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Article

Towards Sustainable Healthcare: Exploring Factors Influencing Use of Mobile Applications for Medical Escort Services

by
Fan Xu
1,
Jing Hu
2,
Duanduan Liu
2 and
Chao Zhou
3,*
1
Robotics Institute, Ningbo University of Technology, Ningbo 315211, China
2
Department of Industrial Design, College of Design, Hanyang University, Ansan 15588, Republic of Korea
3
Department of Industrial Design, School of Art and Communication, China Jiliang University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(14), 6058; https://doi.org/10.3390/su16146058 (registering DOI)
Submission received: 25 May 2024 / Revised: 11 July 2024 / Accepted: 12 July 2024 / Published: 16 July 2024
(This article belongs to the Special Issue Design for Behavioural Change, Health, Wellbeing, and Sustainability)
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Abstract

:
The aging population is placing pressure on the healthcare system, and the private sector is innovating healthcare through digitalization. Mobile applications for medical escort services (MA-MES) could become a sustainable healthcare tool, assisting adult children in scheduling escorts to accompany their elderly parents to medical appointments. This creates new collaborative methods and service processes for healthcare services. This research applies the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model to identify the intentions of adult children to use MA-MES for their elderly parents. Data were gathered from 350 individuals in the age group of 20 to 59 years and analyzed using structural equation modeling (SEM). The findings indicated that the performance expectancy (PE), effort expectancy (EE), social influence (SI), price value (PV), and perceived trust (PT) significantly increased behavioral intentions (BI). Perceived risk (PR) mediated the relationship between PT and BI, while age moderated the effects of PT on BI. Cohabitation with elderly parents moderated the effects of SI and PT on BI. This research proposes a unique model to predict the intentions of adult children to use MA-MES for their aging parents. It provides insights enabling managers to carry out continuous innovation in MA-MES.

1. Introduction

Society is aging with each day, which is one of the most significant issues in public health today [1]. The older people become, the more prevalent frailty and chronic diseases become; thus, the elderly are major users of health and social care services [2]. Such a changing demographic paints a contrasting picture of an upsurge in the demand for healthcare on one hand and, on the other, limited resources in the healthcare system to deliver the corresponding services to this older age group [3].
Aging places pressure on the healthcare sector, prompting various disciplines and departments to innovate in pursuit of sustainability in healthcare. In this context, sustainability extends beyond environmental considerations to encompass the well-being of patients, healthcare professionals, and the community. Healthcare must adhere to the principles of equity, inclusivity, and social justice, emphasizing culturally sensitive and responsive care to meet the diverse needs of the population [4,5].
To enhance the efficiency of healthcare workers, inclusively accommodate the elderly, and help communities to create better home care environments, involving external stakeholders in the healthcare sector has emerged as an innovative approach to sustainability. Consequently, the private sector has introduced mobile applications for medical escort services (MA-MES), a mobile health (mHealth) tool designed to arrange escorts to accompany elderly patients to their hospital appointments.
MA-MES have the potential to be a sustainable tool in healthcare. They introduce a new stakeholder—escorts—thereby fostering new collaboration methods and appointment processes. This facilitates improved communication and cooperation between healthcare providers and patients, enabling the continuous provision of services. By involving escorts in the dialogue between elderly patients and doctors, errors can be reduced, better outcomes achieved, and patient trust increased. In this vision, patients, healthcare professionals, and medical escorts share the responsibility for sustainable development goals.
Despite the numerous benefits that MA-MES offer, a significant gap remains between the actual number of users and the expected demand. For instance, Enjoy Accompany, a one-stop medical service platform, operates in 200 districts across China. However, data from 2021 show that the Enjoy Accompany app was downloaded only 100,000 times [6]. These findings underscore concerns about the sustainability of MA-MES and highlight the need for further research into the reasons behind the low user adoption of MA-MES.
For any organization or enterprise to achieve sustainable development, it must adequately meet the fundamental needs of all stakeholders [7]. Although MA-MES are designed to serve the elderly, many older adults find these applications difficult to use independently due to the digital divide, often relying on their adult children to use them on their behalf. Therefore, understanding the perspectives of adult children on the use of MA-MES is crucial, as they have become the actual users, in order to more effectively serve the interests of the elderly recipients and meet the practical needs of the users.
The effective implementation of any technology or system depends on user acceptance [8], as users have the autonomy to choose the technologies that they prefer. Understanding the motivations behind these choices will help to design more practical technologies [9]. The UTAUT 2 is widely used to measure users’ intentions to use mobile health [10]. Perceived trust (PT) plays a crucial role in relationship marketing, significantly affecting the maintenance of enduring relationships between users and platforms and encouraging positive user behavior [11]. Given mHealth’s close relation with personal health and safety, the perceived risk (PR) may also influence user behavior. Therefore, the objective of this research is to utilize the UTAUT 2 model, along with the PT and PR factors, to explore the factors influencing adult children’s use of MA-MES.
To achieve these objectives, this research adapts the UTAUT 2 model to the context of MA-MES in China, incorporating the PT and PR factors. A total of 18 hypotheses are proposed and tested, marking both theoretical and practical contributions. First, it extends the application of the UTAUT 2 model by examining MA-MES. Second, it predicts the BI of adult children to use MA-MES, with the findings providing valuable insights enabling MA-MES companies to design better services, improve the application usage rates, and achieve sustainable development.
The remainder of this research is structured as follows. Section 2 provides a literature review on MA-MES and the UTAUT 2. Section 3 analyzes the applicability of the UTAUT 2 and makes adjustments according to the research objectives, proposing the research model. Section 4 describes the research methodology. Section 5 presents the empirical analysis and hypothesis results. Section 6 discusses the findings, including the implications, limitations, and directions for future research. Section 7 concludes the research.

2. Literature Review

2.1. Importance and Implementation of MA-MES

The regional disparity in China has prompted a large number of patients to seek medical care in the first- and second-tier cities, causing a resource shortage for the one-on-one assistance provided to elderly patients in care institutions [12]. This could intensify the aging crisis [13], since more and more older people are living alone or with only their old-age spouses. This means that insufficient intergenerational support within families has made it challenging for the elderly to attend medical appointments.
Many elderly individuals find it challenging to navigate the complexities of the current healthcare environment, particularly during medical consultations. Elderly individuals face three main challenges during medical consultations: (1) the concentration of well-equipped medical facilities in urban core areas, leading to long travel distances for elderly patients; (2) difficulties using smart devices and systems within hospitals due to the digital divide, hindering smooth interaction with electronic registration machines, electronic payment machines, and online appointment systems [14]; (3) a dependence on family members for accompaniment and assistance during medical visits, impacting the family’s activities [15].
Numerous studies have underscored the positive role of companions during medical visits among elderly patients. Companions typically include spouses, adult children, and close friends. Schilling et al. [16] highlighted the beneficial effects of family companions, who assist by providing additional information (helping patients to recall details, directly informing doctors, explaining medical instructions, and aiding patient understanding) and facilitating information-seeking behaviors (asking questions, assisting with note taking) during medical encounters. Family involvement in patient healthcare decisions [17] correlates with higher satisfaction with physician care, treatment adherence [18], healthcare process quality [19], overall health [20], and mortality rates [21].
However, it is noteworthy that 9.7% of elderly individuals residing alone and 39.6% of those cohabiting with elderly partners report difficulties in accessing familial companionship and escort services [22]. Elderly patients without family support rely on volunteers or professional caregivers for assistance. Martin [23] demonstrated that volunteer-based door-to-door services ameliorate transportation challenges for elderly individuals attending healthcare appointments. Sheehan [24] highlighted the critical role of medical visit companions (MVCs) for elderly patients in the United States, illustrating their substantial impact. They suggested that guidance from volunteers in using smart services significantly enhances elderly patients’ trust and readiness to adopt such technologies [25].
In response, various private sector entities in China have developed services to mitigate the challenges faced by the elderly, encompassing logistics support (transportation, physical assistance, and appointment scheduling), engagement in the medical consultation process, communication (relaying patient information to doctors, note taking, posing questions, clarifying physicians’ directives, and language translation), and providing companionship. In this study, these services are collectively referred to as MES [26]. With the proliferation of mobile technology, a hybrid service integrating MES with mobile applications, known as MA-MES, has emerged in the Chinese market.
MA-MES represent a category of mHealth, delivering a service that merges internet technology with on-demand companionship for medical appointments, aiding patients who may encounter obstacles in accessing healthcare independently [27]. Users engage with the application to place online orders, selecting the hospital, department, service duration, and specifics of the service required. Patients subsequently receive personalized offline services from medical escort personnel, including door-to-door transport, queue management, in-person consultations, prescription collection, report retrieval, and support during surgical procedures. Escort personnel are well trained, holding professional qualifications and possessing extensive medical knowledge. The operational framework of MA-MES is depicted in Figure 1.
Since the launch of the ePeiZhen app (www.epeizhen.com, accessed on 10 March 2024) in March 2015, over ten MA-MES programs have been introduced in the market. Internet medical platforms, such as the private doctor services initiated by the Ping An Good Doctor app (www.pagd.net, accessed on 10 March 2024), have integrated MA-MES. Currently, MA-MES operate predominantly in major urban areas like Beijing, Shanghai, and Nanjing, targeting elderly individuals, pregnant women, disabled persons, and those living alone.

2.2. Application of UTAUT 2 in mHealth

Research on technology acceptance and use in mHealth commonly employs established theoretical frameworks. Prominent among these are the Technology Acceptance Model (TAM) [28], the Theory of Planned Behavior (TPB) [29], and the Theory of Reasoned Action (TRA) [30]. Wu et al. [31] utilized a model combining the TPB and TAM to investigate mHealth usage among healthcare professionals, emphasizing perceived usability and individual IT innovation. Wang et al. [32] merged the TPB and TAM frameworks, incorporating three patient-centric factors to assess patients’ acceptance of mHealth. The TAM primarily addresses technological elements, neglecting personal factors, while the TPB elucidates personal BI through a self-regulatory lens. Samsuri et al. [33] applied the TRA to examine user satisfaction and ongoing usage intentions in mHealth. While the TRA is based on psychological dimensions of individual behavior, to account for behavior in total, external variables must be added. Although they remain helpful, there is a need for the continued development and refinement of these models to give a greater level of explanation.
Venkatesh et al. [34] went one step further by synthesizing eight theories, including the TAM and TRA, into a single theoretical model, UTAUT. This model emphasizes the influence of four significant constructs that affect either behavioral intention or the actual use of new technologies: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). The UTAUT model is used pervasively in the various domains of telemedicine, digital medicine, mHealth, and electronic health (eHealth) applications.
Based on this, Venkatesh et al. [35] generalized the original UTAUT model to add consumer contexts, including hedonic motivation (HM), habit (HB), and price value (PV), creating the UTAUT 2. This model has been proven effective in explaining factors associated with consumer adoption intentions regarding new technologies, showing a better explanation effect compared with other models [36,37]. The theory emphasizes that the public intention to use services needs to be considered when they are in their start-up phase and during the promotion process.
The UTAUT 2 has found widespread application within the field of mHealth. Gao et al. [10] applied the UTAUT 2 in combination with protection motivation theory (PMT) and privacy calculus theory (PCT) to study the adoption of medical wearable devices, finding significant impacts of all constructs on adoption, with more pronounced impacts originating from self-efficacy, perceived severity, and the perceived privacy risk. The study by Ravangard et al. [38] used the UTAUT 2 to validate PV, hedonic motivation, habit, and usability as significant determinants of mHealth adoption. Another study by Schomakers et al. [39] incorporated the UTAUT 2 model with trust and PR in the context of mHealth technologies’ adoption. The studies discussed above support the utility of the UTAUT 2 in forecasting user intentions to adopt mHealth technologies. Furthermore, by incorporating additional variables addressing practical concerns, these studies have improved their predictive accuracy, identifying PT and PR as key influencers of BI.

3. Theoretical Framework and Hypothesis Development

3.1. Behavioral Intention (BI)

BI refers to the anticipated level of technology usage [34]. Many researchers focus on the intention to use technology in their studies, as it reflects the decision-making process that precedes actual adoption. This research defines behavioral intention as the extent to which adult children are willing to use MA-MES for their elderly parents.

3.2. Performance Expectancy (PE)

PE is the extent to which a user believes that using the technology will be helpful [33]. It is the most prominent determinant of BI to adopt a technology. Studies by Brenčič et al. [40] and Sun et al. [41] showed a positive influence of PE on the intention to use mHealth services. This research conceptualizes PE as the belief that using MA-MES will improve the efficacy and quality of medical service delivery to elderly parents; thus, the following hypothesis is proposed.
H1. 
PE positively influences BI.

3.3. Effort Expectancy (EE)

EE is the perceived ease of a technology [35]. In their study, Hoque and Sorwar [42] found that EE significantly impacted users’ intentions to adopt mHealth services. In this case, EE alludes to the ease with which adult children perceive the use of MA-MES; in essence, it involves the perception of the ease of downloading, installing, and scheduling. Based on the above review, the following hypothesis is raised.
H2. 
EE has a positive impact on BI.

3.4. Social Influence (SI)

SI has been defined as the degree to which an individual believes that essential others think that they should or should not adopt a technology [35]. Dwivedi et al. [43] confirmed that SI is one of the critical determinants when studying users’ intentions to adopt mHealth. In this study, SI refers to the extent to which significant others believe that adult children should adopt MA-MES instead of traditional methods when attending a medical consultation. Therefore, this study hypothesizes the following.
H3. 
SI has a positive impact on BI.

3.5. Price Value (PV)

PV is the perceived balance between the benefits of using a technology and its cost [35]. It reflects the willingness to pay for specific features or services and the satisfaction derived from them. As MA-MES are paid services, considering their cost is essential. This research defines PV as the perceived trade-off between the benefits of using MA-MES over traditional medical visits and the associated costs. Consequently, this research proposes the following hypothesis.
H4. 
PV has a positive impact on BI.

3.6. Perceived Trust–Perceived Risk Relationship (PT–PR Relationship)

PT is defined as an individual’s trust in a system [44]. Trust is crucial in reducing uncertainty and facilitating the adoption of new technologies [45]. Numerous studies have demonstrated the positive influence of PT on the intention to use; for instance, Guo et al. [46] found that trust in mHealth service providers mitigates individual privacy concerns and enhances adoption intention. In this study, PT refers to the trust of adult children in the brand, application, medical escort personnel, and services of MA-MES. Consequently, we propose the following hypothesis.
H5. 
PT has a positive impact on BI.
PR is recognized as a factor that influences PT [47]. Bugshan and Attar [48] describe PR as a critical element of the PT model, establishing the PT–PR relationship. PR encompasses the uncertainties concerning potential outcomes from using health-related applications [49]. When the PRs surpass the benefits, adoption is less likely. mHealth applications, which collect private and health data, can heighten security and trust concerns, thus affecting the intention to adopt. This study defines PR as the risks that adult children perceive when utilizing MA-MES in uncertain scenarios. Therefore, we hypothesize the following.
H6. 
PR mediates the positive impact of PT on BI.
H6a. 
PT positively affects PR.
H6b. 
PR negatively affects BI.

3.7. Moderating Role of Age

In the realm of mHealth, age often serves as a significant moderating factor [42]. Research consistently indicates that younger individuals are more inclined to adopt new technologies [50]. This study identifies age as a moderating factor and proposes the following hypotheses.
H7a. 
Age moderates the impact of PE on BI.
H7b. 
Age moderates the impact of EE on BI.
H7c. 
Age moderates the impact of SI on BI.
H7d. 
Age moderates the impact of PV on BI.
H7e. 
Age moderates the impact of PT on BI.

3.8. Moderating Role of Cohabitating with Elderly Parents

Adult children residing with their elderly parents are more likely to accompany them to hospital visits, primarily due to convenience. Zhou et al. [26] verified that the geographical proximity to elderly parents affects adult children’s willingness to utilize elder MA-MES. Accordingly, this study considers cohabitation with elderly parents as a moderating factor and proposes the following hypotheses.
H8a. 
Cohabitation with elderly parents moderates the impact of PE on BI.
H8b. 
Cohabitation with elderly parents moderates the impact of EE on BI.
H8c. 
Cohabitation with elderly parents moderates the impact of SI on BI.
H8d. 
Cohabitation with elderly parents moderates the impact of PV on BI.
H8e. 
Cohabitation with elderly parents moderates the impact of PT on BI.
This model does not incorporate the UTAUT 2 factors of HM, HB, and FC. HM, the pleasure derived from using technology, is omitted as MA-MES are not intended for entertainment. HB, defined as the tendency to perform behaviors automatically due to learning, is excluded due to MA-MES’s nascent stage and limited adoption. While FC are known to influence BI, they are deemed essential only for elderly individuals lacking technological proficiency [34]. Considering the PE and EE structures of this research, FC are not anticipated to predict BI [51]. Consequently, the study does not include elderly individuals within these expectancy structures and thus excludes the FC factor.

3.9. Hypothetical Model

Based on the previously stated hypotheses, the conceptual model of this research is depicted in Figure 2. It encompasses the five UTAUT 2 factors (PE, EE, SI, PV, and BI), along with the expanded factors (PT and PR) and the moderating influences of age and cohabitation with elderly parents.

4. Methodologies

4.1. Measurement

The measurement items for PE, EE, and SI were adapted from Venkatesh et al. [34]. The items for PV were adapted from Dwivedi et al. [43], those for PT from Thompson et al. [52], and those for PR from Chopdar et al. [53]. The items for BI were adapted from Venkatesh and Davis [54].
The questionnaire was divided into three sections. The first section provided an introduction to MA-MES (Appendix A) and a video demonstration (Appendix B) of its usage. Upon reviewing these materials, respondents proceeded to the subsequent sections. The second section collected demographic information, such as gender, age, educational level, and cohabitation status with elderly parents. The third section comprised 24 questions related to the various constructs within the research model. All items were assessed on a 7-point Likert scale, ranging from strongly disagree (=1) to strongly agree (=7). To ensure consistency and accuracy between the Chinese and English versions of the questionnaire, a translation–back-translation procedure was implemented. Initially, the English questionnaire was translated into Chinese, followed by a retranslation into English by another translator; the two versions were then compared.
Subsequently, five experts (including three researchers, one doctor, and one MA-MES practitioner) revised the descriptions of the measurement items to enhance their effectiveness. These revised items were pre-tested with a group of respondents (15 individuals) to assess the clarity and comprehensiveness of the item meanings. Feedback from this pre-test led to further modifications. The finalized measurement items are detailed in Table 1.

4.2. Sample and Data Collection

This research targeted individuals aged 20–59 whose parents were aged 60 and above and who were experienced smartphone users. Statistical analysis for the structural equation model (SEM) suggested a sample size of 200 for adequacy [55] and 300 for robust results. In SEM, the sample size should be at least ten times the number of measurement items [56]. Considering that the model included 24 items, this research aimed for a sample size of over 300.
After determining the sample size, an online survey was conducted using Wen Juan Xing (wjx.cn). Invitations were sent to 450 participants through social media platforms such as WeChat, Weibo, forums, and email, with an incentive of CNY 2 (USD 0.3) offered to each participant. Data collection occurred from 15 August 2023 to 26 September 2023, resulting in 362 completed questionnaires. After discarding responses with repetitive or missing choices, 350 valid questionnaires remained.

4.3. Analysis Method

This study employed SEM techniques, utilizing SPSS 25 and AMOS 26 to analyze the relationships among constructs. The analysis proceeded in two phases: initially, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to assess reliability and validity. Subsequently, SEM was used to examine the relationships among the constructs.

5. Date Analysis and Results

5.1. Descriptive Statistics

Table 2 presents the demographic information of the respondents. The sample consisted of 59.7% males and 40.3% females, showing a male predominance. The age distribution was mainly middle-aged: 9.6% were aged 20–29 years, 36.8% 30–39 years, 47.4% 40–49 years, and 5.9% 50–59 years. The educational level was high, with 61.9% holding at least an associate degree. The majority, namely 56.8%, reported not living with their elderly parents.

5.2. Reliability and Validity

Before evaluating the structural model, establishing the reliability and validity of the constructs was essential [57]. The results for reliability and convergent validity are detailed in Table 3. The Cronbach’s alpha coefficients for each construct ranged from 0.839 to 0.928, significantly exceeding the 0.6 threshold recommended by Lee et al. [58]. The composite reliability (CR) scores varied from 0.841 to 0.929, surpassing the 0.7 standard set by Fornell and Larcker [59], indicating strong reliability across all factors.
The validity assessment included content validity and structural validity (encompassing convergent and discriminant validity) [60]. Initially, the scales were refined through expert modifications and a pre-test involving 15 participants, enhancing their content. Thus, the scales demonstrated robust content validity. Secondly, the average variance extracted (AVE) values ranged from 0.64 to 0.814, well above the critical threshold of 0.5 recommended by Fornell and Larcker [59]. The factor loadings ranged from 0.752 to 0.949, all exceeding the benchmark value of 0.7, indicating strong convergent validity.
Discriminant validity was assessed by examining the distinctiveness between the constructs. According to Fornell and Larcker [59], discriminant validity is confirmed when the square root of the AVE for each construct exceeds its correlations with other constructs. As indicated in Table 4, all diagonal elements surpassed the non-diagonal elements in their respective rows and columns, with all inter-correlation estimates below 0.458 [59]. Thus, the model exhibited excellent discriminant validity.

5.3. Model Fit Adequacy

Model fit indices were analyzed to evaluate the consistency between the theoretical model and the data, as presented in Table 5. The value of CMIN/DF = 2.2836 was below the cutoff value of 3, NFI = 0.787 exceeded the recommended threshold of 0.5, and both CFI = 0.923 and TLI = 0.914 surpassed the cutoff value of 0.900 suggested by Fornell and Larcker [59]. Additionally, RMSEA = 0.073 was below the cutoff value of 0.08 [61]. These indices collectively confirmed the satisfactory model fit, indicating that the model employed in this research was acceptable.

5.4. Testing Hypotheses

5.4.1. Testing the Impact of Core Factors on BI

After assessing the model fit indices, the relationships between the constructs were analyzed, as shown in Figure 3 and Table 6. The results demonstrate that PE positively influences BI (β = 0.22, p < 0.05), EE positively impacts BI (β = 0.205, p < 0.05), SI positively affects BI (β = 0.18, p < 0.05), PV positively influences BI (β = 0.144, p < 0.05), PT positively impacts BI (β = 0.229, p < 0.05), and PR negatively impacts BI (β = −0.23, p < 0.05). Thus, H1, H2, H3, H4, H5, and H6b are all supported.

5.4.2. Testing the Influence of PT on PR

According to Figure 3 and Table 6, PT positively influences PR (β = 0.229, p < 0.05), thus supporting H6a.

5.4.3. Mediating Role of PR

Bootstrap analysis was utilized to assess the mediating effect of PR, with the findings detailed in Table 7. The effect value (estimate) is 0.059, p < 0.05, with a 95% confidence interval of [0.024, 0.113], excluding 0. This confirms the significant mediating role of PR in the relationship between PT and BI, supporting hypothesis H6.

5.4.4. Modulation Effect of Age

The sample was divided into two age groups, the youth group (20–39 years) and the middle-aged group (40–59 years), with 163 (n = 163) and 187 participants (n = 187), respectively. The outcomes of the group analysis are presented in Table 8.
The results in Table 8 show significant positive impacts in the youth group of EE on BI (β = 0.224, p < 0.05), SI on BI (β = 0.158, p < 0.05), PV on BI (β = 0.197, p < 0.05), and PT on BI (β = 0.501, p < 0.05). In the middle-aged group, PE positively influences BI (β = 0.259, p < 0.001), EE positively impacts BI (β = 0.230, p < 0.05), SI positively affects BI (β = 0.279, p < 0.001), and PV positively influences BI (β = 0.236, p < 0.05).
Notably, the impact of PT on BI is significantly stronger in the youth group compared to the middle-aged group, with a C.R. difference exceeding 1.96, demonstrating the notably stronger effect of PT on BI in the youth group. Therefore, H7e is supported. However, no significant differences were found in the impacts of PE, EE, SI, and PV on BI between the two groups; thus, H7a, H7b, H7c, and H7d are not supported.

5.4.5. Modulation Effect of Cohabitation Status

To evaluate the moderating effect of the cohabitation status, the sample was divided into two groups, those cohabitating with their elderly parents and those not cohabitating, with 151 participants (n = 151) and 199 participants (n = 199), respectively. The results of the group analysis are detailed in Table 9.
In the group cohabitating with their elderly parents, EE has a significantly positive impact on BI (β = 0.246, p < 0.05), PV positively influences BI (β = 0.195, p < 0.05), and PT positively influences BI (β = 0.447, p < 0.001). In the group not cohabitating with their elderly parents, PE positively impacts BI (β = 0.252, p < 0.001), EE positively influences BI (β = 0.215, p < 0.05), SI has a positive impact on BI (β = 0.286, p < 0.001), and PV positively influences BI (β = 0.253, p < 0.001).
Comparing the relationship between SI and BI in the two groups (C.R. = 1.94 vs. C.R. = 4.172, C.R. difference = 2.232), the C.R. difference, surpassing 1.96, indicates the significantly stronger impact of SI on BI in the group not cohabitating with their elderly parents compared to those that were. Similarly, comparing the relationship between EE and BI (C.R. = 5.49 vs. C.R. = 1.061, C.R. difference = 4.429), the C.R. difference also exceeds 1.96, suggesting the significantly stronger impact of EE on BI in the group cohabitating with their elderly parents than in those not cohabitating. Therefore, hypotheses H8c and H8e are supported. However, no significant difference in the impacts of PE, EE, and PV on BI between the two groups was observed; hence, H8a, H8b, and H8d are not supported.

6. Discussion

MA-MES hold significant potential to improve healthcare for elderly patients and increase the efficiency of medical institutions, heralding a transformative shift in the healthcare landscape for the elderly. To explore the factors influencing adult children’s intention to encourage their elderly parents to use MA-MES, 350 questionnaires were distributed among adults in China, and a model was constructed for analysis.

6.1. Main Findings

The validation results of the SEM confirm that the UTAUT 2 is a valid model for the prediction of adult children’s intentions to facilitate the use of MA-MES by their elderly parents. The results of the structural model indicate that the factors from the UTAUT 2 (PE, EE, SI, and PV), along with two additional factors (PT and PR), are significantly associated with BI.
Based on the path coefficient analysis, PR, PT, and PE are identified as the most influential factors on BI, with PR and PT having a notable impact on BI. Although PE is generally seen as the strongest predictor of BI, given people’s interest in the benefits of performance [62], its positive influence on BI in this study is also critical.
The analysis also confirms the mediating role of PR in the relationship between PT and BI, aligning with the findings of Bugshan and Attar [48] and highlighting the importance of risk in enhancing the predictive power of trust. This finding underscores the significant role of the PT–PR relationship in the adoption of mHealth. Previous studies have demonstrated that PT positively affects the acceptance of mHealth [11]. In this study, PR moderated this positive impact, suggesting that when adult children perceive the risks to outweigh trust, their intention to use mHealth decreases.
Consistent with prior research, EE is shown to predict BI [10,63], SI remains a crucial predictor of BI [64], and PV is an essential but weaker predictor of BI [65]. This may be attributed to the deeply ingrained culture of filial piety in China, which makes adult children less sensitive to cost when caring for their parents, often resulting in lower sensitivity to financial concerns.
The moderating effects of age reveal that EE, SI, and PV positively impact BI across all age groups, without significant variation. However, when comparing the two age groups, PT has a more significant impact on BI in the younger group. This finding suggests that younger individuals are generally more accepting of new technologies and are more likely to trust the use of MA-MES. Another possible explanation is that younger adult children may have relatively younger elderly parents, whose better physical fitness and cognitive abilities instill greater trust in their adult children.
Regarding the moderating effect of cohabitation with elderly parents, the results indicate that EE and PV positively influence BI across all groups, with no significant differences. However, compared to adult children who do not live with their elderly parents, SI has a more pronounced effect on BI among those not living with their parents, indicating a greater influence from their social circle. In contrast, PT has a more significant impact on BI for adult children living with their elderly parents, suggesting that these adult children often have a better understanding of their parents’ health conditions and a greater propensity to trust the use of MA-MES.
This research developed a research model based on the UTAUT 2 to examine the factors influencing adult children’s intentions to encourage their elderly parents to use MA-MES. The findings confirm that PE, EE, SI, perceived PV, and PT positively influence behavioral intention. Conversely, PR negatively affects behavioral intention and mediates the relationship between PT and behavioral intention. Additionally, the age of the adult children and the living situation with their parents were confirmed to moderate the effects of specific factors and behavioral intention. These findings offer fresh insights into the factors driving adult children’s intentions to facilitate the use of MA-MES by their elderly parents.

6.2. Theoretical Contributions

This research enhances the UTAUT 2 framework and broadens its applicability to MA-MES, with several theoretical contributions.
Firstly, it advocates for the integration of the UTAUT 2 model with the PT–PR relationship to forge a new theoretical model suited for MA-MES. When analyzing the acceptance of MA-MES by adult children, it is critical to assess not only mobile application acceptance but also the task specificity of medical companionship. Sole reliance on the UTAUT 2 as a validation model is inadequate. Thus, integrating the dynamics of PT and PR into the model offers a more detailed understanding of MA-MES.
Secondly, this study expands the domain of mHealth intention research, previously limited to internet hospitals, health management apps, and fitness applications. MA-MES distinguish themselves from these applications by enhancing the patient’s medical experience through integrated online and offline services, which necessitates an empirical analysis of user acceptance. This is the inaugural empirical study focusing on MA-MES, thereby addressing a theoretical void.
Thirdly, this study introduces the cohabitation status of adult children and their parents as a moderating variable, highlighting its influence on PT and BI, as well as SI and BI.
Fourthly, it develops specific measurement items that capture the distinctive attributes of MA-MES, which could be utilized in analogous studies within this field.

6.3. Practical Implications

Sustainable healthcare requires MA-MES to adapt to the evolving market demands, providing equitable and acceptable services to clients. This involves continuously addressing the needs of elderly patients to ensure that they receive comprehensive care during their medical visits. Additionally, it is essential to focus on the needs of the actual users—the adult children of elderly patients—beyond just the user experience of the app. Through MA-MES, the goal is to foster collaboration among elderly patients, their adult children, healthcare professionals, and escort personnel.
As nascent applications, MA-MES require urgent promotion and market recognition. Practitioners can employ the findings of this study to design and manage MA-MES, thus boosting their download rates. Over the long term, the outcomes of this research may enhance users’ sustained engagement, ensuring that MA-MES do not become transient applications. Consequently, the practical significance of this research is to offer recommendations from a sustainability perspective for those involved in MA-MES. The specific recommendations are as follows.
First, the positive impact of PE on BI suggests that MA-MES managers should tailor their marketing strategies to meet the needs of both adult children and their elderly parents, highlighting the advantages of MA-MES in healthcare management.
Secondly, insights regarding the influence of PR and PT on usage intentions indicate that managers should prioritize user privacy, especially given the sensitivity of personal health information for elderly individuals. This can be addressed by providing comprehensive training for escorts to build trust and alleviate concerns among adult children.
Thirdly, while perceived PV has a modest impact, its importance should not be overlooked. To enhance affordability, managers should maintain the costs within reasonable limits or consider alternatives such as medical subsidies.
Fourthly, the moderating effects of adult children’s age and their cohabitation status with their parents offer important considerations. The middle-aged group and those not living with their parents showed reduced trust in MA-MES. Managers should thus develop targeted promotional strategies for these groups to mitigate their distrust.
In summary, these findings provide practical guidance for MA-MES practitioners, addressing marketing strategies, privacy protection, pricing, and targeted promotions to enhance user trust and intention of use.

6.4. Limitations and Future Research Directions

While this research provides valuable theoretical and practical insights, it also has limitations that open avenues for future exploration.
Firstly, this study focused only on participants’ intentions to use MA-MES, rather than their actual behavior. Although intention is a critical determinant of behavior, there is a subtle but significant difference between the two, and intentions may not fully capture real-world usage. Future research should investigate actual usage patterns to validate and extend these findings.
Secondly, the developed model is based on the UTAUT 2, primarily addressing technological factors. Since MA-MES are mHealth applications, other health-related factors, such as health conditions, should also be considered. Future studies could expand the model by integrating additional variables to accommodate these aspects.
Lastly, this study relied solely on quantitative analysis; future research could enrich the understanding by incorporating qualitative data from real users or expert interviews.

7. Conclusions

This research aimed to identify the key factors that influence adult children’s intentions to use MA-MES for their elderly parents. It modified the UTAUT 2 model and established a new research framework. The findings confirm that PE, EE, SI, PV, and PT positively impact BI, while PR negatively affects BI and mediates the relationship between PT and BI. Additionally, the age of adult children moderates the influence of PT on BI, and cohabitation with elderly parents moderates the effect of SI on BI. These results help to broaden the application scenarios for the UTAUT 2 and provide insights for the sustainable management and design of MA-MES.

Author Contributions

Conceptualization, methodology, F.X.; software, C.Z.; validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, visualization, supervision, project administration, F.X. and C.Z.; writing—review and editing, F.X., D.L. and J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Due to privacy restrictions, the data presented in this study are available on request from the corresponding author and are not publicly available. The corresponding author can be contacted by email to request data sharing.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

A brief essay for participants to read before completing the survey questionnaire.
Mobile applications for medical escort services (MA-MES) have ignited discussions in China by creating a platform that merges mobile technology with scheduled patient accompaniment. Users can book services on the platform, specifying the hospital, department, duration, and type of service needed. In person, professional escorts deliver individualized services to patients. These escorts are well-trained, have medical knowledge, and provide a range of services, including door-to-door transportation, assistance with registration, face-to-face consultations, medication pickup, report collection, accompanying during surgeries, and providing meals and companionship.
The main beneficiaries of MA-MES are patients who face challenges in accessing medical care independently, particularly those who are elderly or unfamiliar with hospital settings. The cost of services varies depending on the complexity and length of the appointment; for example, the Enjoy Accompany app charges RMB 398 for a single visit.
MA-MES improve the efficiency and quality of the patient care experience. Although hospitals may have volunteers or caregivers to assist, they do not offer the personalized service that MA-MES escorts provide. However, the lack of regulatory oversight and the low barriers to entry for service providers pose challenges to medical fairness. Moreover, the requirement for patients to share personal information, including ID, medical details, contact numbers, and addresses, with escorts raises concerns about potential privacy breaches.

Appendix B

The questionnaire includes a video of the Enjoy Accompany app in action, available at the following URL: https://www.bilibili.com/video/BV1Vu4y1B72S/?spm_id_from=333.337.search-card.all.click (accessed on 1 May 2024).

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Figure 1. MA-MES service system.
Figure 1. MA-MES service system.
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Figure 2. The hypothesized research model.
Figure 2. The hypothesized research model.
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Figure 3. Hypothesized model results.
Figure 3. Hypothesized model results.
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Table 1. Measurement items.
Table 1. Measurement items.
ConstructItemMeasurementSource
Performance
Expectancy
(PE)		PE1I believe that using MA-MES can bring convenience to the medical visits of elderly parents.[9]
PE2I believe that using MA-MES can enhance the efficiency of medical visits for elderly parents.
PE3I believe that using MA-MES can provide real-time services for the medical visits of elderly parents.
PE4I believe that by using MA-MES, I do not have to accompany elderly parents to medical visits personally; I can attend to my affairs.
Effort Expectancy (EE)EE1It is easy to learn how to use MA-MES.[9]
EE2The interaction of MA-MES is clear and easy to understand.
EE3I can use MA-MES smoothly.
EE4I can proficiently use MA-MES.
Social Influence
(SI)		SI1I would use MA-MES influenced by family or friends.[9]
SI2I would use MA-MES, influenced by colleagues or classmates.
SI3I would use MA-MES influenced by media or internet.
SI4I would use MA-MES influenced by authority or experts.
Price Value
(PV)		PV1In terms of price, the service charges of MA-MES are reasonable.[15]
PV2In terms of price, the service of MA-MES provides high cost-effectiveness.
PV3In terms of price, the service value of MA-MES exceeds the cost.
Perceived Risk
(PR)		PR1I believe that using MA-MES would cause financial losses for elderly parents.[26]
PR2I believe that using MA-MES would provide information about elderly parents to other companies.
PR3I believe that using MA-MES poses risks to the health of elderly parents.
Perceived Trust
(PT)		PT1I believe that MA-MES is professional.[25]
PT2I believe that MA-MES is trustworthy.
PT3I believe that MA-MES is reliable.
Behavioral Intention to Use (BI)BI1I would try to use MA-MES for my elderly parents.[27]
BI2I would learn to use MA-MES for my elderly parents.
BI3I would recommend using MA-MES for elderly parents to others.
Table 2. Respondent demographics (n = 350).
Table 2. Respondent demographics (n = 350).
CharacteristicsnPercentage
GenderMale20959.7%
Female14140.3%
Age20–29349.6%
30–3912936.8%
40–4916647.4%
50–59215.9%
Education levelJunior high school or lower226.3%
High school7722%
College14240.5%
Bachelor’s degree7521.4%
Master’s degree or above349.7%
OccupancyStudent18051.4%
Employee8022.8%
Self-employed4212%
Unemployed195.4%
Housewife/husband298.2%
Cohabitation with elderly parentsyes15143.1%
not19956.8%
Table 3. Convergent validity and internal reliability.
Table 3. Convergent validity and internal reliability.
ConstructItemMean (SD)Standardized
Factor Loading		Cronbach’ αCRAVE
PEPE13.043 (1.008)0.8520.9080.9090.714
PE22.937 (0.967)0.823
PE32.891 (0.954)0.817
PE42.943 (0.968)0.883
EEEE12.929 (1.003)0.8230.8890.890.668
EE22.937 (1.028)0.797
EE32.857 (1.025)0.778
EE42.929 (0.974)0.874
SISI13.151 (0.91)0.7680.9230.9310.779
SI22.986 (0.922)0.916
SI32.914 (0.939)0.827
SI43.089 (1.188)0.955
PVPV12.951 (0.867)0.8660.8740.8760.702
PV22.969 (0.894)0.782
PV33.026 (0.919)0.863
PRPR12.997 (0.994)0.8160.8390.8410.64
PR22.846 (0.93)0.752
PR32.937 (0.958)0.825
PTPT12.929 (1.012)0.810.8760.8780.706
PT22.911 (1.068)0.812
PT32.929 (1.048)0.895
BIBI12.991 (0.988)0.870.9270.9290.814
BI23.057 (0.974)0.888
BI33.146 (0.957)0.949
SD = standard deviation. CR = composite reliability. AVE = average variance extracted.
Table 4. Correlation matrix and square root of the average variance extracted (AVE).
Table 4. Correlation matrix and square root of the average variance extracted (AVE).
PEEESIPVPRPTBI
PE0.845
EE0.4580.817
SI0.110.2280.883
PV0.4050.3930.0770.838
PR−0.424−0.41−0.216−0.4280.8
PT0.2270.1980.1340.167−0.2820.84
BI0.4550.4560.2790.4020.470.3620.902
The bold data on the diagonal are the square roots of the AVE.
Table 5. Structural model fitness indices.
Table 5. Structural model fitness indices.
IndexAcceptance LevelIndex ValueComments
CMIN-694.830Fulfilled
DF-245Fulfilled
CMIN/DF<32.2836Fulfilled
RMSEA<0.080.073Fulfilled
IFI>0.90.924Fulfilled
TLI>0.90.914Fulfilled
CFI>0.90.923Fulfilled
PGFI>0.50.689Fulfilled
PNFI>0.50.787Fulfilled
Table 6. Results of the hypothesis testing.
Table 6. Results of the hypothesis testing.
HypothesisRelationshipEstimateS.E.C.R.pResult
H1PE→BI0.220.0464.239***Supported
H2EE→BI0.2050.0463.937***Supported
H3SI→BI0.180.0343.586***Supported
H4PV→BI0.1440.0492.7650.006Supported
H5PT→BI0.2290.0464.073***Supported
H6aPT→PR−0.3180.053−5.199***Supported
H6bPR→BI−0.230.055−3.975***Supported
*** p < 0.001. S.E. = standard error. C.R. = critical ratio.
Table 7. Mediating effect results.
Table 7. Mediating effect results.
HypothesisRelationshipEstimateBias-Corrected 95%CIResult
LowerUpperp
H6PT→PR→BI0.0590.0240.1130.001Supported
Table 8. Moderating effect of age.
Table 8. Moderating effect of age.
HypothesisRelationshipYouth (20–39)Middle-Aged (40–59)C.R.
Difference		Result
EstimateC.R.pEstimateC.R.p
H7aPE→BI0.1341.8730.0610.2593.581***1.708Not
Supported
H7bEE→BI0.2243.0290.0020.233.2030.0010.174Not
Supported
H7cSI→BI0.1582.270.0230.2793.945***1.675Not
Supported
H7dPV→BI0.1972.7020.0070.2363.2280.0010.526Not
Supported
H7ePT→BI0.5016.48***−0.014−0.1930.8476.494Supported
*** p < 0.001. C.R. = critical ratio.
Table 9. Moderating effect of cohabitation.
Table 9. Moderating effect of cohabitation.
HypothesisRelationshipCohabitation with
Elderly Parents		No Cohabitation with
Elderly Parents		C.R.
Difference		Result
EstimateC.R.pEstimateC.R.p
H8aPE→BI0.141.8430.0650.2523.585***1.742Not
Supported
H8bEE→BI0.2463.1170.0020.2153.10.0020.017Not
Supported
H8cSI→BI0.1441.940.0520.2864.172***2.232Supported
H8dPV→BI0.1952.5020.0120.2533.553***1.051Not
Supported
H8ePT→BI0.4475.49***0.0741.0610.2894.429Supported
*** p < 0.001. C.R. = critical ratio.
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Xu, F.; Hu, J.; Liu, D.; Zhou, C. Towards Sustainable Healthcare: Exploring Factors Influencing Use of Mobile Applications for Medical Escort Services. Sustainability 2024, 16, 6058. https://doi.org/10.3390/su16146058

AMA Style

Xu F, Hu J, Liu D, Zhou C. Towards Sustainable Healthcare: Exploring Factors Influencing Use of Mobile Applications for Medical Escort Services. Sustainability. 2024; 16(14):6058. https://doi.org/10.3390/su16146058

Chicago/Turabian Style

Xu, Fan, Jing Hu, Duanduan Liu, and Chao Zhou. 2024. "Towards Sustainable Healthcare: Exploring Factors Influencing Use of Mobile Applications for Medical Escort Services" Sustainability 16, no. 14: 6058. https://doi.org/10.3390/su16146058

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