Available online at www.HighTechJournal.org HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 720 ISSN: 2723-9535 Evaluating the Determinants of Young Runners' Continuance Intentions toward Wearable Devices Zhaoxia Guo 1*, Guoqing Liu 1, Zhiguo Liu 2, Asif Khan 3, 4 1 College of Physical Education, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China. 2 College of Physical Education, Yantai University, Yantai, Shandong, 264005, China. 3 Southern Taiwan University of Science and Technology, Tainan, Taiwan. 4 ANScientistify INC., Tainan, Taiwan. Received 31 August 2023; Revised 12 November 2023; Accepted 18 November 2023; Published 01 December 2023 Abstract Running has gained popularity as a fitness activity in China, with a growing number of young runners utilizing wearable devices to monitor their running routines and engage in quantified self-practices. The continuous evolution of wearable devices in terms of products and services has expanded the choices available to young runners. Therefore, there is a need to analyze the factors influencing the continuance intention of young runners, providing insights into how to promote the sustained growth of these products or services in the market. This study is grounded in the Technology Acceptance Model and the Theory of Planned Behavior, with an extension incorporating the quantified self to explore the impact of users' continuance intentions to use wearable devices. A survey was conducted among 468 young runners who already used wearable devices, and the data collected were analyzed using PLS-SEM. The results indicate that perceived usefulness and attitudes from the Technology Acceptance Model positively influence intentions for continued use. Additionally, subjective norms according to the Theory of Planned Behavior positively influence continuance use intentions. However, perceived behavioral control does not have a significant effect on continuance use intentions. Conversely, the Quantified- Self positively influences continuance use intentions and partially mediates the relationship between perceived usefulness and continuance use intentions. This research has several theoretical implications for the Theory of Planned Behavior, the Technology Acceptance Model, and the Quantified-Self research construct. Moreover, this study has practical implications for practitioners concerning the adoption and acceptance of wearable devices by young people. This approach enables practitioners to target and implement precise strategies to meet the current demands of the young runner market. Keywords: Partial Least Squares (PLS); Structural Equation Model (SEM); Young Runners; Wearable Devices; Attitude; Quantified-Self; Continuance Intentions to Use. 1. Introduction Wearable devices, defined as mobile electronic devices worn on the body or integrated into the user's clothing or accessories [1], operate on various systems, akin to smartphones, impacting their market and product shares. Common forms of wearable devices include smart watches, fitness bands, earworm devices, and smart glasses. Notably, according to Statista, the leading suppliers of wearable devices are Apple, Xiaomi, Samsung, and Huawei [2]. As the production technology for wearable devices continues to improve, costs are reduced while enhancing functionality to meet the needs * Corresponding author: 904136621@qq.com http://dx.doi.org/10.28991/HIJ-2023-04-04-02  This is an open access article under the CC-BY license (https://creativecommons.org/licenses/by/4.0/). © Authors retain all copyrights. https://creativecommons.org/licenses/by/4.0/ https://orcid.org/0000-0002-5498-6077 HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 721 of different consumers. In the sports realm, wearable devices are being increasingly applied, spanning everyday activities to professional sports and contributing to health monitoring during exercise [3], healthcare applications [4, 5], and training monitoring [6]. With the growing popularity of running as a fitness activity, an increasing number of young runners are using wearable devices to track their running routines, utilizing data such as heart rate and stride length to monitor progress. Additionally, these devices motivate young runners to follow and share their fitness data online through platforms such as Fitbit, which enables friendly competition and the exchange of encouragement or advice through leaderboards. However, previous research on wearable devices has predominantly concentrated on investigating users' initial adoption of such devices [3, 7], with limited studies on the continuance usage intentions (CIs) of active individuals in the sports domain. Research examining the factors influencing users' usage or continued usage intentions has utilized mainly the technology acceptance model [8-11] and the theory of planned behavior [12, 13]. Furthermore, in addition to the CI of users [14], several theories exist for analyzing consumers’ attitudes (ATT) toward the current digitalization of products. However, the technology acceptance model (TAM) is considered to be the most relevant and popular theory for measuring 'consumers' ATT toward a product [11, 15]. The original TAM model includes perceived usefulness (PU) and perceived ease of use (PE) as the main antecedents for determining the ATT of users and their CI toward a technology or product [8, 16]. If a certain technology is deemed easy to use, the users will harbor a positive ATT toward that technology [17]. Hence, this research aims to analyze the relationship between the ATT and CI of young runners toward smart wearable devices. Moreover, the TAM was chosen as a key theoretical framework for its extensive applicability in analyzing user adoption across diverse technologies [17-22]. Previous research on TAM covered a wide range of technologies, including tablets [23], smartphones [24, 25], long-term evolution (LTE), cloud technology [26], and e-books [27]. Hence, this study employed the TAM to measure the impacts of PU and PE on the ATT and CI of young runners. This research also employed the theory of planned behavior (TPB) to gain further insights into the CI and ATT of young runners. According to the TPB, subjective norms (SNs) and perceived behavioral control (PBC) impact both the ATT and CI of users [28]. The SN reflects the degree to which a user is important for engaging in a certain conduct [29], suggesting that the more users value a certain conduct, the more motivated they are to perform it. Previous research has demonstrated the significant impact of the SN on CI [29, 30]. Furthermore, PBC is related to the difficulty or ease of engaging in a certain conduct [29]. Users will experience greater PBC if there are adequate resources to support that conduct; hence, PBC can be considered to have a significant impact on CI [29-31]. Similarly, users are likely to have a higher degree of PBC for smart wearable devices if they possess the necessary resources to support their conduct. Consequently, this study aimed to explore the impact of SN and PBC on the CI of young runners. With the rapid advancement of information technology, wearable devices and their associated products or services are undergoing continuous upgrades and transformations, leading to intensified market competition. Manufacturers consistently strive for distinctive designs to augment added value and competitiveness. However, despite this, young runners, as users of wearable devices, possess unique needs that have not been adequately addressed in prior research on these devices. Consequently, this study concentrates on young Chinese runners as research subjects, investigating how they perceive and sustain the use of intelligent wearable devices at an individual level. The aim is to provide insights that foster the sustainable growth of wearable device products or services in the market. Since its inception in 2007, quantified self (QS) has garnered considerable attention globally. QS involves capturing real-life events using mechanical devices and converting them into readable data to meet human needs or assist in decision-making, evaluation, and comparison between humans and computers [32]. With the escalating use of intelligent devices and applications to generate substantial data about personal behaviors, self-tracking has become increasingly popular. Wearable devices, such as smartwatches, have emerged as favorable tools for QS [33, 34]. Gathering information during the QS process enables users to receive more accurate and potentially experiential services, prompting self-reflection. Some studies suggest that users with prominent features of quantified design primarily engage in the information feedback process [35, 36]. Young runners can employ wearable devices in conjunction with mobile smart QS activities to experience the essence of this quantification practice. Hence, this study aimed to explore the relationship between QS and CI in young runners. This research seeks to analyze the factors influencing young runners’ CI. The study encompasses the following research objectives. First, this research aimed to explore the relationship between young 'runners' ATT and CI. Second, this research aims to explain the impacts of PE and PU on the ATT and CI of young runners. Third, this research aims to analyze the impacts of 'TPB's SN and PBC on CI. Finally, this research investigated the relationship between the QS and CI of young runners. This study introduces QS variables and combines them with the Technology Acceptance Model and the Theory of Planned Behavior to understand the factors influencing young runners' intentions to continue using wearable devices, providing insights into the enduring impact of wearable devices in sports activities. HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 722 2. Theoretical Background and Hypothesis Development 2.1. Technology Acceptance Model (TAM) Based on the Theory of Reasoned Action, Davis [8] proposed the Technology Acceptance Model (TAM) to investigate users' understanding of the acceptance of information technology. Lee et al. [32] considered the TAM the most widely used model for explaining consumer behavior in technology adoption. TAMs have been applied in acceptance studies of various information technologies, including wearable devices [4, 9]. Due to its robust theoretical foundation and ease of modification and expansion, this study employs the TAM to identify the variables influencing young runners' continued use of wearable devices. The TAM posits that perceived usefulness (PU) and perceived ease of use (PE) are fundamental determinants of user technology acceptance. As defined by Davis [33], PU reflects the extent to which users believe a specific system will enhance their job performance, while PE pertains to the ease with which users believe that using a particular system will occur. Behavior intention to use denotes the strength of a user's intention to perform a specific behavior, while attitudes signify positive or negative emotions toward task behavior [34]. When individuals encounter a new information technology, their perception of its usefulness increases with an increase in PE, consequently positively impacting user attitudes toward adoption [33]. User attitudes influence behavioral intention to use, subsequently affecting actual use [35]. 2.1.1. Attitude and Continuance Intention CI use refers to the user's intention to continue using a system after the initial trial; this is also known as consistent usage intention [36]. In the context of wearable devices, it signifies the subjective intention to continue using a particular product or service in the future [7]. CI is a key outcome in consumer research, contributing to the sustained growth of a product or service in the market. Therefore, we consider this parameter the outcome variable for predicting young runners' CIs when wearing wearable devices. Attitudes refer to an individual's positive or negative evaluation of a specific behavior [12] and are one of the essential factors for predicting behavioral intention. Attitude is a significant influencing variable in the TAM. Previous research has supported the significant relationship between attitudes and CI [33, 37, 38]. In the sports domain, Song et al. [39] found a positive correlation between consumers' attitudes toward smart-connected sports products and their CIs. If young runners hold positive attitudes toward wearable devices after using them, their intention to continue using them may be more decisive. Therefore, we hypothesize the following. Hypothesis 1 (H1): Young runners' attitudes are positively related to their continuance intention to use wearable devices. 2.1.2. Perceived Usefulness and Continuance Intention PU is one of the most influential variables in the TAM for technology adoption. In the context of wearable devices, previous research has identified PU as a significant predictive factor for user intentions to adopt such wearable devices [5, 40]. For example, the PU of smartwatches has been found to positively impact users' CIs [46, 47]. However, some studies suggest that PUs do not significantly influence users' confidence in smart fitness wearables [41]. Additionally, related research has shown that PUs positively impacts user attitudes [29, 42] and can subsequently influence CI through attitudes [3]. For young runners, a crucial factor influencing their CI is whether wearable devices can enhance their exercise efficiency and effectiveness. If individuals perceive fitness benefits or improved athletic performance from using wearable devices, they may develop favorable attitudes toward the devices, thereby influencing their CI. Therefore, we propose the following hypotheses: Hypothesis 2 (H2): Young runners' perceived usefulness of wearable devices is positively related to their intention to continue using wearable devices. Hypothesis 3 (H3): Young runners' perceived usefulness of wearable devices positively influences their attitude toward wearable devices. Hypothesis 3a (H3a): Perceived usefulness positively influences young runners' intention to use wearable devices through their attitudes. 2.1.3. Perceived Ease of Use and Continuance Intention PE stands out as a key factor in the TAM and is deemed reliable for predicting user adoption of new technologies. This finding suggests that if a technology is easy to use, users are more likely to continue using it. For instance, Ashfaq et al. [43] identified PE as an important predictive factor for users' continued use of chatbots. Previous research has also demonstrated a significant relationship between PE and attitude. For example, Yu and Huang [38] discovered that PE positively influenced users' attitudes toward using the CMG Mobile App to view the Olympics (β=0.241, P<0.01). This effect could further impact users' intention to reuse technology through the mediating role of attitude [44]. HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 723 Moreover, in the TAM model, PE positively influences PU, a belief variable [8]. This implies that if users perceive a new technology or system as easy to use, they are more likely to accurately recognize the value of these technologies and systems. Prior research has also revealed that the ease of use of wearable devices positively influences PU [4]. For young runners, if wearable devices are perceived as easy to use, they are more likely to accurately recognize the value of these technologies and services, cultivate positive attitudes toward their continued use, and subsequently influence their intention to use them. Therefore, we hypothesize the following: Hypothesis 4 (H4): Young runners' perceived ease of use is positively related to their perceived usefulness of the tool. Hypothesis 5 (H5): Young runners' perceived ease of use positively influences their attitude toward the continued use of wearable devices. Hypothesis 6 (H6): Young runners' perceived ease of use positively influences their ability to use wearable devices. Hypothesis 4a (H4a): Perceived ease of use positively influences continuance intention through attitudes. Hypothesis 4b (H4b): Perceived ease of use positively influences attitudes toward the continued use of wearable devices through perceived usefulness. Hypothesis 4c (H4c): Perceived ease of use positively influences continuance intention through perceived usefulness. Hypothesis 4d (H4d): Perceived ease of use positively influences continuance intention through perceived usefulness and attitudes. 2.2. Theory of Planned Behavior (TPB) The theory of planned behavior (TPB), akin to the TAM, is rooted in the theory of reasoned action and posits that an individual's attitude, subjective norms (SNs), and perceived behavioral control (PBC) influence behavioral intentions [12]. The TPB has been widely used to explain intentions to use various forms of information technology [29, 45]. This study employs SN and PBC as factors for young runners' confidence in wearing wearable devices. 2.2.1. Subjective Norms and Continuance Intention A SN pertains to an individual's perception of how significant others, such as family, friends, and peers, want them to engage in a particular behavior [46]. It reflects the social pressures and expectations that a decision maker feels about making or refraining from the behavior in question [12]. Users share their fitness data through wearable devices and receive support or advice from others. In this process, social influence or peer pressure motivates them to engage in sports and competitions [47]. Previous research on wearable devices has shown that SNs positively impact users' intention to use [13] and actual usage behavior [3]. Young runners are influenced in their decisions to engage in physical activity by sharing and receiving relevant information through the use of wearable devices. Therefore, we hypothesize the following: Hypothesis 7 (H7): Subjective norms positively influence young runners' continuance intention to use wearable devices. 2.2.2. Perceived Behavioral Control and Continuance Intention PBC refers to the perceived ease or difficulty of performing a behavior [12]. It is assumed to be based on accessible control beliefs that can facilitate or hinder behavioral performance. Ajzen [48] suggested that high levels of PBC can strengthen an individual's intention to perform a behavior and increase their effort and persistence. PBC may be a crucial factor influencing users' CI. Previous research in the context of wearable devices has also shown that PBC positively affects CI [39, 47]. Therefore, we hypothesize the following: Hypothesis 8 (H8): Perceived behavioral control positively influences young runners' intention to use wearable devices. 2.3. Quantified-Self and Continuance Intention Wearable devices have the capability to continuously track users' physiological and behavioral data, providing them with the ability to analyze their QS data to gain insights into their health status and establish exercise plans, among other benefits. These devices can precisely quantify metrics such as steps, heart rate, pace, and energy expenditure. In innovative research focusing on wearable device applications, heart rate and pace stand out as frequently used quantified indicators. For example, Sunne et al. developed a mobile application that enables users to compete based on real-time heart rate data gathered at the gym [49]. Mauriello et al. introduced "social fabric fitness", a wearable application that supports group running by displaying heart rate and pace on the back of runners' shirts using dynamic electronic textiles [50]. Other studies have employed caloric intake as a quantified indicator of users' emotional and social responses [51]. HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 724 In addition to individual support, some wearable device applications incorporate built-in social features. Yang et al. discovered that through the front display screen of SocialBike, cyclists can share competitor data, perceive the presence of rivals, and enhance their intrinsic motivation during cycling [52]. This study posits that young runners use wearable devices to self-track, employing quantified indicators such as heart rate, pace, and caloric intake. By monitoring their activities and decision-making efficiency, young runners can make informed behavioral choices. Hassan identified the significant impact of QS on users' intention to continue using wearable devices [51]. Throughout the quantification process, young runners immerse themselves in tracking activities, which can enhance their ability to use wearable devices. Moreover, prior research has suggested that the utility and user friendliness of mobile intelligent and wearable device information platforms can stimulate users' engagement in QS experiences [53]. Qualitative studies have also indicated that when users perceive the functionality of the data, accessing it will motivate individuals and further engage them in QS activities [54]. For young runners, the usability and usefulness of wearable devices can stimulate their participation in QS activities, thereby influencing their CI. Therefore, the hypotheses of this study are as follows: Hypothesis 9 (H9): Perceived usefulness positively influences young runners' engagement in the quantified self. Hypothesis10 (H10): Perceived ease of use positively influences young runners' engagement in the quantified self. Hypothesis 11 (H11): The self-quantification positively influences young runners' continuance intention to use wearable devices. Hypothesis 11a (H11a): Perceived ease of use positively influences users' continuance intention to use wearable devices through the quantified self. Hypothesis11b (H11b): Perceived usefulness positively influences users' continuance intention to use wearable devices through the quantified self. Hypothesis 11c (H11c): Perceived ease of use positively influences the quantified self through perceived usefulness. Hypothesis11d (H11d): Perceived ease of use positively influences users' continuance intention to use wearable devices through perceived usefulness and the quantified self. Based on the above analysis, this study proposes the following hypothetical model (see Figure 1): PE, PU, SN, and PBC are the independent variables; attitude and QS are the mediating variables; and continuance intention is the dependent variable. Continuous Intention Perceived Behavioral Control Subjective Norms Quantified -Self Perceived Usefulness Attitude Perceived Ease of Use H1 H4 H10 Figure 1. Research framework HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 725 3. Research Design and Methodology 3.1. Data Collection In this study, data were gathered from young runners aged 15 to 34 years who were already utilizing wearable devices. The data collection spanned from April to August 2022. The survey was distributed through online platforms, such as "Marathon Enthusiasts Group" and "Running Enthusiasts Group," using a voluntary and self-administered questionnaire approach. To ensure the anonymity and consent of the users, the questionnaire included the following informed consent statement at its outset. The respondent in the study could withdraw from responding to the questionnaire at any time without providing any reason. Furthermore, the engagement of the respondent in this study was voluntary, and responses to the questionnaire items were not tracked back to the respondent to ensure their anonymity (see Appendix I). Incentives were offered upon completion of the questionnaire to maintain response quality. By August 20, 2021, 525 questionnaires were collected, 468 of which were considered valid. The characteristics of the valid questionnaire sample are presented in Table 1. Among the young runners already utilizing wearable devices, 265 were males and 203 were females. Regarding education level, 189 participants had a bachelor's or associate's degree, while 256 participants had a master's degree or above. The majority of runners (25.85%) had 1-2 years of running experience. The wearable devices used by the participants included smart wristbands, smartwatches, smart running shoes, and smart glasses, among others. Table 1. Characteristics of Young Runner-Wearable Devices Survey Participants (N=468) Variable Category Frequency Percentage Gender Male 265 56.63% Female 203 43.37% Age 15～20 42 0.90% 21～25 255 54.48% 26～30 121 25.58% 31～35 50 10.68% Education Below Junior High 3 0.64% High School/Technical School 20 4.27% Bachelor's/Associate Degree 189 40.38% Master's Degree or above 256 54.7% Running Experience Less than one year 123 26.28% 1-2 years 121 25.85% 2-3 years 81 17.30% 3-4 years 57 12.17% 4-5 years 21 4.48% 5-6 years 15 3.20% More than six years 50 10.68% Type of Wearable Devices Used Smart Wristbands 287 61.32% Smartwatches 242 51.7% Smart Running Shoes 56 11.96% Smart Glasses 41 8.76% Others 19 4.05% 3.2. Measurement Instruments The Technology Acceptance Model (TAM) was used to measure variables, including PU, PE, attitude, and CI, using scales developed by Davis [33] and Lee [29]. Each variable consists of four items. The theory of planned behavior (TPB) variables, including SN and PBC, were measured using scales developed by Ajzen and Driver [46]. Each variable consists of three items. The QS was measured using scales from studies by Hassan [51] and Jin [53], among others, composed of four items. All the items were rated on a 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7). Please refer to Table 2 for further details. HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 726 Research Methodology Data Methodology Perceived Usefulness, Perceived Ease of Use, attitude, and Continuous Intention by Davis s (1989) and Lee s (2010) scale. Subjective Norms and Perceived Behavior Control, by Ajzen and Driver s (1992) scale. Quantified- Self by Hassan s (2019) and Jin s (2020) scale. Data Collection and Sampling Methodology Data was collected from young runners aged 15 to 34 who were already using wearable devices. The survey was distributed through online platforms, such as "Marathon Enthusiasts Group" and "Running Enthusiasts Group," using a voluntary and self-administered questionnaire approach. Research Instrument Questionnaire with 7-point Likert Scale items Data Analysis Methodology This study employed partial least squares structural equation modeling (PLS-SEM) technique to conduct the analysis. In the first phase, the validity of constructs were analyzed. In the second phase, the correlations among the research constructs were analyzed. Figure 2. Research Methodology Flowchart 4. Measurement Model and Structural Model 4.1. Measurement Model Analysis This study employed partial least squares structural equation modeling (PLS-SEM). According to Hair et al. [55], the measurement model should evaluate the factor loadings, reliability, convergent validity, and discriminant validity of the items (see Table 2). The factor loading of each item should be > 0.708 [55]. In this sample, all factor loadings ranged from 0.757 to 0.901, surpassing the threshold of 0.708 and meeting the requirement. The reliability of the measurement model can be assessed by α > 0.70 [56]. In this study, the α values ranged from 0.823 to 0.912, all of which exceeded the recommended threshold, indicating high reliability, as shown in Figure 3. Convergent validity was evaluated using composite reliability (CR) and average variance extracted (AVE) [57, 58]. In this sample, both CR (>0.7) and AVE (>0.50) exceeded the recommended thresholds, as indicated in Figures 4 and 5, respectively, indicating good convergent validity. Discriminant validity was assessed using the square root of the AVE [57] and the Heterotrait–Monotrait ratio (HTMT) (<0.85). The square root of the AVE for each construct was greater than the correlation with other constructs (see Table 3 and Figure 6), indicating good discriminant validity. HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 727 Table 2. Reliability and Validity Testing of Wearable Devices for Young Runners Construct Item Mean Factor Loading VIF α CR AVE Perceived usefulness (PU) Using wearable devices helps me monitor my physical health condition 5.532 0.870 2.371 0.878 0.916 0.731 Using wearable devices helps me improve my physical health condition 5.184 0.831 2.095 Using wearable devices enhances the efficiency of monitoring my physical health condition 5.487 0.870 2.517 Based on my perception of wearable devices, I believe they have excellent functionality 5.438 0.849 2.067 Perceived ease of use (PE) Interacting with wearable devices is clear and understandable 5.423 0.845 2.022 0.845 0.896 0.683 It is easy for me to use wearable devices proficiently 5.583 0.813 1.774 I think it is easy to continue using wearable devices to do what I want 5.308 0.825 1.999 I think continuing to use wearable devices requires minimal effort 5.333 0.823 1.827 Attitude (ATT) I am interested in continuing to use wearable devices 5.605 0.872 2.700 0.887 0.922 0.747 I think continuing to use wearable devices is a good idea 5.630 0.899 2.957 I believe continuing to use wearable devices is enjoyable 5.509 0.871 2.484 I like using wearable devices to monitor my physical health condition 5.526 0.813 1.833 Continuance intention (CI) I am willing to frequently use wearable devices 5.524 0.871 2.340 0.912 0.935 0.741 I plan to continue using wearable devices in the future 5.603 0.882 2.556 I would recommend the use of wearable devices to my family and friends 5.316 0.829 2.010 I will make an effort to continue using wearable devices in the next six months 5.380 0.875 2.510 Subjective norms (SN) Important people to me would think I should continue using wearable devices 4.994 0.880 2.342 0.908 0.936 0.784 Influential people would think I should continue using wearable devices 4.827 0.901 2.607 Many people similar to me think I should continue using wearable devices 5.011 0.850 1.985 Perceived behavioral control (PBC) I can use wearable devices effectively to monitor my physical health condition 5.308 0.869 1.984 0.838 0.903 0.756 I believe continuing to use wearable devices will be entirely within my control 5.404 0.870 2.058 I think I have the resources, knowledge, and ability to continue using wearable devices 5.393 0.869 1.884 Quantified-self (QS) Recording step count 5.647 0.810 1.847 0.823 0.883 0.653 Recording exercise details (pace, distance, etc.) 5.829 0.809 1.713 Recording calorie expenditure 5.374 0.757 1.549 Recording heart rate 5.682 0.854 1.985 Figure 3. Cronbach’s alpha values of the research constructs HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 728 Figure 4. Composite Reliability values of the research constructs Figure 5. Average variance extracted values of the research constructs Table 3. Discriminant validity Construct Square Root of AVE HTMT ATT CI PBC PE PU QS SN ATT CI PBC PE PU QS ATT 0.864 CI 0.695 0.865 0.781 PBC 0.542 0.542 0.869 0.626 0.626 PE 0.613 0.572 0.584 0.826 0.705 0.657 0.693 PU 0.641 0.665 0.657 0.634 0.855 0.720 0.749 0.764 0.733 QS 0.619 0.610 0.503 0.528 0.630 0.808 0.721 0.709 0.603 0.627 0.736 SN 0.458 0.506 0.505 0.507 0.532 0.404 0.887 0.520 0.577 0.593 0.591 0.611 0.476 Note: ATT stands for attitude; CI represents continuance intention; PBC stands for perceived behavioral control; PE indicates perceived ease of use; PU represents perceived usefulness; QS stands for quantified self; SN indicates subjective norms; the diagonal represents the square root of AVE values. HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 729 Figure 6. Heterotrait–Monotrait ratio graph of the research constructs Before examining the structural model, the model’s fit was evaluated using the standardized root mean square residual (SRMR), with a recommended threshold of SRMR < 0.08 [59, 60]. The SRMR for this study was 0.053, which is below the suggested maximum value of 0.08, indicating a good fit for the model. According to Hair et al. [61], the variance inflation factor (VIF) was used to test for multicollinearity, with a threshold of VIF < 5. The results showed that all the measurement indicators had VIF values less than 3 (Table 1), indicating a low likelihood of multicollinearity issues in the model. This study collected data using self-report survey methods, necessitating an assessment of common method bias (CMB). The Harman single-factor test was employed, where if a single factor accounts for more than 50% of the variance, CMB may exist [62]. The results of this study indicated that the variance accounted for by a single factor was 46.025%, which was lower than the 50% total variance, suggesting a low possibility of CMB. 4.2. Structural Model The validity of the structural equation model was assessed by evaluating the explained variance (R2) [63], predictive relevance (Q2) [64], and goodness-of-fit index (GOF) [65] of the endogenous variables. In this study, the R2 values of the endogenous variables were greater than 0.33, indicating moderate explanatory power (Figure 7). Mainly, the R2 for CI was 0.597. All the endogenous variables in this study had Q2 values greater than 0, indicating strong predictive relevance, with a Q2 value of 0.270 for QS. Additionally, the computed GOF value (as the square root of the product of average communality and average R2) was 0.578, exceeding the maximum threshold of 0.36 for goodness-of-fit. These findings suggest that the structural model has good validity. 4.3. Direct Effects After confirming the reliability and validity of the measurement and structural models, bootstrapping with 5000 resamples was applied to test the proposed hypotheses and path coefficients. The specific results are shown in Figure 7 and Table 4. According to the TAM, attitude (β = 0.348, t = 4.272, p < 0.001) and PU (β = 0.218, t = 2.498, p < 0.05) had significant positive effects on CI in the use of wearable devices. However, PE did not significantly influence CI use of wearable devices (β = 0.052, t = 0.610, p > 0.05). PE had a significant positive effect on PU (β = 0.634, t = 12.560, p < 0.001), HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 730 and PE (β = 0.347, t = 4.355, p < 0.001) and PU (β = 0.420, t = 5.100, p < 0.001) had significant positive effects on attitude. Therefore, in the TAM model, H1, H2, H3, H4, and H5 were supported, while H6 was rejected. According to the TPB, SN had a significant positive effect on CI (β = 0.120, t = 2.842, p < 0.05), but PBC did not significantly influence CI when wearable devices were used (β = 0.038, t = 0.604, p > 0.05). Thus, H7 was supported, while H8 was rejected. Finally, PU (β = 0.494, t = 8.221, p < 0.001) and PE (β = 0.214, t = 3.167, p < 0.01) had significant positive effects on QS. QS also significantly positively affected CI in the use of wearable devices (β = 0.163, t = 2.170, p < 0.05). Therefore, H9, H10, and H11 were supported. Table 4. Results of Direct Effects Testing Hypothesis Path Beta Standard Deviation (std) t value p value Results H1 ATT→CI 0.348 0.081 4.272 0.000 Supported H2 PU→CI 0.218 0.087 2.498 0.013 Supported H3 PU→ATT 0.420 0.082 5.100 0.000 Supported H4 PE→PU 0.634 0.050 12.560 0.000 Supported H5 PE→ATT 0.347 0.080 4.355 0.000 Supported H6 PE→CI 0.052 0.085 0.610 0.542 Rejected H7 SN→CI 0.120 0.042 2.842 0.004 Supported H8 PBC→CI 0.038 0.062 0.604 0.546 Rejected H9 PU→QS 0.494 0.060 8.221 0.000 Supported H10 PE→QS 0.214 0.068 3.167 0.002 Supported H11 QS→CI 0.163 0.075 2.170 0.030 Supported Note: ATT stands for attitude; CI represents continuance intention; PBC stands for perceived behavioral control; PE indicates perceived ease of use; PU stands for perceived usefulness; QS represents quantified self; SN stands for subjective norms; the diagonal indicates the square root of the AVE values. Continuous Intention Perceived Behavioral Control Subjective Norms Quantified- Self Perceived Usefulness Attitude Perceived Ease of Use 0.348*** 0.634*** 0.214** Note: * p value < 0.05; ** p value < 0.01; *** p value < 0.001. Figure 7. Framework of the Model Results 4.4. Indirect Effects The indirect effects between variables were examined using bootstrapping with 5000 iterations (see Table 5). First, in the TAM, PU (β=0.146; t=2.999; p<0.01) and PE (β=0.121; t=3.043; p<0.01) had significant positive effects on attitudes toward and CI use of wearable devices. PE had a significant positive effect on attitude (β=0.266; t=4.483; HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 731 p<0.001) and CI toward wearable devices (β=0.139; t=2.488; p<0.01) through PU. Additionally, PE significantly positively affected CI in relation to wearable devices through PU and attitude (β=0.093; t=2.605; p<0.01). Therefore, H3a, H4a, H4b, H4c, and H4d were supported. Second, QS mediated the relationship between PU and CI when wearable devices were used (β=0.080; t=2.240; p<0.05), but it did not mediate the relationship between PE and CI when wearable devices were used (β=0.035; t=1.637; p>0.05). PU partially mediated the relationship between PE and QS (β=0.313; t=6.214; p<0.001). H11a and H11c were supported, while H11b was rejected. Finally, PU and QS had chain mediating effects on the relationship between PE and CI use of wearable devices (β=0.051; t=2.320; p<0.05). H11d was supported. Table 5. Results of Indirect Effects Hypothesis Path Beta Standard Deviation(std) t value p value Results H3a PU→ATT→CI 0.146 0.049 2.999 0.003 Supported H4a PE→ATT→CI 0.121 0.040 3.043 0.002 Supported H4b PE→PU→ATT 0.266 0.059 4.483 0.000 Supported H4c PE→PU→CI 0.139 0.056 2.488 0.013 Supported H4d PE→PU→ATT→CI 0.093 0.036 2.605 0.009 Supported H11a PU→QS→CI 0.080 0.036 2.240 0.025 Supported H11b PE→QS→CI 0.035 0.021 1.637 0.102 Rejected H11c PE→PU→QS 0.313 0.050 6.214 0.000 Supported H11d PE→PU→QS→CI 0.051 0.022 2.320 0.020 Supported Note: ATT stands for attitude; CI stands for continuance intention; PBC stands for perceived behavioral control; PE stands for perceived ease of use; PU stands for perceived usefulness; QS stands for quantified self; SN stands for subjective norms; the diagonal represents the square root of AVE values. 5. Discussion and Implications 5.1. Main Findings of the Study This study delved into young runners' CIs to use wearable devices by integrating the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) with the inclusion of the QS variable. The findings demonstrated that the TAM/TPB integrated model had moderate explanatory power for young runners' CIs regarding wearable devices. Additionally, young runners' QS positively influenced their ability to use wearable devices. Therefore, QS in the TAM/TPB integrated model provides a better understanding of young runners' CIs toward wearable devices. 5.2. Comparison with Other Studies The findings revealed that PU and attitude significantly influenced CI use of wearable devices among young runners, further validating the validity of the TAM theory in the context of sports and wearable devices. Specifically, the study revealed that PU (β=0.218; t=2.498; p<0.05) had a significant positive effect on young runners' CI from using wearable devices, which aligns with the findings of previous research [8, 43, 66]. This finding also corresponds with the results of Ong et al. [67], who observed a positive effect of PU on CI among online game users. However, the study showed that PE did not have a significant impact on young runners' ability to use wearable devices, contradicting the findings of Davis et al. [33]. Nevertheless, these findings align with the results of Chang et al. [66] and Ashfaq [43], who found no significant relationship between PE and the intention to use wearable devices. Interestingly, the study showed that the PU fully mediated the relationship between PE and CI use of wearable devices (β=0.139; t=2.488; p<0.01). This finding suggested that young runners' expectations for the continued use of wearable devices are primarily determined by their perception of usefulness rather than by ease of use, consistent with the view of Davis et al. [33], who argued that usefulness has a stronger relationship with usage than does ease of use. However, it should be noted that PE still positively impacts CI's use of wearable devices through its influence on PU, indicating that it is still a factor worthy of consideration. This study revealed that attitude had a highly significant positive impact on young runners' CI in using wearable devices (β=0.348; t=4.272; p<0.001), which is consistent with the findings of previous research [68]. The study also established that young runners' PU of wearable devices had a highly significant positive effect on their attitude (β=0.420; t=5.100; p<0.001). Furthermore, attitude partially mediated the relationship between PU and CI when wearing devices were used, aligning with the findings of Lunney et al. [3] in their study on wearable fitness technology. Additionally, PE had a significant positive impact on attitudes toward the continued use of wearable devices (β=0.347; t=4.355; p<0.001), which is consistent with the results of Davis et al. [33] and Venkatesh et al. [69]. Furthermore, the study revealed a significant chain mediating effect of PU and attitude between PE and CI toward wearable devices (β=0.093; t=2.605; p<0.01). These findings underscore the importance of attitude as a significant predictor of young runners' CI when wearing wearable devices. Moreover, PE and PU indirectly influence young runners' CI through their impact on attitude. HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 732 This study utilized the theory of planned behavior (TPB) to scrutinize the predictive role of SN and PBC in young runners' CIs when using wearable devices. These findings revealed a significant positive effect of the SN on CI use of wearable devices (β=0.120; t=2.842; p<0.05), aligning with previous findings [3, 70]. SNs reflect individuals' perceptions of how important others, such as family members, friends, and peers, are in their lives and expect them to engage in a particular behavior. It has been suggested that individuals are more likely to be influenced by in-group information than out-group information [71]. Given that young runners often participate in group running events or engage in running and fitness activities with others, they are susceptible to the influence of their peers. Moreover, wearable devices themselves have social functionalities, such as ranking users and their friends based on daily (or weekly) step counts or calories burned and facilitating communication and interaction among users. These social aspects also expose young runners to the influence of others. However, the coefficient for the ability of the SN to predict young runners' CI when wearing devices was used was relatively low, suggesting that its practical impact may be limited. One possible reason for this difference is that running is primarily an individual activity and is less influenced by social or interpersonal factors. Furthermore, this study revealed that PBC had no significant effect on young runners' CI in using wearable devices (β=0.038; t=0.604; p>0.05). This finding contrasts with those of several previous studies [72, 73] but aligns with research that found no significant impact of PBC on usage intention [74]. One possible explanation for this discrepancy is that PBC's role may vary across user groups and technology products. As wearable devices represent a rapidly evolving technology, young runners may have limited knowledge and weaker control over these new technologies. Additionally, wearable devices may raise privacy concerns, which can influence the decision-making process. This study demonstrated that QS positively impacted young runners' intention to use wearable devices (β=0.163; t=2.170; p<0.05). Furthermore, PE (β=0.214; t=3.167; p<0.01) and PU (β=0.494; t=8.221; p<0.001) had significant positive effects on QS, aligning with previous research [14]. Previous studies have indicated that linking the quantified self with data helps users contextualize events and that combining quantitative data and qualitative experiences strengthens long-term usage habits [75, 76]. The quantitative results of this study support the qualitative findings mentioned earlier. Additionally, the results revealed that PU, rather than PE, significantly positively influenced young runners' intention to use wearable devices through QS. PE had an impact only through PU, indicating that users highly value the functionality and purpose of wearable devices. Young runners utilize wearable devices to track their relevant data in real time, allowing them to experience their physical and mental states within specific data areas. This feeling contributes to the achievement of their health and performance goals. 5.3. Implication and Explanation of Findings This study yields various academic and practical implications concerning the use of smart wearable devices. This study aimed to provide a complete theoretical background by integrating the TPB, TAM, and QS to analyze the ATT and CI of young runners. This study revealed a significant and positive association between the ATT and CI of young runners. These findings indicate that practitioners in the health care industry, such as healthcare professionals and sports managers, can plan, design, and implement strategies that will enhance the ATT of young runners toward CI to use smart wearable devices. Furthermore, this study employed the TPB to analyze the impacts of SN and PBC on CI [28]. These findings indicated that SN and PBC were positively related to CI. This implies that healthcare practitioners should develop promotional strategies targeting the young population, emphasizing societal pressures to motivate the young target market to use smart wearables. Thus, the ATT and CI of young runners should be increased to use smart wearables. Additionally, this study employed the PE and PU of the TAM to further explore the ATT and CI of young runners. The findings of this research revealed that 'TAM's PE and PU significantly impacted the ATT and CI of users. Hence, TAM score was a significant predictor of 'users' CI, which was in accordance with previous research [17, 25, 77]. The major contribution of this study was to theoretically integrate the TAM with QS and the TPB, providing an extended modified TAM framework. Finally, this research utilized QS to analyze the CI of young runners toward smart wearable technologies [78]. The results indicated a significant relationship between QS and CI. These findings encourage healthcare practitioners to emphasize the use of QS for healthcare tracking by users with the help of smart wearable devices. Furthermore, healthcare practitioners and managers are encouraged to include various interactive activities beyond exercise tracking, such as monitoring food and water consumption, to enhance user engagement. The findings provide further guidelines for practitioners regarding the design and implementation of QS-related strategies, directing them toward more human- centered strategies by ensuring the inclusion of widely used constructs such as autonomy, understanding, choice, and consciousness. 5.4. Strengths and Limitations First, the participants in this study consisted of young runners from various provinces in China, providing a certain level of representativeness among these demographic variables. However, the sample scope remains limited, and future research could delve into the quantified experiences and usage behavior of wearable devices among middle-aged and HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 733 elderly individuals. Second, this study did not dynamically track the participants' perceived differences in various stages of wearable device usage. Future research could investigate the behavioral changes in users' continued use of wearable devices over time. Finally, this study did not address the privacy and security issues associated with the use of wearable devices. Subsequent research could focus on exploring these aspects. 6. Conclusion, Recommendations, and Future Directions For young runners, the most crucial aspect influencing their continued selection of wearable devices is their usefulness. Continuous improvement in product performance should be directed toward improving product performance to meet the specific needs of this user group. This can be achieved by offering accompanying fitness apps that provide personalized health reports and analyze user data trends, among other features. For instance, a device equipped with a six-axis sensor can track the wearer's running posture, accurately identify running posture, and correct any incorrect postures. By providing users with more effective information during usage, their PU can be enhanced. Some wearable devices can offer more in-depth data monitoring metrics to help consumers gain a more comprehensive understanding and offer them more accurate guidance and recommendations. For young runners, the PE of wearable devices is an important factor. They should be able to understand the user instructions and use the devices easily. This involves accurately identifying errors and understanding the causes of those errors. The devices should prioritize lightweight and user-friendly designs, ensuring smooth system performance and compatibility. The user interface should be simple and intuitive, minimizing elements and operations on the screen. Voice control can facilitate usage while running. Recognizing the social nature of many young individuals by adding interactive features to the device interface, such as social sharing and competition, can encourage communication, challenge each other, and foster the sharing of running experiences. However, users continue to use wearable devices for data collection and for quantified self-engagement. However, during interviews, some participants raised concerns about discrepancies between the displayed heart rate data and their actual experience. For example, with certain fitness bands, heart rate data for "recovery time" and "training effect" functions may show high values after aerobic exercise but low values after strength training, which contradicts users’ actual experiences. Therefore, continuous optimization of product performance is crucial for enhancing users' authentic experience and improving their sense of immersion. 7. Declarations 7.1. Author Contributions Conceptualization, Z.G., G.L., Z.L., and A.K.; methodology, Z.G., G.L., and Z.L.; formal analysis, Z.G., G.L., Z.L., and A.K.; writing—original draft preparation, Z.G., G.L., Z.L., and A.K.; writing—review and editing, Z.G., G.L., Z.L., and A.K. All authors have read and agreed to the published version of the manuscript. 7.2. Data Availability Statement The data presented in this study are available in the article. 7.3. Funding The authors appreciate the support of The Philosophy and Social Sciences Research Project of Colleges and Universities in Shanxi Province (201801015) and the Shanxi Postgraduate Education Innovation Project (2021-27). 7.4. Institutional Review Board Statement Not applicable. 7.5. Informed Consent Statement Not applicable. 7.6. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 8. References [1] Xie, J., Chen, Q., Shen, H., & Li, G. (2020). Wearable graphene devices for sensing. Journal of the Electrochemical Society, 167(3), 037541. doi:10.1149/1945-7111/ab67a4. [2] Bhatti, D. S., Saleem, S., Imran, A., Iqbal, Z., Alzahrani, A., Kim, H. J., & Kim, K. 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HighTech and Innovation Journal Vol. 4, No. 4, December, 2023 738 Appendix I: Questionnaire (Research Instrument) Constructs Codes Questions Perceived usefulness [15, 79] PU1 Using wearable devices helps me monitor my physical health condition PU2 Using wearable devices helps me improve my physical health condition PU3 Using wearable devices enhances the efficiency of monitoring my physical health condition PU4 Based on my perception of wearable devices, I believe they have excellent functionality Perceived ease of use [10, 80] PE1 Interacting with wearable devices is clear and understandable PE2 It is easy for me to use wearable devices proficiently PE3 I think it is easy to continue using wearable devices to do what I want Attitude [46, 81, 82] ATT1 I am interested in continuing to use wearable devices ATT2 I think continuing to use wearable devices is a good idea ATT3 I believe continuing to use wearable devices is enjoyable ATT4 I like using wearable devices to monitor my physical health condition Continuous intention [4] CI1 I am willing to frequently use wearable devices CI2 I plan to continue using wearable devices in the future CI3 I would recommend the use of wearable devices to my family and friends CI5 I will make an effort to continue using wearable devices in the next 6 months Subjective Norms [13, 29] SN1 Important people to me would think I should continue using wearable devices SN2 Influential people would think I should continue using wearable devices SN4 Many people similar to me think I should continue using wearable devices Perceived behavioral control [13, 29] PBC1 I can use wearable devices effectively to monitor my physical health condition PBC2 I believe continuing to use wearable devices will be entirely within my control PBC3 I think I have the resources, knowledge, and ability to continue using wearable devices Quantified-self (QS) [51] QS1 Recording step count QS2 Recording exercise details (pace, distance, etc.) QS4 Recording calorie expenditure QS5 Recording heart rate