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Abstract

Objective: While smart healthcare services have shown potential in improving healthcare efficiency and effectiveness, significant barriers remain for consumers’ long-term engagement in such services. The study sought to propose and validate a theoretical framework to investigate the continuous use of smart healthcare services. Methods: The research model integrates commitment-trust theory with the information system success model, empirically validated through partial least squares structural equation modeling. Data were collected via a Chinese online survey platform, targeting 355 active users of smart health services. Results: The proposed model explained 61.4% of the variance in continuous usage intention. Affective commitment, trust, and satisfaction significantly affected continuous usage intention (p’s < 0.01). Trust and satisfaction were found to significantly influence affective commitment (p’s < 0.001). Satisfaction and perceived value were found to be significant determinants of trust (p’s < 0.05). Perceived value also significantly influenced satisfaction (p < 0.001). The relationships were also moderated by age, gender, and AI literacy. Conclusion: This study represents rare attempts to explore continuous usage intention of smart healthcare services from the commitment-trust theory perspective. Practitioners should prioritize trust-building measures (e.g., transparent data usage policies) and personalized features (e.g., adaptive health recommendations) to enhance long-term engagement. Demographic characteristics should also be considered when designing such services.

Keywords

smart healthcare service continuous use affective commitment trust perceived value satisfaction

Introduction

Healthcare services worldwide have been evolving in response to the rapid proliferation of digital health technologies.¹ In recent years, with the rapid development of intelligent technology (e.g., AI, cloud computing, and natural language processing), smart healthcare services, as one of typical digital health technologies, have gradually redefined the approach for healthcare service delivery, providing more personalized and diversified services for patients and even healthy consumers.^2–4 Smart healthcare services have been utilized in many contexts, including self-diagnosis, mental health and therapy, rehabilitation, and health management,³ and this trend is expected to be accelerated.⁵ The implementation of such services provides huge potential for the improvement of the capacity and efficiency of healthcare service delivery, facilitating accessibility of health information, and promoting better decision-making.⁶

Despite implementation efforts and potential benefits, studies on acceptance of smart healthcare services are limited, concentrating primarily on initial adoption of the services.^3,7–9 However, it is important to recognize that continued use behaviors are more crucial than initial adoption,¹⁰ as they play a vital role in determining the long-term viability and success of a service.¹¹ Among the limited number of research works concentrating on factors influencing continuance intention of smart healthcare services, technology attributes have been the most commonly examined aspects.^12,13 For instance, Liu et al.¹² examined patients’ continuance intention to use AI-supported service robots at hospitals based on the Technology Adoption Model, and the indicated that patients’ perceptions of usefulness, ease of use and enjoyment were significant determinants of continuous intention of the service robots. The study Dias et al.¹⁴ examined factors affecting users’ perceptions of an AI-based health app for nutrition based on a modified Technology Acceptance Model (TAM), and the study found that usage intention of the system was found to be influenced by perceived usefulness, perceived ease of use, perceived novelty, and perceived personalization. However, these factors are not able to sufficiently explain continuous use. Several researchers have highlighted that sustained use of information systems relies heavily on users’ maintenance of their relationships with the systems,¹⁰ which can be largely influenced by cognitive psychological factors, such as trust and relationship commitment.^10,15 These factors are believed to play an even more crucial role in influencing sustained use behaviors of smart healthcare services. For instance, such services may help chronically ill patients to relieve their long-term negative emotions, leading to patient strengthened emotional bond to the services.¹⁶ However, how these factors affect users’ intentions of continued use remains unclear.

China has witnessed rapid development and widespread application of smart healthcare services in recent years, and represents a vast user base for these services.¹⁷ However, despite the promising adoption rates, existing research specifically addressing the continuous use intention of smart healthcare services among Chinese users remain limited. Therefore, to address the aforementioned research gaps, the present study sought to propose and validate a theoretical model for examining users’ continuous use intention of smart healthcare services through the lens of commitment-trust theory in the Chinese context. In addition, we also aimed to examine whether user characteristics (e.g., gender, age, and AI literacy) could moderate the relationships in the proposed model.

Research model devevlopment

The conceptual foundation of the proposed model (Figure 1) draws upon the commitment trust theory and information systems success model. The definition of each variable was presented in Table 1.

Figure 1.

Hypothesized research model.

Table 1.

The variables proposed in the research model.

Variables	Type	Definition
Continuous use intention	Endogenous	Users’ intention to persistently use smart healthcare services
Affective commitment	Endogenous	Emotional attachment to the service as a long-term health partner
Trust	Endogenous	The extent to which one views smart healthcare services as reliable, dependable, and trustworthy in supporting one’s healthcare activities
Satisfaction	Endogenous	The extent to which the use of smart healthcare services exceeds users’ expectations
Perceived value	Exogenous	The extent to which individuals perceived that smart healthcare services provide good value

Commitment-trust theory

The Commitment-Trust Theory (CTT) proposed by Morgan and Hunt¹⁸ highlights the significance of trust and commitment in relationship marketing. The theory indicated that trust between exchange parties can enhance relationship commitment, which in turn facilitates development and maintains a long-term relationship.^18–20 The theory was originally proposed in the context of relational exchanges, but has received considerable attention by information system researchers in recent years.^10,21,22 Previous literature indicated that the commitment-trust theory can be used to explain the long-term use of information system, as continued usage behavior can be viewed as a long-term relationship between individuals and the systems.^10,23 For instance, Yuan et al.¹⁰ found that commitment and trust are significant factors that affect users’ intention of continued use of internet banking. Rehman et al.²¹ indicated that commitment and trust moderated the purchase intention-online shopping behavior relationship. Hashim and Tan²² confirmed that members’ continued online knowledge sharing intentions are influenced by satisfaction, affective commitment, and identification trust. The theory has also been applied to the healthcare context.^16,24,25 For instance, Yadav et al.²⁴ found that trust, affective commitment, and calculative commitment affected individuals’ continuous usage intention of healthcare apps.

Affective commitment

Meyer and Allen²⁶ divided commitment into three components: affective, continuance, and normative commitment. Researchers focused most on affective commitment (reflecting an emotional attachment in a relationship²⁶) in the information system context.^22,23,27 In the current study, it refers to the degree to which one believes that he/she has an emotional attachment to smart healthcare services. Previous literature has indicated that users who had a strong emotional bond with an information system would more likely continue to use it.^10,16,22 For instance, relationship commitment was found to be an important determinant of users’ stickiness intention of shopping websites.²⁸ Another research found that affective commitment significantly influenced users’ satisfaction with Internet banking, which subsequently affected their continuous usage intention.¹⁰ In healthcare context, Zhang and colleagues¹⁶ examined the role of emotional attachment on the usage of mobile health-monitoring services among chronically ill patients, and the results indicated that patients’ emotional attachment can facilitate active use of mobile health services. Chiu et al.²⁵ also found that users’ relationship commitment to health apps had a positive effect on their continuance intention. Therefore, we hypothesized.

Affective commitment yields a positive effect on continuous usage intention.

Trust

Lee and See29 defined trust as “the attitude that an agent will help achieve an individual’s goals in a situation characterized by uncertainty and vulnerability.” The present study defined trust as the extent to which one views smart healthcare services as reliable, dependable, and trustworthy in supporting one’s healthcare activities.³ Trust has received substantial attention in a range of information system contexts.^21,30,31 Rahi et al.³² found that trust was a significant factor in shaping users’ intention of continued use of internet banking. Meng et al.³⁰ studied elderly users’ usage of m-health services and found that both affective and cognitive trust enhanced their continued intention to use such services. The role of trust is even more important when deploying AI-supported technologies.³³ For instance, trust was found to be the most critical factor affecting drivers’ attitudes towards automated vehicles.³⁴ Wang et al.⁶ reported that the influence of trust on continuous intention become increasingly significant, largely due to the fact that AI-augmented healthcare services have been increasingly implemented recently, which makes trust an important issue. We hypothesized that.

Trust exerts a positive effect on continuous usage intention.

Based on commitment-trust theory,¹⁸ trust is an important antecedent of commitment. A trustworthy relationship between relationship partners confers a sense of safety and goodwill, which leads to a strengthened affective commitment.³⁵ Hashim and Tan²² found that trust directly influence affective commitment in online knowledge sharing context. Yuan et al.¹⁰ demonstrated that if users perceive that an internet banking service is reliable, their positive emotions are strengthened, which in turn enhances their willingness to continue using the service. This mechanism may also apply to the field of smart healthcare services. Specifically, the more individuals trust smart healthcare services, the more likely they are to develop an emotional connection with them. We thus proposed.

Trust has a positive effect on affective commitment.

Information system success model

The Information systems success model established relationships among six variables, including system quality, information quality, use, user satisfaction, individual impact, and organizational impact.³⁶ The model, originally developed by Delone and McLean in 1992, was further updated by them in 2003.³⁷ The updated information system success model indicated that an individual’s satisfaction and use intention are affected by quality variables (i.e., system quality, information quality, and service quality). User satisfaction and use/use intention are closely interrelated, and the two variables are found to be significant determinants of net benefits. In turn, users perceived net benefit of using the system may also influence their subsequent use and satisfaction. In 2008, Wang re-specified the information system success model.³⁸ The empirical evidence indicated that whether a user intent to repeatedly use an e-commerce system is largely determined by perceived value and satisfaction, which are further affected by quality factors. Although the information system success model was originally validated in e-commerce settings; it has been applied in various information system contexts to explain usage behavior in recent years.^15,28,39

Satisfaction

Satisfaction refers to the extent to which the use of smart healthcare services exceeds users’ expectations.⁴⁰ Users’ satisfaction represents users’ overall evaluation of information systems.⁴¹ It is also considered to be a factor that reflects users’ affective (feeling of pleasure or disappointment) commitment to the use of information systems.^11,42 In healthcare contexts, satisfaction is one of the most frequently explored factors in studies that examine users’ continuous use intention of mobile health services.⁶ For instance, Cho⁴³ examined factors affecting health app users’ post-adoption behaviors; the results indicated that satisfaction was significantly associated with users’ continuance intention. Similarly, Wang et al.⁴⁴ found that users’ satisfaction of mobile health apps significantly affected their continuous use intention. Consequently, we proposed that when users perceive that the functionality of smart healthcare services surpasses their anticipations, their inclination to persist in using these services will likely increase.

Satisfaction shows a positive effect on continuous usage intention.

Moreover, the significance of users’ experience to commitment has been well documented. If users are satisfied with a service, their emotional attachment to the service is deepened.¹⁶ It is likely that satisfaction (retrospective evaluation) acts like a psychological basis for the affective commitment (forward-moving evaluation).⁴⁵ Iglesias et al.⁴⁶ found that customers’ brand experience influenced their loyalty through affective commitment. Hashim et al.²² demonstrated that satisfaction is a significant antecedent of affective commitment in the online knowledge sharing context. In health informatics contexts, previous literature indicated that patients’ satisfaction with mobile health services have significant effects on their emotional attachment to the services.¹⁶ We thus hypothesized that.

Satisfaction has a positive effect on affective commitment.

Furthermore, if users are satisfied, their trust in the service will increase. Kassim and Abdullah⁴⁷ confirmed that consumer satisfaction would significantly affect trust in e-commerce settings. Dabholkar and Sheng indicated that trust could act as a mediator in the effect of satisfaction on consumer intention to purchase when using online recommendation agents.⁴⁸ We thus proposed that.

Satisfaction has a positive effect on trust.

Perceived value

Perceived value refers to the extent to which individuals perceived that m-health services provide a good value.^38,49 This reflects users’ assessment of “the ratio of perceived quality and perceived sacrifice”,^38,50 which is considered as a more appropriate measure of users’ overall evaluation of a service.³⁸ Perceived value was consistently found to be a significant determinant on customers’ continuous intention.^51–53 For example, Li and Shang⁵¹ found that perceived value had significant effect on continuous use intention and satisfaction among e-government users. Hossain investigated m-health use in Bangladesh and found that perceived value significantly influenced users’ continuous use intention.⁵² Yuan et al.⁵³ found that perceived value acted as a significant determinant for behavioral intention of health and fitness apps. We thus proposed.

Perceived value has a positive effect on continuous use intention.

Moreover, guided by the information system success model,³⁸ we hypothesized that perceived value is a significant determinant of satisfaction. In support of this, Hossain⁵² demonstrated that if m-health users perceive that the service provides good value for health, they would be more likely to feel satisfied with it. Chen⁵⁴ investigated behavioral intention of airline passengers and found that perceived value positively affected satisfaction. Therefore, we proposed that.

Perceived value positively affects satisfaction.

As for the perceived value-trust relationship, Karjaluoto et al.⁵⁵ found that perceived value relates to users’ loyalty in the telecommunications industry, and trust serves as a mediator. Similarly, perceived value was found to be a significant antecedent of repurchase and advocacy intention in B2B express delivery services.⁵⁶ Sharma et al.⁵⁷ found that consumers’ perceived value of online group buying significantly influences perceived trust, which subsequently influences their intention to participate. We thus proposed that.

Perceived value has a positive effect on trust.

The relationship between perceived value and affective commitment has been examined as well. Users are more likely to show higher affective commitment toward a brand if the brand could provide high-value products.^58,59 Milan et al.⁶⁰ found that perceived value significantly affected consumers’ repurchase intention of smartphone through affective commitment. In healthcare contexts, Zhang et al.¹⁶ found a significant relationship between perceived value and affective commitment; if patients perceive that m-health services have effectively met their expectations and addressed their needs, they would be inclined to develop a heightened emotional connection or attachment to these services. Therefore, we assumed that.

H10

Perceived value positively affects affective commitment.

Methods

Study sample

A cross-sectional design was adopted in this study, which could collect data at a single time point to allow for the examination of the strength and direction of relationships between latent variables in our proposed model. Accordingly, a cross-sectional survey was conducted through a reputable and professional web-based online survey company (Sojump, www.sojump.com) in December 2020. Sojump contained 2.6 million registered members with diverse sociodemographic characteristics and geographical distribution in China.^3,61 Eligible participants were adults who had experience in using smart healthcare services and were able to understand written Chinese. To reduce potential bias and ensure ethical and privacy compliance, participants were explicitly informed that the anonymous survey would collect their demographic data and perceptions of smart healthcare services for research purposes only. Then, informed consent was obtained from the participants when they agreed to complete the survey. A total of 355 valid samples were obtained through simple random sampling for analysis. This sample size satisfied structural equation modeling requirements by exceeding the 10-times rule⁶² and achieving 95% statistical power for detecting medium effects via G*Power analysis.⁶³ Participants obtained 5 Chinese Yuan RMB as reward for their response. Data were analyzed using partial least squares structural equation modeling (PLS-SEM). The study was approved by the Institutional Review Board of Shenzhen University.

Survey instruments

The questionnaire was composed of three sections. The first section explains the purpose of the survey. Additionally, to ensure that each participant understands the concept of smart healthcare services, this section provides definition of smart healthcare services, which was read as follows: “Smart healthcare services refer to healthcare services that were supported by advanced techniques such as AI, big data, cloud computing, natural language processing, and computer vision. Examples of smart healthcare services included AI health consultation, health chatbot, etc.” The second section collects participants’ demographic information, including age, gender, education level, AI literacy, and the types of smart healthcare services they have used. AI literacy is assessed by asking participants, “How would you rate your ability to use smart technology?” Each participant responds on a 5-point Likert scale, where 1 represents “very unskilled” and 5 represents “very skilled.” The third section includes measurement items for the constructs proposed in the research model. The items used to measure continuous usage intention,⁶⁴ affective commitment,^16,22,28 trust,^3,31 satisfaction,²⁸ and perceived value⁴⁹ were created according to validated and widely used measurement scales in prior information system research and acceptance studies in healthcare technologies. We adjusted the items to ensure the wording of the questions is aligned with the smart healthcare service context. All the items were first developed in English and then translated into Chinese. Participants were required to rate on a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree). The questionnaire used in this study was structured and closed-ended (see Appendix Table 1).

Data analysis

This study utilized partial least squares structural equation modeling (PLS-SEM) approach for model testing (using Smart PLS 3.0⁶⁵). The measurement model was examined based on the following criteria^66,67: (1) satisfactory internal consistency reliability as indicated by Cronbach’s alpha and composite reliability values larger than 0.7; (2) acceptable convergent validity as indicated by average variance extracted (AVE) larger than 0.5; (3) acceptable discriminant validity as indicated by Fornell-Larcker criterion. Path coefficients (β) quantify the strength and direction of relationships between constructs. Path coefficients and their significance levels were estimated (by running 5000 bootstrap subsamples^66,68), with significance determined at p < 0.05. We also calculated the coefficients of determination (R²) of the endogenous constructs. R² values indicate how much of the variance in the endogenous latent variables is accounted for by their independent latent variables, with R² values of 0.75, 0.50, or 0.25 for endogenous latent variables in the structural model can be described as substantial, moderate, or weak, respectively.⁶⁶ Multi-group analyses were conducted to compare whether the path coefficients were different across age (median = 31 years as the cut-off), gender (male vs female), and AI literacy (median = 4 as the cut-off).

Results

Sample characteristics

Table 2 presents the demographic characteristics of the 355 participants and their experience in using smart healthcare services. The mean age of the patients was 31.0 ± 7.5 years. Most of them (57.8%) were female. More than 90 percent (90.4%) of the participants used smart healthcare services to inquire medical/health information.

Table 2.

Demographic information of the study sample.

Characteristics	n (%)/Mean ± SD
Age, years	31.0 ± 7.5
Gender
Male	150 (42.2%)
Female	205 (57.8%)
Education
High school or lower	24 (6.8%)
University/college	305 (85.9%)
Postgraduate	26 (7.3%)
AI literacy	3.8 ± 0.9
Type of AI-augmented healthcare service used
Inquiry of medical/health information	321 (90.4%)
Diagnosis/Consultation	307 (86.5%)
Appointment	296 (83.4%)
Fitness/exercise	294 (82.8%)
Diet management	287 (80.9%)
Medication reminder/management	265 (74.7%)
Sleep management	208 (58.6%)
Mental health management	215 (60.7%)
Chronic disease management	150 (42.3%)

Measurement model assessment

The assessment results showed good internal consistency (all Cronbach’s alpha and composite reliability values >0.7, except that the Cronbach’s alpha for affective commitment was 0.66), acceptable convergent validity (all AVE values >0.5), and satisfactory discriminant validity (Fornell-Larcker criterion was fulfilled) for all constructs (see Appendix Tables 2 and 3). VIFs were all below 5, indicating there was no collinearity issues among constructs.

Structural model assessment

Figure 2 and Table 3 present the structural model assessment results. All the hypotheses, except for H7 and H8, were supported. Affective commitment and trust significantly affected continuous usage intention. Trust had a significant positive influence (β = 0.32, p < 0.001) on affective commitment. Satisfaction was found to be significantly influence continuous usage intention (β = 0.24, p = 0.002), affective commitment (β = 0.37, p < 0.001), and trust (β = 0.59, p < 0.001). Perceived value was found to be a significant antecedent for trust (β = 0.16, p = 0.02) and satisfaction (β = 0.67, p < 0.001), but not for continuous use intention and affective commitment. Continuous usage intention was mostly influenced by satisfaction, followed by perceived value, trust, and affective commitment (Table 4). The proposed model overall explained 61.4% of the variance in continuous usage intention.

Figure 2.

Structural model evaluation results.

Table 3.

Hypotheses results of the research model.

Hypothesis	β	t-value	p-value	Hypothesis supported
H1: AC→CUI	0.21	4.66	<0.001***	Yes
H2: TRU→CUI	0.40	7.16	<0.001***	Yes
H3: TRU→AC	0.32	4.53	<0.001***	Yes
H4: SAT→CUI	0.24	3.11	0.002**	Yes
H5: SAT→AC	0.37	5.45	<0.001***	Yes
H6: SAT→TRU	0.59	10.47	<0.001***	Yes
H7: PV→CUI	0.07	1.38	0.17	No
H8: PV→AC	0.01	0.22	0.83	No
H9: PV→TRU	0.16	2.39	0.02*	Yes
H10: PV→SAT	0.67	23.31	<0.001***	Yes

CUI: continuous usage intention; AC: affective commitment; TRU: trust; SAT: satisfaction; PV: perceived value. ***p < 0.001, **p < 0.01, *p < 0.05.

Table 4.

The direct, indirect, and total effects on continuous usage intention examined in the model.

	Direct effect	Indirect effect	Total effect
AC→CUI	0.21***	-	0.21***
TRU→CUI	0.40***	0.07**	0.46***
SAT→CUI	0.24***	0.35***	0.59***
PV→CUI	0.07	0.47***	0.56***

CUI: continuous usage intention; AC: affective commitment; TRU: trust; SAT: satisfaction; PV: perceived value. ***p < 0.001, **p < 0.01, *p < 0.05.

The study investigated the mediating roles of trust and affective commitment. Results showed that trust and affective commitment partially mediated the satisfaction-continuous usage intention relationship (see Table 5). Trust fully mediated the perceived value-continuous usage intention relationship.

Table 5.

Mediating effect tests of trust and affective commitment.

Direct effect		Mediating effect		Mediating role
PV→CUI	0.07	PV→AC→CUI	0.003	-
PV→CUI	0.07	PV→TRU→CUI	0.06*	Full mediation
SAT→CUI	0.24**	SAT→AC→CUI	0.08**	Partial mediation
SAT→CUI	0.24**	SAT→TRU→CUI	0.24***	Partial mediation

CUI: continuous usage intention; AC: affective commitment; TRU: trust; SAT: satisfaction; PV: perceived value. ***p < 0.001, **p < 0.01, *p < 0.05.

Multigroup analyses

The results of multi-group analyses indicated that some of the path coefficients of the theoretical hypotheses differed by gender, age, and AI literacy (results see Appendix Tables 4-6). For age, the path coefficient for trust-continuous usage intention [path coefficient difference (younger adults-middle-aged adults) = 0.31, p = 0.004]. In addition, the satisfaction-continuous usage intention relationship was found to be significant among middle-aged adults but not younger adults. The perceived value-trust relationship was found to be significant only among younger adults. For gender, there were significant differences between males and females for such path coefficients as perceived value-continuous usage intention [path coefficient difference (male-female) = 0.24, p = 0.01] and perceived value and satisfaction [path coefficient difference (male-female) = −0.18, p = 0.003]. As for AI literacy, the satisfaction-continuous usage intention and trust-affective commitment relationships were found to be significant among individuals having higher AI literacy, while the perceived value-trust relationship was found to be significant among individuals having lower AI literacy.

Discussion

Main findings

Users’ continued usage intention of smart healthcare services is significantly affected by trust and affective commitment. This result was consistent with the evidence reported in other IS contexts,^22,28,32 which showed that cognitive psychological factors play important roles in facilitating continuous usage intention in post-adoption situation. This is probably that a higher level of trust and affective commitment to a relationship can promote a cooperative environment,²² which subsequently makes users more willing to maintain the relationship in the long term. Furthermore, our analysis revealed a noteworthy correlation between trust and affective commitment, implying that affective commitment is rooted in perceptions of trust. Users who perceived that smart healthcare services are trustworthy and dependable would be more likely to cultivate positive affective states.⁶⁹

Satisfaction influenced continuous use intention both directly and indirectly. Consistent with the information system success model,³⁸ if users are satisfied with the smart healthcare services they used, they would be more likely to keep using them in the near future. In addition, the effect is partially mediated by trust and affective commitment. Users’ trust and affective commitment towards smart healthcare services are consequences derived from a prolonged and positive experience (for instance, the ability of smart healthcare services to fulfill users’ needs and meet their expectations over time). Higher levels of trust and affective commitment, in turn, have significant effects on users’ continuous use intention. Comparable findings were observed in Zhang et al.¹⁶ which corroborate the notion that emotional attachment plays a mediating role in the relationship between device satisfaction and the frequency and intensity of usage among users of m-health services.

We found that perceived value did not directly influence continuous use intention, which diverges from the results found in existing literature.⁵³ However, perceived value was found to be a significant determinant of satisfaction and trust. Previous literature indicated that perceived value shapes users’ perceptions of a piece of technology by evaluating the trade-off between benefits and sacrifices.^70,71 Therefore, if users of smart healthcare services perceive that the services can provide more benefits (e.g., convenience, personalized feedback) compared with sacrifices (e.g., perceived loss of privacy, monetary cost, storage space), they would be more likely to trust and be satisfied with the services.^52,57

Finally, the multigroup analyses indicated that gender, age, and AI literacy were found to be important moderators. Although trust was found to be a significant factor influencing the intention to continue using smart healthcare services across all age groups, this effect is more pronounced among younger individuals. Additionally, among middle-aged users, satisfaction directly influenced the continuous usage intention of smart healthcare services. However, among younger users, satisfaction did not directly influence continuous usage intention. Instead, it affected trust and affective commitment, which in turn indirectly impact their intention to continue using the services. This suggests that younger adults place greater importance on building trust and emotional connections with technology when using smart healthcare services, rather than relying solely on their usage experience. Moreover, when younger adults perceived smart healthcare services as valuable, they are more likely to develop trust in them. As for gender, male users with a higher perceived value of smart healthcare services exhibited a stronger intention to continue using them. This effect, however, is not observed among female users. Instead, for female users, perceived value indirectly influenced their intention to continue use through affecting satisfaction. This may be because males tend to prioritize the functionality and efficiency of smart healthcare services, while females place greater emphasis on the overall service experience.^3,49 Moreover, the trust-affective commitment and satisfaction-continuous use intention relationships were found to be significant only for users with higher AI literacy. Perceived value was found to have significant impact on trust only for those who had lower AI literacy level. This indicates that individuals with different levels of AI literacy establish trust and emotional connection in smart healthcare services through different mechanisms.

Theoretical implications

We contribute to the literature in several ways. First, to the best of our knowledge, the present study represents one of rare attempts that examined users’ continuous use intention of smart healthcare services with a focus on the influence of cognitive psychological factors, different from previous literature that mainly examined technological factors (e.g., based on TAM or Unified Theory of Acceptance and Use of Technology). Therefore, a model was proposed that draws on the commitment-trust theory and information system success theory. Our results provided strong support for the validity of the proposed model, as it explained 61.4% of the variance in continuous usage intention of smart healthcare services. The study showcased the applicability of the commitment-trust theory, initially formulated within the realm of relationship marketing, to the context of utilizing smart healthcare services. Second, the results offered support for the pivotal significance of trust and affective commitment in shaping users’ persistent intention to utilize smart healthcare services. The model also explains the mechanism regarding how users’ trust and affective commitment mediate the relationship between the information system success model constructs and continuous usage intention in smart healthcare service context. Third, we found that path coefficients were different across age, gender, and AI literacy, highlighting that the mechanisms influencing the continued use of smart healthcare services may differ among populations. The theoretical model developed in this research provides a valuable for future studies on continuous usage of information systems.

Practical implications

First, affective commitment should be considered during the design and application of smart healthcare services to enhance users’ long-term willingness to use them. Therefore, it is recommended that healthcare practitioners design features to increase users’ affective commitment to the services. For instance, Zhang et al.¹⁶ suggested that healthcare services developed for chronically ill patients can involve features that motivate users to treat such services as an essential part of their daily routine. Personalized feedback and the use of anthropomorphic features may also increase the emotional bond between users and the services, which in turn promotes their continued use. Second, trust is another important factor that needs to be considered because building and maintaining users’ trust is essential to facilitate users’ long-term use of the technology. For AI-augmented healthcare services, the need for increasing amounts of users’ personal data may raise trust issues.^3,33,72 Therefore, although health service providers need to provide more personalized health services to meet the needs of different people, they equally need to address and minimize users’ concerns about privacy issues by clearly explaining how data will be used.³ Third, patients’ perceptions of satisfaction and higher value facilitate their continued use of m-health systems. It is therefore recommended that healthcare providers approach every feature with the aim of providing users with a positive experience. To achieve this, a user-centered design process should be employed, and iterative human-factor evaluation implemented to improve the usability and effectiveness of systems.^73–76 In addition, practitioners should make such services more cost-effective. It should be noted that although many services are free, users may still abandon them if they are not able to add value.⁵³ Fourth, when designing smart healthcare services, it is essential to consider different demographic characteristics. For instance, service value can be emphasized for male users, while for female users, enhancing user experience through emotionally engaging interactions may be more effective.^3,77

Limitations

Our study has several limitations. First, the participants are predominantly younger users with high education levels, which may not fully represent the broader population of smart healthcare service users. Future studies should aim for a more diverse sample, including a wider range of ages and varying levels of education and technology proficiency. Second, the cross-sectional survey approach limits the ability to infer causality and understand the evolution of users’ intentions over time. Future studies can consider employing a longitudinal or mixed study design to provide insights into how users’ perceptions and usage intentions change with ongoing experience with smart healthcare services. Third, this study relied on self-reported data. Future research should incorporate objective usage metrics (e.g., app logins, service frequency) for measurement.

Conclusion

Smart healthcare services have developed rapidly in recent years and attracted much research attention. Despite their increasing popularity, users do not always maintain their use of the services. The present study is among the earliest attempts to examine continuing usage intention of smart healthcare services from the perspective of psychological cognition. The research emphasizes the notable roles that trust and affective commitment serve in fostering users’ intentions to persist in utilizing smart healthcare services. Study results contribute to an enhanced comprehension of how individuals engage with smart healthcare services, offering valuable theoretical and practical insights for this domain.

Supplemental Material

Supplemental Material - Determinants of continuous use intention of smart healthcare services: Evidence from a commitment-trust theory perspective

Supplemental Material for Determinants of continuous use intention of smart healthcare services: Evidence from a commitment-trust theory perspective by Kaifeng Liu, Qinyue Li, Da Tao in Health Informatics Journal

Footnotes

Acknowledgements

We would like to thank all participants for their time and effort in sharing their experiences and opinions in the survey. Dr. Kaifeng Liu would like to thank the support of Xiaomi Young Talents Program (Xiaomi Foundation).

ORCID iD

Da Tao

Ethical considerations

The study was approved by the Institutional Review Board of Shenzhen University (approval no. 72101161). Informed consent was obtained from all subjects involved in the study.

Author contributions

KL and TD contributed to the study conception and design, and funding acquisition. TD performed data collection. KL and QL performed data analysis and drafted the manuscript. All authors contributed to the manuscript revision, read, and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the National Natural Science Foundation of China [Grant No. 72104176, 72101161], the Science Fund for Distinguished Young Scholars of Guangdong Province, China (Grant No. 2024B1515020007) and the Foundation of Shenzhen Science and Technology Innovation Committee [Grant No. JCYJ20230808105219038].

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

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