Determinants of health-promoting behavior among eHealth consumers in South Korea: a longitudinal path analysis

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J Korean Biol Nurs Sci. 2024;26(3):206-217

Publication date (electronic) : 2024 August 22

doi : https://doi.org/10.7586/jkbns.24.017

Hanna Choi¹, Meiling Jin^,2

¹Department of Nursing Science, Nambu University, Gwangju, Korea

²Korea University Ansan Hospital, Ansan, Korea

Corresponding author: Meiling Jin Korea University Ansan Hospital, 123 Jeokgeum-ro, Danwon-gu, Ansan 15355, Korea Tel: +82-31-8099-3258 E-mail: jinmeiling04@gmail.com

Received 2024 June 6; Revised 2024 July 27; Accepted 2024 July 27.

Abstract

INTRODUCTION

The coronavirus disease 2019 (COVID-19) crisis has brought enormous changes to numerous citizens’ daily lives [1]. In particular, consumers try to acquire health related information online when they have a health related problem [2]. Obtaining information online regarding health care-related tasks such as searching for the signs and symptoms of a disease, learning about someone else’s experience, finding the name of a hospital, or discovering aftercare information before visiting the doctor is easy. Thus, online health information (OHI) benefits consumers’ self-care and their valuable decision-making process [1].

In South Korea, 91.5% of the population uses internet services, ranking first among 157 nations in the global Information and communications technology development index. This underscores the country's significant reliance on information technology [3]. Thus, the data on Korean consumers reflects the phenomenon of increasingly seeking OHI and using eHealth to participate more in health care management.

The World Health Organization describes eHealth as the efficient and secure application of information and communication technologies to support health and health related activities. This includes the provision of healthcare services, health monitoring, medical literature, health education, as well as the dissemination of knowledge and research [4].

The advancement of information and communication technology (ICT) positively impacts health promoting behaviors (HPBs) by increasing access to health information, encouraging behavioral changes, enabling personalized health management, providing remote healthcare services, and offering psychological support. These changes enhance individuals' health management capabilities and contribute to the improvement of overall health status [5].

There has been increased research on consumers’ use of OHI to investigate this recent trend [2,6,7]. Unfortunately, despite the great debate on OHI’s effects, it is challenging to find quantified indicators about health and the effect OHI has on the health promotion of consumers. The effect of OHI on actual HPB needs to be investigated [8]. Previous research has mainly pertained to understanding individuals’ healthcare–seeking behavior, such as using web portals or web tools [9,10]. These have focused mainly on behavioral intention, present behavior, or short-term changes [11,12]. Predictive research on behavioral models considers various factors, but often excludes the information factor when evaluating the influence of health information [13]. Regarding behaviors associated with health promotion, prior studies have focused on intention [14], and lifestyle changes [15]. Little is known about HPB related to OHI seekers and eHealth consumers. Thus, a systematic review has also pointed out that additional research is required to determine the influences of real health related behavior [16].

The connections established between health related behaviors, technology usage, and health promotion outcomes provide a solid basis for integrating these findings into the theoretical framework of basic nursing science. This integration enhances the understanding of how eHealth tools can be effectively utilized to foster better health outcomes, aligning with core principles of fundamental nursing practice and education. This study can significantly contribute to the development of biological and physiological nursing knowledge by providing insights into how eHealth consumers engage with health information over time and the subsequent impact on their health-promoting behaviors.

Theoretical background

To predict the usefulness and accuracy of health information obtained online for consumers' HPB, this study is grounded in the health-promotion model (HPM) [17] and the technology acceptance model (TAM) [18].

The TAM is well known in technology-related research. However, despite the increased recognition of the importance of OHI during the COVID-19 pandemic, its significance in terms of health promotion or health behavior has not been fully addressed. The HPM provides useful ideas in terms of prevention and promotion, and studies have included the perceived benefits and self-efficacy variables. However, the HPM has received little validation among eHealth consumers. Using both models, the study predicts the health-promotion behavior of eHealth consumers.

Prior health related behaviors included in HPM are considered important to form habits so that previous behaviors are coded and memorized and so that they can naturally participate in future behaviors without much effort [19]. Prior health related behaviors naturally induce behaviors with healthy lifestyle habits that have been maintained so far, and they directly affect perceived benefits and self-efficacy. In addition, they affect intentions and HPBs directly or indirectly [17,20].

As a result of identifying HPBs and related factors, we have found that prior health related behaviors of Iranian women [19] and female worker [21] were related to perceived benefits, self-efficacy, behavioral intentions, and HPBs. Yang and Kim [22] and Lee [23] confirmed the relationship between prior health related behaviors and perceived benefit as a motivator, and Lee [23] and Shin et al. [24] confirmed perceived efficacy as a motivator.

Perceived ease of use and perceived usefulness, pivotal components in the TAM, have been extensively utilized as factors to elucidate behavioral intention and actual behavior in relation to technology utilization [25,26]. Perceived ease of use is defined as the degree to which a consumer believes that using OHI would be free from effort, and perceived usefulness is defined as the degree to which a consumer believes that using OHI would enhance his or her performance. The TAM is widely accepted because, in contemporary society, consumers tend to depend on technology to seek useful health information themselves rather than depending on passive methods (TV, newspaper etc.) [18,27].

On the other hand, perceived ease of use and perceived usefulness as an antecedent not only lead to perceived benefit, which facilitates health behavior, but also affect HPBs through the medium of self-efficacy [17,28]. Similarly, behavioral intention has been proven to have effects on perceived benefit [24,29] and self-efficacy [30,31].

Perceived benefit has been commonly employed to denote individual motivations. In this study, we also focused on personal motivation, which is more likely to change within the individual dimension. Consumers anticipated benefits from engaging in health behaviors. This is because consumers tend to maintain health behavior longer when they realize that health information benefits them [32]. Perceived self-efficacy of HPB is a mediating factor. Because HPB is the final goal, we focused on the self-efficacy of health behavior in the study rather than internet or technology efficacy. This is a construct of the HPM [17] and has been proven to be the most important predictor of lifestyle changes in meta-analyses [33,34].

Finally, with regard to behavior, behavioral intention refers to a specific plan of action, including when, where, and how, for an organized health behavior [17]. In addition to the HPM, several theories including the Theory of Planned Behavior, the Theory of Reasoned Action, and the TAM have demonstrated that behavioral intention is a significant predictor of health behavior [25,35].

Based on the studies mentioned above, Figure 1 outlines the identified predictors. Prior health related behavior has an effect on perceived benefit, perceived self-efficacy, behavioral intention, and HPB. Perceived ease of use and usefulness impact perceived benefit and perceived self-efficacy, which in turn affect behavioral intentions and HPB. Additionally, there is a direct relationship between behavioral intentions and HPB.

Figure 1.

Path diagram of HPB for consumers of OHI .

The objective of this study is to investigate the effect of information utilization on the intentions and behaviors associated with health promotion among eHealth consumers who use OHI , within the frameworks of HPM and TAM.

METHODS

RESULTS

1. Characteristics of participants

The general characteristics of the 360 respondents were analyzed, including age, sex, occupation, level of education, household income/month in KRW 10,000, cohabitation, and number of chronic diseases (Table 1).

Table 1.

Participant Characteristics and Health Information Technology in Health Promotion (N = 360)

3. Endogenous variables

As endogenous variables, the average score for prior health related behavior was 18.07 (SD = 3.72), that for perceived ease of use was 11.68 (SD = 2.02), and that for perceived usefulness was 13.78 (SD = 2.81), within a score range from 4 to 20 for each item. The average score for perceived benefit was 15.96 (SD = 5.96) within a score range from 5 to 25, that for self-efficacy was 10.91 (SD = 1.77) with a range from 3 to 15, that for behavioral intention was 21.61 (SD = 3.47 ) with a range from 6 to 30, and that for HPB was 65.07 (SD = 11.18 ) with a range from 20 to 100 (Table 2).

Table 2.

Descriptive Statistics of the Measured Variables

4. Path analysis

As a result of testing univariate normality, the absolute value of skewness of all variables was from -.64 to .06, less than 2, and the absolute value of kurtosis was from -.09 to .34, less than 7, satisfying the assumption of univariate normality. Therefore, the data set was adequately modeled by a normal distribution because univariate normality is a necessary condition for multivariate normality. The parameters that fit the information were estimated utilizing the maximum likelihood method. Before model verification, correlations among variables and multicollinearity were examined. The absolute values of the correlation coefficients in this study were from .09 to .51, whereas a correlation of above .7 is generally considered to be a very strong correlation. Thus, there was no multicollinearity problem.

As a result of testing the significance of hypothetical paths and the goodness-of-fit of the hypothetical path model, both were found to be satisfactory. Nine out of 13 OHI consumers’ HPB paths were found to be significant. The fit indices were computed as follows: χ² = 7.92, GFI = .99, AGFI = .94, Normed χ² =2.64, RMSEA = .068, NFI = .99, CFI = .99. The fit indices of the hypothesis model satisfied all fit criteria except PNFI.

Nine out of 13 paths of the model were significant. The squared multiple correlations (SMCs), which indicate the explanatory power of behavioral intention and HPB as consequence variables, were 10.4% and 15.3%, respectively. The SMC of perceived self-efficacy, a factor affecting the consequence variables, was 15.2% (Table 3).

Table 3.

Model Standardized Estimates and Predictive Effects in OHI Consumers (N = 360)

Perceived ease of use and usefulness directly and positively influenced perceived benefit, and prior health related behavior and Perceived ease of use directly and positively influenced perceived self-efficacy. Prior health related behavior, perceived benefit, and self-efficacy directly influenced behavioral intention. Prior health related behavior and behavioral intention directly influenced HPB, whereas perceived ease of use, perceived usefulness, perceived benefit, and perceived self-efficacy indirectly influenced HPB (Table 3).

DISCUSSION

The research serves two primary objectives. Firstly, this study validated factors affecting HPB based on the HPM [17] and the TAM [18]. Through this investigation, we identified that prior health related behavior, perceived benefit, perceived ease of use, perceived usefulness, perceived self-efficacy, and behavioral intention significantly affect HPB. Secondly, by revealing specific predictors for e-health consumers' HPB, we empirically substantiated the behavioral changes among adult consumers further than behavioral intention.

Among exogenous variables, prior health related behaviors were uniquely identified as exerting significant indirect or direct influence on both the behavior intention and HPBs. This finding aligns with conclusions drawn from prior research [20,42]. This study verified the alternative model of patient health engagement in the context of e-health consumers [43], the premise that patients act in response to their thoughts and emotions. Psychosocially, individuals are more influenced by others’ previous actions than their cognitive and emotional states.

In other words, emphasizing prior health related behaviors can also be considered as obstructing behavioral changes by maintaining their health behavior. However, health information providers can assist in promoting health behavior by identifying the subject's condition based on prior health related behaviors before acquiring desired information from consumers and providing customized information to facilitate behavioral change. Artificial intelligence can recommend relevant online content based on a user’s patterns of information search and health management experiences presents an opportunity to enhance support for health information seekers [7].

Perceived ease of use is a crucial independent variable that influences behavior intention. In the TAM proposed by Davis, he explains that in his research, perceived ease of use has an indirect influence on intention to use. Based on the model and the results of this study, it can be concluded that perceived ease of use given an HPM does not affect the intention to use but through perceived benefit indirectly [25].

The perceived usefulness of OHI influenced perceived benefit but did not impact perceived self-efficacy. Consumer behavior in utilizing the internet for health promotion is influenced by perceived ease of use, while those seeking disease-related health information are more affected by perceived usefulness [44,45]. In the original TAM by Davis, perceived usefulness, and perceived ease of use were significant factors affecting technology adoption [27]. In Korea, OHI seekers predominantly search for health information weekly, using around 1.78 mobile devices or personal computer applications. In this study, perceived usefulness demonstrated greater predictive power than perceived ease of use [25], although the latter significantly influence consumers, however, its impact in the context of HPB remains uncertain. The nature of influence depends on individual purposes, which supports the view that consumers evaluate health promotion-related content based on entertainment value and disease-related content based on practical value [46].

The variable of perceived benefit exhibited the most substantial explanatory power in the study, accounting for 27.5% of the variance, directly and indirectly influencing HPBs regarding behavioral intentions. Perceived benefits can be broadly categorized into internal and external benefits. For instance, internal benefits reduced nervousness and fatigue, while external benefits involve monetary rewards or social recognition [17]. Given the unprecedented nature of the COVID-19 pandemic, individuals experience heightened uncertainty about their health, often stemming from a lack of accurate health related knowledge [6]. Online resources minimize internal uncertainty by disseminating accurate information and enhancing the effectiveness of health related decision-making [1]. Moreover, health-promotion practices like physical activities, nutrition, and weight management shape consumers' intentions regarding concrete actual health behaviors [17].

The perceived ease of use of OHI also influenced perceived self-efficacy in behavioral skills. This aligns with the findings of previous studies indicating that the perceived ease of use of online patient portals positively impacts perceived self-efficacy in adults [47].

In this study, perceived self-efficacy can be interpreted as the belief that consumers can transfer the knowledge acquired through exploring OHI into behaviors. The results of this study suggest that perceived self-efficacy did not directly impact health promotion behaviors but exerted an indirect and significant influence through the behavioral intention to engage in health promotion behaviors. Among young adults, perceived self-efficacy was a significant predictor for seeking mental health counseling services [48].

The behavioral intention for health promotion behaviors directly impacted health promotion behaviors through the influence of motivation and behavioral skills factors. This direct and significant influence of intention on behavior was reaffirmed in this study, consistent with previous research in HPM and information motivation behavioral skills model, as well as in extended theories of planned behavior and planned behavior, where emotional responses to behavior are linked through intention to actual behavior [49]. Therefore, when providing health promotion-related content, it would be beneficial to thoroughly assess consumers’ intentions, such as asking a few questions with checklists. Setting differentiated goals based on these intentions can facilitate a smoother transition from intention to health promotion behaviors. This approach ensures that health promotion behaviors are naturally and effectively connected once intentions are enhanced. This study's findings align with research on determinants influencing health promotion behaviors in individuals at high risk of consumers or patients, where behavioral intention had a positive effect on HPB [50].

This study addresses a gap in the literature by elucidating the factors influencing behavioral intentions and HPB, focusing on motivational and behavioral skill factors to enhance consumers' self-engagement in health management. The findings underline the guidance that health information providers can derive from prior health related behavior, perceived ease of use, usefulness, perceived benefit, self-efficacy, and behavioral intention. Additionally, categorizing health information into general and disease-specific health information warrants consideration of various influencing factors, such as perceived benefit and perceived usefulness.

However, despite the robustness of the findings, this study has limitations. First, the subjects were adults willing to participate in both survey phases, possessing experience in seeking OHI. Generalizing these findings requires broader participation and further studies. Second, OHI use and HPBs relied on self-reporting through surveys, introducing potential recall bias and subjectivity. The study excluded variations in OHI use based on different characteristics of OHI services each participant used, focusing instead on subjective indices. Finally, the study found that the average age of the participants was 33.9 years old and nearly 98% of the sample has received a university-level education or higher. Therefore, premature generalization of the study's findings should be avoided. Further research is necessary to comprehensively understand how these effects vary across different consumer subgroups. Lastly, there have been significant advancements in digital technology since data collection. Although some time has passed, continuous impact of digital technology on HPBs reinforces the relevance of the data. Also, this research continues to hold significant value by providing baseline data for future comparisons and demonstrating methodological rigor, such as adherence to the Declaration of Helsinki.

CONCLUSION

This study makes a novel contribution to the literature by identifying factors influencing behavioral intention and HPB of consumers of OHI based on the HPM and TAM. Among these factors, perceived benefits demonstrated the greatest explanatory power and directly affected the intention of health promotion. Notably, perceived usefulness was a key factor for consumers seeking disease-specific health information, whereas perceived ease of use was identified as a strong determinant for consumers seeking health promoting information. Consequently, to enhance consumers’ HPB, strategies should focus not only on behavioral intention but also on perceived benefit, perceived usefulness, prior health related behavior, perceived ease of use, and self-efficacy.

This study demonstrates a new perspective for eHealth consumers by offering important factors. Ultimately, the findings of this study will enhance the development of scientific theory and its practical application for eHealth consumers in the post-COVID-19 era.

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