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Book cover

International Conference on Advanced Intelligent Systems and Informatics

AISI 2020: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020 pp 100–112 Cite as

The Impact of the Behavioral Factors on Investment Decision-Making: A Systemic Review on Financial Institutions

  • Syed Faisal Shah 19 ,
  • Muhammad Alshurideh 19 , 20 ,
  • Barween Al Kurdi 21 &
  • Said A. Salloum 22  
  • Conference paper
  • First Online: 20 September 2020

3072 Accesses

17 Citations

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1261)

The purpose of the study is to identify the effects of behavioral factors (cognitive biases) on financial decision-making. A systematic review method was implemented and selected 29 research published studies between the years 2010-2020 and were critically reviewed. The main findings of the study indicate that the most common factors appear in papers were overconfidence (18), anchoring bias (11), herding effect (10) and loss aversion (9), which has a significant impact on the financial decision making process. Moreover, almost half of the articles were survey-based (questionnaire), quantitative method and the rest of the articles were the qualitative and mixed-methods. The study concluded that the overall impact of behavioral/psychological factors highly influence on financial decision-making. However, the time and search of the key terms in the papers’ title were considered as the key limitations, which prevent in-depth investigation of the study. For future research, those most repetitive cognitive bias should be measured during COVID-19 pandemic uncertain situation.

  • Behavioral finance
  • Behavioral factor
  • Cognitive biases
  • Decision-making
  • Systemic review

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Syed Faisal Shah & Muhammad Alshurideh

Faculty of Business, University of Jordan, Amman, Jordan

Muhammad Alshurideh

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Shah, S.F., Alshurideh, M., Kurdi, B.A., Salloum, S.A. (2021). The Impact of the Behavioral Factors on Investment Decision-Making: A Systemic Review on Financial Institutions. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_9

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Unleashing the behavioral factors affecting the decision making of Chinese investors in stock markets

Roles Data curation, Methodology

Affiliation Business School, University of Southampton, Southampton, United Kingdom

Roles Conceptualization, Investigation, Writing – original draft

* E-mail: [email protected]

Affiliation Division of Management and Administrative Science, Department of Economics, University of Education, Lahore, Pakistan

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  • Yuzhu Xia, 
  • Ghulam Rasool Madni

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  • Published: February 13, 2024
  • https://doi.org/10.1371/journal.pone.0298797
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Table 1

This research paper delves into the behavioral factors that have impact on decision making of Chinese investors in stock markets. As one of the world’s most dynamic and rapidly evolving financial landscapes, stock markets of China have witnessed significant growth and transformation in recent years. However, the role of behavioral biases in shaping investment decisions remains a relatively understudied aspect. Drawing upon a detailed review of studies, psychological theories, and empirical studies, this research explores various behavioral factors affecting the decision of investors at Beijing, Shanghai and Shenzhen stock markets. Through a structured questionnaire and by collecting a sample of 521 respondents, this paper investigates that herding, overconfidence, prospect, market, gamble’s fallacy, and anchoring-ability bias often lead investors to deviate from rational decision-making and contribute to market inefficiencies. While herding, prospect, and heuristic affect the investment performance in stock markets of China. Moreover, the research underscores the need for investor education programs and regulatory interventions that acknowledge the presence of behavioral biases and encourage more informed decision-making. By shedding light on these dynamics, it provides valuable insights for policymakers, financial institutions, and investors seeking to navigate the intricacies of this rapidly growing financial landscape.

Citation: Xia Y, Madni GR (2024) Unleashing the behavioral factors affecting the decision making of Chinese investors in stock markets. PLoS ONE 19(2): e0298797. https://doi.org/10.1371/journal.pone.0298797

Editor: Andrea Flori, Politecnico di Milano, ITALY

Received: September 19, 2023; Accepted: January 30, 2024; Published: February 13, 2024

Copyright: © 2024 Xia, Madni. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Finance pertains to the administration of funds, possessions, debts, and investments by individuals, companies, governments, and other entities. It encompasses a broad spectrum of tasks linked to the distribution, procurement, and utilization of monetary assets with the aim of accomplishing diverse objectives [ 1 ]. Finance constitutes a pivotal element in both individual and corporate spheres, aiding people and entities in making knowledgeable choices concerning the adept handling and expansion of their economic assets. Similarly, behavioral finance stands as an area of research that melds psychological principles with conventional financial theory, aiming to grasp how emotional and psychological factors impact decision-making within financial markets [ 2 ]. It acknowledges that individuals and participants in the market frequently do not solely make rational decisions when dealing with financial management and investment selections [ 3 ]. In contrast to conventional financial theories that posit participants in the market consistently make reasonable and optimal choices, behavioral finance acknowledges that human conduct frequently diverges from rationality due to psychological elements, feelings, and cognitive restrictions. Despite the multitude of theories offered by economics and finance over time, these disciplines were unable to account for instances when individuals make irrational financial judgments. Individuals are drawn to stocks due to their potential for “long-term capital growth, dividends, and as a safeguard against the erosive impact of inflation on purchasing power” [ 4 ].

While Chinese stock markets have experienced substantial growth in terms of the quantity of listed stocks and transaction values, the volatility in price trends appears to be unpredictable across various timeframes. Moreover, there exists a restricted comprehension of the behaviors exhibited by individual investors and the behavioral components that influence their choices in investment [ 5 ]. Investor decision-making is significantly influenced by behavioral aspects, which include psychological components like emotions and cognition [ 6 ].

Numerous theories [ 7 – 9 ] assume that investors respond rationally by thoroughly evaluating all accessible information prior to making investment choices. Some other studies [ 10 – 12 ] have indicated that the rationality assumption does not align with reality. It is highlighted that investors do not conform to the rational suppositions of conventional finance theories. It has been emphasized that investors do not function as “calculative utility maximizing machines,” as postulated by conventional finance theories [ 12 ]. In greater detail, individuals are susceptible to their emotions and sentiments, often leading to cognitive errors [ 13 ]. To make more precise forecasts and decisions for endeavors of investors, it is crucial to delve into the behavioral variables impacting individual investors’ decision-making processes. Behavioral finance, grounded in psychology, proves advantageous in this context, as it elucidates the reasons behind individuals’ stock purchases or sales [ 6 ].

Many scholars believe that using behavioral finance as a framework will help them to better understand that how emotion and cognitive biases affect how they make financial decisions [ 14 ]. Advocates of behavioral finance claim that by including social sciences like psychology, it is possible to understand how the stock market behaves and how market bubbles and crashes occur [ 15 ]. The significance and appeal of applying behavioral finance to the Chinese stock markets stem from two key reasons. Firstly, behavioral finance remains a relatively novel area of study. It is only recently that it has been acknowledged as a viable model for elucidating the decision-making processes of financial market investors and their subsequent influence on financial markets themselves [ 16 ]. Secondly, Asian investors in particular have been found to be more prone to cognitive biases than people from other cultural backgrounds, according to a combination of subjective, academic, and experimental findings [ 16 ]. As a result, behavioral considerations must be considered when examining the variables that influence Chinese investors’ decision-making processes.

Behavioral finance investigations have predominantly been conducted within the developed economies of USA and Europe [ 17 ]. However, compared to their use in industrialized markets, behavioral finance ideas have been applied far less frequently in developing economies. The goal of this paper is to understand the motivations behind the actions taken by investors in Chinese stock markets via the lens of behavioral finance. The pursuit of comprehending and adequately explaining investor decisions necessitates an exploration into the behavioral factors influencing these decisions within the context of the Chinese stock markets, as well as an examination of the resultant impacts on investment performance [ 18 ].

This study, which departs from earlier research that was mostly based on conventional financial theories, applies behavioral finance principles. Moreover, while preceding research has often limited its focus to specific dimension of behavioral factor [ 19 ], this research adopts a more comprehensive array of behavioral determinants in evaluating their impact on Chinese investors. Furthermore, this study expands the application of principles of behavioral finance in context of emerging stock markets of China. Additionally, the methodology employed for gauging investment performance in this research diverges from past approaches that drew on secondary data from investors’ outcomes in the securities markets. Instead, this study directly engages individual investors by asking them to assess their own success in light of factors like investment return rate and fulfilment levels. These findings extend beyond individual investors, proving useful for security organizations as a reference point for market trend analysis and prediction.

2. Earlier literature and hypothesis

The Efficient Markets Hypothesis (EMH) holds that markets are rational. Additionally, the theory posits that markets formulate unbiased forecasts instead of attempting to predict the future. In contrast, behavioral finance departs from this notion by asserting that financial markets can occasionally lack informational efficiency [ 20 ]. This is due to the recognition that individuals are not always guided by rationality, often making financial decisions driven by behavioral biases. When decisions deviate from rational reasoning, it becomes imperative to recognize the impact of these behavioral biases [ 21 ]. Recent investigations have delved into the consequences arising from the actions of less-than-rational agents, employing various theoretical models. Researchers have empirically tested behavioral biases [ 11 ], providing evidentiary support. However, despite the controlled environment that can be achieved through well-designed experiments [ 22 ], testing behavioral finance theories has only occasionally used a small number of experiments.

The variables related to decision-making of investors can be divided into four groups: heuristic, prospect, market, and herding. Heuristics are practical rules that help with decision-making, especially when situations are complex and uncertain [ 23 ]. Heuristics are mental shortcuts or rules of thumb that simplify decision-making. In investment, heuristics allow investors to make quick judgments based on limited information [ 24 ]. They can be beneficial as they streamline complex investment choices into manageable decisions. For Chinese investors, heuristics might help in rapidly evaluating stocks or investment opportunities in a fast-paced market environment. They make it easier to estimate probabilities and predict values by breaking them down into simpler judgments [ 10 ]. The heuristic theory includes two factors called the Gambler’s fallacy and Overconfidence [ 25 ]. The idea that a small group can represent the larger overall population it comes from is called the "law of small numbers" [ 12 , 26 ], and this can lead to the Gambler’s fallacy happening [ 27 ]. In context of stock market, Gambler’s fallacy materializes when individuals inaccurately anticipate reversals in trends that are perceived as the culmination of favorable (or unfavorable) market returns [ 6 ]. The five distinct heuristic components under investigation in this study are overconfidence, anchoring, availability bias, gambler’s fallacy, and representativeness. shares. Representativeness denotes the extent to which an event bears similarity to its parent population or exhibits resemblance to that population [ 28 ]. This concept can introduce certain biases, such as the tendency for individuals to assign excessive significance to recent experiences and disregard the overall long-term average [ 29 ]. Additionally, representativeness contributes to what is referred to as “sample size neglect,” which raised when an individual attempt to infer conclusions from an insufficient number of samples [ 12 ]. Within context of stock market, application of representativeness becomes apparent when investors opt to purchase “hot” stocks as opposed to those that have performed poorly. This inclination towards representativeness offers an explanation for investor overreactions. When people overestimate dependability of their abilities and knowledge, overconfidence results [ 28 , 30 ]. A range of studies [ 31 ] have indicated that excessive trading is one of the consequences observed among investors due to this phenomenon. Research offers evidence that financial analysts tend to adjust their evaluations of a company at a gradual pace, even when strong indications suggest that the initial valuation is no longer correct [ 32 ]. Overconfidence is posited to enhance traits such as persistence, mental agility, and risk broad-mindedness. Essentially, it can contribute to strengthening professional enactment. It’s worth noting that overconfidence can also influence how others perceive an individual’s capabilities, potentially expediting promotions and extending investment horizons [ 33 ].

Prospect theory elucidates the theoretical relationship between prospects and investment decisions. The theory posits that individuals make decisions based on potential gains and losses relative to a reference point, rather than focusing solely on final outcomes [ 34 ]. Prospect theory suggests that individuals perceive outcomes relative to a reference point, often the status quo or an initial investment value. Investors evaluate gains and losses based on this reference point [ 35 ]. It is also highlighted by the theory that losses loom larger than equivalent gains. Investors tend to exhibit loss aversion, being more sensitive to potential losses than to gains of equal magnitude. This asymmetry significantly influences investment decisions, leading to risk-averse behavior to avoid potential losses [ 22 ]. Prospect theory shapes risk preferences in investment choices. When faced with potential gains, individuals might exhibit risk-seeking behavior, willing to take chances for further gains. Conversely, when facing potential losses, they tend to become risk-averse, prioritizing capital preservation over potential gains [ 18 ]. Understanding prospect theory aids in comprehending how investors evaluate investment opportunities, perceive risks, and make decisions based on potential gains and losses. The asymmetrical impact of gains and losses, reference points, and framing effects significantly shape investment choices, risk preferences, and the overall decision-making process in financial markets [ 36 ].

Efficient Market Hypothesis (EMH) suggests that markets efficiently incorporate all available information, making it challenging to outperform the market consistently. Under this theory, investors believe that attempting to beat the market through individual security selection or timing is futile, leading to passive investment strategies like index funds [ 13 ]. Behavioral finance challenges the EMH by highlighting investor behavior that deviates from rationality, leading to market anomalies [ 21 ]. Psychological biases, herding behavior, overreaction, and under reaction to news or market events can cause price distortions or inefficiencies. Investors identifying these anomalies might capitalize on mispriced assets, impacting their investment decisions. Market sentiment, driven by collective investor emotions, influences investment decisions [ 32 ]. Positive sentiment might lead to increased buying and inflated asset prices, while negative sentiment can trigger selling pressure and price declines. Investors might adjust their portfolios based on market sentiment, impacting their decisions [ 9 ]. The degree of market efficiency influences investors’ choice of investment strategies. In highly efficient markets, investors might opt for passive strategies, while in less efficient markets, active strategies that attempt to exploit mispriced assets might be preferred [ 37 ]. The market significantly influences investors’ perceptions of risk and return. In bull markets with rising asset prices, investors might perceive lower risk and higher potential returns, leading to more aggressive investment decisions [ 26 ]. Understanding the relationship between the market and investment decisions involves considering market efficiency, behavioral biases, sentiment, analysis methods, and the interplay between risk and return perceptions. These factors collectively shape how investors interpret market information and make investment decisions within financial markets [ 38 ].

The relationship between herding behavior and investment decisions is rooted in the influence of social dynamics on investor behavior within financial markets. Herding refers to individuals imitating the actions of a larger group rather than making independent decisions. In investment contexts, herding occurs when investors follow the crowd, mimicking others’ actions without conducting thorough individual analysis [ 25 ]. Herding often emerges due to information cascades, where individuals base their decisions on the actions of others rather than on private information. Initially, a few investors might make decisions based on their information or analysis. Subsequent investors observe these actions and may choose to follow suit, leading to a cascade of others mimicking these decisions, irrespective of personal information or analysis [ 31 ]. Herding behavior can lead to market inefficiencies and price distortions. When a significant number of investors herd towards certain assets, it can create artificial demand or supply, causing prices to deviate from their fundamental values [ 11 ]. This creates opportunities for contrarian investors who recognize the herd’s influence and trade against prevailing market sentiment [ 23 ]. Herding behavior can significantly influence investment decisions. Fear of missing out (FOMO) or assuming safety in numbers might drive investors to join the herd. Conversely, some investors might be contrarians, leveraging opportunities created by herd-driven mispricing [ 39 ]. Acknowledging the impact of herding behavior helps investors navigate market trends and potentially capitalize on opportunities arising from herd-driven mispricing [ 40 ]. In light of above discussion, hypothesis H1 is put forth as follows:

  • H1 : The behavioral elements such as heuristics , prospect , market , and herding have positive influence on investment decisions of Chinese investors .

Behavioral factors can positively influence the investment performance of Chinese investors through several theoretical perspectives. Behavioral finance theory suggests that market inefficiencies arise due to investors’ behavioral biases. These biases, such as overreaction to news, herding behavior, or cognitive errors, create opportunities for skilled investors to exploit mispricing [ 23 ]. Behavioral biases might lead to certain stocks being undervalued or overvalued, allowing astute investors to identify mispriced assets and capitalize on them, ultimately enhancing investment performance [ 12 ]. Adaptive Market Hypothesis posits that markets continuously adapt to the behavior of investors. Investors’ behavioral tendencies, such as herd behavior or biases in risk perception, might contribute to temporary price distortions [ 34 ]. However, over time, market mechanisms adjust, and prices reflect fundamental values. Skilled investors who recognize and navigate these behavioral fluctuations can outperform the market by exploiting short-term misalignments before market corrections occur [ 26 , 41 ]. Traditional portfolio theory assumes rational, utility-maximizing investors. However, behavioral portfolio theory acknowledges investors’ behavioral biases and their influence on portfolio construction. Investors might exhibit biases like home country bias (overweighting domestic assets) or familiarity bias (preferring familiar stocks), which might lead to portfolios deviating from efficient diversification [ 35 ]. However, if investors understand and manage these biases effectively, they can construct portfolios that capitalize on market opportunities and enhance performance. Prospect theory suggests that investors’ risk attitudes differ when facing gains or losses [ 18 ]. Understanding how investors perceive gains and losses can lead to better risk management strategies. Investors might adopt strategies that focus on capital preservation when facing potential losses and pursue more aggressive strategies to capitalize on gains. Effectively managing risk based on these behavioral tendencies can positively impact investment performance [ 33 ]. In summary, behavioral factors can positively influence investment performance by providing opportunities to exploit market inefficiencies, adapt to behavioral fluctuations, construct portfolios aligned with behavioral biases, and employ effective risk management strategies based on investors’ perceptions of gains and losses. Understanding these behavioral aspects enables investors to navigate the market more strategically, potentially leading to improved investment outcomes.

In their research [ 34 ], Lin and Swanson examined investment performance using five different time spans and three measures of returns. The study found that investors can attain higher returns, especially in the short run. This increased performance isn’t necessarily due to taking on more risk, but rather is connected to short-term price trends [ 42 ]. However, when compared to performance over longer periods, this impressive performance becomes weaker or declines. This suggests that short-term factors like a higher demand for previously successful stocks and/or an oversupply of stocks that have performed poorly in the past contribute to achieving outstanding enactment. In short run, but not over long time periods, behaviors have the effect of pushing up equities with a history of high performance and pushing down stocks with a history of negative performance [ 36 ]. The predominance of short run performance, especially when powered by the momentum of winners rather than losers, suggests that buying behavior adds fresh knowledge to market.

The study conducted by [ 35 ] explores the degree of impact that overconfidence exerts on investment performance, as measured by two primary indicators: investment return rate and swapping experience. The investment coming back rate, or profit, serves as an objective measure of investment performance, gauged by investors in relation to the profit rates achieved by their peers [ 43 ]. On the other hand, an investor’s trading experience signifies the duration for which an individual has been participating in securities markets. The finding of research show that while overconfidence does not significantly influence investment profit, it does exert an impact on the trading practice of separable investors [ 12 , 28 – 31 ].

In conclusion, there exists a plethora of methods for evaluating stock investment performance. Previous researchers have predominantly relied on secondary data gathered from investors’ activities in the securities markets to gauge investment performance [ 16 , 34 ]. In order to go deeper, the return on stock investments is evaluated using both objective and subjective criteria. Comparing actual return degrees to the normal return rate of the securities market is what is required for impartial assessment [ 25 ]. Furthermore, this study adds another factor to evaluate investment performance: level of satisfaction with investment decisions. In real-world scenarios, certain investors might be pleased with their investment performance even profits aren’t significant, whereas others might be dissatisfied despite having relatively high profits [ 18 ]. Based on the discussions provided, it’s logical to suggest that behavioral factors have an impact on the investment choices. This idea forms the basis for creation of hypothesis H3 :

  • H2 : Behavioral factors have positive influence on investment performance of Chinese investors .

3. Materials and methods

3.1. sample and data collection.

To comprehend the typical behaviors exhibited by individual investors, a cross-sectional design is chosen as the most appropriate approach, in contrast to case studies, experiments, or longitudinal-designs [ 13 ]. Specifically, experimental designs often prove effective for investigating the relationships between variables. Among the array of data collecting approaches available, the self-completion method is selected for gathering quantitative data. Self-completed questionnaires are widely employed in quantitative research due to their convenience for respondents, particularly when sensitive information is involved [ 36 ]. The convenient sampling method is adopted to collect the data to ensure representation across various demographics because it is best way to get the highest response rate and saving the money and time. It is worth noting that convenience sampling is a type of non-probability sampling and its results cannot be generalized [ 37 ]. Questionnaires are distributed to investors via email and social media platforms. Regarding geographical distribution, the respondents were sourced from various regions across China, contributing to a broader representation within the study. The questionnaires were sent to individual investors randomly through brokers of security companies registered in Beijing, Shanghai and Shenzhen stock markets of China. The investors from ten leading securities companies have been selected. The survey was carried out from February 1, 2023 to May 30, 2023. The “Ethical Committee” of University of Education approved the study. A written consent was gained from participants of the study. A total of 700 questionnaires are disseminated to investors to facilitate the research objectives. There are three parts of questionnaire including personal details, behavioral factors affecting investment choices, and investment performance. A set of questions has been developed to encompass all aspects described in theories of behavioral finance. Within these parts, we’ve utilized a 6-point Likert scale, a commonly employed technique for collecting opinions and attitudes from participants [ 38 , 39 ]. Individual investors are requested to express their consent to express the impact of behavioral factors on their investment decisions and their alignment with statements related to investment performance. The scale covers a range from 1 to 6, encompassing options such as "strongly disagree," "disagree," "somewhat disagree," "somewhat agree," "agree," and "strongly agree." To ensure the soundness of opinion poll, draft is tested by experts in the field as well as by fifty individual investors. Subsequent to these iterations, the final version of the questionnaire is prepared.

The data acquired from the questionnaires offer foundational insights into the factors that influence investors’ decisions. Given the research’s focus on exploring behavioral factors, it’s advisable to employ a comparatively large sample size. A larger sample size enhances representativeness and consequently bolsters the reliability of the findings [ 37 ]. According to [ 38 ], for quantitative research, a minimum of 100 respondents is recommended to ensure compatibility with statistical methods of data analysis.

3.2. Methodology

The gathered data is subjected to processing and analysis through the utilization of SPSS and AMOS software. The initial step involves data cleaning, where questionnaires showing poor quality, such as excessive misplaced values or biased ratings, are excluded from the dataset. Subsequently, a range of statistical methods is employed to meet the research objectives. Descriptive statistics, factor analysis, the Cronbach’s Alpha test, and structural equation modelling (SEM) are among the used approaches.

The exploratory factor analysis (EFA) in this study employs several criteria, including Factor Loadings, Kaiser-Meyer Olkin Measure of Sampling Adequacy (KMO), Total Variance Explained, and Eigenvalue. When factor loadings are above 0.5, it indicates practical significance in EFA [ 38 ]. A KMO score in range of 0.5–1.0 (with a significance level below 0.005) confirms the appropriateness of factor analysis for the data [ 40 ]. The study uses Cronbach’s Alpha Test to assess reliability of variables [ 41 ]. According to Nunnally’s suggestion [ 42 ], the value of Cronbach’s alpha of 0.7 is considered to indicate measurement reliability. However, some statisticians consider an alpha value higher than 0.6 to be acceptable [ 43 ].

SEM is utilized to validate how behavioral factors influence investment performance of individual investor. Additionally, it’s used to estimate the strength of relationships among these factors. The model is considered satisfactory if it meets the following criteria: “a squared error of approximation (RMSEA) equal to or less than 0.10, a comparative fit index (CFI) equal to or greater than 0.90, and a parsimonious fit index (PFI) equal to or greater than 0.60” [ 44 ].

Respondents are provided with comprehensive and pertinent information to enable them to willingly participate in the research. Each questionnaire includes a cover page that furnishes adequate details about the study, thereby allowing respondents to make informed decisions regarding their participation. Furthermore, the distribution of questionnaires occurs through email and social media platforms, granting respondents the autonomy to choose whether or not to respond. It’s important to emphasize that respondents were informed about the survey’s commitment to maintaining their anonymity and confidentiality throughout the process.

3.3. Empirical findings

Out of the 700 questionnaires distributed to individuals, 521 respondents have been recorded, resulting in a response rate of 80%. This rate stands as moderately high, particularly for a postal questionnaire survey. A breakdown of the respondent sample by characteristics such as gender, age, duration of participation in the stock market, total investment amount, and more, has been carried out.

The gender composition of the sample shows that female investors account for 47% of the participants, while male investors account for 53%. In terms of age distribution, 77% of stock investors (or 77% of the whole sample) are in the age of 26–35. Furthermore, 12% of respondents are in age of 36–45, while 9% are in age of 18–25. This distribution emphasizes that a significant number of individual investors are under the age of 35, indicating that this study is likely to substantially represent the investment behaviors of this age group. Regarding the age skew toward younger respondents, this might reflect a higher engagement of younger demographics in stock market participation.

An analysis of the duration of participation in the stock market reveals that 33% of respondents have been engaged for less than 3 years, 22% have participated for 3 to fewer than 5 years, 15% have been involved for less than 1 year, while 29% have taken part in the stock marketplace for over five (5) years but less than ten (10) years. A mere 1% have an extensive history of more than 10 years of participation in stock market.

Concerning investment ranges, respondents cover the spectrum from US$2000 to US$30000. Notably, the greater proportions of individual stockholders in the sample are invested within the ranges of US$2000–10000: 35% investing less than US$2000, 32% investing between US$2000–4000, and 30% investing between US$4000–10000. Moreover, a small portion of the sample, 3%, invests substantially higher amounts exceeding US$30000.

The EFA has been applied to set of communicative variables and investment in order to discern the underlying factors to which these variables pertain. Through several iterations involving the removal of unsuitable variables, the remaining variables have been successfully grouped into six distinct factors. These factors consist of five factors associated with communicative variables and one factor linked to investment performance. The decision to stop at an Eigenvalue of 1.00 has been made, resulting in a Kaiser-Meyer Olkin (KMO) measure of 0.77 (with a significance level of 0.000). This measure, along with a percentage of total variance explained at 71.41%, validates the suitability and acceptance of the factor analysis for these variables. Moreover, all factor loadings have exceeded the threshold of 0.5. The combination of these indices serves as conclusive evidence supporting the appropriateness of conducting factor analysis on these variables. Further details and findings are outlined in Table 1 .

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https://doi.org/10.1371/journal.pone.0298797.t001

Composite reliability (CR) and Cronbach’s α are used to assess the dependability of constructs. The values of Cronbach’s α and CR demonstrate strong internal reliability. Next, discriminant and convergent validity are evaluated. To establish convergent validity, factor loadings should be at least 0.50, and Average Variance Extracted (AVE) coefficients should also reach 0.50. The findings revealed that all items exhibited factor loadings surpassing 0.50, and AVE coefficients exceeded 0.50, indicating robust convergent validity. Several goodness of fit indices are employed to determine the adequacy of model. The ratio of (x2/df) was less than 5, and the RMR score was 0.045, with the RMSEA score at 0.048, both falling below the acceptable threshold of 0.08. Moreover, the values of GFI, CFI, RFI, NFI, and IFI all exceeded the recommended threshold of 0.90 [ 38 ]. Thus, based on these indicators, the data demonstrated a strong fit to the measurement model.

The herding, prospect, and market variables are combined into a single interconnected factor, as indicated in Table 1 . Conversely, the heuristic variables are split between two factors: overconfidence-gambler’s fallacy and anchoring-ability bias. This finding slightly differs from Hypothesis H1, which suggested classifying behavioral variables into four groups. As a result, there are five main behavioral aspects that influence individual investors’ investment decisions. The Cronbach’s α test ensures the consistency of the items within these categories determined by factor analysis. This evaluation ensures the measurements’ dependability for future usage. The results reveal that the Cronbach’s α values for all factors are greater than 0.6, and the significance of the F-test for each factor is less than 0.05. These findings show that the items within the factors are reliable, making them acceptable for further investigation.

The average values of each variable in the sample are employed to gauge the extent of influence that behavioral variables have on investment decisions. Likewise, the investment performance variables are evaluated by computing the average ratings given by respondents for each variable. Since 6-point scale is used, their average value act as indicators of their effect on investment decision-making, as outlined below:

  • Mean value less than 2 shows very little impact from the factors.
  • Mean values ranging from 2–3 show that variables have little impact.
  • Mean values ranging from 3–4 show that variables have moderate impact.
  • Mean values ranging from 3–4 show that variables have significant impact.
  • Mean values greater than 5 show that variables have substantial impact.

The impact of each factor is shown in the following Table 2 :

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https://doi.org/10.1371/journal.pone.0298797.t002

This study shows that the representativeness-related characteristics are not reliable enough to be taken into account as behavioral influences on the choices made by individual investors. On the other hand, overconfidence, anchoring, and ability bias have only little effects on how individual investors make decisions. Contrarily, the gambler’s fallacy has no impact on their choice of investments. All three behavioral tendencies; regret aversion, loss aversion, and mental accounting are depicted through variables in the Prospect dimension that affect investment decisions of stock investors. Individual investors exhibit a moderate level of mental accounting, regret aversion, and loss aversion. Notably, these investors demonstrate a strong inclination toward treating each element of their investment portfolio as a separate entity. This implies that individuals tend to thoroughly assess all accessible stock market information, including general data, historical price patterns, and current price changes, before finalizing investment choices. When contrasted with their average values, the relatively significant standard deviations of these variables suggest that certain investors attach substantial importance to market-related factors while making decisions about which stocks to purchase. Individual investors exhibit a moderate inclination to follow the trading decisions of their peers. However, it appears that the steering effect does not have an immediate impact on their stock investment adoptions.

Collectively, the mainstream of behavioral variables across the Heuristic, Prospect, and Herding factors exert reasonable impact on decision making of investors. A small number of items exhibit low impacts on investors’ choices. Conversely, certain variables within the Market and one variable within the Prospect factor are reported to exert significant influence on investment choices. The findings do not support second hypothesis that proposed all behavioral finance factors would strongly influence individuals’ investment decisions.

SEM, or structural equation modelling, is used to show how different variables relate to one another. SEM is a framework that combines elements of multiple regression and factor analysis. CFA, a component of SEM is used to confirm that factors and each of their individual components—which were discovered by Exploratory Factor Analysis, as was previously mentioned—are appropriate for inclusion in the overall model. The second part also includes multiple regression, which determines the correlation coefficients between behavioral factors, which serve as independent variables, and the investment performance factor, which serves as a dependent variable.

The results of SEM are presented in Fig 1 . The GFI (Goodness-of-Fit Index) for the structural model stands at 0.95, the TLI (Tucker-Lewis Coefficient) at 0.96, the CFI at 0.95, the RMSEA at 0.08, the CMIN/df at 2.25, and the p-value is 0.00. These results indicate a strong fit between the model and data. These indicators highlight the model’s effectiveness in predicting the studied data accurately. The estimated weights for variables in a regression are displayed in Fig 1 .

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Note: Regression weights are mentioned above the arrows. https://docs.google.com/document/d/1olllsnBahvyWQj_Fo9ZItL6gw8g9TbpZ/edit?usp=sharing&ouid=104935469773789258540&rtpof=true&sd=true .

https://doi.org/10.1371/journal.pone.0298797.g001

Three elements, Herding, Prospect, and Heuristic have an impact on Investment Performance. The fact that there are greater than 0.5 factor loading between each factor and its component variables supports the congruent validity of the data measures. A regression estimate of 0.71 (p 0.01) shows that the Overconfidence and Gambler’s fallacy heuristic behaviors have the greatest positive influence on investment performance. A regression estimate of 0.42 (p 0.01) shows that herding behaviors have a beneficial impact on investment performance, whereas a regression estimate of -0.25 (p 0.05) shows that prospect behaviors, such as following the herd, have a negative effect on investment success. These three sorts of actions together explain 52% of the variation in the success of individual investors.

The findings imply that enhancing both heuristic and herding behaviors while being mindful of the adverse effects of prospect behaviors could lead to an enhancement in investment performance. Surprisingly, a notable finding is that, despite the perception that market factors have a substantial impact on investing decisions, investors’ performance is not significantly impacted. The results derived from SEM contradict Hypothesis H2, which posits that all behavioral factors contribute positively to investment performance. In reality, only the herding and heuristic factors are recognized to exert favorable influences on investment performance. Conversely, the market factor lacks impact, and the prospect factor exhibits a negative impact on investment performance.

4. Analysis and discussion

The findings indicate a differentiation within the heuristics components, now labeled as Overconfident & Gambler’s fallacy and Anchoring & Availability bias. The results highlight that investors’ decisions are affected by the information available to them, particularly among the four heuristic factors, which have demonstrated sufficient reliability and consistency in measurement. Investors exhibit a preference for local stocks over international ones, attributed to the ease with which they can obtain information about local stocks through their social connections, like friends and relatives. This is consistent with a previous study’s findings [ 12 , 45 – 47 ], which showed that 96% of investors examined have a home-market bias and disregard the benefits of diversifying their investment portfolios. The findings also reveal that individual investors possess a moderate level of confidence. This can be attributed to the fact that the security market is still in its emerging stages, resulting in complex and unpredictable fluctuations. These fluctuations can occur irrespective of the performance of the listed companies issuing the stocks [ 48 ].

While the Gambler’s fallacy has been established as a dependable variable influencing investors’ decision-making, its impact level remains quite low. This implies that investors typically lack the capability to foresee the outcomes of positive or negative market trends. This outcome is readily understandable given the substantial fluctuations in market trends, coupled with investors’ moderate level of confidence. Notably, this finding diverges significantly from the results of a previous study [ 45 ], which suggests that a majority of investors possess the ability to accurately anticipate stock price changes. Loss aversion, regret aversion, and mental accounting are the three significant prospect factors that notably impact investors’ decisions. Additionally, mental accounting serves as the fourth influential factor [ 49 ]. This finding affirms the tendency of investors to treat each component of their investment portfolio in isolation, effectively disregarding potential connections between different investment opportunities, as previously noted in a study [ 46 ]. However, this approach can lead to inefficiencies and inconsistencies in decision-making processes.

It is found that the herding variables’ significant observed influence on investors’ judgments is merely minimal. Herding tendencies are more prevalent in emerging markets than in established ones [ 37 , 50 ], because of greater government intervention and lower-quality information disclosure [ 47 ]. It’s said that herding behaviors had a huge impact on Asian nations in 1997–1998. The stock market has been operational for more than a decade, which may account for the herding factor’s mild impact. As a result, investors might have accumulated greater knowledge and skills, allowing them to leverage diverse information sources prior to making investment decisions. Consequently, the effect of herding may have diminished over time [ 48 , 51 , 52 ]. Farber, et al. [ 53 ] emphasize the significant prevalence of the herding effect within Vietnam’s stock market, particularly directed toward positive market returns. Chen, et al. [ 54 ] posit that herding tendencies are more pronounced in emerging markets compared to developed ones due to higher levels of government intervention and lower quality of information disclosure. Additionally, Kaminsky and Schmukler [ 55 ] assert that during the period spanning 1997–1998, herding behaviors notably influenced the trajectories of Asian countries.

Three distinct groups of factors-herding, prospect, and heuristics-exert effects on investment performance. Heuristics and herding have favorable effects within these, whereas prospect has a detrimental impact on investing success. The heuristics factor, in particular, exerts a positive impact, implying a direct correlation between overconfidence, gambler’s fallacy, and investment outcomes. Investors tend to believe that heightened confidence leads to more resolute actions. In the realm of business, making decisive choices is crucial for capitalizing on significant opportunities [ 56 ]. Confident investors are likely to leverage their expertise and knowledge in specific situations, potentially enhancing investment outcomes. Regarding loss aversion, investors acknowledge it as a prevalent behavior among their peers; however, they recognize that it can result in poor trading decisions that negatively influence investors’ wealth. This perspective aligns with the viewpoints of previous studies [ 52 ]. The notion is that prior gains can fuel a sense of greed, pushing investors to seek even greater profits by investing more capital. Consequently, when unforeseen events occur, resulting losses tend to be considerably higher than anticipated. Conversely, experiencing a loss prompts individuals to become more risk-averse and cautious [ 57 ]. They approach decisions with a heightened sense of circumspection, seeking extensive information and analyzing it meticulously. They only commit to investments when confident of success. This viewpoint aligns with the perspective put forth by Odean [ 56 ]. Manager A suggests that following a profit, individuals often experience increased confidence in their decision-making abilities, prompting them to rush their judgments. This haste might cause them to undervalue or disregard crucial information that could impact investment outcomes. Notably, individuals in this scenario tend to prioritize their own judgments over external opinions, potentially leading them to overestimate the probability of success. The earlier research [ 57 , 58 ] posit that during declining periods, market liquidity significantly diminishes. This occurs as buyers aim to purchase stocks at the lowest possible price while sellers seek to offload stocks at the highest possible price. Consequently, the process of selling underperforming stocks becomes more challenging compared to selling those that have gained value.

Numerous authors also assert the benefits of overconfidence. Choosing common stocks that outclass the market is a challenging endeavor, characterized by low predictability and noisy feedback. Consequently, stock selection stands as a task where people exhibit heightened overconfidence [ 24 ]. According to some researchers, overconfident investors trade at levels that are much greater than those of rational investors, potentially having a considerable impact on trading volume, market depth, wealth distribution, and other outcomes [ 29 , 59 ]. Severe under-confidence and severe overconfidence are unlikely to continue over the long term, according to a study [ 49 ]. Moderate overconfidence, however, has the potential to outlast and rule logical behavior. Additionally, under certain conditions, overconfident traders may perform so well that they eclipse "rational" traders. These analytical findings have their origins in the psychological finding that overconfidence is a characteristic that is often present [ 35 , 60 ].

Herding factor has a beneficial effect on investment performance in addition to the heuristics factor, represented by overconfidence. It has been proposed that in the securities market, overconfidence can encourage herding behaviors [ 50 ]. Herding behavior, often referred to as the “crowd effects,” occurs when individuals replicate others’ decisions and is frequently linked with significant stock price fluctuations or excessive volatility [ 51 ]. Consequently, emulating these prevailing trends might assist investors in enhancing their investment outcomes. This is especially important for investors who are not risk-takers because doing as the "crowd" suggests is a wise move to ensure at least average returns. Furthermore, herding contributes to notable increases in trading volumes [ 15 , 61 ], consequently bolstering liquidity. As a result, investors stand to benefit from rapid capital turnover, translating to improved returns. However, it’s important to note that while herding investors aim to match their peers’ performance, non-herding traders strive to surpass competitors [ 22 , 60 ]. Therefore, achieving higher returns necessitates careful consideration of the potential positive and negative effects of herding before making choices regarding investments [ 25 ].

However, it’s essential to acknowledge that every investment inherently carries a degree of risk, and without assuming some level of risk, high returns cannot be pursued. While careful consideration is prudent before making decisions, excessive caution might lead to delayed actions, potentially causing investors to miss out on favorable investment opportunities and subsequently reducing their prospects for achieving substantial profits [ 31 ].

5. Conclusions

Five key behavioral factors, including herding, market, prospect, overconfidence, and anchoring-ability bias, influence the investing choices of investors at Chinese stock markets. Four behavioral aspects of the herding factor are all connected to imitating the activities of other investors. Price variations, market knowledge, and previous stock movement are the three elements that make up the market factor. The prospect factor consists of four components: mental accounting (with two sub-variables), mental accounting (with loss aversion and regret aversion), and mental accounting. Two components make up the heuristic variables: overconfidence-gambler’s fallacy and anchoring-ability bias. In contrast to the anchoring-ability bias component, which consists of two variables: ability bias and anchoring, the overconfidence-gambler’s fallacy factor consists of two variables: overconfidence and gambler’s fallacy. The decisions that investors make are affected by all of these variables taken together.

The results of the investigation show that Hypothesis H1 is largely supported. Prospect, herding, and heuristic (containing two sub-elements) are the behavioral factors that have the most moderate influence on individual investors’ decisions. Herding speed and the gambler’s fallacy are two elements that have less of an effect on the decisions made by investors. It’s interesting to note that when making investing decisions, three market factors—price movements, market knowledge, and historical stock trends—as well as one prospect component element—mental accounting—come into play. Only three variables—herding, prospect (which includes loss aversion, regret aversion, and mental accounting), and heuristic (which includes overconfidence and gambler’s fallacy)—have been found to significantly affect investing success. Among these, heuristic behaviors instead of herding behaviors have a more favorable impact on the outcome of investments. The favorable influence of heuristic behaviors is bigger than that of the other two components. Conversely, prospect behaviors yield a negative impact on investment performance. These findings diverge from Hypothesis H2, which proposed that all behavioral factors would positively impact investment performance.

The findings underscore that a certain level of overconfidence exerts a positive effect on investment enactment. As a result, single investors should maintain a measured degree of overconfidence to effectively apply their expertise and knowledge in specific situations, thus enhancing investment outcomes. Particularly in times of uncertainty, overconfidence can prove beneficial by empowering investors to tackle challenging tasks and aiding them in forecasting future trends. In addition to overconfidence, herding is also observed to have a favorable impact on investment performance. Therefore, single investors are advised to establish partnerships or alliances with competent investment peers, leveraging their insights as valuable references for making informed decisions. Creating platforms for mutual support and information-sharing within forums can contribute to accessing reliable stock market information. Prospect considerations, on the other hand, have a detrimental effect on investment performance, according to the data. In light of their possible negative impact on investing decision-making, investors should proceed cautiously when dealing with loss aversion, regret aversion, and mental accounting.

The recommendations for investors include being diligent in their investment decisions, while also avoiding excessive fixation on prior losses for subsequent investment choices. Striking this balance is crucial to prevent missed investment opportunities, mitigate potential negative psychological impacts, and ultimately improve investment performance. Given certain limitations, the current research concentrates solely on the actions of individual stockholders in the Chinese stock markets. To gain a comprehensive understanding of the Chinese stock market landscape, further research should encompass the behaviors of institutional investors. This broader perspective would contribute to a more holistic and well-rounded assessment of the market dynamics. The study has a limited sample size due to resource constraints, potentially failing to represent the broader diversity of Chinese investors. Findings of this study are not applicable in general due to regional, cultural, or economic variations within China. Moreover, the study used convenient sampling so findings of the study cannot be generalized. Reliance on self-reported data or surveys could introduce response bias or inaccuracies due to subjective perceptions of participants. Market conditions and behavioral trends are dynamic. The study captures a snapshot of behavior at a specific time, which might not reflect long-term patterns or changes in market dynamics. Moreover, the study did not account for external factors such as regulatory changes, geopolitical events, or economic fluctuations, which can significantly influence investor behavior. Focusing on specific behavioral aspects might overlook other influential factors like socioeconomic status, education, or personal experiences that also shape investment decisions. Addressing these limitations often strengthens the credibility and applicability of such studies, allowing for a more comprehensive understanding of the behavioral dynamics influencing investment decisions among Chinese investors.

Supporting information

https://doi.org/10.1371/journal.pone.0298797.s001

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This paper is in the following e-collection/theme issue:

Published on 19.2.2024 in Vol 26 (2024)

Media Use Behavior Mediates the Association Between Family Health and Intention to Use Mobile Health Devices Among Older Adults: Cross-Sectional Study

Authors of this article:

Author Orcid Image

Original Paper

  • Jinghui Chang 1 * , PhD   ; 
  • Yanshan Mai 2 *   ; 
  • Dayi Zhang 2   ; 
  • Xixi Yang 1   ; 
  • Anqi Li 1 , MSc   ; 
  • Wende Yan 2   ; 
  • Yibo Wu 3 , PhD   ; 
  • Jiangyun Chen 1 , PhD  

1 School of Health Management, Southern Medical University, Guangzhou, China

2 School of Public Health, Southern Medical University, Guangzhou, China

3 School of Public Health, Peking University, Beijing, China

*these authors contributed equally

Corresponding Author:

Jiangyun Chen, PhD

School of Health Management

Southern Medical University

Number 1023, South Shatai Road

Baiyun District

Guangzhou, 510515

Phone: 86 1 858 822 0304

Email: [email protected]

Background: With the advent of a new era for health and medical treatment, characterized by the integration of mobile technology, a significant digital divide has surfaced, particularly in the engagement of older individuals with mobile health (mHealth). The health of a family is intricately connected to the well-being of its members, and the use of media plays a crucial role in facilitating mHealth care. Therefore, it is important to examine the mediating role of media use behavior in the connection between the family health of older individuals and their inclination to use mHealth devices.

Objective: This study aims to investigate the impact of family health and media use behavior on the intention of older individuals to use mHealth devices in China. The study aims to delve into the intricate dynamics to determine whether media use behavior serves as a mediator in the relationship between family health and the intention to use mHealth devices among older adults. The ultimate goal is to offer well-founded and practical recommendations to assist older individuals in overcoming the digital divide.

Methods: The study used data from 3712 individuals aged 60 and above, sourced from the 2022 Psychology and Behavior Investigation of Chinese Residents study. Linear regression models were used to assess the relationships between family health, media use behavior, and the intention to use mHealth devices. To investigate the mediating role of media use behavior, we used the Sobel-Goodman Mediation Test. This analysis focused on the connection between 4 dimensions of family health and the intention to use mHealth devices.

Results: A positive correlation was observed among family health, media use behavior, and the intention to use mHealth devices (r=0.077-0.178, P<.001). Notably, media use behavior was identified as a partial mediator in the relationship between the overall score of family health and the intention to use mHealth devices, as indicated by the Sobel test (z=5.451, P<.001). Subgroup analysis further indicated that a complete mediating effect was observed specifically between family health resources and the intention to use mHealth devices in older individuals with varying education levels.

Conclusions: The study revealed the significance of family health and media use behavior in motivating older adults to adopt mHealth devices. Media use behavior was identified as a mediator in the connection between family health and the intention to use mHealth devices, with more intricate dynamics observed among older adults with lower education levels. Going forward, the critical role of home health resources must be maximized, such as initiatives to develop digital education tailored for older adults and the creation of media products specifically designed for them. These measures aim to alleviate technological challenges associated with using media devices among older adults, ultimately bolstering their inclination to adopt mHealth devices.

Introduction

The 2022 United Nations report on “World Population Prospects” predicted that by 2050, the global population will reach 9.7 billion. Within this demographic shift, 1.5 billion individuals aged 65 and above are anticipated, constituting 16% of the total population [ 1 ]. Notably, the trend of population aging is intensifying. In the context of population dynamics, China, as a heavily populated nation, is undergoing significant and intricate transformations. The Seventh National Population Census of China revealed that there are 264 million individuals aged 60 or older in the country, comprising 18.7% of the overall population [ 2 ]. This underscores the profound changes in China’s demographic landscape. The rapidly increasing aging rate in China poses substantial challenges for the future development of the country’s medical services. Over 180 million older adults in China grapple with chronic diseases, and a staggering 75% of them contend with multiple chronic illnesses [ 3 ]. This places older individuals in a high-risk and vulnerable category, imposing considerable financial and operational burdens on China’s medical and health sector.

Mobile health (mHealth) devices typically encompass mHealth programs and wearable devices [ 4 ]. Functioning as portable tools leveraging internet communication technology, these devices continuously monitor diverse physiological conditions. They have the capability to track and record users’ daily lifestyle and health status data in real-time [ 5 ]. These real-time data are instrumental for users to make informed adjustments to their health behaviors, facilitated by prompt feedback on health information [ 6 ]. The utilization of mHealth devices addresses the emerging need for self-monitoring and self-management within the expanding medical service market, aligning with heightened health awareness among consumers. These devices play a pivotal role in enabling early diagnosis, intervention, clinical treatment, and monitoring of various diseases by continuously supervising vital signs in real-time. However, it is noteworthy that despite the potential benefits, mHealth devices are not widely embraced by older individuals [ 7 ]. Consequently, the robust functionalities and inherent advantages of these devices remain underutilized within this demographic group. Emerging as an inevitable outcome of the internet era and the aging society, mHealth holds substantial potential to offer a promising solution to meet the escalating demands for medical services in developing countries [ 8 ]. Recognizing that older individuals constitute the most frequent and substantial users of health services [ 9 ], it becomes imperative to cultivate a new social trend, encouraging the integration of older individuals with mHealth [ 10 ].

Prior research has demonstrated that mHealth can significantly enhance the health, well-being, and longevity of older individuals in the digital era. However, it also introduces a new social governance challenge—the digital divide among older individuals [ 11 , 12 ]. This divide arises from challenges in accessing or utilizing information infrastructure coupled with a lower level of digital education, resulting in difficulties for older individuals to stay abreast of social, economic, and technological advancements [ 13 ]. As outlined in the 50th Statistical Report on the Development of the Internet in China by the China Internet Network Information Center, individuals aged 60 and above constitute the predominant group of non-netizens, comprising 41.6% of this demographic [ 14 ]. A confluence of personal, family, social, and technological factors collectively contributes to the estrangement of older individuals from engaging with new media, such as the internet [ 15 ]. Research indicates that the motivation for older individuals to actively seek health information on the internet is closely tied to their interactions with family or friends [ 16 ]. Older adults primarily rely on their families for social support, and the cohesion within the family unit significantly influences their overall health status [ 17 , 18 ].

Family health represents a collective resource that emerges from the interconnected well-being of each family member, encompassing their health, interactions, capacities, and the family’s overall physical, social, emotional, economic, and medical resources [ 19 ]. As an interdisciplinary concept, evaluating family health necessitates a thorough examination of various factors, including but not limited to family functioning, emotional support, financial resources, and access to external services [ 20 ]. Existing literature demonstrates that family support plays a pivotal role in motivating older individuals to seek medical services [ 21 ]. Additionally, family function and overall health serve as crucial indicators for assessing the mental well-being of older individuals [ 22 ]. Communication within the family, involving interactions with children, grandchildren, and peer groups, influences older individuals’ inclination to adopt smart senior care solutions [ 23 ]. While numerous articles predominantly explore family health from a singular dimension [ 24 - 26 ], there exists a research gap concerning the specific influence of family health on older individuals’ intention to adopt mHealth devices.

The evolution of mHealth is intricately linked to the technical backing of media. Media technology plays a dual role—it not only generates visual data representing health conditions detected by mHealth devices [ 27 ] but also serves as a platform for the public to exchange and share medical information. In the case of older adults, their acceptance of new health services and access to health information are influenced in distinct ways by the utilization of media devices [ 28 , 29 ]. A Chinese empirical analysis revealed a fundamental correlation between media use and the health level of older adults [ 30 ]. Social media communication is considered an intervention measure to alleviate the loneliness experienced by older adults, achieved by enhancing social support and contact levels, thereby fostering positive responses to emerging technologies [ 31 , 32 ]. Furthermore, the utilization of mobile phones and other media significantly influences disparities in medical care. Increasing the frequency of contact and sustained use of media by older individuals can contribute to unlocking the considerable potential of mobile medical technology in the health care of older individuals [ 33 ].

In summary, there is an immediate and practical need to reduce the digital divide among older adults. The willingness of older individuals to embrace mHealth devices, as reflected in surveys, signifies their acceptance of new health technologies and, to a certain extent, their integration into the era of mHealth. Previous research on factors influencing the intention to use mHealth devices among older adults has predominantly centered on understanding the behavioral motivations and mechanisms behind users’ intentions to use, emphasizing the impact of technical and social aspects on actual usage behavior [ 34 ]. Research on influencing factors has primarily delved into age, gender, education level, BMI, income, and health status, among other individual aspects [ 35 - 37 ]. However, there is a paucity of studies examining external environmental factors, notably the influence of family and social dynamics, particularly among the older adult population in China. A previous study indicated that family internet access enhances older adults’ cognitive function and increases the frequency of media use [ 38 ]. Moreover, family support has been identified as a crucial factor aiding older adults in overcoming barriers to the utilization of mHealth services [ 39 ]. Considering the substantial impact of family factors on the proactive health information-seeking behavior of older individuals [ 40 - 43 ], it becomes imperative to delve deeper into the relationship between family health, media use behavior, and the older individual’s intention to use mHealth devices. Additionally, exploring the mediating role of media use behavior between family health and the older individual’s intention to use mHealth devices is crucial. This comprehensive investigation aims to facilitate the integration of older individuals into the “digital age” starting from the family level, foster the adoption of mHealth in the health care sector, enhance societal healthy aging, and contribute to the realization of the objectives outlined in the “Healthy China 2030 Plan.”

In this study, information pertaining to family health, media use behavior, and the intention to use mHealth devices among older adults was gathered from the Psychology and Behavior Investigation of Chinese Residents (PBICR) study. The primary objective of this study was to examine the impact of family health and media use behavior on the intention of older individuals to use mHealth devices in China. Furthermore, the study aimed to assess whether media use behavior acts as a mediating factor in the relationship between family health and the intention to use mHealth devices among older adults. Drawing upon the insights gained from the literature review, the following hypotheses were formulated: (1) family health has a direct impact on the intention to use mHealth devices among older adults; (2) family health exerts an indirect influence on the intention to use mHealth devices through the mediating factor of media use behavior; in other words, media use behavior serves as a mediator in the relationship between family health and the intention to use mHealth devices.

Study Design and Setting

The data for this study were sourced from the PBICR survey, a comprehensive cross-sectional survey initiated by the Peking University School of Public Health in 2022. The survey encompasses 148 cities spanning 23 provinces, 5 autonomous regions, and 4 municipalities directly under the central government in China. Using a multistage sampling approach, the survey uses a stratified sampling method in cities, districts, counties, and communities, and uses a quota sampling method from the community level down to the individual level.

The survey was carried out by adeptly trained investigators. Electronic questionnaires (developed previously [ 44 ]) were distributed directly to the public through one-on-one, face-to-face interactions on-site. Respondents could access the questionnaire by scanning the provided QR code. In situations where face-to-face investigations were impeded due to the constraints of the COVID-19 epidemic, investigators distributed the electronic questionnaire on a one-on-one basis through instant communication tools such as WeChat (Tencent Holdings Ltd.). Additionally, online video investigations were conducted through platforms such as Tencent Meeting (Tencent Holdings Ltd.)and WeChat video [ 45 ].

Within the PBICR survey, investigators underwent comprehensive training in sampling methods, research tools, and quality control. Only those investigators who strictly adhered to the trained survey procedures were deemed qualified and eligible to participate in the study. Furthermore, during the data processing phase, 2 researchers were designated to perform logical checks. Questionnaires that did not meet the predetermined screening criteria were excluded, ensuring the quality and reliability of the data. Additionally, in this study, further screening was implemented to eliminate questionnaires completed in an excessively short time, those containing outliers, or those with missing values.

In the 2022 PBICR survey, a total of 23,414 questionnaires were collected. Following logical checks and the elimination of outliers, 21,916 questionnaires were deemed valid. For the purposes of this study, the focus will be confined to the age group of 60 years and above. Consequently, the final sample size included 3712 older adults after sorting.

Participants

A total of 21,916 questionnaires were collected, with the screening criterion being individuals aged 60 years and above, ensuring the absence of missing data and logic errors. Following a meticulous summary and screening process, 3712 valid survey responses were obtained for analysis in this study.

The inclusion criteria for participants in this study were as follows: (1) age between 18 and 60 years old; (2) possession of the nationality of the People’s Republic of China; (3) status as a Chinese permanent resident with an annual travel time of 1 month or less; (4) willing participation in the study and voluntary completion of the informed consent form; (5) ability to independently complete the questionnaire survey or do so with the assistance of investigators; (6) capacity to comprehend the meaning of each item in the questionnaire.

The exclusion criteria for participants in this study were as follows: (1) individuals with unconsciousness or mental disorders; (2) individuals with cognitive impairment; (3) those currently participating in other similar research projects; and (4) individuals unwilling to collaborate or reluctant to participate in the study.

Ethics Approval

The study adhered to the principles outlined in the Declaration of Helsinki. Ethical approval for all experimental protocols was granted by the ethics research committees of the Health Culture Research Center of Shaanxi (approval number JKWH-2022-02) and Second Xiangya Hospital of Central South University (approval number 2022-K050). The cover page of the questionnaire provided a clear explanation of the study’s purpose and assured participants of anonymity, confidentiality, and the right to refuse participation. Informed consent was obtained from all participants involved in the study.

The questionnaire cover used in this study provided a detailed explanation of the study’s purpose and ensured participants of anonymity, confidentiality, and the right to refuse participation. All participants were required to voluntarily sign an informed consent form before engaging in the study. While respondents did not directly benefit from the survey, their input contributed to a more comprehensive and systematic understanding of the physical and mental health status of the public. The data from this study will be strictly managed and used in accordance with the Statistics Law of the People’s Republic of China. The research data are intended for academic purposes only, and when the research findings are published, no information about individual participants will be disclosed or adversely affected.

Measurements

General situation survey information.

The basic demographic information of the older individuals included gender, age rank, nationality, religion, BMI rank, political status, status of occupation, education level, chronic diseases, and family type (conjugal family, core family, backbone family, and other family).

Family types were defined as follows:

  • Conjugal family: a family consisting of only husband and wife.
  • Core family: a family consisting of parents and unmarried children.
  • Backbone family: a family consisting of parents and married children.
  • Other family: other families including joint families, single-parent families, DINK (dual income, no kids) families, and single families.

Short-Form of the Family Health Scale

The assessment of family health in this study used the Chinese version of The Short-Form of the Family Health Scale (FHS-SF), developed by Crandall et al [ 20 ]. Wang et al [ 46 ] introduced the FHS-SF cross-culturally to create a Chinese version as a quantitative tool for evaluating family health issues in China. The scale comprises 10 items, encompassing 4 dimensions: family social and emotional health processes, family health lifestyle, family health resources, and family external social supports. A 5-point Likert scale was used for each item of the FHS-SF, with response options ranging from 1=strongly disagree to 5=strongly agree. Items with negative wording were scored in reverse. The final score on the scale ranged from 10 to 50, where higher scores indicated higher levels of family health. Wang et al [ 46 ] reported that the Cronbach α for the FHS-SF was .83. Additionally, the Cronbach α for the 4 subscales ranged from .70 to .90, and the retest reliability of the scale was 0.75.

In our study, the composite reliability values for the 4 dimensions were 0.912, 0.848, 0.781, and 0.806, respectively. All these values surpass the reliability threshold of 0.7. The average variance extracted values for the dimensions were 0.775, 0.736, 0.553, and 0.677, respectively, all of which exceed the threshold of 0.5. The Cronbach α of the FHS-SF was .90, and the factor loadings ranged from 0.73 to 0.90, all within an acceptable range.

Media Use Behavior Scale

The frequency of media use in this study was gauged using the Media Use Behavior Scale developed by the PBICR survey of Peking University. The scale encompasses various media channels such as newspapers, radio, television, the internet, and mobile phones. Comprising 6 items related to social contact, self-presentation, social behavior, leisure and entertainment, access to information, and business transactions, the scale uses options that signify the degree of media use frequency, ranging from “1=infrequent” to “5=frequent.” The total score on the scale ranges from 6 to 30, with higher scores indicative of more frequent use of the media [ 45 ].

In this study, the composite reliability for the Media Use Behavior Scale was 0.894, and the average variance extracted was 0.585. The Cronbach α for the Media Use Behavior Scale was .89, indicating strong internal consistency. Additionally, the standardized factor loadings obtained from the validation factor analysis were above 0.50, all falling within acceptable limits.

Intention to Use mHealth Devices

The intention to use mHealth devices in this study was assessed through subjective evaluations. Participants were required to provide a numerical response ranging from 0 to 100 based on their individual subjective awareness. This formed a continuous variable, where a higher numerical value indicated a stronger intention to use mHealth devices.

Data Analysis

Continuous variables were assessed for normality using the Kolmogorov-Smirnov test and presented as the median and IQR. Categorical variables were reported in terms of frequency and percentage. Nonparametric methods were used to test the differences in characteristics related to the total score of the intention to use mHealth devices. Specifically, the Mann-Whitney U test was used for dichotomous variables, while the Kruskal-Wallis H test was used for multicategorical variables. The partial correlation coefficient between family health scores, media use behavior scores, and intention to use mHealth devices scores was calculated using a regression model. Linear regression models were used to assess the association between family health scores and media use behavior/intention to use mHealth devices scores, both with and without adjustment for covariates. The associations between media use behavior and intention to use mHealth devices scores were also examined. The results are reported as coefficients along with 95% CIs. Covariates, determined based on previous studies and general knowledge, were included in the models for adjustment. To examine the mediating role of media use behavior scores in the association between family health scores and intention to use mHealth devices scores, we conducted a Sobel-Goodman Mediation Test. This analysis was performed while controlling for all selected covariates. The significance of the indirect effect, direct effect, and the total effect was determined using the bootstrap algorithm.

All P values were 2-sided, with a significance level (α) of .05 used to define statistical significance. The data were analyzed using IBM SPSS Statistics 26 and R version 4.1.3 (R Foundation).

Subgroup Analysis

Indeed, empirical studies have consistently indicated a positive association between education and health. Individuals with higher levels of education often exhibit a tendency to adopt healthier lifestyles, and their increased income may lead to greater investment in health-related expenses [ 47 ]. Furthermore, education is closely linked to varying levels of internet participation. Generally, individuals with higher educational attainment are more likely to use online platforms for accessing health-related information [ 48 ]. In diverse educational and cultural backgrounds, individuals may exhibit varying levels of concern regarding health risks, subsequently influencing their acceptance of health care technology [ 49 ]. Additionally, preliminary analysis in our study revealed significant differences in the total score of family health across different education levels ( P <.001). Building on the established influence of education on health behavior and media use, as outlined in the existing literature and supported by our results, this paper intends to analyze education level as a subgroup. The aim is to comprehensively explore the mediating role of media use behavior among older adults with different education levels in the relationship between family health and their intention to use mHealth devices.

General Characteristics

A total of 3712 older individuals aged 60 and above participated in this study, with an average age of 69.23 (SD 6.13) years. The majority of older adults (3036/3712, 81.79%) fell within the age range of 60-74 years. Basic demographic data for the 3712 older adult participants are detailed in Table 1 . Among them, 1839 were males (49.54%) and 1873 were females (50.46%). The majority identified as Han nationality (3370/3712, 90.79%) and nonreligious (3416/3712, 92.03%), with the majority expressing mass political views (3151/3712, 84.89%). There were noteworthy differences in the willingness to use mHealth devices among older adults with varying political statuses, occupational statuses, and chronic disease conditions ( P <.001). However, no significant differences were observed in the willingness to use mHealth devices among older adults with different family types ( P =.97; Table 1 ).

a Median (IQR) was used to describe the continuous variable, whereas n (%) was used to describe the categorical variable.

Association Analysis

After adjusting for covariates, the intention to use mHealth devices exhibited a positive correlation with the total score of family health ( r =0.077, P <.001) and the media use behavior score ( r =0.178, P <.001). Additionally, the total score of family health was positively correlated with the media use behavior score ( r =0.079, P <.001; Table 2 ).

a The model was adjusted for various covariates, including religion, BMI rank, political status, occupational status, education degree, and chronic diseases. Variables achieved statistical significance at P ≤.05.

b N/A: not applicable.

Relationship Between Family Health and Media Use Behavior Score/Intention to Use mHealth Devices

In the linear regression models before adjustment, the 4 dimensions of family health (ie, family socialization, family healthy lifestyle, family health resources, and family external social support) and the total score were significantly ( P <.001) associated with media use behavior. Moreover, they were significantly ( P <.001) related to the intention to use mHealth devices, except for family health resources ( P= .15). After adjusting for gender and age rank, as well as political status, nationality, religion, BMI rank, occupation status, education level, family type, and chronic diseases, all dimensions remained statistically significant ( P <.001) except for family health resources ( P= .29; Table 3 ).

a Data were adjusted for gender and age rank, political status, nation, religion, BMI rank, status of occupation, education degree, family type, and chronic diseases.

Relationship Between Media Use Behavior Score and Intention to Use mHealth Devices

In the linear regression models before adjustment, media use behavior was significantly ( P <.001) associated with the intention to use mHealth devices. After adjusting for gender and age rank, as well as political status, nationality, religion, BMI rank, occupation status, education level, family type, and chronic diseases, the association remained statistically significant ( P <.001; Table 4 ).

Mediation Analysis

The family health total score demonstrated a positive association with the intention to use mHealth devices among older adults. Mediation analysis, including media use behavior, revealed that the relationship between the total score of family health and the intention to use mHealth devices was mediated through media use behavior. In this study, media use behavior partially mediated the association between family health and the intention to use mHealth devices. The mediating variable accounted for nearly a quarter (22.46/100) of the association when adjusting for covariates. The total score of family health was associated with media use behavior (β=.088, P <.001) and intention to use mHealth devices (β=.244, P <.001). Additionally, media use behavior was linked to the intention to use mHealth devices (β=.810, P <.001). The final mediation models depicting the independent variable (total score of family health), the mediating variable (media usage behavior), and the dependent variable (intention to use mHealth devices) are illustrated in Figure 1 .

research paper behavioral finance

The 4 dimensions of family health were positively associated with the use of mHealth devices among older adults, except for the dimension of family health resources, which had a nonsignificant association ( P= .72). The mediation analysis involving media use behavior indicated that the direct and total effects of family health resources were not significant ( P =.72 and P =.20, respectively). Media use behavior acted as a full mediator when adjusting for covariates. Media use behavior partially mediated the relationship between family social, family healthy lifestyle, family external social support, and the intention to use mHealth devices, with mediating effects of 35.18/100, 31.78/100, and 31.33/100, respectively, under adjusted covariates ( Table 5 ).

a The Sobel-Goodman Mediation Test was applied in adjusted models for religion, BMI rank, political status, occupation status, education level, and chronic diseases.

b The Sobel test was used to assess the hypothesis that the indirect role was equal to 0, adjusting for covariates such as religion, BMI rank, political status, occupation status, education level, and chronic diseases. Values reach statistical significance at P ≤.05.

Subgroup analyses based on education degrees are presented in Table 6 . Among the older adult population with primary school education and below, media use behavior showed no mediating effect between the total score of family health and the intention to use mHealth devices ( z =–0.942; indirect effect=–0.019, P =.35; direct effect=0.252, P =.007). Additionally, the mediating effect of media use behavior between family healthy lifestyles and the intention to use mHealth devices was not significant ( z =1.953, P =.052). Media use behavior fully mediated the association between family health resources scores and intention to use mHealth devices scores in different education degrees among the older adult population: primary school and below degree older adult population ( z =–5.832; indirect effect=–0.331, P <.001; direct effect=0.218, P= .29), middle school/vocational school/high school degree older adult population ( z =–3.439; indirect effect=–0.136, P <.001; direct effect=–0.066, P =.76), and college and above degree older adult population ( z =–2.516; indirect effect=–0.212, P= .01; direct effect=0.026, P =.93).

a The Sobel-Goodman Mediation Test was applied in adjusted models for religion, BMI rank, political status, status of occupation, and chronic diseases.

Principal Findings

Previous studies have consistently demonstrated that family factors play a crucial role in influencing the frequency of media use and the acceptance of mHealth among older adults [ 50 ]. The findings of our study further confirm that family health positively contributes to increasing the willingness of older adults to use mHealth devices. Additionally, a high frequency of media use behavior emerges as a significant driver for the utilization of mHealth devices, a behavior that is profoundly influenced by the state of family health. The results align with previous research on the digital divide among older adults, indicating that those with higher family health scores tend to engage in more frequent media contact behaviors. This heightened connectivity to the internet makes them more adaptable to a big data–based mHealth environment, fostering a greater willingness to use mHealth devices. Before conducting the mediation analysis, the study also observed, through univariate analysis, that older individuals over 90 years and those who were unemployed exhibited a lower willingness to use mobile medical devices. The results confirm the existence of differences in the digital divide among age groups, especially with older age groups experiencing inequalities in social and economic support [ 51 , 52 ]. These disparities may further impact their access to and utilization of media devices.

In addition to the descriptive findings, this study delves into the intricate relationship between family health and the willingness to use mHealth devices, uncovering the mediating role of media use behavior. Primarily, the study supports the positive impact of media use behavior, which partially mediates the influence of overall family health levels on the intention to use mHealth devices. Furthermore, the results indicate that media use behavior serves as a fully mediating variable in the dimension of family health resources. In essence, the findings suggest that older adults lacking family health resources completely lose their willingness to use mHealth devices, primarily due to their challenges in accessing or using media. This underscores the crucial role of family health resources in integrating older adults into the internet sphere and enabling them to benefit from mHealth technology. The study emphasizes the practical importance of addressing resource-related health inequities, with financial support from the family being identified as a critical factor in the daily lives of seniors [ 52 ]. To address the imbalance in the distribution of resources among families in different regions at the societal level, it is crucial for the government to assist socioeconomically disadvantaged older adults in gaining greater access to various devices. This can be achieved through economic empowerment initiatives and the development of policies aimed at bridging the digital divide [ 53 ].

Building upon the crucial role of media contacts in linking family health resources and the willingness to use mHealth devices among the older population, there is an opportunity to further motivate the desire for mHealth device usage. Leveraging the positive influence of family health resources to increase the frequency of media exposure can enhance the motivation of older individuals. Effective communication within the family emerges as a catalyst for improving the technology literacy and information-seeking skills of older adults [ 16 ]. Family members play a crucial role in supporting seniors to build confidence in using internet technology while alleviating their anxiety and fear of new technologies. Encouraging older adults to adapt and learn information technology, such as WeChat and health-related mobile apps, through straightforward and repeated demonstrations can be an effective strategy [ 54 ]. Additionally, family support may help mitigate the economic challenges associated with using health care services by influencing older adults’ subjective perceptions of financial accessibility [ 55 ]. To address financial challenges and enhance older adults’ access to technology, a comprehensive approach can be adopted. This involves leveraging both the financial support within the family and external economic resources. Encouraging family members to provide suitable financial assistance to each other, coupled with ensuring stable financial security for older individuals, can be achieved by gradually increasing pensions for retirees. This approach aims to augment the purchasing power of older adults, enabling them to acquire media devices and enhancing their ability to use technological devices in the health care sector to a greater extent.

The subgroup analysis further indicated that media use behavior did not mediate the relationship between the total family health score and the intention to use mHealth devices among older adults with primary school education or below. However, it did partially mediate the association among those with primary school education and above, aligning with the study hypothesis. Given that the older adult population with low education levels may experience relatively weak cognitive function and lack personal health literacy [ 56 , 57 ], the mechanisms by which they are influenced by family, social, and economic environments in the acceptance of new health technologies become more intricate. Conversely, older adults with a high school education or higher often perceive themselves as having an above-average ability to learn, making them less uncomfortable with the changing social environment brought about by technological developments [ 58 ]. Moreover, older individuals with limited education often lack access to information technology education or the ability to operate mobile devices [ 59 ]. For these individuals, exposure to media devices or mHealth devices is relatively homogeneous. Consequently, they may lack a progressive transition from regular media contact behaviors to the use of mHealth devices.

Disparities in internet participation levels due to education constitute a significant barrier hindering older adults from using media devices to access the mHealth era. To bridge the “digital divide” and enhance the effective use of mHealth devices among older individuals, it is imperative to consider implementing relevant education measures. These measures can focus on improving their ability to use smart technology, thus empowering them to navigate and benefit from the advancements in health care technology. In alignment with the comprehensive “Smart Senior Care” action plan in China [ 60 ], communities can implement health education initiatives through a blend of technology-supported learning and traditional lectures. For instance, using touchscreen tablets for courses on healthy diet and nutrition guidance can enhance the older individual’s interest in the internet while imparting essential health and hygiene knowledge [ 61 ]. This approach serves to bridge the transition from traditional modes of access to mobile health care. Adopting adaptive behaviors and learning strategies can further enhance the efficiency and effectiveness of mobile health care apps [ 62 ]. In the mHealth era, the design of mHealth devices should be tailored to the cognitive abilities and mindset of older individuals. Full consideration should be given to their eHealth literacy, incorporating improvements in usability, emphasizing the responsiveness of operations, and integrating monitoring functions that align with the physical activities of older individuals [ 63 ]. Such considerations aim to enhance the overall satisfaction of older individuals with mobile health care apps [ 64 ]. Moreover, due to prevailing stereotypes about older people, digital platforms often harbor ageist mechanisms that categorize them as users uninterested in technology [ 65 ]. This results in an unfavorable digital environment for older individuals. In general, the development and application of internet technology must not overlook the realistic capacity and objective demands of older individuals [ 66 ]. Digital platforms should strive to create more inclusive algorithms and use statistical models of social digital media practices that cater to all literacy levels [ 65 ]. This may involve reducing complex and lengthy text that is difficult to understand, avoiding in-depth and complex hierarchical options, and adopting simple page designs [ 67 ] to mitigate the impact of technological differences on the accessibility of digital health care for older adults.

Strength and Limitations

This study contributes significantly to the existing literature by evaluating the connection between family health, media use behavior, and the intention to use mHealth devices among older adults, using cross-sectional data from the PBICR survey. The findings of this study support our hypothesis that media use behavior serves as a mediator between family health status and the intention to use mHealth devices among older adults. Furthermore, a subgroup analysis based on education level revealed that the impact of family health on the willingness to use mHealth devices through media use behavior was not significant among older adults with lower education levels, indicating a nuanced mechanism at play. All of the aforementioned studies contribute to the body of research on the digital divide among older individuals.

Despite comprehensive consideration, the results of this study have several limitations. First, due to the exploratory cross-sectional design, no causal inferences can be drawn. Second, the majority of seniors included in this study were in the young-old age group (60 to 74 years old), lacking representation of the entire age spectrum of older adults and potentially neglecting variations in social background associated with age factors. Third, the results obtained in this study may be influenced by economic factors and psychological variables. As mHealth devices represent an evolving component of the health system, their development trajectory is still undergoing exploration. It is possible that various latent factors influencing the relationship between family health, media use behavior, and the intention to use mHealth devices are yet to be uncovered.

Conclusions

In conclusion, this study highlights the substantial impact of family health and media use behavior on the intention of older adults to use mHealth devices. Media use behavior acts as a mediator in the relationship between family health and the intention to use mHealth devices, with more intricate dynamics observed among older adults with lower educational levels. These findings emphasize that robust family health, particularly sufficient family health resources, plays a crucial role in enhancing the media engagement of older individuals, ultimately fostering their interest in embracing mHealth devices. The insights from this work provide valuable recommendations for bridging the gap in digital health adoption among older adults. Furthermore, encouraging teaching by family members can create a supportive environment for seniors to embrace mobile technology, while financial support can enhance their accessibility to health-related mobile devices. Additionally, developing age-specific digital education programs and media products tailored to the needs and preferences of older individuals can contribute to overcoming technological barriers and fostering a positive digital experience for older adults in the realm of mobile health care. These strategies align with the goal of promoting inclusive and user-friendly digital solutions for seniors, ensuring they can benefit from advancements in health technology.

Acknowledgments

This study was conducted with the support of data from the Psychology and Behavior Investigation of Chinese Residents (PBICR). We appreciate all the participants who showed great patience in answering the questionnaires. None of the portions of this article used generative artificial intelligence. This work was supported by the 2023 Guangdong Province Education Science Planning Project (Specialized in Higher Education; 2023GXJK252), the Science and Technology Program of Guangzhou (grant numbers 2023A04J2267 and 2024A04J02668), the Guangdong Basic and Applied Basic Research Foundation (grant number 2021A1515110743), the Health Economics Association of Guangdong Province (grant number 2023-WJMZ-51), the Student Innovation and Entrepreneurship Training Program of Guangdong Province (grant number S202312121283), the Key Laboratory of Philosophy and Social Sciences of Guangdong Higher Education Institutions for Health Policies Research and Evaluation (grant number 2015WSY0010), and the Research Base for Development of Public Health Service System of Guangzhou.

Data Availability

The data sets generated and analyzed during this study are not publicly available because the data still need to be used for other research but are available from the corresponding author on reasonable request.

Authors' Contributions

JHC, YBW, and JYC designed and conducted this study. YBW collected data. YSM, AQL, and XXY participated in the data screening. DYZ and WDY conducted data analysis. JHC and YSM wrote the first draft of the paper. JYC contributed to supervising data analysis and developing the manuscript. All authors made contributions to the critical revision of the manuscript. The authors read and approved the final manuscript.

Conflicts of Interest

None declared.

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Abbreviations

Edited by T de Azevedo Cardoso; submitted 18.06.23; peer-reviewed by R Sun, X Zhang; comments to author 08.08.23; revised version received 29.08.23; accepted 28.01.24; published 19.02.24.

©Jinghui Chang, Yanshan Mai, Dayi Zhang, Xixi Yang, Anqi Li, Wende Yan, Yibo Wu, Jiangyun Chen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.02.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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    The behavioral finance consists of two elements: Cognitive psychology and limits to arbitrage, where cognitive implies human subjective thinking. ... The research articles have been included in this systematic literature review from a broad search of existing research papers using various databases (ProQuest One Academic, Google scholar ...

  15. (PDF) Literature review of Behavioral Finance: Then and Now

    To know the status of research on the topic, these 44 papers are classified based onvarious variables,like behavioral biases, investors' investment decisions and demographical factors, etc....

  16. Behavioural Finance: A Synthetic Review of Literature and Future ...

    It ignites the spark of behavioural finance, explaining irrational exuberance and anomalies in asset price. There are two possible directions for further research, either backwards or forwards. Backward research refers to neural mechanisms for financial decision-making while forward research is about developing sound behavioural asset pricing ...

  17. Full article: Behavioral influence and financial decision of

    Behavioral finance does not overshadow the existence of any finance theory, ... The rest of the paper is organized as follows: ... S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293-334.

  18. Behavioral Finance by Robert J. Bloomfield :: SSRN

    Behavioral finance began as an attempt to understand why financial markets react inefficiently to public information. One stream of behavioral finance examines how psychological forces induce traders and managers to make suboptimal decisions, and how these decisions affect market behavior. Another stream examines how economic forces might keep ...

  19. PDF Behavioral Corporate Finance

    Many of the research papers identi ed as \Corporate Finance" deal neither with corporations nor with nancing decisions. In this chapter of the Handbook, I rst conceptualize the breadth and boundaries of Corporate Finance research, ... Two more general insights have emerged from Behavioral Corporate Finance research on high-level decision-makers ...

  20. Research

    JAN 2022 Using historical data on postwar financial crises around the world, the authors show that the combination of rapid credit and asset price growth over the prior three years is associated with a 40% probability of entering a financial crisis within the next three years.

  21. (PDF) Behavioral Finance: A Conceptual Review

    Behavioral Finance: A Conceptual Review Authors: Pooja Patil Dr. D. Y. Patil Institute of Management Studies Shraddha Rajan Joshi Dr. D.Y. Patil bschool pune Archana Verma Abstract

  22. PDF A Study on Behavioral Finance in Investment Decisions of Investors in

    This paper seeks to find out the major influence of certain behavioral finance concepts such as overconfidence, perception, Representative, anchoring cognitive Dissonance, Regret Aversion, narrow framing and mental accounting on the decision-making process of individual investors in stock market.

  23. Unleashing the behavioral factors affecting the decision making of

    This research paper delves into the behavioral factors that have impact on decision making of Chinese investors in stock markets. As one of the world's most dynamic and rapidly evolving financial landscapes, stock markets of China have witnessed significant growth and transformation in recent years. However, the role of behavioral biases in shaping investment decisions remains a relatively ...

  24. (PDF) AN IMPACT OF BEHAVIOURAL FINANCE ON INVESTMENT ...

    This article contributes to the understanding of behavioural finance approach to investment decisions. Discover the world's research 3).pdf Content uploaded by Aakash Shivaji Yadav Author...

  25. Journal of Medical Internet Research

    Background: With the advent of a new era for health and medical treatment, characterized by the integration of mobile technology, a significant digital divide has surfaced, particularly in the engagement of older individuals with mobile health (mHealth). The health of a family is intricately connected to the well-being of its members, and the use of media plays a crucial role in facilitating ...