User Interface Characteristics Influencing Medical Self-Service Terminals Behavioral Intention and Acceptance by Chinese Elderly: An Empirical Examination Based on an Extended UTAUT Model

Abstract

Medical self-service terminals (MSTs) offer potential advantages for optimizing workflows and enhancing patient experience in hospitals, particularly for the elderly. Despite this, the uptake of MSTs among older adults in China remains a challenge. This research aims to identify the key factors influencing behavioral intention (BI) to adopt MSTs in this age group, with a particular emphasis on user interface (UI) attributes. We extend the Unified Technology Acceptance and Use Theory (UTAUT) model to include these UI elements. Our empirical analysis examines seven variables, which include three critical UI attributes and four core UTAUT elements. The results highlight the importance of performance expectancy (β = 0.40, p < 0.001), effort expectancy (β = 0.50, p < 0.001), and social influence (β = 0.25, p < 0.05) in shaping BI. Importantly, the design of the user interface shows a strong positive correlation with both performance expectancy (β = 0.89, p < 0.001) and effort expectancy (β = 0.81, p < 0.001). These findings illuminate the complex relationship between objective UI features and subjective UTAUT factors. Our study enriches the understanding of how UI design affects the willingness and acceptance of MSTs, especially among China’s elderly population, emphasizing the need to incorporate their viewpoints for successful technology integration in healthcare.

1. Introduction

Hospital resources are becoming increasingly scarce and challenging to access, primarily due to the rapidly expanding elderly population [1,2]. The integration of new technological tools in healthcare delivery has proven to be a pivotal factor in enhancing both the scalability and value of patient care. In alignment with global sustainable development goals, advancements in internet technology—particularly through the incorporation of the Internet of Things and artificial intelligence—have made self-service machines increasingly prevalent in everyday activities [3]. To mitigate the strain on healthcare resources, medical self-service terminals (MSTs) have been strategically introduced in hospitals with the aim of streamlining various patient-related processes [4,5]. Although some academic research has already been conducted on the utility and effectiveness of these self-service terminal systems [6,7,8,9,10,11], these platforms notably offer added convenience, particularly for the elderly, who are a significant user group. However, the inclination of China’s elderly population to positively engage with MSTs is not as high as desired. This shortfall can be attributed to a range of factors, such as a general unfamiliarity with emerging digital technologies [12,13], less-than-intuitive user interface (UI) designs [14,15], and complex or unclear operational procedures [16,17]. Therefore, to foster greater acceptance and utilization of MSTs among the elderly in China, it becomes imperative to identify and understand the specific UI characteristics that most significantly influence their behavioral intention (BI).

The technology acceptance model (TAM), originally introduced by Davis in 1989 [18], serves as a cornerstone framework in the field of information technology (IT). It has been extensively utilized to investigate the factors that influence the acceptance and subsequent usage of emerging IT solutions, particularly in professional and organizational settings [19,20,21]. Over the years, TAM has gained widespread applicability and has been adapted to study technology adoption across a myriad of sectors, ranging from healthcare to education and beyond [19]. One of the fundamental tenets of TAM is that it seeks to understand the behavioral intention (BI) to use a technology, in this case, MSTs, particularly among older adults. According to TAM, this BI is largely shaped by the users’ subjective attitudes toward the technology, which can include their perceptions of its usefulness and ease of use. Therefore, TAM provides a valuable lens through which to examine and interpret the willingness—or lack thereof—of older adults to engage with MSTs in healthcare settings.

Among the various theories related to the TAM, the Unified Theory of Acceptance and Use of Technology (UTAUT) has gained significant attention [19]. UTAUT identifies four key determinants—performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC)—that notably influence the behavioral intention (BI) of older individuals toward MSTs. Aguilar-Flores et al. conducted research using UTAUT to explore the factors affecting the use of information technology among older adults and revealed a 55% IT utilization rate among this demographic based on latent variables [22]. Soh et al. applied UTAUT to examine elderly Malaysians’ perceptions of online shopping and found that PE, SI, and FC were the primary factors encouraging online shopping among this group [23]. Yang et al. employed a meta-UTAUT model to investigate the intent and behavior related to smartphone usage among senior citizens, recruiting 311 participants aged between 60 and 75. They found that PE was the most significant factor affecting BI [24]. Rój J. also used the UTAUT model to study the acceptance and utilization of e-health among individuals aged 60–69, highlighting the strong influence of PE, followed by EE and SI [25]. Extending the UTAUT framework, Park et al. explored factors affecting Korean attitudes toward mobile government services and found that trust in these services influenced adoption primarily through PE and EE [26]. While these studies have provided valuable insights into the subjective factors affecting older adults’ acceptance of healthcare information systems, they have largely overlooked the objective impact of usability, a factor that varies significantly between MSTs and other types of hardware or software.

Evaluating the usability of UI is essential for improving mobile sensing technology. Heuristic evaluation serves as a prevalent theoretical approach for optimizing UI usability, especially for products targeting elderly users [27,28]. Nimmanterdwong Z et al. employed heuristic evaluation to investigate mobile health application designs for the elderly, revealing that a human-centered design can effectively create mHealth solutions for this demographic with positive outcomes [29]. Tsai et al. utilized Nielsen’s heuristics to elucidate the usability of UI design and applied ISO 9241-11 standards to assess the usability and acceptability of an exergame system among 101 older adults, finding a robust correlation between interface design and system usability [30]. Kim et al. identified that “simple” and “intuitive” UI language significantly influences elderly users’ preferences for interfaces and interaction methods, aligning with the views presented in [31]. Amid the pandemic, Hamid Reza Saeidnia et al. assessed smartphone app UIs tailored for the elderly, emphasizing the importance of customized design [32]. Similarly, Zhou et al. found that older users favored a straightforward interface, and C-Life’s white login screen facilitated routine tasks [33]. While these studies offer valuable insights into the factors affecting usability in healthcare products, they fall short in addressing the subjective experiences of older adults adequately.

Recent research has underscored the link between UI usability and the BI of older adults [34]. Sumak et al. found that the quality of the user interface (UIQ) has a direct bearing on users’ perceptions of performance PE and EE [35]. Their study further revealed that UIQ plays a significant role in influencing user acceptance of technology across various stages. Turetken et al. highlighted that both PE and EE are substantially affected by how familiar users are with a system’s navigation [36]. This familiarity with NAV is tied to task-related information and users’ cognitive abilities. Despite these insights, there remains a gap in understanding how specific UI characteristics influence the BI of the elderly, particularly in the context of MST acceptance. A more comprehensive understanding of these UI attributes and their implications for elderly users’ intentions is crucial for designing MST systems that cater to their unique needs and preferences, ultimately fostering better healthcare experiences.

In this study, the Unified Theory of Acceptance and Use of Technology model is extended by integrating the Nielsen-Shneiderman heuristics to explore the influence of UI characteristics on the BI of older adults in China towards MSTs [37]. The proposed model is bifurcated into two key components: UI characteristics, assessed through the Nielsen-Shneiderman heuristics, and technology acceptance, as outlined by the UTAUT framework. To substantiate the model, an empirical study was carried out, featuring the testing and evaluation of MSTs among a cohort of elderly Chinese participants. The empirical findings affirm the applicability of the proposed model within the MST research context. This validated model serves as a valuable resource for researchers, designers, and policymakers aiming to boost MST acceptance and usage among the elderly. By presenting a comprehensive framework that melds subjective user perceptions with objective design elements, this study adds to the growing body of work promoting sustainable, user-focused technology solutions specifically designed for an aging demographic.

2. Materials and Methods

2.1. Research Framework

The research framework, illustrated in Figure 1, is composed of multiple elements. UI characteristics are evaluated based on three key factors: system support (SS), user interface design (UID), and navigation (NAV). Both UID and NAV jointly contribute to the perceived PE and EE of the system. Furthermore, SS, PE, EE, SI, and FC act as predictive variables for assessing the BI of older adults in China towards MST.

2.2. Hypotheses

2.2.1. UTAUT

To explore how UTAUT defined the subjective cognition of older adults, the hypotheses H1, H2, H3, and H4 were proposed.

H1. 

PE is significantly correlated with the BI of the elderly to MST.

H2. 

EE is significantly correlated with the BI of the elderly to MST.

H3. 

SI is significantly correlated with the BI of the elderly to MST.

H4. 

FC is significantly correlated with the BI of the elderly to MST.

2.2.2. Nielsen–Shneiderman Heuristic

To explore the influence of UI characteristics on the BI of older adults using MST, this study employed the Nielsen-Shneiderman heuristics [37] as a usability metric. Based on prior research [38], these UI characteristics were categorized into three main factors: SS, user UID, and NAV (as detailed in

Table 1. Measurements.

Dimension	Variable	Sub-Heuristics	Item Number
User Interface Characteristics	System Support	H7. Agility	5
		H8. Remind
		H9. Warning
		H11. Reversible
		H12. Language
	User Interface Design	H1. Conformity	5
		H2. Visualization
		H6. Respond
		H10. Ending
		H13. Control
	Navigation	H3. Matching	4
		H4. Compendious
		H5. Remember
		H14. Support
Behavioral Intention	Performance Expectancy	/	3
	Effort Expectancy	/	4
	Social Influence	/	3
	Facilitating Conditions	/	4
	Behavioral Intention	/	4

). Consequently, the study put forth the hypotheses H5, H6, H7, H8, and H9:

H5. 

SS is significantly correlated with the BI of the elderly to MST.

H6. 

UID is significantly correlated with the PE of the elderly to MST.

H7. 

UID is significantly correlated with the EE of the elderly to MST.

H8. 

NAV is significantly correlated with the PE of the elderly to MST.

H9. 

NAV is significantly correlated with the EE of the elderly to MST.

2.3. Data Collection

In this study, the questionnaire was adapted from English versions commonly used in prior research and was translated and expanded to fit the Chinese context. The questionnaire was divided into three sections:(1) demographic information, (2) a UTAUT questionnaire, and (3) a heuristic assessment scale. The demographic section collected five pertinent variables. Participants’ satisfaction levels were gauged using an 11-question UTAUT questionnaire and a 54-question heuristic assessment scale. Both questionnaires employed a five-point Likert scale, ranging from 1 (very dissatisfied) to 5 (very satisfied). To enhance the survey’s relevance, adjustments were made based on a preliminary experiment. For instance, the term “system” was replaced with “MST” to better align with the study’s focus. Additionally, irrelevant items (H8. Message and H14. Document) were eliminated from the questionnaires.

The study engaged urban older adults aged 50 and above living in mainland China, an age bracket selected in line with the concept of the digital divide, generally acknowledged to begin at age 50 [39] (

Table 2. The Chinese Internet Penetration Rate of Population of All Ages in 2018.

Age	10–19	20–29	30–19	40–49	50–59	Over 60
%	19.6	30.0	23.5	13.2	5.2	5.2

). For the experimental framework, a 19-inch touch-screen display was employed to emulate the self-service registration process. A high-fidelity MST prototype (Figure 2) was sourced from a Grade III Level A hospital in Hangzhou, China, which offers public service terminals in communal areas. As outlined in

Table 3. Four Tasks of Participants.

	Tasks
1	To assume that you had a cold, and to complete a “registered today”
2	To assume that you had a stomachache, and to complete an “appointment registration”
3	To complete a self-service payment
4	To complete an account recharge

, four distinct tasks were selected for UI usability evaluation. Subsequent to the usability tests, participants were invited to fill out a series of questionnaires. To maintain uniformity and mitigate potential cognitive variances among the older adults, post-task interviews were conducted. During these interviews, participants shared their insights and experiences related to MST usage.

The research hypotheses were evaluated through a structural equation modeling approach. Confirmatory factor analysis was performed using Amos 24 software to validate the model.

3. Results

3.1. Data Collection

A total of 78 valid questionnaires were obtained for this study, collected from Chinese adults aged 50 and above, who were sourced from senior communities in Hangzhou, China. Detailed demographic characteristics, familiarity with MST, and internet usage frequency of the participants are outlined in

Table 4. Questionnaire data summary.

Demographic Category		Frequency	Percentage (%)
Gender	Male	29	37.2
	Female	49	62.8
Age	50–54	22	28.2
	55–60	10	12.8
	61–64	11	14.1
	65–70	25	32.1
	71 or older	10	12.8
Education	Primary school	2	2.6
	Middle school	28	35.9
	High school	25	32.1
	College (3 years degree)	14	17.9
	University (4 years degree)	8	10.3
	Graduate school	1	1.3
MST Experience	Experienced	27	34.6
	Inexperienced	51	65.4
Internet Use Frequency	Monthly or less	8	10.3
	Weekly	20	25.6
	Everyday	18	23.1
	Daily	32	41.0

. Of the respondents, 49 (62.8%) were female. A significant majority (59%) fell within the age bracket of 55–70 years, while only 12.8% were aged 71 or older. Regarding educational background, 70.6% had completed primary, middle, or high school. Notably, 65.4% had no previous experience with MSTs, even though 65.1% reported daily internet usage. Age groups 50–54 and 65–70 constituted significant portions, comprising 28.2% and 32.1% of the sample, respectively. Additionally, a substantial number of participants had either completed middle school (35.9%) or high school (32.1%).

3.2. Measurement Model

The composite reliability (CR) values of PE, EE, SI, FC, BI, SS, UID, and NAV ranged between 0.798 to 0.919, signifying robust reliability in this study (see

Table 5. Results of reliability and validity tests.

Variable	Cronbach’s Alpha	Factor Loading	CR	AVE
PE	0.896	0.715–0.884	0.846	0.648
EE	0.839	0.630–0.790	0.824	0.540
SI	0.726	0.728–0.772	0.798	0.569
FC	0.838	0.728–0.815	0.843	0.574
BI	0.961	0.777–0.898	0.919	0.740
SS	0.726	0.667–0.737	0.801	0.501
UID	0.731	0.624–0.827	0.858	0.549
NAV	0.758	0.749–0.790	0.815	0.594

) [40]. Similarly, the average variance extracted (AVE) values for these variables ranged from 0.501 to 0.740, surpassing the benchmark value of 0.5 (see Table 5) [41]. These results further corroborate the study’s strong composite reliability.

To establish discriminant validity and differentiate measurement structures, a discriminant validity assessment is essential [42]. Discriminant validity is confirmed when the average variance extracted (AVE) value for each variable exceeds the squared correlation coefficients between variables [38]. As shown in

Table 6. Results of discriminant validity tests.

Variable	Mean	S.D.	BI	FC	SI	EE	PE	NAV	UID	SS
BI	4.148	0.691	0.860
FC	4.278	0.668	0.695	0.758
SI	3.833	0.696	0.673	0.622	0.754
EE	4.003	0.540	0.805	0.674	0.574	0.735
PE	4.257	0.664	0.793	0.552	0.585	0.709	0.805
NAV	4.138	0.394	0.492	0.395	0.302	0.409	0.310	0.771
UID	4.350	0.479	0.648	0.538	0.341	0.526	0.681	0.912	0.741
SS	4.048	0.448	0.669	0.567	0.422	0.574	0.646	0.881	0.986	0.708

, BI, FC, SI, EE, and PE all exhibit satisfactory discriminant validity, while the remaining variables do not meet this criterion. Overall, 62.5% of the variables in this study demonstrated strong discriminant validity.

3.3. Hypothesis Testing

As shown in Figure 3, among the extended UTAUT model, PE (β = 0.40, p < 0.001, |t| > 1.96), EE (β = 0.50, p < 0.001, |t| > 1.96), and SI (β = 0.25, p < 0.05, |t| > 1.96) were definitely associated with BI, while FC (β = 0.18, p > 0.05, |t| < 1.96) was not associated with it (H1, H2, and H3 were supported; H4 was not supported). In terms of UI characteristics, UID was positively associated with PE (β = 0.89, p < 0.001, |t| > 1.96) and EE (β = 0.81, p < 0.001, |t| > 1.96) (H6 and H7 were succeeded). NAV (β = −0.32, p < 0.01, |t| > 1.96; β = −0.11, p > 0.05, |t| < 1.96) was negatively correlated with PE and was not associated with EE (H8 was supported; H9 was not supported). Path analysis results shown that SS (β = 0.15, p > 0.05, |t| < 1.96) was not correlated with BI (H5 was not supported).

It can be identified that UID is indeed a significant factor for MST acceptance by Chinese older adults. In the light of the path analysis results shown in Figure 3, UID has a comparable impact on PE and EE. In addition, the impact of UID on BI was 0.89 × 0.40 + 0.81 × 0.50 = 0.76, which indicated that UID was an important factor in determining MST acceptance among older adults. Overall, as shown in

Table 7. Results of the hypothesis tests.

Hypotheses	Standardized Regression Coefficient	T-Value	Support
H1: PE → BI	0.40	2.983 **	Yes
H2: EE → BI	0.50	3.824 **	Yes
H3: SI → BI	0.25	2.466 *	Yes
H4: FC → BI	0.18	1.911	No
H5: SS → BI	0.15	1.456	No
H6: UID → PE	0.89	7.914 ***	Yes
H7: UID → EE	0.81	5.054 ***	Yes
H8: NAV → PE	−0.32	−3.019 **	Yes
H9: NAV → EE	−0.11	−0.830	No

*** p < 0.001, ** p < 0.01, * p < 0.05.

, more than 60% of the research hypotheses were consistent with previous studies.

4. Discussion

4.1. Theoretical Implications

The aim of this study is to scrutinize the influence of UI attributes on the behavioral BI to adopt MSTs among older adults in China, utilizing an augmented UTAUT model. The expanded model incorporates four core UTAUT determinants—PE, EE, SI, and FC—alongside three UI elements: SS, UID, and NAV. Our findings reveal that six of the nine formulated hypotheses, which connect UI features to user acceptance, are positively corroborated. Notably, UID exerts a substantial impact on BI by significantly influencing both PE and EE.

4.2. Managerial Implications

This study provides two sets of academic implications.

Firstly, concerning the four subjective technology acceptance factors from the UTAUT model, several key insights emerge. The validated Hypothesis 1 (H1) suggests that planners can leverage MSTs’ value-added features to boost older adults’ PE. For example, hospitals could highlight MST benefits like advanced access to doctor and department information, streamlined registration processes, and time-saving features [4,43,44]. In the next place, the confirmed Hypothesis 2 (H2) underscores the importance of user-friendliness in MST interactions. To improve the interface’s learnability and usability, simplifying the information delivery process is crucial [31,45]. Moreover, optimizing UID proves more effective in enhancing older adults’ BI than other strategies like increasing motivation. Thirdly, the supported Hypothesis 3 (H3) indicates that governments could employ targeted, large-scale channels, aligned with the sustainable development goals [46], such as community gatherings for seniors, to facilitate effective information dissemination. Lastly, the unsupported Hypothesis 4 (H4) implies that enhancing MSTs’ technical support and bridging the digital divide could improve acceptance among older adults.

Secondly, regarding the three objective usability factors of UI, the supported Hypotheses 6–8 suggest that designers should focus on UID to minimize cognitive barriers during initial use. Enhancing UI visualization and providing clear system support (SS) are recommended, aligning with suggestions from [32,33]. For example, standardizing and anchoring frequently used NAV buttons like “Back” and “Home” could be beneficial. Developers should also optimize the UI layout to elevate the user experience, differentiating content levels through adjustments in kerning, line spacing, font size, font weight, and color.

Furthermore, the lack of support for Hypotheses 5 and 9 may indicate that current MST systems lag behind personal terminals. Designers could create personalized systems that cater to individual UI interaction preferences.

Lastly, the observed discrepancies between the study’s outcomes and its hypotheses could be due to its localized scope in Hangzhou, China. Cognitive factors among Chinese older adults may differ from those in foreign studies cited in the literature.

Overall, to better cater to the needs of China’s elderly population, future research could delve into specific strategies for enhancing MST interfaces. For instance, adopting a more intuitive and user-friendly interface could mitigate potential cognitive burdens. Additionally, educational and training programs aimed at older users could be instrumental in boosting MST acceptance. These initiatives could bolster user understanding and confidence in utilizing MSTs. Engaging directly with older users to gather their feedback is a crucial step in refining the MST interface. This could be accomplished through periodic user feedback sessions or surveys, ensuring that MST designs align with user expectations and requirements. Furthermore, governmental bodies and healthcare providers might consider implementing policies and incentives to encourage MST usage among the elderly. This could range from subsidies to incentive programs that alleviate the financial constraints often associated with adopting new technologies. Research has also indicated a positive link between educational levels and health awareness [47]. Individuals with higher education are generally more informed about health issues, healthy living, and healthcare options. Therefore, elevating educational standards could indirectly enhance overall health literacy. Future studies could approach this from multiple angles, examining various factors that influence elderly decision-making regarding MST, with the aim of gathering more comprehensive data for ongoing MST improvement.

4.3. Limitations and Further Research

Several limitations in this study warrant acknowledgment and further discussion. First and foremost, the cross-sectional nature of our survey design poses a limitation as it may not adequately capture the cognitive variances that naturally emerge due to the aging process among the older population. This is a critical aspect that future research should delve into. Moreover, the study’s geographic confinement to Hangzhou, China, significantly restricts the broader applicability and external validity of our findings related to MST acceptance on a national or global scale. Second, the limited sample size in the studies we reviewed further constrains the generalizability of our conclusions, making them less universally applicable. As a recommendation for future research, there is a pressing need to amass more comprehensive and diverse data sets. This would not only bolster the robustness of cognitive studies focusing on older adults but also help to refine and validate the structural integrity of the existing model.

While this study underscores the significance of UI features in the acceptance of MST, it is important to acknowledge that MST acceptance is likely influenced by a myriad of other factors that were not exhaustively covered in this research. For instance, future research could expand the scope to include other determinants of MST acceptance, such as cultural nuances, educational backgrounds, and individual predilections, to provide a more holistic and comprehensive understanding of the subject matter [48]. Therefore, caution should be exercised when applying these insights to different settings. Despite these limitations, the insights gleaned from this research offer valuable guidelines for MST design and broader application. However, for practical implementations, it’s crucial to consider other potential variables and contextual elements that could influence MST acceptance in real-world scenarios.

5. Conclusions

The primary objective of this study is to conduct an in-depth examination of the role that UI characteristics play in influencing the behavioral intention (BI) to adopt MSTs among the older adult population in China. To achieve this, we extend the UTAUT model to create an augmented framework. This enhanced model not only incorporates the four foundational UTAUT variables—PE, EE, SI, and FC—but also integrates three critical UI elements: SS, UID, and NAV.

Our study culminates in three pivotal conclusions: Firstly, PE, EE, SI, and UID emerge as crucial determinants that significantly impact the BI to use MSTs among China’s older adults. In the next place, UID shows a positive correlation with both PE and EE, indicating that a well-designed interface can enhance both the perceived usefulness and ease of use of MST. Lastly, our findings reveal a substantial relationship between UID and BI, thereby affirming the importance of UI design in influencing technology adoption decisions among older adults.

By establishing a constructive association between objective UI elements and subjective UTAUT variables, this research significantly enriches our understanding of the specific UI attributes that can drive or hinder the adoption and acceptance of MST among the elderly in China. Furthermore, the study underscores the critical need to consider the unique perspectives and requirements of older adults when designing UI elements. The insights gleaned from this research provide actionable recommendations for healthcare providers, policymakers, and UI designers, aimed at tailoring UI elements to better meet the needs of the older population. This, in turn, is expected to facilitate greater acceptance and more widespread utilization of MST in healthcare settings.