Investigation of the Antecedents of Personal Saving Behavior: A Systematic Literature Review Using TCM-ADO Framework

Abstract

This paper reviews the current research landscape on Personal Saving Behavior, focusing on its antecedents and outcomes. Using bibliographic analysis of publication trends—highlighting productive authors, journals, countries, and keywords—the literature is synthesized. A framework-based systematic review is conducted to understand factors influencing saving behavior and its effects, employing the TCM framework to analyze theory, context, and methods across selected studies. Additionally, the ADO framework is used to discuss antecedents, decisions, and outcomes related to personal saving behavior. The review consolidates 112 articles from 2000 to 2025, grouping unique antecedents into nine categories. It also examines how specific antecedents positively or negatively impact saving decisions and outcomes. Finally, using the TCM and ADO frameworks, the study identifies research gaps and discusses future directions, especially from the perspectives of behavioral economics and critical incidents.

1. Introduction

Saving behavior involves setting aside a portion of one’s current income for future needs, rather than spending it all on current consumption. This factor also contributes to economic growth and stability in people’s lives by providing access to resources that can be invested in national priorities. The significance of savings in developing a country’s financial and economic systems has been well-established for a considerable period (Athukorala & Suanin, 2024). Saving enhances the independence of individuals, households, businesses, financial institutions, and the overall national economy. Households should allocate funds for two primary purposes: to reduce the effects of unexpected events, including natural disasters, job loss, health issues, and disabilities, and to make informed investment decisions. The Organisation for Economic Co-operation and Development (OECD 2016) considers a prudent financial strategy beneficial, as it enables people to handle unforeseen emergencies efficiently and creates opportunities for launching investment initiatives. A personal savings account enhances one’s sense of security and reduces anxiety by offering a financial safety net (Ouyang et al., 2025). However, the focal point is that some people save and use it to invest, earning a return on their investment, while others do not; it is a paradox. Thus, saving behavior is affected by several factors. This can be answered by the parlance of “What”, “Who”, and “Why”. What is the person trying to save? Are they saving money for investments, retirement, or unexpected expenses?

It is also essential to consider who is more likely to save: people with greater work experience or those who have just started their careers. It is commonly believed that individuals with higher incomes often have larger savings accounts because they can better allocate a portion of their earnings. Some believe that gender and age impact saving (Zainal Alam et al., 2023). Why do they want to save? Market environment shifts, changing social attitudes, and evolving consumer habits, combined with the introduction of new financial products, can all impact and alter savings patterns (Gargano & Rossi, 2024).

Understanding saving behavior is essential, as it can result in poor saving decisions or the selection of inappropriate institutions or instruments. When it comes to life savings, the stakes are considerably higher due to the amplified risks involved. Additionally, it is important to note that, in many developed countries, savings and investment choices have typically been viewed as private and confidential matters. The household sector typically contributes significant savings in developing nations (Aktas et al., 2012; Burney & Khan, 1992). Therefore, when formulating policies to encourage saving and investing, it is crucial to understand the characteristics of household saving behavior (Muradoglu & Taskin, 1996). This perspective seeks an investment mechanism that connects economic growth to savings.

According to Smyth et al. (1993), saving is typically viewed as a flow concept calculated by subtracting personal consumption expenses from disposable income. This definition identifies saving as a portion of income not immediately consumed and that can accrue interest or alternative returns during the saving period. Lunt and Livingstone (1991) state that saving is crucial for regulating consumption patterns throughout a lifetime. According to Ramanathan (1969), saving is the change in the value of net assets (assets adjusted for liabilities) during a specific period. In addition to the financial aspects of saving, the concept of “saving behavior” also considers psychological elements such as emotions, fear, and risk perceptions (Ranyard, 2017). According to Giné et al. (2018), understanding the factors that influence behavioral change is crucial, as it may impact how policies are designed and how they improve the welfare of individuals and society.

Process theories provide a more comprehensive explanation of behavior by including both automatic reactions and reflective aspects such as intention (Alós-Ferrer & Strack, 2014; Hofmann et al., 2008). They suggested that behavior is shaped by two systems: impulsive and reflective. Controlled procedures, deliberate thought, and goals make up the reflective system. In contrast, automatic tendencies and associative processes are part of the impulsive system. Impulsive purchasing is one impulsive behavior that can greatly influence saving habits. Dual-process theories of saving behavior suggest that a person’s desire to save money often conflicts with their impulse to spend, impacting their financial decisions.

However, the effects of these systems might depend on other factors, which can also indirectly affect behavior through the related systems (Strack & Deutsch, 2006). In particular, those with higher self-control are more likely to have reflective processes guide their actions, as shown by the influence of intention on behavior (Allom et al., 2018). Similarly, savings behavior and individual savings outcomes need to be distinguished. Personal savings result from saving habits and likely reflect a person’s ability to manage finances, which depends on various factors such as socialization and peer influence. In summary, various academic fields such as economics (Zhu & Chou, 2018), social psychology (Brown & Taylor, 2006), and marketing (DeVaney et al., 2007) have extensively studied saving behavior at both the household and aggregate levels, highlighting its importance for economic stability and personal finance.

Therefore, it can be concluded that exploring the causes and effects of savings behavior is essential for several reasons. Many countries have shifted from traditional defined-benefit plans and pensions to defined-contribution plans like 401(k) and private social security accounts, placing the responsibility for savings on individual workers. This significant change underscores the importance of proactive financial planning and personal investment strategies for ensuring a stable future (e.g., Cronqvist & Thaler, 2004; Poterba et al., 2007). Understanding why some people choose to save while others do not is becoming increasingly crucial as autonomy over finances increases. Current empirical models only partially explain what discourages people from saving.

In summary, this article explores an untapped area: a framework-based systematic review that integrates theories and concepts from multiple disciplines while focusing on individual saving behavior.

The article addresses the following research questions.

• What are the temporal trends and publication patterns of the published research on saving behavior over the years?
• Identify the authors significantly contributing to the study of individual saving behavior and what they bring to the field
• What are the emergent themes based on keyword analysis in the research landscape of saving behavior?
• What are the antecedents, decisions, and outcomes related to individual saving behavior, along with their theoretical foundations?
• What are the key contextual settings within the research field of individual saving behavior?
• What are the major research methods used to collect and analyze data in the reviewed research papers?

Furthermore, our research has implications for policy design and illustrates how sensitive savings behavior is to public policy intervention. Below, we outline how our research adds to the current understanding of saving behavior.

Several review articles summarize studies focused on household saving behavior. For example, Jumena et al. (2022) reviewed 124 articles to examine factors influencing saving behavior, which are categorised into 15 factors divided into two groups: external (social influence, macroeconomic conditions) and internal (financial literacy, psychological features). The results indicate that over the past decade, research on saving behavior has been conducted in 185 countries across ten regions, utilising 18 analytical methods. It highlights how legislative changes and financial education can influence individuals’ savings. Brochado and Mendes (2021) conducted a systematic review covering 183 articles that employed hybrid methodologies, including descriptive, semantic network, and narrative analyses. Their lexical analysis of the abstracts revealed 11 distinct themes, including financial education, financial literacy, and personal finance, among others. Another review examined 12 papers (Zainudin & Shaharuddin, 2022). The study, an experimental review examining savings by and for the poor, focused on developing nations (Karlan et al., 2014). This article provides a comprehensive overview of research on saving behavior since 2000, emphasizing the importance of examining both the factors that lead to and the effects of saving behavior in the literature.

Many researchers are interested in bibliometric analysis of published research. This approach utilizes statistics to organize and map ideas and trends within a large body of literature (Hemrajani et al., 2023). It measures research outcomes, effectiveness, and significance. It also examines the influence of authors, journals, organizations, and documents within a specific field (Chiu & Ho, 2007). According to our knowledge, there are no studies based on bibliometric analysis of focused individual or household saving behavior; however, there are existing studies mainly centered on financial literacy and financial behavior (Ingale & Paluri, 2022; Vijay Kumar & Senthil Kumar, 2023; Goyal & Kumar, 2021) and personal financial planning behavior or retirement planning (Tomar et al., 2021; Rani & Goyal, 2024). Thus, this research aims to conduct a comprehensive study focusing on bibliometric and systematic literature reviews on individual saving behavior. This review provides a comprehensive analysis of the articles about individual saving behavior retrieved from the Scopus database. In particular, it offers several significant contributions to the existing body of knowledge:

• We propose a hybrid strategy that includes a comprehensive literature review and bibliometric analysis, allowing us to present the knowledge bases and outline a research agenda for the future in the field of “saving behavior.”
• Identifies both micro- and macro-level antecedents based on research from 2020 to 2025, unlike earlier reviews that only examined these factors in a fragmented manner.
• This study employs a systematic review method based on two frameworks: the TCM (Theory, Context, and Methods) framework by Paul et al. (2024) and the ADO (Antecedents, Decisions, and Outcomes) framework by Paul and Benito (2018). This enhances the organization and structure of the review concerning saving behavior research, in contrast to the (semi-)structured review methods used in previous studies.
• Unlike previous reviews that relied on a single-level categorization, this comprehensive analysis introduces a robust multi-level categorization of antecedents. This innovative approach provides deeper insights and greater clarity, enhancing the overall understanding of the subject matter.
• Our review highlights significant research gaps that future studies should address using the TCM and ADO frameworks.

This research used a hybrid review approach that combines bibliometric analysis with a framework-based method, providing a systematic way to synthesize findings from the studies reviewed. This method is based on the ADO and TCM frameworks, which are discussed further.

This review is organized as follows: The upcoming section explains the systematic literature review methods by Paul and Criado (2020) and outlines the PRISMA (The Preferred Reporting Items for Systematic reviews and Meta-Analyses protocol used to identify and select articles. Section 3 presents statistics on publication years and the journals in which they appear. The subsequent section uses the TCM framework to analyze the theories and methods in the reviewed studies. In Section 5, we discuss the findings related to ADO through the ADO framework. The following section explores implications for government authorities, policymakers, and institutions. Section 7 summarizes the results and suggests directions for future research. Lastly, the concluding section provides a summary of the study’s main outcomes.

2. Methodology

Systematic literature reviews serve several purposes: to prevent redundant research that does not significantly advance knowledge, to inform the design of new studies aimed at making meaningful contributions, and to substantiate claims of originality by contrasting existing and new insights (Paul et al., 2021). Paul and Criado (2020) categorized systematic reviews into four primary types.

• Domain-based reviews concentrate on examining knowledge pertaining to the topic, split into five categories:

  (a)

  Structured reviews aim to synthesize theories, constructs, contexts, and prevalent methods documented in literature on specific research themes (Canabal & White, 2008).

  (b)

  Framework-based reviews rely on established models like the ADO, TCM, or the 7P framework to structure the review and extract valuable insights related to the research subject (Lim, 2025; Lim et al., 2021; Paul & Benito, 2018). Authors might develop their own frameworks for the reviewing process.

  (c)

  Bibliometric reviews emphasize statistics regarding publication years, journals, and authors (Donthu et al., 2021).

  (d)

  Hybrid reviews integrate elements from various domain-specific review types (Lim et al., 2021; Paul et al., 2024).

  (e)

  Reviews dedicated to theory development aim to formulate theoretical models, hypotheses, or propositions for future validation (Paul, 2020; Sharma & Kushwah, 2025).

• Theory-based reviews offer a comprehensive examination of how different theories influence a particular research area (Gilal et al., 2020, 2023; Lim et al., 2021).
• Meta-analytical reviews evaluate effect sizes of various variables, highlighting the strength of their relationships using statistical techniques such as the weighted average method (Rana & Paul, 2020; Tang & Buckley, 2020).
• Method-focused reviews concentrate on the current knowledge surrounding specific methods employed in the field (Jia et al., 2012).

This article introduces a hybrid review combining bibliometric analysis with a framework-based approach, as discussed by Chakma et al. (2021). This method offers a systematic way to synthesize the distinct findings from the reviewed studies. Based on Lim et al. (2021), we combine the ADO framework by Paul and Benito (2018) with the TCM framework from Paul et al. (2024) to unify key approaches and results. This combined view provides a broader understanding of the factors influencing and resulting from saving behavior, while also addressing the limitations of each individual framework (Lim et al., 2022, 2021).

Review Approach

This review paper follows the PRISMA protocol, as per Figure 1, which involves four stages: identification, screening, eligibility assessment, and inclusion (Moher et al., 2009), to analyze the articles included in the review. Figure 1 presents the flow diagram summarizing each stage. During the identification phase, journal articles were searched in the Scopus database, covering publications from 2000 to 5 May 2025. A 25-year review period provides a comprehensive view of the field’s development. The search was limited to journal articles indexed in Scopus, ensuring the quality of included studies and capturing a significant portion of peer-reviewed literature. Keywords were chosen based on the review’s scope. Since the primary focus is on understanding household saving behavior, all selected keywords are combined with the term ‘saving behavior’. The initial keyword selection was based on the authors’ subject expertise and also included terms from existing review articles in related fields. To ensure comprehensive coverage, the authors added related keywords such as “saving behavior”, “personal saving behavior”, “household saving behavior”, and “individual saving behavior”. The final list of keywords was finalized through expert consultation and discussions among the authors.

The keyword search was performed in the title, abstract, and keywords sections of the paper. An initial total of 2649 articles were retrieved. In the next step, the search was narrowed to include only journal articles published in English, focusing on Economics, econometrics, finance, Social Sciences, Business Management, and Accounting. Using these criteria, 425 articles were selected from the Scopus database. During screening, these articles were checked for duplicates, resulting in the removal of 29 duplicates. The full texts of the remaining 396 articles were then downloaded from Google Scholar or publishers’ websites. In the eligibility stage, these full texts were manually reviewed based on specific inclusion criteria:

• The study aims to understand individual saving behavior.
• Articles on energy-saving behavior or government saving were excluded. The authors manually reviewed 396 articles. Using the inclusion criteria noted earlier, along with discussions and consensus among the authors, 112 articles were chosen for this review. During the inclusion process, a detailed content analysis was conducted, focusing on extracting, coding, and organizing data from these articles.

The final dataset comprises 112 English-language journal articles published between 2000 and 2025, primarily in the fields of Economics, econometrics and finance, Social Sciences, and business management and accounting. This review follows a protocol that assesses (a) bibliometric features, (b) the theories, contexts, and methods employed, and (c) ADO of individual saving behavior. The following sections present the results of the detailed content analysis.

3. Bibliographic Analysis

To assess the productivity and impact of academic papers, the scientific community employs essential metrics, including the total number of documents, citation counts, and average citations per paper. Total link strength reflects an item’s connectivity, while the global citation score indicates its overall influence. Collectively, these metrics offer valuable insights into the significance of academic research.

All these metrics are regarded as significant (Hirsch, 2005). As a result, the authors examined relevant documents related to research on saving behavior using these indicators to determine the most influential studies based on the Scopus database of 2649 articles. The results are shown in Table 1, Table 2, Table 3, Table 4 and Table 5.

3.1. Authors Who Are Highly Prolific in Saving Behavior Research

A comprehensive analysis identified a total of 992 authors, from which a co-authorship map was developed, focusing on those with a minimum of two publications. Among these authors, 47 met the specified criteria for further examination. As presented in Table 1, the most productive and highly cited authors within the research field include Patti J. Fisher, Michael S. Gutter, Sherman D. Hanna, Charles Y. Horioka, Cazilia Loibl, and Annkathrin Wahbi, each contributing three publications. Notably, Charles Y. Horioka stands out as one of the most cited authors. It is also important that, of the six authors with three publications, only Horioka appears in the top 10 list for most citations (1471) in the field of saving behavior.

Table 1. Highly productive authors.

Most Productive Authors		Most Cited Authors
Author	Total Publications	Author	Total Citations
Fisher Patti J.	3	Horioka Charles Y.	1471
Gutter Michael S.	3	Lensink Robert	132
Hanna Sherman D.	3	Price Debora	130
Horioka Charles Y.	3	Börsch Supan A	128
Loibl Cäzilia	3	Cook Christopher J.	124
Wahbi Annkathrin	3	Mauldin Teresa	123
Adami Roberta	2	Cho Soo Hyun	105
Alessie Rob	2	Kim Jinhee	93
Athukorala Prema-Chandra	2	Gough Orla	90
Benartzi Shlomo	2	Hermansson C	73

Table 1. Highly productive authors.

Most Productive Authors		Most Cited Authors
Author	Total Publications	Author	Total Citations
Fisher Patti J.	3	Horioka Charles Y.	1471
Gutter Michael S.	3	Lensink Robert	132
Hanna Sherman D.	3	Price Debora	130
Horioka Charles Y.	3	Börsch Supan A	128
Loibl Cäzilia	3	Cook Christopher J.	124
Wahbi Annkathrin	3	Mauldin Teresa	123
Adami Roberta	2	Cho Soo Hyun	105
Alessie Rob	2	Kim Jinhee	93
Athukorala Prema-Chandra	2	Gough Orla	90
Benartzi Shlomo	2	Hermansson C	73

3.2. Highly Productive Documents on Saving Behavior Research

Over the decades, numerous influential articles have appeared across various journals. A common method to identify these key papers is by examining their citation counts (Merigó et al., 2015). Llanos-Herrera and Merigo (2019) also noted that citation numbers can indicate an article’s impact. As a result, Table 2 lists the most cited articles, each with at least 50 citations. The top article is “Save More Tomorrow: Using Behavioral Economics to Increase Employee Saving” (Thaler & Benartzi, 2004) from the Journal of Political Economy, which has accumulated over 1448 citations, making it the most cited, followed by “Mutual Funds Flows and Performance in Rational Markets” (Berk & Green, 2004).

Table 2. Most cited articles with respective author, title and journal.

Year	Author	Global Citation Score	Document Title	Journal
2004	Thaler Richard H	1448	Save more tomorrow: Using behavioral economics to increase employee saving	Journal of Political Economy
2004	Berk, Jonathan B	1048	Mutual fund flows and performance in rational markets	Journal of Political Economy
2002	Glaeser, Edward L	926	An economic approach to social capital	Economic Journal
2001	Appadurai, A.	566	Deep democracy: Urban governmentality and the horizon of politics	Environment and Urbanization
2004	Dynan, Karen E	490	Do the rich save more?	Journal of Political Economy
2007	Puri, Manju	459	Optimism and economic choice	Journal of Financial Economics
2005	French, Eric	396	The effects of health, wealth, and wages on labour supply and retirement behaviour	Review of Economic Studies
2009	Ersner-Hershfield	313	Don’t stop thinking about tomorrow: Individual differences in future self-continuity account for saving	Judgment and Decision Making
2003	Zimmerman, Frederick J.	299	Asset smoothing, consumption smoothing and the reproduction of inequality under risk and subsistence constraints	Journal of Development Economics
2005	Fankhauser, Samuel	263	On climate change and economic growth	Resource and Energy Economic

Table 2. Most cited articles with respective author, title and journal.

Year	Author	Global Citation Score	Document Title	Journal
2004	Thaler Richard H	1448	Save more tomorrow: Using behavioral economics to increase employee saving	Journal of Political Economy
2004	Berk, Jonathan B	1048	Mutual fund flows and performance in rational markets	Journal of Political Economy
2002	Glaeser, Edward L	926	An economic approach to social capital	Economic Journal
2001	Appadurai, A.	566	Deep democracy: Urban governmentality and the horizon of politics	Environment and Urbanization
2004	Dynan, Karen E	490	Do the rich save more?	Journal of Political Economy
2007	Puri, Manju	459	Optimism and economic choice	Journal of Financial Economics
2005	French, Eric	396	The effects of health, wealth, and wages on labour supply and retirement behaviour	Review of Economic Studies
2009	Ersner-Hershfield	313	Don’t stop thinking about tomorrow: Individual differences in future self-continuity account for saving	Judgment and Decision Making
2003	Zimmerman, Frederick J.	299	Asset smoothing, consumption smoothing and the reproduction of inequality under risk and subsistence constraints	Journal of Development Economics
2005	Fankhauser, Samuel	263	On climate change and economic growth	Resource and Energy Economic

3.3. Key Journals in Savings Behavior Research

Many journals publish articles on saving behavior, showing the field’s significant growth and wide range of academic sources. Research on saving behavior spans multiple academic journals, including psychology and economics. Table 3 highlights the top 10 most productive journals ranked by citation counts. A citation map was also created for analysis, using a threshold of at least two documents and two citations per source.

Out of 263 sources examined, 62 satisfy the threshold criteria. Table 3 indicates that the Journal of Political Economy, published by the University of Chicago Press, is the most prominent journal with 34368 citations and four total link strengths. Although it has only seven documents related to saving behavior, it remains the most cited journal in the saving behavior field, reaffirming its leading position. The Economic Journal from Oxford University Press ranks second, comprising 49 documents, 1273 citations, and two total link strengths.

Table 3. Key journals in saving behavior research. Total link strength reflects the collaborative effort of journals (sources) in the saving behavior field.

Source	Documents	Citations	Total Link Strength
Journal of Political Economy	7	3468	4
Economic Journal	9	1273	2
Journal of Development Economics	11	661	8
Review of Economic Studies	3	489	0
World Bank Economic Review	7	467	12
Judgment and decision making	2	321	2
journal of human resources	6	320	1
Oxford Economic Papers	4	254	9
Journal of Financial Counseling and Planning	7	248	10
Journal of Economic Psychology	8	182	3

Source: The data presented is from journals indexed in Scopus.

Table 3. Key journals in saving behavior research. Total link strength reflects the collaborative effort of journals (sources) in the saving behavior field.

Source	Documents	Citations	Total Link Strength
Journal of Political Economy	7	3468	4
Economic Journal	9	1273	2
Journal of Development Economics	11	661	8
Review of Economic Studies	3	489	0
World Bank Economic Review	7	467	12
Judgment and decision making	2	321	2
journal of human resources	6	320	1
Oxford Economic Papers	4	254	9
Journal of Financial Counseling and Planning	7	248	10
Journal of Economic Psychology	8	182	3

Source: The data presented is from journals indexed in Scopus.

3.4. Key Author Keywords in Saving Behavior Research

Researchers identified 1641 keywords related to saving behavior. To focus on the most relevant ones, they only considered keywords that appeared at least five times in a co-occurrence map. This narrowed the list down to 117 keywords. The authors highlighted the ten most important keywords based on their connections, which are shown in Table 4. Singh and Dhir (2019) explained that keywords that are close together and share similar colors show a stronger similarity than those that are farther apart with different colors.

Table 4. Key author keywords.

Keyword	Occurrences	Total Link Strength
Savings Behavior	276	937
Saving	73	150
Saving Behavior	61	105
Investment	51	199
Savings	48	237
Consumption Behavior	41	163
Income	30	149
Saving Behaviour	29	45
Financial Literacy	25	78
Retirement	25	157

Source: The data is sourced from journals indexed in Scopus.

Table 4. Key author keywords.

Keyword	Occurrences	Total Link Strength
Savings Behavior	276	937
Saving	73	150
Saving Behavior	61	105
Investment	51	199
Savings	48	237
Consumption Behavior	41	163
Income	30	149
Saving Behaviour	29	45
Financial Literacy	25	78
Retirement	25	157

Source: The data is sourced from journals indexed in Scopus.

Major Themes (Cluster Analysis)

Figure 2 illustrates a network visualization of keywords relevant to savings behavior literature, comprising seven interconnected thematic areas and a total of 117 keywords. Central to this visualization is the theme of savings behavior, encompassing concepts such as banking, credit, financial knowledge, and decision-making, particularly in developing contexts like China. Cluster 1, in red, focuses on “Saving Behavior and Retirement and Income,” emphasizing women. Gaining interest from 2015 to 2020, this research examines long-term planning, including pensions and social security, supported by frameworks such as the Life Cycle Hypothesis (Ando & Modigliani, 1963) and the Permanent Income Hypothesis (Friedman, 1957). Cluster 2, shown in green, focuses on “Saving Behavior and Financial Systems,” emphasizing the shift from informal to formal saving sectors. Cluster 3, in dark blue, explores Economic and Geographic Factors, mainly drawing on research from Western economies such as the UK, USA, and Western Europe, starting around 2005. This cluster also covers studies on the influence of microfinance and regional disparities on saving behaviours in developing countries. Cluster 4, in light green, explores “Saving Behavior and Cognitive Factors,” particularly the role of financial knowledge in savings practices. Financial Literacy and Inclusion stress the importance of education and access, thus enhancing decision-making and responsible saving (Becker, 1964; Huston, 2010).

Cluster 5 examines Consumption and Investment Behavior, analyzing how spending and investment intersect with saving tendencies in Western economies (Solow, 1956). Cluster 6 emphasizes Empirical and Methodological Approaches, focusing on theoretical models and regression techniques that support financial behavior research. Lastly, Cluster 7, illustrated in orange, centers on “Saving Behavior and Macroeconomic Conditions,” addressing the impacts of macroeconomic factors on saving behavior. Together, these themes create a concise framework for understanding the complexities of saving behavior.

In conclusion Table 5 highlights the themes and exemplar keywords identified.

Table 5. Themes identified and formation of clusters.

Cluster	Themes Identified	Keywords
Cluster 1 (Red)	Saving behavior and retirement and income	Adult, aged, female, income, pension
Cluster 2 (Green)	Saving behavior and financial system	Banking, credit provision, financial market, financial services, informal sector
Cluster 3 (Dark blue)	Saving behavior and economic and geographic factors	Asia, Eurasia, comparative study, developing world, economic growth
Cluster 4 (Light green)	Saving behavior and cognitive factors	Financial education, financial knowledge, financial literacy, financial planning, household finance
Cluster 5 (Purple)	Saving behavior and consumption and investment behavior	Consumption behavior, household expenditure, household income, income distribution, precautionary savings
Cluster 6 (Light blue)	Saving behavior and empirical and methodological approaches	Decision-making, economic analysis, empirical analysis, methodology, regression analysis
Cluster 7 (Orange)	Saving behavior and macro-economic factors	Capital market, fiscal policy, inflation, macroeconomics, monetary policy

Table 5. Themes identified and formation of clusters.

Cluster	Themes Identified	Keywords
Cluster 1 (Red)	Saving behavior and retirement and income	Adult, aged, female, income, pension
Cluster 2 (Green)	Saving behavior and financial system	Banking, credit provision, financial market, financial services, informal sector
Cluster 3 (Dark blue)	Saving behavior and economic and geographic factors	Asia, Eurasia, comparative study, developing world, economic growth
Cluster 4 (Light green)	Saving behavior and cognitive factors	Financial education, financial knowledge, financial literacy, financial planning, household finance
Cluster 5 (Purple)	Saving behavior and consumption and investment behavior	Consumption behavior, household expenditure, household income, income distribution, precautionary savings
Cluster 6 (Light blue)	Saving behavior and empirical and methodological approaches	Decision-making, economic analysis, empirical analysis, methodology, regression analysis
Cluster 7 (Orange)	Saving behavior and macro-economic factors	Capital market, fiscal policy, inflation, macroeconomics, monetary policy

3.5. Countries That Are Highly Productive in Researching Saving Behaviors

The citation map, which includes countries with at least two documents and one citation each, indicates that out of 84 countries, only 47 meet these criteria.

Table 6. Data sourced from journals indexed in Scopus.

Country	Documents	Citations	Total Link Strength
United States	137	9631	62
United Kingdom	53	1140	31
Netherlands	25	784	18
France	17	560	10
Germany	25	508	24
Australia	19	336	16
China	23	283	15
Italy	22	271	20
Chile	8	189	6
Canada	17	164	8

reveals that the United States leads in both productivity and impact. While Germany has produced many publications, the Netherlands has had a relatively greater influence. Regarding collaboration, the United States, United Kingdom, and Germany possess the most prominent networks.

4. Review of Studies—TCM-Based Framework

Theory

Theories underpin the findings of studies (Lim et al., 2022). Research on saving behaviors and their impact on financial decision-making has been explored through various theoretical frameworks, as shown in Table 6. The reviewed literature on saving behavior employs a total of 22 theories, which are listed in Table 7 alongside their citations and exemplar studies. The most frequently used theories are (a) Life Cycle Hypothesis (24 votes), (b) Permanent Income Hypothesis (eight votes), (c) Behavioral Life Cycle (six votes), and (d) Theory of Planned Behavior (five votes).

Various disciplines have examined savings behavior through different theoretical approaches. Economists typically focus on observable actions, often neglecting or barely addressing the underlying motivations for saving (Wärneryd, 1999). Recent research has incorporated sociological ideas to explore influences from parents and peers (Garrison & Gutter, 2010; Gutter et al., 2010). Other studies take a psychological perspective (DeVaney et al., 2007; Xiao, 2008; Xiao & Anderson, 1997; Xiao & Wu, 2008). The next section offers a detailed discussion of the most common theories used in savings behavior research, organized by their level of prominence.

The life-cycle hypothesis explains how people save and spend across their lifetime, noting that they tend to save during youth and working years, then dissaving in old age or retirement. The absolute income hypothesis states that people’s saving and spending decisions are based on their current income levels. In contrast, the relative income hypothesis emphasizes how income distribution affects individuals’ capacity to save. The permanent income hypothesis, proposed by Friedman (1957), suggests that savings and consumption are driven by ‘permanent income’ rather than short-term income fluctuations. Although these theories focus on income, they overlook personal differences and employment sector variations that affect financial decisions, leading to the development of the Behavioral Life-Cycle Hypothesis (BLC). This model suggests that psychological factors, not just rational calculations, shape saving behaviors. Unlike traditional models assuming individuals aim to smooth consumption based on expected income, the BLC recognizes that mental biases influence financial choices, explaining why many under save for retirement or maintain inconsistent consumption. It underscores the importance of psychology in personal finance, rather than assuming purely logical behavior. Similarly, the Theory of Planned Behavior emphasizes that intentions influence actual saving behavior. It asserts that attitudes, subjective norms, and perceived behavioral control form the basis for behavioral intention. This intention, shaped by one’s attitude toward the behavior, beliefs about social approval, and perceived ease of performing it, can ultimately lead to the behavior itself.

Recent studies highlight the significance of internal factors like values, attitudes, beliefs, knowledge, and perceptions in shaping financial behavior, which differ among individuals (Hira, 2012; Lachance, 2012). Functional Theory of Values, which was tested (Gouveia et al., 2014) illustrating how values serve both psychological and social roles. This theory proposes that values guide behavior through two primary functions, helping to clarify personal decisions, cultural variations, and social interactions. In contrast, Hofstede’s Culture Theory (Hofstede, 2001) offers a framework for cross-cultural communication by demonstrating how societal culture affects members’ values and behaviors, explaining why some countries tend to save more than others. Additionally, Regulatory Focus Theory (Higgins, 1997) links psychological factors to saving objectives, suggesting that ‘promotion-oriented’ individuals aim for achievement, while ‘prevention-oriented’ individuals prioritize safety.

Another important theory that describes saving behavior is the Dual Process theory, initially proposed by William James (1890). It offers a comprehensive view of behavior by combining both reflective and automatic processes (Alós-Ferrer & Strack, 2014; Hofmann et al., 2008). According to this model, behavior arises from two distinct systems: the reflective system, which involves deliberate, controlled thinking, and the impulsive system, which functions automatically. An important impulsive factor affecting saving behavior is buying impulsiveness. Kahneman (2003) described these processing styles as intuition and reasoning. Intuition (or system 1), similar to associative reasoning, is characterized as quick, automatic, and often influenced by strong emotional links. Kahneman (2003) also noted that this reasoning type is rooted in ingrained habits, making change difficult. In contrast, reasoning (or system 2) is slower, more flexible, and depends on conscious judgments and attitudes.

Many theories influenced by social factors, which are exemplified in a book by J. Newman (1996), such as Cognitive Role Theory, which closely resembles Social Cognitive Theory (Bandura, 1989) by highlighting how the social environment affects development. It describes how individuals in committed relationships develop perceptions, role expectations, and behaviors. The theory suggests that taking on new roles prompts individuals to modify their behavior to match the expectations tied to those roles (B. M. Newman & Newman, 2022). Whether these changes are beneficial or detrimental depends on the specific role and context. In addition to psychological and social foundations, institutional theories of saving reveal that institutional processes shape individual and household saving behaviors. Beverly and Sherraden (1999) stressed the importance of institutions, stating that “individual and household saving behavior is shaped by the institutional processes through which saving occurs” (p. 463).

They identified four main institutional factors influencing savings. Later research (Beverly et al., 2003; Sherraden et al., 2003; Sherraden & Barr, 2005) expanded this framework by including seven additional institutional dimensions: access, security, incentives, information, facilitation, expectations, and limits.

Table 7. List of Theories.

Theory	Origin	No. of Articles	Exemplar Studies
Life cycle hypothesis	Ando and Modigliani (1963)	24	(Yoon & Hanna, 2024; Midamba et al., 2024a; Ghosh & Chaudhury, 2023; Khawaja, 2023; Amoah et al., 2024)
Behavioral Life cycle hypothesis	Shefrin and Thaler (1988)	4	(Yoon & Hanna, 2024; Thaler & Benartzi, 2004; Zainal Alam et al., 2023; Ouyang et al., 2025)
Theory of Mental accounting	Thaler (1985)	1	(Yoon & Hanna, 2024)
Theory of Planned Behavior	Ajzen (1991)	6	(Copur & Gutter, 2019; Uy et al., 2024; Núñez-Letamendia et al., 2025; Rahayu et al., 2024; Alshebami & Seraj, 2021)
Prospect Theory	Kahneman and Tversky (1979)	1	(Harahap et al., 2022)
Permanent Income Hypothesis Theory	Friedman (1957)	7	(Khawaja, 2023; Amoah et al., 2024; Matenge et al., 2019; Swasdpeera & Pandey, 2012; Temel Nalın, 2013)
The Functional Theory of Values	Gouveia (1998)	1	(Cruz et al., 2025)
Theory of Values Culture	Hofstede (2001)	1	(Cruz et al., 2025)
Financial Socialization Theory	Gudmunson and Danes (2011)	1	(Lössbroek & Van Tubergen, 2024)
Integrated Behavioral Model	Ajzen and Fishbein (2000)	1	(Lopez et al., 2024)
Unified Theory of Acceptance and use of Technology (UTAUT)	Venkatesh et al. (2003)	1	(Hii et al., 2025)
Time Perspective Theory	Zimbardo (1999)	1	(Trzcińska et al., 2022)
Expected Utility Theory	Bernoulli (2011)	1	(Filipski et al., 2019)
Dual Process Theory	James (1890)	1	(Allom et al., 2018)
Social Cognitive Theory of Self-Regulation,	Bandura (1989)	3	(Copur & Gutter, 2019; Asebedo & Seay, 2018; Lown et al., 2015)
Cognitive Role Theory	Newman (1996)	1	(Grable et al., 2021)
Institutional Theory of Saving Behavior	Beverly and Sherraden (1999)	2	(S. Heckman & Hanna, 2015; Loaba, 2022)
Regulatory Focus theory	Higgins (1997)	1	(Cho et al., 2014)
Absolute Income Hypothesis	Keynes (1936)	1	(Harris et al., 2002)
Relative Income Hypothesis	Duesenberry (1948)	1	(Harris et al., 2002)
Subsistence Consumption Theory	Smith (1776)	1	(Ozcan et al., 2003)

Table 7. List of Theories.

Theory	Origin	No. of Articles	Exemplar Studies
Life cycle hypothesis	Ando and Modigliani (1963)	24	(Yoon & Hanna, 2024; Midamba et al., 2024a; Ghosh & Chaudhury, 2023; Khawaja, 2023; Amoah et al., 2024)
Behavioral Life cycle hypothesis	Shefrin and Thaler (1988)	4	(Yoon & Hanna, 2024; Thaler & Benartzi, 2004; Zainal Alam et al., 2023; Ouyang et al., 2025)
Theory of Mental accounting	Thaler (1985)	1	(Yoon & Hanna, 2024)
Theory of Planned Behavior	Ajzen (1991)	6	(Copur & Gutter, 2019; Uy et al., 2024; Núñez-Letamendia et al., 2025; Rahayu et al., 2024; Alshebami & Seraj, 2021)
Prospect Theory	Kahneman and Tversky (1979)	1	(Harahap et al., 2022)
Permanent Income Hypothesis Theory	Friedman (1957)	7	(Khawaja, 2023; Amoah et al., 2024; Matenge et al., 2019; Swasdpeera & Pandey, 2012; Temel Nalın, 2013)
The Functional Theory of Values	Gouveia (1998)	1	(Cruz et al., 2025)
Theory of Values Culture	Hofstede (2001)	1	(Cruz et al., 2025)
Financial Socialization Theory	Gudmunson and Danes (2011)	1	(Lössbroek & Van Tubergen, 2024)
Integrated Behavioral Model	Ajzen and Fishbein (2000)	1	(Lopez et al., 2024)
Unified Theory of Acceptance and use of Technology (UTAUT)	Venkatesh et al. (2003)	1	(Hii et al., 2025)
Time Perspective Theory	Zimbardo (1999)	1	(Trzcińska et al., 2022)
Expected Utility Theory	Bernoulli (2011)	1	(Filipski et al., 2019)
Dual Process Theory	James (1890)	1	(Allom et al., 2018)
Social Cognitive Theory of Self-Regulation,	Bandura (1989)	3	(Copur & Gutter, 2019; Asebedo & Seay, 2018; Lown et al., 2015)
Cognitive Role Theory	Newman (1996)	1	(Grable et al., 2021)
Institutional Theory of Saving Behavior	Beverly and Sherraden (1999)	2	(S. Heckman & Hanna, 2015; Loaba, 2022)
Regulatory Focus theory	Higgins (1997)	1	(Cho et al., 2014)
Absolute Income Hypothesis	Keynes (1936)	1	(Harris et al., 2002)
Relative Income Hypothesis	Duesenberry (1948)	1	(Harris et al., 2002)
Subsistence Consumption Theory	Smith (1776)	1	(Ozcan et al., 2003)

Contexts

The context of a study includes its specific circumstances and the surrounding economic or political environment (Lim et al., 2021). In this review, studies are categorized by country.

Table 8. List of countries.

Contexts	No. of Articles	Contexts	No. of Articles
United States	18	Australia	1
India	8	Belarus	1
China	6	Botswana	1
Indonesia	6	Brazil	1
Malaysia	6	Britain	1
Vietnam	4	Canada	1
Japan	3	Ethiopia	1
Saudi Arabia	3	Gorgia	1
Thailand	3	Greece	1
Turkey	3	Gulf Cooperation Council (GCC) region	1
United Kingdom	3	Hongkong	1
Germany	2	Iran	1
Ghana	2	Ireland	1
Korea	2	New Zealand	1
Netherland	2	European Union (9 new members)	1
Pakistan	2	Philippines	1
Portugal	2	Poland	1
Singapore	2	Russia	1
Spain	2	Rwanda	1
Sweden	2	South Africa	1
Uganda	2	Taiwan	1
Chile	1	UAE	1
West Africa	1	Ukraine	1

The sum total of above countries is 108 as one study did not mention the context, one was experiment-based and two were conceptual based.

shows the contexts of these studies. Most studies, totaling 18 articles, were conducted in the United States, followed by India with eight articles. China, Indonesia, and Malaysia each contributed six articles. One study focused on saving behavior in the European Union and the Gulf Cooperation Council (GCC) region. Overall, the data include twelve Asian countries, and one study spans forty-eight nations.

Methods

Methods include the systematic gathering and analysis of data for empirical research. This review evaluates the data collection methods and analysis techniques used in the 112 articles examined, based on the research by Lim et al. (2021).

Table 9. Data collection method.

Methods of Data Collection	No. of Articles	Exemplar Studies
Survey	74	(Kim et al., 2025; Ramli et al., 2022; Yadav & Banerji, 2025; Uy et al., 2024; Ammer & Aldhyani, 2022)
Secondary Data	21	(Athukorala & Suanin, 2024; Gröbel & Ihle, 2018; Fang et al., 2022; Ang, 2009; Cho et al., 2014)
Experiment Based	6	(Trzcińska et al., 2022; Sayinzoga et al., 2016; Durante & Laran, 2016; Cho et al., 2014; Thaler & Benartzi, 2004)
Interview	3	(Pandey, 2018; Swasdpeera & Pandey, 2012; Garcia et al., 2011)
Epidemiological Approach	1	(Fuchs-Schündeln et al., 2020)
Case Study	1	(Jongwanich, 2010)
Critical Analysis	3	(Gomes, 2025; Hopf, 2006; Browning, 2000)
Randomized Field Experiment	3	(Behr & Jacob, 2024; Pomeranz & Kast, 2024; Calderone et al., 2018)

summarizes the data collection methods used in 112 articles, while

Table 10. Data analysis method.

Quantitative Data Analysis (72)	No. of Articles	Exemplar Studies
Regression Method
Logistic Regression	7	(Yoon & Hanna, 2024; Pavlo & Dariia, 2023; Amari et al., 2020; Nguyen et al., 2017; S. Heckman & Hanna, 2015)
Multinomial Logit Model	2	(Loaba, 2022; Temel Nalın, 2013)
Probit Regression	8	(Walstad & Wagner, 2023; Boto-García et al., 2022; Fang et al., 2022; Brown & Taylor, 2016; Erskine et al., 2006)
Binary Logit Model	1	(Lössbroek & Van Tubergen, 2024)
OLS Regression	3	(Topa & Herrador-Alcaide, 2016; Asebedo & Seay, 2018; Puri & Robinson, 2007)
Panel Data Regression Analysis	2	(Gröbel & Ihle, 2018; Kiiza & Pederson, 2001)
Multiple regression model	3	(Ramli et al., 2022)
Binary logistic regression analysis	2	(Doan, 2020; Cho et al., 2014)
Copula based Bivariate probit regression model	1	(Gilenko & Chernova, 2021)
Heckman Probit model	1	(Canare et al., 2019)
Ordered Logistic Regression	1	(Swasdpeera & Pandey, 2012)
Difference in Difference Estimator	2	(Hermansson & Song, 2016; Pomeranz & Kast, 2024)
Ordered Probit Estimates	1	(Harris et al., 2002)
Generalized Quantile Regression	1	(D. Yao et al., 2019)
Multinomial logistic regression	2	(Gutter et al., 2012; Cruz et al., 2025)
Multivariate linear regression	2	(Raue et al., 2020; Grable et al., 2021)
Pooled Logistic Regression Model	1	(Ghosh & Chaudhury, 2023)
Factor Analysis	1	(Zulaihati & Widyastuti, 2020)
Chi square test	4	(Midamba et al., 2024a; Trzcińska et al., 2022; Copur & Gutter, 2019; Chudzian et al., 2015)
Hierarchical Logistic Regression	1	(Chou et al., 2014)
Linear Regression Model	1	(Raue et al., 2020)
ANCOVA	1	(Calderone et al., 2018)
ANOVA	2	(Durante & Laran, 2016; Copur & Gutter, 2019)
PLS-SEM method	19	(Kim et al., 2025; Ammer & Aldhyani, 2022; Harahap et al., 2022; Rahayu et al., 2024; Lopez et al., 2024)
AMOS-SEM method	1	(Yadav & Banerji, 2025)
M Plus–SEM method	1	(Anvari-Clark & Ansong, 2022)
t-test	1	(Midamba et al., 2024b)
Others (18)
Fixed effects model	1	(Amoah et al., 2024)
MLE Linear Random Effects Model	1	(Cronqvist & Siegel, 2015)
Likelihood Ratio Test	1	(Yoon & Hanna, 2024)
Augmented Dickey- Fuller (ADF) test	5	(Khawaja, 2023; Jongwanich, 2010; Agrawal et al., 2010; Ozcan et al., 2003; Athukorala & Tsai, 2003)
Panel data using the Im-Persara-Shin (CIPS) test	1	(Athukorala & Suanin, 2024)
Finite mixture model with maximum likelihood (ML)	1	(Gerhard et al., 2018)
Phi coeffecient	1	(Ouyang et al., 2025)
Two-stage least squares (2SLS)	2	(Sayinzoga et al., 2016; Al-Awad & Elhiraika, 2003)
Mean approximation	3	(Filipski et al., 2019; Thaler & Benartzi, 2004; Burton, 2001)
Autoregressive Distributed Lag(ARDL)and the dynamic ordinary least squares (DOLS)	1	(Ang, 2009)
Random Forest Method	1	(Zainal Alam et al., 2023)
Qualitative data analysis
Conceptual/Theoretical Model	3	(Landman & Mthombeni, 2021; Grable et al., 2021; Rolison et al., 2017)

outlines the different data analysis techniques applied. These methods are divided into quantitative and qualitative approaches. In the quantitative group, regression was the most common, mentioned 48 times. Among these, logistic and probit regressions received the most attention, with 7 and 6 mentions respectively. PLS SEM was used in 20 studies, and both AMOS and Mplus were employed in 1 study each. Additionally, many researchers favored the Augmented Dickey–Fuller (ADF) test, which was mentioned 5 times.

Articles under review employ both primary and secondary data collection methods. Primary methods include surveys, interviews, and experiments. The data shows that 21 articles used secondary data to explore individuals’ saving behaviors. Among the primary methods, surveys are the most common, with 74 mentions, followed by experiments with 6 mentions. Only three studies used qualitative methods, including Van Manen’s hermeneutic phenomenology approach for developing a theoretical model.

5. Review of Studies Based on ADO Framework

Antecedents

In recent decades, saving behavior has become a key concern for individuals. Understanding the various factors that influence personal saving decisions is essential. A thorough review of the literature shows that many factors affect saving behavior, including demographic and socioeconomic traits, psychological and personality elements, social and cognitive influences, technological factors, cultural influences, and situational and macroeconomic factors (see

Table 11. Summary table of the antecedents of saving behavior and their established relationships.

Typology	Antecedent	Citations	Association with SB
Demographic and socio-economic Factors	Age	(Zainal Alam et al., 2023; Harahap et al., 2022; Matenge et al., 2019; R. Yao & Cheng, 2017; Copur & Gutter, 2019; Gutter et al., 2012; Swasdpeera & Pandey, 2012)	Significant/Positive
		(Midamba et al., 2024a; Sakyi et al., 2021; Horioka, 2019; Rezaei, 2022)	Significant/Negative
		(Vadde, 2015)	Insignificant
	Neighbourhood social capital	(Behr & Jacob, 2024)	Significant/Positive
	Housing wealth	(Gröbel & Ihle, 2018)	Significant/Positive
	Marital status	(Midamba et al., 2024a; Zainal Alam et al., 2023; Matenge et al., 2019; R. Yao & Cheng, 2017; Gutter et al., 2012; Swasdpeera & Pandey, 2012)	Significant/Positive
		(Vadde, 2015)	Insignificant
	Gender	(Zainal Alam et al., 2023; R. Yao & Cheng, 2017),	Significant/Positive
		(Midamba et al., 2024a; Ghosh & Chaudhury, 2023; Amoah et al., 2024; Muhamad et al., 2021)	Insignificant
	Experience Group membership	(Midamba et al., 2024a)	Significant/Positive
	Education	(Zainal Alam et al., 2023; Harahap et al., 2022; Amoah et al., 2024; Matenge et al., 2019; R. Yao & Cheng, 2017; Gutter et al., 2012; Swasdpeera & Pandey, 2012)	Significant/Positive
		(Sakyi et al., 2021; R. Yao & Cheng, 2017; Vadde, 2015)	Significant/Negative
	No. of children	(Núñez-Letamendia et al., 2025; Swasdpeera & Pandey, 2012)	Significant/Negative
	Family size	(Núñez-Letamendia et al., 2025; Matenge et al., 2019)	Significant/Negative
		(Garcia et al., 2011)	Insignificant
	No. of dependents	(Zainal Alam et al., 2023; Amoah et al., 2024; Khawaja, 2023; Athukorala & Suanin, 2024; Ang, 2009; Athukorala & Tsai, 2003)	Significant/Negative
	Income	(Harahap et al., 2022; Amoah et al., 2024; Matenge et al., 2019; Amari et al., 2020; Copur & Gutter, 2019; Gutter et al., 2012; Vadde, 2015; Garcia et al., 2011; Harris et al., 2002)	Significant/Positive
	Home ownership	(R. Yao & Cheng, 2017; Copur & Gutter, 2019; Gutter et al., 2012)	Significant/Positive
	National Origin	(Lössbroek & Van Tubergen, 2024)	Significant disparity
	Employment Status	(Amoah et al., 2024; Sakyi et al., 2021; R. Yao & Cheng, 2017)	Significant/Positive
	Occupation	(Vadde, 2015)	Significant positive
	Earning disparities among partners	(Browning, 2000)	Significant/Positive
	Family Income	(R. Yao & Cheng, 2017)	Significant/Positive
	Religion	(Lössbroek & Van Tubergen, 2024; Ahmad et al., 2023; Alfi & Yusuf, 2022)	Significant disparity
	Ethnicity/Race	(R. Yao & Cheng, 2017; Gutter et al., 2012)	Significant disparity
Cognitive Factors	Objective Financial Literacy	(Kim et al., 2025; Núñez-Letamendia et al., 2025; Van et al., 2024; Ammer & Aldhyani, 2022; Harahap et al., 2022; Lopez et al., 2024; Ananda et al., 2024; Zulaihati & Widyastuti, 2020; Sayinzoga et al., 2016; Worasatepongsa & Deesukanan, 2022; Alshebami & Seraj, 2021; Kusairi et al., 2019; Fang et al., 2022; Nguyen et al., 2017; Rolison et al., 2017; Gilenko & Chernova, 2021; Doan, 2020; Mohanta & Dash, 2022; S. Heckman & Hanna, 2015; Sabri & Juen, 2014; Henager & Mauldin, 2015)	Significant/Positive
		(Sakyi et al., 2021)	Significant/Negative
	Financial knowledge	(Ramli et al., 2022)	Significant/Positive
	Mental Accounting	(Zainal Alam et al., 2023; Landman & Mthombeni, 2021)	Significant/Positive
Psychological Factors	Self-control/Perceived control	(Yoon & Hanna, 2024; Ammer & Aldhyani, 2022; Rahayu et al., 2024; Lopez et al., 2024; Satsios & Hadjidakis, 2018; Rabinovich & Webley, 2007; Thaler & Benartzi, 2004)	Significant/Positive
		(Alshebami & Seraj, 2021)	Significant/Negative
	Intention to save	(Uy et al., 2024; Satsios & Hadjidakis, 2018)	Significant/Positive
	Performance Expectancy	(Hii et al., 2025)	Significant/Positive
	Attitude towards saving	(Uy et al., 2024; Núñez-Letamendia et al., 2025; Van et al., 2024; Rahayu et al., 2024; Copur & Gutter, 2019; Tonsing & Ghoh, 2019; Satsios & Hadjidakis, 2018; Cho et al., 2014; Garcia et al., 2011; Chudzian et al., 2015)	Significant/Positive
	Motivation	(Núñez-Letamendia et al., 2025)	Significant/Positive
	Self-efficacy	(Núñez-Letamendia et al., 2025; Lopez et al., 2024; Kusairi et al., 2019; Muhamad et al., 2021; Copur & Gutter, 2019; Asebedo & Seay, 2018; Lown et al., 2015; Gutter et al., 2012)	Significant/Positive
	Distrust	(Mohanta & Dash, 2022; Gutter et al., 2012)	Significant/Negative
	Materialism	(Gutter et al., 2012)	Insignificant
	Anxiety	(Gutter et al., 2012)	Significant/Negative
	Subjective Financial Literacy/Financial Confidence	(Núñez-Letamendia et al., 2025; Ananda et al., 2024; Chou et al., 2014; Henager & Mauldin, 2015)	Significant/Positive
		(Nguyen et al., 2017)	Insignificant
	Subjective Norms	(Van et al., 2024; Lopez et al., 2024; Rahayu et al., 2024; Copur & Gutter, 2019; Satsios & Hadjidakis, 2018)	Significant/Positive
	Risk Tolerance	(Ammer & Aldhyani, 2022; Ananda et al., 2024; Matenge et al., 2019; Nguyen et al., 2017; Rolison et al., 2017; Copur & Gutter, 2019; Doan, 2020; Gutter et al., 2012)	Significant /Positive
	Fear (loss of life/property)	(Canare et al., 2019; Kinugasa et al., 2024; Filipski et al., 2019)	Significant/Negative
	Time Preference	(Lopez et al., 2024; Trzcińska et al., 2022; Choi & Han, 2018; Matenge et al., 2019; Rolison et al., 2017)	Significant/Positive
	Perceived saving barriers	(Matenge et al., 2019; Copur & Gutter, 2019; Gutter et al., 2012)	Significant/Negative
	Impulsiveness	(Allom et al., 2018; Copur & Gutter, 2019; Gutter et al., 2012)	Significant/Negative
	Personality	(Gerhard et al., 2018; Topa & Herrador-Alcaide, 2016)	Significant disparity
	Financial vulnerability (stress)	(Durante & Laran, 2016)	Significant disparity
	Optimism	(Gerhard et al., 2018; Puri & Robinson, 2007)	Significant/Negative
	Propensity to Plan	(Rolison et al., 2017; Copur & Gutter, 2019; Zulaihati & Widyastuti, 2020)	Significant/Positive
	Saving motives	(Gerhard et al., 2018; Asebedo & Seay, 2018; Tonsing & Ghoh, 2019; S. Heckman & Hanna, 2015; Burton, 2001)	Significant/Positive
	Perception of partner’s saving	(Grable et al., 2021)	Significant/Positive
	Saving habits	(Anvari-Clark & Ansong, 2022; Allom et al., 2018)	Significant/Positive
Cultural Factors	Personal & Cultural values	(Cruz et al., 2025; Fuchs-Schündeln et al., 2020; Al-Awad & Elhiraika, 2003)	Significant disparity
	Social norms	(Horioka, 2019; Carpenter & Jensen, 2002)	Significant/Positive
Social Factors	Financial Socialization	(Ammer & Aldhyani, 2022; Lössbroek & Van Tubergen, 2024; Garcia et al., 2011; Alshebami & Seraj, 2021; Raue et al., 2020; Fang et al., 2022; Copur & Gutter, 2019; Doan, 2020; S. Heckman & Hanna, 2015; Gutter et al., 2012; Brown & Taylor, 2016; Erskine et al., 2006)	Significant/Positive
Technological Factors	Content-specific text messages	(Behr & Jacob, 2024)	Significant/Positive
	Mobile banking services (Digital Payments)	(Loaba, 2022; Gurusamy & Balachandar, 2024)	Significant/Positive
	Digital Financial Literacy	(Kim et al., 2025; Yadav & Banerji, 2025; Rahayu et al., 2024; Setiawan et al., 2022)	Significant/Positive
Situational Factors	Financial shocks	(Anvari-Clark & Ansong, 2022; D. Yao et al., 2019; González & Özcan, 2013)	Significant/Negative
	Income Volatility	(Anvari-Clark & Ansong, 2022)
	Liquidity constraint	(Toussaint-Comeau, 2021; Ozcan et al., 2003)	Significant/Negative
	Perceived safety net	(Ouyang et al., 2025)	Significant Disparity
	Compulsary/Voluntary Financial Education	(Walstad & Wagner, 2023; Calderone et al., 2018; Henager & Mauldin, 2015)	Significant/Positive
Macro Factors	Growth rate of GDP, Interest Rate, Budget Surplus, Foreign Direct Investment (FDI)	(Khawaja, 2023; Gomes, 2025; Jongwanich, 2010)	Significant/Positive
	Inflation	(Khawaja, 2023; Ang, 2009)	Significant/Negative
		(Temel Nalın, 2013; Jongwanich, 2010)	Significant disparity
	Export orientation	(Athukorala & Suanin, 2024)	Significant/Positive
	Domestic credit availability	(Athukorala & Suanin, 2024; Jongwanich, 2010; Athukorala & Tsai, 2003)	Significant/Negative
Others	Financial Products	(Anvari-Clark & Ansong, 2022; Pomeranz & Kast, 2024)	Significant/Positive
	Money anthropomorphism	(Wang et al., 2023)	Significant/Positive
	Economic populism	(Pavlo & Dariia, 2023)	Significant/Negative
	Institutional access	(Pandey, 2018; S. Heckman & Hanna, 2015; Agrawal et al., 2010; Kiiza & Pederson, 2001)	Significant/Positive
	Professional Financial advice	(Fang et al., 2022; Zulaihati & Widyastuti, 2020; Mohanta & Dash, 2022; Hermansson & Song, 2016)	Significant/Positive
	Genetics	(Cronqvist & Siegel, 2015)	Significant

).

Demographic Factors

Research on the link between age and savings shows mixed results. Some studies find a positive relationship between age and saving behavior (Browning & Lusardi, 1996; Johnson & Widdows, 1985; Zainal Alam et al., 2023; Harahap et al., 2022). However, others indicate that people under 30 tend to save more based on their current income (Yuh & Hanna, 2010), while savings among older adults do not necessarily decrease (Avery & Kennickell, 1991; Midamba et al., 2024a). The life cycle theory describes a nonlinear relationship between savings and age (Ando & Modigliani, 1963; Browning & Crossley, 2001). Households with children often face high caregiving costs, which can reduce their ability to save. The dependency ratio is also a key social factor; a higher ratio means more income is spent, leading to lower savings (Khawaja, 2023). The positive link between income and savings suggests that higher income encourages saving a larger portion of earnings. Employment also has a positive effect, as employed individuals tend to save at higher rates than the unemployed (Amoah et al., 2024). Economic factors, such as average monthly income, occupation, and homeownership, are statistically significant and influence household savings behavior (Vadde, 2015). Gender is a well-documented factor affecting saving habits, with men generally saving more than women. Other socio-economic and demographic variables—such as income, occupation, household size, education, number of dependents, marital status, and ethnicity—also significantly impact total household savings and savings rates (Hua & Erreygers, 2020; Zainal Alam et al., 2023; Copur & Gutter, 2019). Married individuals tend to have more positive cash flow and savings patterns. Additionally, religiosity influences savings intentions and behaviors, albeit to a lesser extent (Alfi & Yusuf, 2022). Since wives are often younger and tend to live longer than their husbands, they may have a stronger incentive to save for retirement. Consequently, household consumption largely depends on how income is shared within the household (Copur & Gutter, 2019). Social factors like neighborhood social capital and account usage significantly influence saving habits in low-income countries (Behr & Jacob, 2024).

Psychological factors

Psychological factors influencing saving behavior can be categorized into two main groups. The first includes direct influences such as the intention to save, performance expectancy, attitudes towards saving, distrust, materialism, anxiety, subjective norms, fear (related to potential loss of life or property), time preference, financial vulnerability (stress), saving motives, perceptions of a partner’s spending habits, established saving practices, financial confidence, and perceived safety nets. The second group consists of indirect factors that shape an individual’s personality, including traits such as agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience, as well as attributes like optimism, planning tendencies, self-control or perceived control, self-efficacy, and risk tolerance.

Among these factors, attitude plays the most important role in shaping the intention to save, thereby affecting saving behavior across generations (Rahayu et al., 2024). Attitude is an evaluative judgment about objects, people, or events, consisting of cognitive (belief), affective (emotion), and behavioral (intent) components (Nga & Yeoh, 2015). Essentially, it’s “how someone feels about something,” representing internal beliefs linked to expected outcomes. Research has demonstrated a strong connection between financial attitude and saving behaviour (Hilgert et al., 2003), as key money management activities like spending or saving are influenced by individuals’ feelings about finance. Therefore, having a clear understanding of their saving goals is vital to bridging the gap between the desire to save and actual saving behaviour (Otto, 2013).

In technology adoption, “performance expectancy” measures how much the technology makes saving easier. For example, compared to traditional methods requiring visits to financial centers, saving through Internet Wealth Management (IWM) is more efficient and may lead to higher saving rates (Hii et al., 2025). Subjective norms (SN) refer to the support or pressure from influential individuals, such as parents, spouses, friends, and teachers (Ajzen, 1991). Within the frameworks of TRA (Theory of Reasoned Action) and TPB (Theory of Planned Behavior), which focus on reasoned action, subjective norms help predict behavioral intentions, which in turn forecast actual behavior (Rahayu et al., 2024). Research has highlighted saving habits as a crucial behavioral factor affecting saving behavior. This encompasses activities like budgeting, paying bills, exercising self-control, and planning long-term goals (Netemeyer et al., 2018; Anvari-Clark & Ansong, 2022). Time preference refers to prioritizing a smaller, immediate reward over a larger, delayed one, as explained by (Lopez et al., 2024). Numerous studies in economic psychology reveal that time horizons significantly influence financial decisions (Trzcińska et al., 2022). For instance, a longer perceived future time horizon strongly encourages saving (Hershey & Mowen, 2000; Loibl et al., 2011; Lusardi, 2000), increasing the likelihood of consistent and effective savings (Lopez et al., 2024). Perceived barriers are a significant factor discouraging saving, offering insights into personal saving behaviors (Copur & Gutter, 2019; Matenge et al., 2019). Research shows that saving is often seen as complicated (Perry & Morris, 2005; Hershey et al., 2010). Factors such as low financial literacy (Perry & Morris, 2005; Lusardi, 2008) and difficulties with financial institutions (Radipotsane, 2006) hinder saving. Lusardi and Mitchell (2011) found that limited financial knowledge and awareness decrease overall savings. Barriers such as limited access to services, low returns, withdrawal restrictions, and high deposit requirements also impede saving (Matenge et al., 2019). Additionally, individuals with higher buying impulsiveness tend to spend more than those with lower impulsiveness (Allom et al., 2018; Copur & Gutter, 2019; Gutter et al., 2012).

Household savings are driven by two primary motivations: the promotion system, which focuses on achieving financial benefits, and the prevention system, which aims to avoid losses. The prevention motive corresponds to Keynes’s (1936) precautionary motive, where households save to prepare for emergencies or income drops. In contrast, the promotion motive aligns with Keynes’s (1936) concept of the improvement motive. Browning and Lusardi (1996) highlight that savings motives differ, with wealthier people possibly saving for different reasons than those with less wealth (Gerhard et al., 2018; Asebedo & Seay, 2018). Financial institutions can help close the trust gap between savers and investors. From this perspective, institutional factors are also seen as important. Because many public sector banks are naturally robust, it is essential to work on enhancing people’s trust in financial institutions to promote financial inclusion (Mohanta & Dash, 2022). Several studies (Ajayi, 2016; Beckmann & Mare, 2017) recommend applying information asymmetry theory to understand how trust influences decision-making, highlighting the importance of clear communication and information sharing for building trust. Stress is a physiological and mental response to challenges that threaten one’s ability to cope (Lazarus, 1966). Saving money creates a sense of control by keeping resources accessible when needed. Conversely, strategic spending can also help regain a feeling of control. During times of stress, consumers are more likely to focus on essentials rather than non-essentials, as this reassures them that their daily needs are covered and readily available (Durante & Laran, 2016).

The pandemic has significantly changed people’s lifestyles and spending habits. Heightened fears of death or increased mortality risks may lead individuals to save less, as they doubt they will live long enough to enjoy their savings. A fear of crimes involving physical violence decreases the likelihood of saving but raises the chance of saving through formal channels, thereby affecting saving behavior and decisions (Canare et al., 2019). Richins and Dawson (1992) investigated how possessions are perceived as indicators of success and discovered that family structure can influence material values. Since materialism may influence saving behaviors, this could explain differences in savings among households with various family setups (Gutter et al., 2012). Additionally, anxiety influences saving choices (Gutter et al., 2012). As often said, perceptions shape reality—especially among married couples. How one regards a partner’s spending and saving habits can reflect their financial satisfaction and influence their saving decisions (Grable et al., 2021).

The “Big Five” personality traits model—used in psychology and economic research—evaluates five traits—agreeableness, conscientiousness, extraversion, neuroticism, and openness—that influence behaviors such as saving and indirectly affect income (Borghans et al., 2008). The concept of self-efficacy, applied to financial decisions, suggests that individuals who are more confident in their financial skills are better equipped to handle economic challenges. They perceive problems as challenges to be mastered rather than threats to be avoided (Bandura, 1989; Bandura et al., 1987; Farrell et al., 2016), leading to greater success and better financial outcomes (Núñez-Letamendia et al., 2025; Lopez et al., 2024). Optimism, generally seen as a positive outlook on the future (Gerhard et al., 2018; Puri & Robinson, 2007), can influence saving behavior; more optimistic individuals are less likely to feel the need to save for future setbacks. Investors’ risk attitudes significantly influence their asset allocation choices, with risk-averse individuals preferring savings accounts. In contrast, those who are comfortable with higher risk tend to invest in more volatile assets, such as stocks (Ammer & Aldhyani, 2022). Recent studies have expanded the idea of financial literacy to include behavioral self-confidence, which is found to be as important as actual knowledge in shaping saving habits. This trait has been measured through self-assessment questions about perceived knowledge, as shown in studies by (Lusardi & Mitchell, 2011), and Ananda et al. (2024). Remund (2010) further expanded the concept by including motivation in financial literacy, noting that “... to be financially literate requires knowledge, skills, motivation, and confidence.” This model aligns with the Theory of Planned Behavior. Therefore, individual confidence greatly affects savings behaviors (Ananda et al., 2024). Past research shows that overconfident investors often believe they can make decisions on their own but face a higher risk of falling for scams (Drew & Cross, 2013). On the other hand, underconfident people tend to avoid stock trading, which leads to lower investment returns (Xia et al., 2014).

Individuals rely on self-control mechanisms to resist temptations, emphasizing their crucial role in decision-making. This supports the view that self-control tools are essential for helping people make better choices (Yoon & Hanna, 2024). Perceived behavioral control reflects how individuals perceive their ability to perform specific actions, including their assessment of how easy or difficult a task is, which significantly influences intentions.

Situational Factors

Financial shocks are incidents impacting individuals or households, potentially causing stress and requiring adaptation to external hardships. Examples include job loss, major car repairs, or expensive medical bills, which can significantly affect financial well-being based on the monetary impact, disruption to family life, perceptions of the situation (Asebedo & Wilmarth, 2017), and available resources to address them. Income volatility refers to fluctuations in monthly income (Morduch & Schneider, 2017). These fluctuations often occur due to employment opportunities and employer schedules. The intensity of income swings can impair short-term savings, planning for future needs, addressing immediate expenses, or coping with financial shocks. Both factors are generally beyond a family’s immediate control and can heavily influence saving decisions (Anvari-Clark & Ansong, 2022).

Consequently, subjective expectations of mortality play a vital role in shaping saving behaviors (Kinugasa et al., 2024). Additionally, worries about accidents, chronic illnesses, and disasters also influence saving intentions. Natural disasters, therefore, affect economies not only through physical destruction but also by impacting the emotional and psychological well-being of survivors (Filipski et al., 2019).

A financial safety net, utilizing external support, serves as a temporary buffer, influencing long-term decisions. The traditional view suggests that liquidity limits mainly affect low-income, asset-poor individuals (Campbell & Hercowitz, 2019; Ouyang et al., 2025). However, recent research indicates that middle-class households also face these restrictions, although the extent to which they influence savings remains unclear. These households often have a high marginal propensity to consume temporary income because of different savings motivations—they are wealthy but live paycheck to paycheck (Sahm, 2019). These studies tend to overlook recent challenges, such as income shocks and rising debt costs, which could hinder their ability to save and impact their saving behaviors (Toussaint-Comeau, 2021).

Another important factor that affects individual saving behaviour is participant self-selection of educational programs, which can impact course evaluations (J. J. Heckman, 2010). In financial education, whether participants choose to volunteer or are required might influence the effectiveness of the education on financial behaviours. For instance, volunteers might have a greater interest in financial education, leading to more favorable financial outcomes compared to those who participate out of obligation (Walstad & Wagner, 2023).

Social factors

These factors strongly influence financial behaviors, which are often passed from parents to children through socialization. From a young age, children observe their parents managing money or discuss financial and investment topics with them (Ammer & Aldhyani, 2022). The confidence developed during family interactions can impact investment choices and future saving habits. Early childhood financial socialization has a positive impact on later financial decisions, particularly in the areas of saving and asset accumulation. While some saving preferences may be partly inherited, environmental influences and parental guidance play a significant role in differences in saving rates among individuals (Cronqvist & Siegel, 2015; Garcia et al., 2011). As a result, migrant and non-migrant parents are likely to develop different values and practices related to saving, which they pass on to their children. These cultural influences are shaped not only by the country where their parents grew up but also by their religion. Therefore, a parent’s country of origin probably affects both saving behaviors and the methods used for saving.

When making social comparisons, people tend to seek information about others relative to themselves, often unconsciously (Raue et al., 2020). This may involve direct or indirect comparisons, such as news stories or word of mouth (Wood, 1996). People often compare their performance, possessions, and well-being with similar others, thinking, “I did better than X, I have more money than Y,” rather than evaluating themselves in absolute terms (Festinger, 1954).

Cognitive Factors

Financial literacy is the ability to understand and use financial information to solve financial problems. It helps people understand financial topics and make appropriate financial choices. Research also shows a connection between financial literacy and saving habits among older adults (55 years and above), with a direct impact on this group (Kim et al., 2025). Moreover, the effect of financial education or literacy on financial behaviors probably differs based on factors like measurement methods, the nature of educational programs, specific behaviors studied, content included, and the duration of instruction (Lusardi & Mitchell, 2011; Allgood & Walstad, 2016; Kaiser & Menkhoff, 2017, 2020; Ammer & Aldhyani, 2022). Another study uses machine learning to predict saving habits by analyzing mental accounting—a theory that categorizes wealth and spending into distinct, non-fungible groups. It finds that spending on discretionary items and current income are key predictors of saving behavior (Zainal Alam et al., 2023). Therefore, it involves mentally setting aside funds for specific goals, like savings, to address self-control problems. It helps overcome behavioral biases that block saving and makes money designated for savings seem less available for other expenses. Karlan et al. (2014) suggest that mental accounting connects current savings with future goals.

Technological Factors

Mobile phones have shifted from simple communication tools to vital financial resources for both wealthy and low-income households. These devices are more affordable, easy to use, support frequent small transactions, and accelerate payments and purchases benefits that can improve people’s resource management and potentially increase their savings. Digital financial literacy enhances this effect by helping elderly individuals use digital tools for managing their finances and adopting more effective savings strategies (Ramli et al., 2022). Targeted text messages have improved savings habits among women in low-income countries, benefiting both the supply and demand for financial services and increasing financial inclusion (Behr & Jacob, 2024). As a result, mobile banking plays a key role in saving behavior, especially in developing countries where women often rely on informal savings; however, their ability to save officially rises with mobile banking use (Loaba, 2022). Digital financial literacy combines financial and digital skills, including understanding digital financial products, managing risks, and seeking redress (Lyons & Kass-Hanna, 2021; Rahayu et al., 2024). Furthermore, digital financial literacy positively influences the saving behaviors of both the elderly (Kim et al., 2025) and younger generations (Yadav & Banerji, 2025).

Importantly, the method of payment influences how individuals spend money. The rise in digital payments has changed spending habits and affected savings patterns. Rather than improving saving skills through technology, people are more likely to become better at spending (Gurusamy & Balachandar, 2024; Chhillar & Arora, 2023). Switching to cards instead of cash is an innovative and advantageous development. However, subtle behavioral changes often go unnoticed until they matter. Cash payments can be uncomfortable, while cashless transactions reduce spending restraints and increase the tendency to spend. These methods have significantly altered perceptions of money. Not all payment modes promote saving. Technological advances complicate saving efforts, making financial behavior more complex. Cash naturally encourages self-discipline, but digital transactions can challenge self-control. Although digital payments are widely adopted, the traditional art of saving seems diminished amid technological progress.

Macro-economic Factors

According to Keynes’ consumption theory, saving depends on current income since it is what remains after consumption. Meanwhile, Modigliani (1986) proposes that savings behavior is driven not by current income but by the lifecycle income of individuals, establishing a link between savings and the growth rate of per capita GDP (Khawaja, 2023). The real interest rate is also a significant factor influencing savings. However, literature review shows mixed results regarding its effect on domestic savings in developing countries (Balassa, 1989). Similarly, inflation reflects macroeconomic stability and can indicate economic uncertainty, generally believed to negatively impact savings. Nonetheless, inflation and anticipated inflation may have conflicting effects: Deaton (1977) suggests anticipated inflation can increase savings due to uncertainty, while Stockman (1981) argues it decreases savings. The government budget surplus is also crucial for domestic savings, given the importance of government financing. Additionally, a country’s savings rate strongly correlates with its export orientation. Evidence shows that foreign direct investment (FDI) contributes to private savings, contradicting some studies that argue foreign inflows displace domestic savings. Lastly, easy access to bank credit tends to reduce the incentive to save.

In addition to savings rates, the significant contribution of the labor force to economic growth is well acknowledged. Imbalances in the labor-to-capital ratio can hinder growth, especially as population aging—reducing savings and weakening the labor force—may negatively impact economic development. Consequently, changes in population size or a decrease in the population growth rate are likely to substantially reduce savings levels (Rezaei, 2022). Furthermore, a notable connection exists between migration background and saving habits among young people, influenced in part by socioeconomic factors like parental and children’s resources (Lössbroek & Van Tubergen, 2024).

Cultural Factors

The literature analyzes two primary perspectives on human values: personal values and cultural values. Personal values consist of intrinsic traits and principles that individuals hold, representing broad goals that motivate actions and influence behavior. These values are rooted in individual identity and experiences. In contrast, cultural values refer to the shared principles and beliefs within a society, guiding social interaction and cohesion. While personal values are individualistic, they are shaped by the cultural context, influencing how individuals relate to their communities. Core cultural features serve as benchmarks for social norms and expectations. Research has explored how cultural frameworks influence saving behavior, examining how societal values and beliefs impact financial decisions (Shoham & Malul, 2012). The functional theory of values suggests that individuals who prioritize personal goals and economic security are more likely to save, while those emphasizing social orientation and community engagement tend to save less. This illustrates the interplay between individual aspirations and societal influences on financial decision-making. Culturally, individualistic societies tend to save more, whereas countries that strongly avoid uncertainty generally have lower saving rates (Cruz et al., 2025). Therefore, culture and social norms have an impact on household saving behavior (Horioka, 2019).

Other Factors

Access to liquid savings accounts helps manage consumption during economic shocks, but limited access can lead to borrowing and harm. Financial products, such as checking accounts, savings, life insurance, and retirement accounts, are connected to the formal banking system (Anvari-Clark & Ansong, 2022). Religious beliefs, especially Islamic prohibitions on interest, influence savings behaviors. Promoting savings may require incorporating religious messaging, offering subsidies, and providing support. Economic populism, involving proactive economic attitudes, is associated with lower savings (Pavlo & Dariia, 2023).

Professional financial advice encourages saving habits (Fang et al., 2022). Financial consultants play a crucial role in increasing awareness about available financial services and their advantages, which can enhance financial inclusion, particularly in low-income nations and among groups with limited access (Mohanta & Dash, 2022; Hermansson & Song, 2016). Additionally, research indicates a connection between genes, time, and risk preferences, suggesting that genetic factors partly explain the variations in individuals’ savings behaviors (Agrawal et al., 2010; Cronqvist & Siegel, 2015). Another key aspect is how people’s perception of money changes when they attribute humanlike qualities to it, known as money anthropomorphism, which can significantly boost savings. It is suggested that attributing human traits to money can increase both saving intentions and actual saving behavior because humanized money seems capable of experiencing emotions such as pain or joy, which influences how people perceive its value. Consequently, it appears more vulnerable and in need of protection (Wang et al., 2023).

Sherraden (1991) states that government aid does not promote savings; instead, institutions should encourage risk-taking and asset building. Beverly and Sherraden (1999) highlight that factors such as structured savings programs, targeted financial education, incentives, and easy access shape saving habits, beyond income and personal choice. Foreign and public savings can often have a negative impact on private and household savings. An unexpected rise in house prices leads young renters to save more, while young homeowners tend to cut savings when their housing wealth changes unexpectedly. Young renters increase savings for a down payment, driven by rising house prices to build equity or prepare for higher future costs, especially in retirement (Gröbel & Ihle, 2018).

Decisions

Decisions reflect consumers’ behavioral responses to savings, as discussed by Paul and Benito (2018). The reviewed studies mainly used measures such as average savings balances, savings frequency, savings goals, savings rate, and entrepreneurial intention. Saving behavior is not merely about setting aside money—it is a complex interplay of cognitive processes, emotional triggers, social norms, and future-oriented thinking. Paul and Benito (2018) highlight several behavioral indicators that help decode how and individuals save. Maintaining high average savings balances reflects a strong sense of financial discipline. Individuals who achieve higher balances often demonstrate traits such as self-control, delayed gratification, and risk aversion. A high savings balance may indicate a precautionary motive, while a low balance could suggest either a tendency towards immediate consumption or financial constraints. Several factors influence the maintenance of high savings balances, including trust in financial institutions, perceived economic stability, and fears regarding future uncertainties, such as job loss or health emergencies (Behr & Jacob, 2024).

Frequency reflects the habitual practice of saving. Consistent saving indicates that individuals have made it part of their routines, which can be encouraged through automatic transfers, savings apps, reminders, and cultural norms that promote thriftiness (Midamba et al., 2024b). Goal-oriented saving is crucial for effective financial planning and future success. Clearly defined goals offer essential direction and purpose, making it easier for individuals to stay committed and resist impulsive spending. Using visualization techniques and goal-tracking tools helps individuals stay focused and reach their financial goals.

The savings rate indicates the percentage of income a person consciously allocates for savings, highlighting financial priorities. A higher rate may suggest strong self-discipline, while a lower one could be due to present bias, limited financial knowledge, or economic hardships. Influencing factors include income level and stability, financial literacy, budgeting abilities, and psychological traits like optimism or pessimism (Rezaei, 2022). Entrepreneurial intention signifies a proactive, risk-taking mindset driven by aspirational goals, where money is invested to generate value rather than simply saved for security. Consequently, people often save with the intention of future investments instead of solely focusing on security (Alshebami & Seraj, 2021).

Outcomes

Outcomes are defined as the results of consumers’ behavioral responses (Paul & Benito, 2018). The articles reviewed predominantly addressed key variables, including financial fragility, retirement planning, financial resilience, financial well-being, and personal saving behavior. Financial fragility, as an outcome of saving behavior, refers to the vulnerability of consumers to financial shocks arising from inadequate savings or ineffective financial planning. This condition indicates that individuals or households may struggle to manage unexpected expenses, such as medical emergencies or loss of income. Notably, research has shown that households with higher incomes and greater educational attainment may experience financial fragility more acutely than their middle-income counterparts (Lusardi et al., 2011). This observation underscores the reality that financial fragility can affect diverse segments of society, regardless of income level, race, gender, or age (Ramli et al., 2022).

As global life expectancy increases and populations age, long-term savings become more important for financial well-being, especially with changing policies. A review of 191 studies shows cognitive and non-cognitive factors affect retirement planning, highlighting the need for interdisciplinary research (Ingale & Paluri, 2025). Research suggests financial risk tolerance and herd behaviour mediate the link between financial literacy and retirement planning (Harahap et al., 2022). Retirement preparation requires not just finances but also psychological readiness and lifestyle changes driven by long-term savings. Financial resilience refers to the capacity to withstand and recover from financial shocks and is closely linked to consistent saving behavior. This capacity is fostered by specific financial practices, including regular saving. Recent research identifies digital financial literacy (DFL) as a key predictor of financial resilience, especially within the rapidly evolving digital landscape (Rahayu et al., 2024). Financial resilience extends beyond maintaining a bank balance. It involves preparedness for economic disruptions such as job loss or medical emergencies, reduced financial anxiety, increased confidence in managing future expenses, and the ability to sustain financial stability without resorting to high-interest debt. Furthermore, research demonstrates that financial resilience is a multidimensional construct, encompassing financial literacy, financial inclusion, and access to emergency funds (Kamble et al., 2025). Financial well-being (FWB) has gained significant attention in recent years (Kempson et al., 2017). Multiple scales and definitions have been developed to measure FWB (Netemeyer et al., 2018). The Consumer Financial Protection Bureau (CFPB) (2017) defines FWB as a person’s subjective sense of security and freedom of choice, both now and in the future. Research indicates that financial hardship reduces well-being, while regular saving serves as a protective factor, lessening hardship and its psychological impact. Saving also strengthens financial health, which in turn supports overall well-being. Higher self-efficacy is associated with greater financial satisfaction and emotional health. There is a positive feedback loop: financial well-being promotes saving, and saving further enhances well-being. This relationship is supported by behavioral economics and life-cycle theories (Hernandez-Perez & Cruz Rambaud, 2025). Studies recommend that efforts to improve financial capability and well-being should include behaviorally-oriented financial education and intervention programs. These policies are crucial to encourage saving across all income levels (Anvari-Clark & Ansong, 2022).

Personal saving behavior is regarded as a form of financial conduct influenced by individuals’ motivations and responsibilities regarding future prospects (social behavior). Accordingly, people tend to save rather than spend all their income; in other words, it reflects their capacity for personal financial balancing (Doan, 2020). Saving behavior is affected by a complex interplay of cognitive, psychological, social, and economic factors. Key influences include cognitive elements, such as financial literacy, where extensive financial knowledge promotes better saving habits (Kim et al., 2025). Psychological factors, such as self-control, help delay gratification and maintain consistent savings, particularly among students (Rahayu et al., 2024). Additionally, risk tolerance plays a role, with lower risk tolerance leading to more conservative saving strategies. Social factors involve parental socialisation, where early exposure to saving practices by parents encourages lifelong habits; peer influence, where social norms and peer behaviour can either promote or hinder saving (Ammer & Aldhyani, 2022); and cultural values, which significantly shape attitudes towards money and future planning (Cruz et al., 2025). Economic and demographic variables include income level, with higher incomes generally enabling greater savings, although effective planning makes saving possible at all income levels; education, as higher educational attainment is linked to a better understanding of the importance of saving; gender, noting certain differences in saving behavior based on gender; and access to financial services, with the availability of banking and digital payment options making saving easier (Zainal Alam et al., 2023).

6. Implications

This comprehensive review of existing literature offers important insights for: 1. Financial institutions such as banks, credit unions, and insurance companies, which play a key role in designing effective saving programs for various consumer groups. 2. Consumers: As the main participants in saving activities, individuals can improve their financial literacy and awareness to better manage future uncertainties. Similarly, by utilizing psychological factors such as self-control, self-efficacy, financial confidence, and good saving habits, saving behavior can be encouraged in consumers. 3. Policy regulators: Governments and financial authorities can foster a saving-friendly environment through incentives, awareness campaigns, training, and protective regulations. Overall, the results highlight how household traits such as demographic, socio-cultural, psychological, technological, and macroeconomic factors influence saving behaviors across different countries. Saving decisions are often linked to socio-economic elements like education and employment opportunities, though these relationships can be bidirectional. For example, households that save more may accumulate resources that support their children’s education, creating a reciprocal relationship between saving habits and educational opportunities. Recognizing factors beyond socio-demographics is essential for a comprehensive understanding of saving behavior, especially among vulnerable groups such as low-income families. While income level remains a primary influence (Amari et al., 2020), families and individuals with unstable or limited incomes can also be heavily impacted by cultural and personal values in their saving choices. Practitioners, regulators, and researchers all seek to address these influences. Additionally, this understanding can help guide policy development to promote effective financial practices. For instance, achieving financial resilience involves adopting strategies to ensure each household has enough resources to withstand economic shocks, through training, literacy programs, behavioral coaching, such as boosting confidence and self-esteem, seeking financial advice, and shifting government policies.

7. Directions for Future Research

This section explores future research directions concerning individual saving choices. Interdisciplinary research is vital for understanding that saving is not only an economic decision but also shaped by social context, lifecycle requirements, and psychological traits of savers (Gutter et al., 2010). In developing countries, many studies on savings factors often neglect sociological and psychological elements such as financial socialization and self-efficacy, mainly because they depend on macro-level data. Embracing a multidisciplinary approach in future research will deepen our understanding of saving behaviors.

In the following section, we explore various aspects of the TCM and ADO framework to discuss future research opportunities.

Future directions: Theory

The following outlines key potential directions for future research in the development of theories related to saving behavior: 1. Integration of Theories—Future studies may focus on how different theories, such as the Unified Theory of Acceptance and Use of Technology (UTAUT) and mental accounting, interact to influence comfort with saving behavior and decision-making processes. 2. Exploring Innovative Concepts—Research could leverage insights from neuroscience and evolutionary psychology to better understand market dynamics, particularly through the lens of neural networks. 3. Cultural and Cross-Cultural Considerations—It is essential to investigate how cultural factors impact existing theories and affect risk preferences across various populations, thereby enriching our understanding of saving behavior globally.

Future directions: Context

The literature review (see Table 3) reveals that most studies 15 focusing on the United States have investigated saving behaviors within a single country. In contrast, only two studies have adopted a cross-national perspective. This presents a significant opportunity for future research: examining saving practices across different countries to explore how cultural norms and religious beliefs shape individual financial choices globally. Such comparative studies could enhance our understanding of how social and belief systems influence decisions regarding the amount saved, savings goals, and preferred financial tools. These insights are especially relevant in today’s globally connected financial environment. Against this backdrop, we propose the following questions for future research.

• In what ways do religious beliefs and cultural practices influence personal saving habits in different regions or societies?
• Do consumers in emerging economies have different saving habits compared to those in developed countries, and what factors shape these differences in saving behavior?

Future directions: Methodology

The data analysis methods in the reviewed articles (see Table 5) mainly depend on traditional statistical techniques such as regression, ANOVA, and factor analysis. Looking ahead, researchers could adopt more advanced techniques like machine learning both supervised and unsupervised and neural networks to analyze big data and gain deeper insights into saving behaviors. However, there is comparatively little focus on qualitative research methods, which may result in biased, inconsistent, or unclear findings. To gain a more comprehensive understanding, more qualitative research is necessary to explore the topic in greater depth and identify key factors impacting saving decisions. We suggest the following study directions focused on methodology for future studies.

• Neuroscientific methodologies employ neuroimaging techniques to investigate brain regions and processes implicated in saving behavior, thereby elucidating the neurological foundations of risk-taking, a field frequently referred to as Neuro finance.
• Big data analytics and machine learning are applied to assess risk tolerance across various populations and to discover new factors affecting saving preferences using large datasets and sophisticated algorithms.
• Experimental designs involve manipulating multiple variables to investigate causal relationships between specific traits and saving behavior, thereby enhancing understanding of the underlying mechanisms.
• Longitudinal and cross-cultural studies provide valuable insights into the evolution and variation of saving behaviors across different societies. These behaviors are significantly influenced by cultural norms and shifts within the social landscape.
• Dynamic modeling builds representations of how individuals’ saving results respond to economic, social, and psychological influences.
• Integrative approaches utilize both quantitative and qualitative methodologies to provide a comprehensive understanding of the interplay between personal traits and environmental factors in influencing saving behavior.

Future Direction: Antecedents

Future studies could examine the impact of less-explored antecedents. For instance, researchers might investigate how perceived financial literacy, distrust in financial institutions, and materialism influence saving behavior. Another potential area for future research is to analyze how key antecedents and their primary effects interact to shape individuals’ saving habits. For example, studies could explore the interaction between household income and inflation, or between cultural and institutional factors in influencing saving intentions and behavior. Investigating these interactions would help clarify how multiple antecedents work together to affect saving decisions. Future research may consider these perspectives, such as:

• In developing countries such as India and other Asian economies, deep-rooted cultural norms particularly those related to patriarchy, masculinity and high power distance may significantly moderate the relationship between various antecedents and individuals saving behaviors and financial outcomes.
• Future research should examine how regulatory orientation affects complex financial tasks like investment decisions, with consumers having a prevention focus potentially achieving better outcomes. Socialization agents, such as parents and media, significantly shape early investment habits, and their influence varies by regulatory orientation. Additionally, addressing cognitive biases and limitations in financial literacy education is vital, and customizing educational programs to align with individuals’ regulatory orientations could enhance their effectiveness.
• The connection between saving intentions and actual behavior has not been widely studied thus it would be interesting to explore how and when intentions merge into behavior.
• Since this is an emerging field, a thorough study could explore the role of financial consultants and analyze how factors like emotions and mood influence savings habits. Moreover, research could investigate why some individuals opt out of savings and investment activities.
• Future studies should gather spender-saver data from both partners, including income and resources, to better understand gender differences linked to income and wealth. Collecting more data on partner finances and attitudes can assist financial therapists working with couples. Researching how partner collaboration on finances affects satisfaction and perceptions of saving and spending, as well as how these perceptions influence relationship outcomes, is also valuable.

Future Direction: Decision

The literature review highlights several promising yet underexplored decision variables, such as individuals’ willingness to save, their intention to increase savings, and changing saving habits over time. These factors extend beyond static measures, offering a more dynamic and psychological perspective on how, why, and when people choose to save.

Future Directions: Outcome

The conceptual diagram in Table 6 illustrates that most reviewed studies primarily investigate how antecedents like income, education, financial literacy, and psychological traits influence saving decisions such as whether individuals save and the amount they save. Fewer studies, however, have created models connecting these antecedents to outcome variables related to saving behavior, like long-term financial security or personal financial growth. This may be due to the limited availability of measurable financial performance indicators at the household or individual level. To expand the research scope, scholars could consider focusing more on non-financial or strategic outcomes of saving.

Future research should go beyond traditional financial metrics and explore how saving behavior impacts personal, emotional, and social outcomes. It is advisable for studies to examine the effects of consistent saving on financial satisfaction and overall well-being, especially as individuals gain greater control over their finances. Additionally, research could analyze how saving enhances quality of life, strengthens relationships, and improves financial stability over time. An important area to consider is how fair and inclusive savings products can help underserved populations access financial services. Researchers should also investigate how saving decreases financial vulnerability, relieves stress, and contributes to long-term happiness. As households save more, their net worth and resilience tend to grow, calling for detailed, long-term studies. Saving habits can also improve financial literacy, leading to better decisions and less anxiety about old age through long-term planning. Ultimately, understanding these effects within overall well-being offers a broader view of the transformative power of saving. Focusing on these aspects can provide valuable insights into the psychological and strategic sides of saving, opening new opportunities for research and innovative financial products.

8. Conclusions

This paper presents a systematic literature review combining ADO and TCM frameworks to explore saving behavior. It reviews 112 articles, revealing numerous unique factors that form a comprehensive knowledge base on what influences saving decisions. The review groups these factors into nine broad categories and creates a conceptual framework showing the relationships among factors, decisions, and outcomes. Figure 3 highlights that psychological factors are the most widely studied, followed by demographic and socioeconomic factors. The framework stresses that positive influences of these factors on saving decisions are common. Additionally, the most frequently studied decision variables are average savings balance and savings frequency, while key outcome variables include retirement planning and financial well-being. The review also assesses the theories, contexts, and methods used in existing research, noting that the Life Cycle Hypothesis is the most popular theory. Most studies depend on survey data and regression analysis, primarily conducted in the United States. Finally, the review points out significant research gaps and offers guidance for future research.