the global population was aged 65 or older in 2022, with projections estimating that this figure will reach 16 % by 2050. This demographic shift is driving increased research interest. Recent studies also highlight a surge in research across ageing-related fields, reflecting a growing academic focus on ageing's implications [74]. The increase in publications mirrors these demographic changes and indicates a rising global concern about the impact of ageing on various aspects, including insurance purchase behaviour. Thus, this growing research area is timely and relevant, addressing the challenges of ageing populations, and will become increasingly significant in the future.Fig. 3 Number and trend of publications per years (by country).

Fig. 3

Regarding research countries, the US has the highest number of published articles (13, or 30 % of the total). However, temporal patterns reveal a different trend (Fig. 3). From 2001 to 2016, the US saw an increase in publications, peaking between 2013 and 2016 with six articles. However, from 2017 to 2023, publications dropped significantly to just two. This decline can be attributed to several factors. The US's ageing population increased from 13.89 % in 2013 to 14.67 % in 2016 [75], surpassing the UN's deep ageing society threshold of 14 % [1]. This focus on ageing issues led to a temporary rise in research. However, as the US exceeded the 7 % ageing threshold well before 1960 [29], research on ageing was long established. Consequently, the transition to deep ageing may have only briefly intensified interest, with attention shifting to new issues as the immediate impact faded. In contrast, China published five articles with a steady trend from 2001 to 2020. From 2021 to 2023, publications surged to seven, making China the second-highest contributor with 12 articles (27 %). This surge coincides with China reaching a deeply ageing society threshold of 14.2 % in 2021 [3]. China crossed the 7 % ageing threshold only in 2000 and rapidly reached 14 % by 2021 [3]. This swift transition suggests that ageing research in China is still developing. With a population over four times larger than the US and unique ageing issues [76], research interest in China is likely to grow for many years, unlike the US's swift decline. Thus, the increased focus on ageing in China underscores its growing importance as a research region in the coming years.

The dominance of the TPB and Utility Maximization Theory reflects a reliance on established behavioural and economic theories to examine insurance purchase intentions in the context of ageing. The frequent use of multiple regression techniques highlights efforts to understand complex relationships and predict outcomes based on various factors. The emphasis on quantitative methods reveals a preference for empirical analysis and statistical validation. Although qualitative and mixed methods are less common, they suggest an interest in exploring context-specific aspects of insurance behaviour. The range of data collection methods, from questionnaires to experiments, demonstrates a multifaceted approach. The prevalence of questionnaires and secondary data indicates a focus on broad-scale analysis, while experiments and focus groups are used to explore specific behaviours and opinions. The limited use of systematic reviews points to the relatively early stage of comprehensive literature reviews in this field.

For the types of insurance covered in the studies, as shown in Table 5, except for social old-age insurance (2 %), which belongs to social insurance, all other types of insurance fall under the broader concept of commercial life insurance (as distinguished from property insurance) [18]. For instance, long-term care insurance (28 %) and medical insurance (11 %) are types of health insurance. When combined with the 30 % of studies focused on health insurance, a total of 69 % of research is centred on the broader category of health insurance. This is understandable, as the health problems and medical risks associated with ageing are most relevant to health insurance. However, given that broad insurance categories have been widely studied, future research should target specific subcategories such as medical insurance, long-term care insurance, and longevity annuity insurance, while reducing focus on general categories like life and health insurance.

In terms of age characteristics, as evident from Fig. 2 and Table 6, research trends rank the age of study subjects from high to low as older adults, prime working-age adults, and young adults. Notably, this analysis may contain inaccuracies as many articles do not specify the age ranges of study subjects. In this review, such articles (13) were assumed to cover ages 18–100 years, resulting in a high proportion of prime working-age categories. If age 65 years is used as the dividing line between ageing and non-ageing populations [1], only 8 of the 44 articles specify that their subjects are below 64 years [43,46,56,59,60,65,72,73]. Thus, only 18 % of studies on the impact of ageing on insurance purchase intentions focus on non-ageing populations, and most studies concentrate on older adults. Therefore, future research should emphasise the influence of ageing on the insurance purchase intentions of prime working-age populations.

The factors associated with ageing were also analysed. Table 7 shows that four variables (age, marital status, health status, and number of dependents) account for over 30 % of the evidence, all within the category of demographic information. This prevalence is due to the widespread collection of such data in empirical studies. However, these factors do not fully capture the multifaceted impact of ageing. Since this review aims to understand the impact of population ageing on individual consumers rather than intrinsic changes as they age, cognitive-related factors are not considered reflective of the 'ageing impact' in this study.

The remaining three categories—family-related, risk-related, and expectation-related variables—capture the nuanced impact of ageing more effectively than demographic information. For example, the variable of interpersonal influence reflects societal pressure from ageing through interactions with peers, older parents, and adult children [43,47,61,68,73]. The variable of caregiving experience indicates whether an individual has a history of providing care, offering insight into their understanding of ageing's challenges [44,47]. By contrast, 'bequest motivation' measures the older demographic's willingness to purchase insurance to increase their estate's inheritance [49,72]. Risk-related variables, such as risk perception [37,38,45,58,68,70,73] and risk propensity [48,63,69], assess an individual's judgement of risks associated with ageing, including their inclination to purchase insurance. Expectation-related variables illustrate ageing's impact on individual consumers. Family insurance expectations (self-insurance) reflect an individual's estimation of self-insurance feasibility and their attitude towards purchasing insurance [31,37,45,69,72]. Anticipated dependence measures pessimism about future independence, influencing the need for commercial insurance [45,61,72,73]. Public insurance expectations gauge confidence in public coverage reliability [45,50]. Life expectancy expectations estimate the extent of old-age risks [45,63], while perceived economic instability relates to expectations for future economic development and personal income under the influence of ageing [47]. These variables collectively evaluate ageing's impact on the willingness to purchase insurance.

The findings of this study have significant implications for policymakers, industry practitioners, and consumers. Policymakers must develop inclusive health insurance policies that cater to the 'young-old' demographic, integrating flexible coverage options, preventive care, and chronic disease management, while considering affordability and accessibility. These policies should prioritise comprehensive care over acute interventions. Industry practitioners can leverage these insights to design and market insurance products that address the specific needs and preferences of consumers under ageing context, such as combining health insurance with wellness programmes and personalised health plans. This research empowers consumers to make informed decisions regarding health insurance by understanding the importance of selecting products that offer comprehensive coverage tailored to their evolving needs. Overall, the study enhances the understanding of ageing's influence on insurance decision-making, aiding in the development of effective, consumer-centric health insurance policies and products.

5 Future directions

There is an increasing interest in how ageing affects consumer insurance purchase intentions, as evidenced by the continual surge in the number of articles on this topic. This indicates that it is an emerging research area warranting further exploration. Therefore, based on the aforementioned analysis, this study proposes future research directions from five perspectives.

In terms of geographical contribution, China is steadily cementing its position as a pivotal contributor in global ageing studies and is anticipated to remain a significant research hotspot in the coming years. This underscores the imperative for future research on this topic to either focus on China or at least reference studies from this region. Moreover, future research could also delve deeper into regional variations, examining how cultural and socioeconomic differences influence consumer behaviour in insurance markets under ageing.

For theories, future research should expand theoretical frameworks by incorporating variables such as risk perception, expectations, and family dynamics into established theories like the Theory of Planned Behavior (TPB) and Utility Maximization Theory. This integration could provide a more nuanced understanding of insurance purchase intentions in the context of ageing. Additionally, for methods, moving beyond purely quantitative methods to include mixed methods approaches will offer richer insights into the contextual factors influencing insurance decisions. Integrating cutting-edge technologies, such as blockchain [77] and deep learning models [78], may assist in uncovering more nuanced insights into consumer behaviour. Furthermore, conducting systematic reviews is also crucial to identify existing gaps in the literature and guide future research efforts more effectively.

Concerning the types of insurance examined, there exists a significant focus on general health insurance, highlighting a research gap. Future studies are thus encouraged to investigate more specific insurance categories within the context of ageing, in order to reveal more detailed insights. For instance, with the introduction and growing emphasis on the United Nations Sustainable Development Goals, comprehensive health coverage has become an objective for many countries. Consequently, the exploration of various health insurance subcategories, such as community-based health insurance, village-based health insurance, government-funded health insurance, and inclusive health insurance, is warranted, particularly in relation to specific contexts.

As to the age characteristics of study subjects in ageing studies, the literature is predominantly centred on the older population, with a dearth of studies involving the 'young-old' population. Considering the extensive societal impact of ageing, which permeates all age groups, the young-old population—a cornerstone of the consumer base—warrants increased scholarly focus in future investigations. Investigating the insurance purchase intentions of younger demographics could provide valuable insights into how these groups are preparing for ageing.

Regarding the variables employed to assess the ageing impact, while numerous studies have incorporated demographic data such as age, this approach alone is inadequate for fully encapsulating the influence of the ageing phenomenon. In contrast, variables pertaining to family dynamics (e.g. interpersonal influence), risk factors (e.g. risk perception) and expectations (e.g. anticipated dependence) are more apt for reflecting the societal impact of ageing on individuals. Future research could also explore other potential variables that may reflect the impact of ageing, such as imitation and information elements [79]. The interplay between these aforementioned variables, within the context of ageing, and consumers' insurance purchase intentions presents a promising research trajectory for future studies.

In conclusion, this study suggests the following future research directions: (1) How do cultural and socioeconomic differences between countries like the USA and China affect individual insurance purchase intentions under ageing? (2) How do qualitative or mixed methods approaches reveal the impact of ageing on insurance decisions? (3) What factors influence consumer decision-making for specific insurance types such as community-based health insurance under ageing? (4) How do insurance purchase intentions and behaviours differ between young-old population and older population in response to ageing? (5) How do factors such as interpersonal influence, risk perception, and anticipated dependence affect consumers’ insurance decision-making under ageing?

6 Conclusion

This scoping review comprehensively examined how ageing impacts insurance purchase intentions, identifying key research gaps and future directions. Out of 1082 articles from four major databases (WOS, Scopus, ScienceDirect, and Emerald Insight), 44 were reviewed. The growing scholarly interest underscores the importance of this emerging topic. The analysis reveals a strong reliance on established theories like the TPB, with a dominance of quantitative methods and regression analyses, alongside an emerging use of qualitative and mixed methods, and a need for more systematic reviews. The findings show a significant focus on general health insurance, highlighting the need for research into various insurance subcategories. The study emphasises the need to explore ageing across different cultures and socio-economic contexts, particularly in countries like the USA and China. It also calls for more focus on the 'young-old' demographic and other underrepresented age groups. Furthermore, expanding the analysis to include family dynamics, risk perceptions, and expectations will provide deeper insights into ageing and consumer behaviour in the insurance sector, offering valuable guidance for policy development and market strategies.

This study faced a limitation in analysing age characteristics as some referenced studies did not specify age ranges, necessitating the assumption that participants spanned the entire adult age spectrum, excluding minors. This assumption may lead to an over-representation of younger individuals within the older population, potentially affecting statistical accuracy. Future research should analyse study content to specify target age groups, such as older or young-old. Additionally, due to the limited number of articles, this study did not perform a quality assessment based on JCR quartile rankings. Given the scoping review's broad evidence mapping goal, future research could undertake a systematic review including quality assessment to better explore the current state and gaps in this field.

Ethics approval statement

Review and/or approval by an ethics committee and consent by participants/respondents were not needed for this study because this is a scoping review article and no empirical data was gathered in the course of this research.

Conflict of interest disclosure

The authors have no competing interests to declare that are relevant to the content of this article.

Funding statement

The authors did not receive support from any organization for the submitted work.

Data availability statement

Data sharing is not applicable to this review article since there was no generation or analysis of new data in the study.

CRediT authorship contribution statement

Zhangwei Zheng: Writing – review & editing, Writing – original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization. Hafizuddin-Syah B.A.M: Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization. Hafizah Omar Zaki: Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization. Qin Lingda Tan: Writing – review & editing, Methodology, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors have no competing interests to declare that are relevant to the content of this article.
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References

1 Cox P.R. The Aging of Populations and its Economic and Social Implications 1956 United Nations, Department of Economic and Social Affairs New York
2 Population Reference Bureau (PRB) Population Handbook. Population Reference Bureau 1980 Inc. Washington, D.C
3 National Bureau of Statistics (NBS) China Statistical Yearbook (2021-2022) 2023 National Bureau of Statistics
4 Outreville J.F. Life insurance markets in developing countries J. Risk Insur. 1996 263 278 10.2307/253745
5 Eastman J.K. Iyer R. The impact of cognitive age on Internet use of the elderly: an introduction to the public policy implications Int. J. Consum. Stud. 29 2 2005 125 136 10.1111/j.1470-6431.2004.00424.x
6 Patterson I. Information sources used by older adults for decision making about tourist and travel destinations Int. J. Consum. Stud. 31 2007 528 533 10.1111/j.1470-6431.2007.00609.x
7 Frees E.W. Sun Y. Household life insurance demand: a multivariate two-part model North Am. Actuar. J. 14 2010 338 354 10.1080/10920277.2010.10597595
8 Yuan C. Zhang R.X. Xu S.W. The inverted U-shaped effect of family aging on commercial health insurance consumption: empirical research based on CHFS 2017 Consum. Econ. 36 2020 31 37
9 Wong W.M. Long H. Wang Y. Su W. Residence after retirement: a review and bibliometric analysis Int. J. Consum. Stud. 47 3 2023 936 952 10.1111/ijcs.12875
10 Feng W. Mason A. Population aging in China: challenges, opportunities, and institutions Population in China at the Beginning of the 21st Century 2007 177 196
11 Sika P. Challenges and opportunities related to aging policy to ensure economic growth and innovation in the Slovak republic In Economic and Social Development (Book of Proceedings), 86th International Scientific Conference on Economic and Social 2022 42
12 Bano S. Liu L. Khan A. Dynamic influence of aging, industrial innovations, and ICT on tourism development and renewable energy consumption in BRICS economies Renew. Energy 192 2022 431 442 10.1016/j.renene.2022.04.134
13 Foster L. Active ageing, pensions and retirement in the UK Journal of population ageing 11 2018 117 132 10.1007/s12062-017-9181-7 29899811
14 Huo C. Xiao G. Chen L. The crowding-out effect of elderly support expenditure on household consumption from the perspective of population aging: evidence from China Front. Bus. Res. China 15 2021 1 20 10.1186/s11782-021-00099-5
15 Korniotis G.M. Kumar A. Do older investors make better investment decisions? Rev. Econ. Stat. 93 2011 244 265 10.1162/REST_a_00053
16 Finke M.S. Howe J.S. Huston S.J. Old age and the decline in financial literacy Manag. Sci. 63 2017 213 230 10.1287/mnsc.2015.2293
17 Frank C.C. Seaman K.L. Aging, uncertainty, and decision making—a review Cognit. Affect Behav. Neurosci. 2023 1 15 10.3758/s13415-023-01064-w
18 Chartered Insurance Institute (CII) M86 Personal Insurance study text The Chartered Insurance Institute 2021
19 Zniva R. Weitzl W. It's not how old you are but how you are old: a review on aging and consumer behavior Management Review Quarterly 66 4 2016 267 297 10.1007/s11301-016-0121-z
20 Guido G. Amatulli C. Sestino A. Elderly consumers and financial choices: a systematic review J. Financ. Serv. Market. 25 2020 76 85 10.1057/s41264-020-00077-7
21 Casanova G. Martarelli R. Belletti F. Moreno-Castro C. Lamura G. The impact of long-term care needs on the socioeconomic deprivation of older people and their families: results from mixed-methods scoping review Healthcare vol. 11 2023 MDPI 2593 10.3390/healthcare11182593 18
22 Yagi B.F. Luster J.E. Scherer A.M. Farron M.R. Smith J.E. Tipirneni R. Association of health insurance literacy with health care utilization: a systematic review J. Gen. Intern. Med. 2022 1 15 10.1007/s11606-021-06819-0
23 Munn Z. Peters M.D. Stern C. Tufanaru C. McArthur A. Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach BMC Med. Res. Methodol. 18 2018 1 7 10.1186/s12874-018-0611-x 29301497
24 Arksey H. O'Malley L. Scoping studies: towards a methodological framework Int. J. Soc. Res. Methodol. 8 2005 19 32 10.1080/1364557032000119616
25 Norsworthy C. Jackson B. Dimmock J.A. Advancing our understanding of psychological flow: a scoping review of conceptualizations, measurements, and applications Psychol. Bull. 147 8 2021 806 10.1037/bul0000337 34898235
26 Pham M.T. Rajić A. Greig J.D. Sargeant J.M. Papadopoulos A. McEwen S.A. A scoping review of scoping reviews: advancing the approach and enhancing the consistency Res. Synth. Methods 5 4 2014 371 385 10.1002/jrsm.1123 26052958
27 Tricco A.C. Lillie E. Zarin W. O'Brien K.K. Colquhoun H. Levac D. Moher D. Peters M.D.J. Horsley T. Weeks L. Hempel S. Akl E.A. Chang C. McGowan J. Stewart L. Hartling L. Aldcroft A. Wilson M.G. Garritty C. …Straus S.E. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation Ann. Intern. Med. 169 2018 467 473 10.7326/M18-0850 30178033
28 Peters M.D.J. Marnie C. Tricco A.C. Pollock D. Munn Z. Alexander L. McInerney P. Godfrey C.M. Khalil H. Updated methodological guidance for the conduct of scoping reviews JBI Evidence Implementation 19 2021 3 10 10.1097/XEB.0000000000000277 33570328
29 United Nations (UN) World Population Prospects 2022 2023 UN Department of Economic and Social Affairs
30 Organisation for Economic Co-operation and Development (OECD) The survey of adult skills: reader's companion OECD Skills Studies third ed. 2019 OECD Publishing Paris
31 Mellor J.M. Long-term care and nursing home coverage: are adult children substitutes for insurance policies? J. Health Econ. 20 2001 527 547 10.1016/S0167-6296(01)00078-9 11463187
32 Dong H. Kouyate B. Cairns J. Mugisha F. Sauerborn R. Willingness‐to‐pay for community‐based insurance in Burkina Faso Health Econ. 12 2003 849 862 10.1002/hec.771 14508869
33 Hanoch Y. Rice T. Can limiting choice increase social welfare? The elderly and health insurance Milbank Q. 84 2006 37 73 10.1111/j.1468-0009.2006.00438.x 16529568
34 Taylor A.J. Ward D.R. Consumer attributes and the UK market for private medical insurance Int. J. Bank Market. 24 2006 444 460 10.1108/02652320610712076
35 Löckenhoff C.E. Carstensen L.L. Aging, emotion, and health-related decision strategies: motivational manipulations can reduce age differences Psychol. Aging 22 2007 134 10.1037/0882-7974.22.1.134 17385990
36 Ying X.H. Hu T.W. Ren J. Chen W. Xu K. Huang J.H. Demand for private health insurance in Chinese urban areas Health Econ. 16 2007 1041 1050 10.1002/hec.1206 17199233
37 Costa-Font J. Rovira-Forns J. Who is willing to pay for long-term care insurance in Catalonia? Health Pol. 86 2008 72 84 10.1016/j.healthpol.2007.09.011
38 Costa-Font J. Font M. Does 'early purchase' improve the willingness to pay for long-term care insurance? Appl. Econ. Lett. 16 2009 1301 1305 10.1080/13504850701720171
39 Rice T. Hanoch Y. Cummings J. What factors influence seniors' desire for choice among health insurance options? Survey results on the Medicare prescription drug benefit Health Econ. Pol. Law 5 2010 437 457 10.1017/S1744133109990272
40 Szrek H. Bundorf M.K. Age and the purchase of prescription drug insurance by older adults Psychol. Aging 26 2011 308 10.1037/a0023169 21534689
41 Shafie A.A. Hassali M.A. Willingness to pay for voluntary community-based health insurance: findings from an exploratory study in the state of Penang, Malaysia Soc. Sci. Med. 96 2013 272 276 10.1016/j.socscimed.2013.02.045 23528670
42 Yeung F. Lai K.K. Lee Y.P. Effect of affordability on insurance purchase in Hong Kong 2013 Sixth International Conference on Business Intelligence and Financial Engineering 2013 IEEE 324 328
43 Nosi C. D'Agostino A. Pagliuca M.M. Pratesi C.A. Saving for old age: longevity annuity buying intention of Italian young adults Journal of Behavioral and Experimental Economics 51 2014 85 98 10.1016/j.socec.2014.05.001
44 Tennyson S. Yang H.K. The role of life experience in long-term care insurance decisions J. Econ. Psychol. 42 2014 175 188 10.1016/j.joep.2014.04.002
45 Costa-Font J. Courbage C. Crowding out of long‐term care insurance: evidence from European expectations data Health Econ. 24 2015 74 88 10.1002/hec.3148 25760584
46 Zhang C. Children, old-age support and pension in rural China China Agric. Econ. Rev. 7 2015 405 420 10.1108/CAER-01-2014-0003
47 Broyles I.H. Sperber N.R. Voils C.I. Konetzka R.T. Coe N.B. Van Houtven C.H. Understanding the context for long-term care planning Med. Care Res. Rev. 73 2016 349 368 10.1177/1077558715614480 26553887
48 Guillemette M.A. Martin T.K. Cummings B.F. James R.N. Determinants of the stated probability of purchase for longevity insurance Geneva Pap. Risk Insur. - Issues Pract. 41 2016 4 23 10.1057/gpp.2015.26
49 Lin H. Prince J.T. Determinants of private long‐term care insurance purchase in response to the partnership program Health Serv. Res. 51 2016 687 703 10.1111/1475-6773.12353 26303435
50 Nosratnejad S. Rashidian A. Mehrara M. Jafari N. Moeeni M. Babamohamadi H. Factors influencing basic and complementary health insurance purchasing decisions in Iran: analysis of data from a national survey World Med. Health Pol. 8 2016 179 196 10.1002/wmh3.187
51 Price M.M. Crumley-Branyon J.J. Leidheiser W.R. Pak R. Effects of information visualization on older adults' decision-making performance in a medicare plan selection task: a comparative usability study JMIR human factors 3 2016 e5106 10.2196/humanfactors.5106
52 Reid R.O. Deb P. Howell B.L. Conway P.H. Shrank W.H. The roles of cost and quality information in Medicare Advantage plan enrollment decisions: an observational study J. Gen. Intern. Med. 31 2016 234 241 10.1007/s11606-015-3467-3 26282952
53 Ampaw S. Nketiah-Amponsah E. Owoo N.S. Gender perspective on life insurance demand in Ghana Int. J. Soc. Econ. 45 2018 1631 1646 10.1108/IJSE-03-2017-0120
54 Sowa P.M. Kault S. Byrnes J. Ng S.K. Comans T. Scuffham P.A. Private health insurance incentives in Australia: in search of cost-effective adjustments Appl. Health Econ. Health Pol. 16 2018 31 41 10.1007/s40258-017-0338-6
55 Wang Q. Zhou Y. Ding X. Ying X. Demand for long-term care insurance in China Int. J. Environ. Res. Publ. Health 15 2018 6 10.3390/ijerph15010006
56 Aziz S. Husin M.M. Hussin N. Afaq Z. Factors that influence individuals' intentions to purchase family takaful mediating role of perceived trust Asia Pac. J. Mark. Logist. 31 2019 81 104 10.1108/APJML-12-2017-0311
57 Paton Schmidt A. The impact of cognitive style, consumer demographics and cultural values on the acceptance of Islamic insurance products among American consumers Int. J. Bank Market. 37 2019 492 506 10.1108/IJBM-02-2018-0033
58 Tolani S. Rao A. Worku G.B. Osman M. System and neural network analysis of intent to buy and willingness to pay insurance premium Manag. Finance 45 2019 147 168 10.1108/MF-04-2018-0156
59 Dragos S.L. Dragos C.M. Muresan G.M. From intention to decision in purchasing life insurance and private pensions: different effects of knowledge and behavioural factors Journal of Behavioral and Experimental Economics 87 2020 101555 10.1016/j.socec.2020.101555
60 Ondruška T. Pastorakova E. Brokešová Z. Determinants of individual life-related insurance consumption: the case of the Slovak republic Ekonomický časopis 68 2020 846 863 10.31577/ekoncas.2020.08.05
61 Xu X. Zhang L. Chen L. Wei F. Does COVID-2019 have an impact on the purchase intention of commercial long-term care insurance among the elderly in China? Healthcare vol. 8 2020 MDPI 126 10.3390/healthcare8020126
62 Colón-Morales C.M. Giang W.C. Alvarado M. Informed decision-making for health insurance enrollment: survey study JMIR Formative Research 5 2021 e27477 10.2196/27477
63 Eling M. Ghavibazoo O. Hanewald K. Willingness to take financial risks and insurance holdings: a European survey Journal of Behavioral and Experimental Economics 95 2021 101781 10.1016/j.socec.2021.101781
64 Rizwan S. Al-Malkawi H.A. Gadar K. Sentosa I. Abdullah N. Impact of brand equity on purchase intentions: empirical evidence from the health takāful industry of the United Arab Emirates ISRA International Journal of Islamic Finance 13 2021 349 365 10.1108/IJIF-07-2019-0105
65 Tam L. Tyquin E. Mehta A. Larkin I. Determinants of attitude and intention towards private health insurance: a comparison of insured and uninsured young adults in Australia BMC Health Serv. Res. 21 2021 1 11 10.1186/s12913-021-06249-y 33388053
66 Wang Q. Abiiro G.A. Yang J. Li P. De Allegri M. Preferences for long-term care insurance in China: results from a discrete choice experiment Soc. Sci. Med. 281 2021 114104 10.1016/j.socscimed.2021.114104
67 Wang Q. Wang J. Gao F. Who is more important, parents or children? Economic and environmental factors and health insurance purchase N. Am. J. Econ. Finance 58 2021 101479 10.1016/j.najef.2021.101479
68 Kai Y. Zhujun K. Zhijie C. Xiaoting S. Wanyue T. Social learning? Conformity? Or comparison?—an empirical study on the impact of peer effects on Chinese seniors' intention to purchase travel insurance Tourism Manag. Perspect. 38 2021 100809 10.1016/j.tmp.2021.100809
69 Yeh S.C.J. Wang W.C. Chou H.C. Chen S.H.S. Private long-term care insurance decision: the role of income, risk propensity, personality, and life experience Healthcare vol. 9 2021 MDPI 102 10.3390/healthcare9010102
70 Choe Y. Kim H. Choi Y. Willingness to pay for travel insurance as a risk reduction behavior: health-related risk perception after the outbreak of COVID-19 Service Business 16 2022 445 467 10.1007/s11628-022-00479-8
71 Li D. Lv J. Jing L. Cao C. Shi Y. Exploring the intention of middle-aged and elderly consumers to participate in inclusive medical insurance IEEE Access 10 2022 71398 71413 10.1109/ACCESS.2022.3187711
72 He A.J. Qian J. Chan W.S. Chou K.L. Willingness to purchase hypothetical private long-term care insurance plans in a super-ageing society: evidence from Hong Kong J. Aging Soc. Pol. 2023 1 26 10.1080/08959420.2023.2182084
73 Wu G. Gong J. Investigating the intention of purchasing private pension scheme based on an integrated FBM-UTAUT model: the case of China Front. Psychol. 14 2023 1136351 10.3389/fpsyg.2023.1136351
74 Hu F. Wen J. Phau I. Ying T. Aston J. Wang W. The role of tourism in healthy aging: an interdisciplinary literature review and conceptual model J. Hospit. Tourism Manag. 56 2023 356 366 10.1016/j.jhtm.2023.07.013
75 Population Reference Bureau 2022 World Population Data Sheet 2023 U.S. Population Reference Bureau
76 Wang Z. Wei L. Zhang X. Qi G. Impact of demographic age structure on energy consumption structure: evidence from population aging in mainland China Energy 273 2023 127226 10.1016/j.energy.2023.127226
77 Zhu P. Hu J. Li X. Zhu Q. Using blockchain technology to enhance the traceability of original achievements IEEE Trans. Eng. Manag. 70 5 2021 1693 1707 10.1109/TEM.2021.3066090
78 Cai Y. Ke W. Cui E. Yu F. A deep recommendation model of cross-grained sentiments of user reviews and ratings Inf. Process. Manag. 59 2 2022 102842 10.1016/j.ipm.2021.102842
79 Zhu P. Miao C. Wang Z. Li X. Informational cascade, regulatory focus and purchase intention in online flash shopping Electron. Commer. Res. Appl. 62 2023 101343 10.1016/j.elerap.2023.101343
