Recent technological advancements in artificial intelligence (AI) have profoundly impacted various industry sectors, including the realm of sports. The extended duration of the COVID-19 pandemic has precipitated a surge in the adoption of non-face-to-face services within leisure sports, a development significantly influenced by AI technologies (Han & Park, 2021). These technologies have been pivotal in the postpandemic era, fostering new business models by leveraging personal health data to deliver customized feedback, thereby transforming the landscape of sports participation (Lattie et al., 2019; Thompson, 2022; J. Wang et al., 2019).
In particular, the emergence of AI coaching technologies marks a notable paradigm shift in the enhancement of personal training and athletic skill development within the sports domain. These innovative services, utilizing data metrics such as exercise volume and heart rate, facilitate the creation of individualized training regimens. This approach not only enhances the efficiency and safety of training but also augments participant engagement; by analyzing physical movements, these AI coaching systems are adept at identifying and rectifying incorrect postures and exercise habits (Yu et al., 2019). Furthermore, the incorporation of voice feedback within these services—providing motivation, commendation, and essential information—has been shown to have a significant positive influence on users’ health behaviors, thereby contributing to the overall training experience (Graßmann & Schermuly, 2021; Y. Lee & Lim, 2015; Terblanche et al., 2023).
The assessment of user acceptance and the intention to utilize technology plays an integral role in determining the success of technological applications (Y. Kim et al., 2017). Scholarly research in this domain, notably through frameworks like the technology acceptance model (TAM; Davis, 1989) and the unified theory of acceptance and use of technology (Venkatesh et al., 2003), has predominantly concentrated on evaluating the advantageous attributes of technology, such as its perceived usefulness and ease of use. However, the value-based adoption model (VAM; H.-W. Kim et al., 2007), which draws upon Zeithaml’s (1988) seminal concept of perceived value, offers a more holistic perspective by integrating both the advantageous and sacrificial elements inherent in the technology-adoption process (H.-W. Kim et al., 2007; S. Y. Lee et al., 2019). This dualistic approach, which gives equal consideration to the benefits and costs associated with technology use, has been empirically demonstrated to exert a significant influence on consumers’ propensity to continue using a technology (Kwon & Son, 2021; Lin et al., 2012; Zeithaml, 1988).
In this scholarly inquiry we delved into the ramifications of AI coaching services as regards participation in leisure sports in the context of the postpandemic landscape, with a specific focus on remote and digital modalities. Our objective was to elucidate user perceptions and ascertain the determinants that drive the adoption of AI coaching services, utilizing the theoretical framework of the VAM. We methodically assessed the perceived advantages, such as utility and enjoyment, alongside the perceived drawbacks, including financial outlay and complexity, and analyzed how these factors collectively and individually predict users’ continued engagement with these services. Artificial intelligence coaching within the realm of leisure sports is characterized as technologically advanced systems that furnish customized coaching and training experiences through applications, online platforms, and virtual reality environments, which are tailored to individual athletic performance metrics and personal objectives. This research will be instrumental in enhancing comprehension of the role of AI coaching in leisure sports and in guiding future innovations and developments within this sector.
Artificial Intelligence Coaching Services
We explored the role of AI coaching services in influencing health behavior changes, focusing in particular on their use in leisure sports contexts. According to Prochaska and Velicer (1997), the process of health behavior change is characterized by a sequence of stages: precontemplation, contemplation, preparation, action, and maintenance. The critical transition from the action to the maintenance stage is pivotal for the enduring success of health behavior modifications, and is typically achieved through social reinforcement provided by mediators (Sundel & Sundel, 2018; Watson & Tharp, 2013). These mediators may encompass social entities that offer either positive reinforcement or corrective feedback.
In the realm of non-face-to-face services, AI coaching services, functioning as surrogate voice feedback coaches, have the potential to emulate social mediators. They dispense not only motivational support and emotional sustenance but also health-related information (Y. Lee & Lim, 2015). We delved into the specific modalities through which AI coaching services influence user engagement and sustained involvement in sports activities, focusing in particular on the capacity of these services to approximate social interaction, a key element in the maintenance of health behavior change.
The Value-Based Adoption Model
The VAM was originally proposed by H.-W. Kim et al. (2007) to explain consumers’ intention to adopt mobile commerce. The researchers argued that existing models like the TAM did not fully capture the factors influencing the adoption of new information and communication technologies. Prior research into technology adoption and usage intentions (Choi & Lee, 2019; C. Luo, 2014; Y. Wang et al., 2004; Yu et al., 2019; Zeithaml, 1988) integrated the VAM’s proposed constructs of perceived benefits and perceived sacrifices, examining their effects on perceived value and the intention to adopt technology. Perceived benefit is the evaluation made by consumers about the superiority and excellence they recognize while using a specific product or service, which then becomes an attitude toward the product or service (Zeithaml, 1988). In examining technology adoption, the VAM stands out by delving into user acceptance nuances. Unlike the TAM and the unified theory of acceptance and use of technology, which focus on facilitators of technology use, the VAM evaluates the net worth of technology from the user’s viewpoint (Pal et al., 2020). It stresses the balance of perceived benefits against perceived sacrifices, offering a dualistic perspective that acknowledges both the advantages and costs associated with technology adoption. The VAM’s consumer-centric angle highlights the subjective nature of value perception, positing it as a crucial factor for adoption intention (Erdmann et al., 2023). This holistic approach makes the VAM particularly relevant where the costs of technology adoption are as significant as the benefits in influencing user decisions.
In line with these studies, perceived benefits, which are typically linked to the usefulness and enjoyment of the technology (Davis, 1989; Venkatesh & Davis, 2000), and perceived sacrifices, which are associated with the costs and complexity of using the technology (H.-W. Kim et al., 2007), have been investigated for their impact on technology acceptance. This study extended this investigation to the domain of AI coaching services, hypothesizing structural relationships among the VAM variables to comprehend users’ continued usage intention. The research model depicted in Figure 1 is based on the interrelations of these concepts as evidenced in the existing literature.
Relationship Between Perceived Benefits of Technology Use and Perceived Value
Usefulness of information technology refers to the degree of belief in performance obtained through the use of this technology (Seo & Kim, 2020). It is a measure of extrinsic motivation and can be used to explain the outcomes of technology acceptance, with users who perceive high (vs. low) usefulness considering the use of technology as a more meaningful act (S. Lee, 2021; Venkatesh et al., 2003). Looking at previous research on the dimensions of perceived benefit, Davis (1989) defined usefulness as the belief that using a new system or technology will enhance one’s job performance, leading to improved achievements or work outcomes. People who feel that a new technology is useful perceive its use as more beneficial than not using it, which represents the total value perceived in the process of using the new technology, and individuals make choices about the evaluation of technology acceptance outcomes and technology acceptance behavior through the perception of usefulness (Venkatesh, 1999).
In adopting information technology, intrinsic motivations (e.g., enjoyment) and extrinsic motivations (e.g., usefulness) play a crucial role (Ahn et al., 2007; K. S. Lee & Lee, 2011). Enjoyment is measured by the positive or negative responses people experience in an environment (Eroglu et al., 2003), and it refers to the degree to which the process of using a product or service is pleasurable, regardless of the outcomes of the usage behavior (Malone, 1981). The hedonic benefit, or pleasure, is the utility derived from the emotional state or feelings formed by the product; when consumers experience or perceive enjoyment from using technology, they are more likely to accept it (Oh, 2017).
According to Davis (1989), the enjoyment perceived in the process of using technology encourages more diverse use of that technology, indicating that the emotion of enjoyment can positively transform the perceived value of adopting new technology. Thus, we formed the following hypotheses:
Hypothesis 1: Usefulness will positively predict perceived value.
Hypothesis 2: Enjoyment will positively predict perceived value.
Relationship Between Perceived Sacrifices Associated with Technology Use and Perceived Value
Perceived sacrifice refers to the monetary and nonmonetary costs that customers incur when purchasing a product or service (Zeithaml, 1988). This can be explained by the complexity related to the costs that must be paid or given up when using a product or service. The monetary element, or perceived cost, signifies the actual financial amount associated with the use of new technology (Biswas et al., 2006). The cost perceived by users is based on their perception of the actual selling price of the product, service, or technology. Users unfamiliar with new technology may find it difficult to assess costs and, therefore, estimate expenses based on similar past experiences (Grewal et al., 1998). Consequently, the cost-related risk increases due to uncertainty about the product, requiring consumers to estimate and pay costs without direct experience (Biswas et al., 2006). I. Lee and Lee (2015) and H.-Y. Wang and Wang (2010) found that perceived costs negatively influence perceived value. Monetary sacrifices are typically understood as direct costs incurred, while time and effort represent nonmonetary sacrifices that also impact the perceived value of a service (Cronin et al., 2000; H.-W. Kim et al., 2007).
Complexity refers to the effort needed by consumers to effectively use the functions of a product (Alba & Hutchinson, 1987). It denotes the degree of difficulty perceived in the use of a service or product and is closely related to acceptance of the service. Greater complexity requires more time and effort from consumers to adopt the technology. Thus, the more easily a new technology is understood and used by the user, the faster the product or service will be accepted. Conversely, greater complexity can lead to dissatisfaction with the new product or service (Rogers et al., 2008). K. T. Kim and Song (2020) found that higher complexity significantly reduces perceived value and the intention to use AI-based mobile applications. Y. Kim et al. (2021) also found in their study with AI platform users that complexity negatively affects perceived value. Thus, we established the following hypothesis:
Hypothesis 3: Cost will negatively predict perceived value.
Hypothesis 4: Complexity will negatively predict perceived value.
Relationship Between Perceived Value and Continued Usage Intention
Value is judged based on the benefits or profits that individuals anticipate from a product or service, playing a pivotal role in consumption decisions and influencing consumer behavior (Yi & La, 2003). Bolton and Drew (1991) and Sirdeshmukh et al. (2002) noted that a positive perception of a product or service’s value can increase the intention to repurchase, while a negative perception can decrease this intention. Therefore, consumers are more likely to continue using a product or service when they perceive its value to be high. This concept is supported by several studies within the framework of the VAM. Thus, we formed the following hypothesis:
Hypothesis 5: Perceived value will positively predict the intention to continue using artificial intelligence coaching services.
Method
Participants and Procedure
We examined general participants involved in leisure sports, who were not necessarily athletes but who had engaged in sports activities facilitated by AI coaching services. In collecting data, we targeted individuals with experience in using AI coaching services for sports activities that utilize motion sensing to enhance interaction and engagement. Participants were required to have taken part in at least one session lasting a minimum of 30 minutes. The study focused on the most-utilized sports in South Korea for AI coaching services—specifically, fitness and golf—where motion sensing plays a critical role in providing real-time feedback and personalized coaching. Data collection was facilitated through an online survey distributed by a survey company during August 5–10, 2023. The survey reached potential participants in the company’s database via email, resulting in 320 valid responses. All participants resided in South Korea. Prior to the survey, informed consent was secured from every participant and they were thoroughly briefed about the survey’s nature and objectives. Detailed demographic and general characteristics of the participants can be found in Table 1.
Table 1. Characteristics of the Participants
Measures
We adapted our questionnaire from items utilized by H.-W. Kim et al. (2007) and H.-Y. Wang and Wang (2010). Variables measured comprised perceived benefits (usefulness and enjoyment), perceived sacrifices (cost and complexity), perceived value, and continued usage intention, encompassing 24 items across these categories. Modifications and additions were made to these items to align them with the specific context of this study. A 5-point Likert response scale (1 = never, 5 = always) was used for all measures. Additionally, to understand the demographic profiles of participants, we asked them to provide their gender, age, number of service experiences, primary considerations in using AI services, main purpose(s) for using these services, and method of discovery of these services.
Factor Analysis and Reliability Testing
We established the validity of the measurement instrument through a rigorous validation process encompassing content validity, construct validity, and model fit assessments. Content validity was initially ascertained through evaluation by an academic peer. Subsequently, we conducted a confirmatory factor analysis to assess construct validity and model fit. As shown in Table 2, the confirmatory factor analysis results satisfied the criteria for a good level of fit to the data according to established criteria (Hu & Bentler, 1999; MacCallum et al., 1996).
Cronbach’s alpha values exceeded .70, confirming the measures’ reliability (Van de Ven & Ferry, 1980). Convergent validity was further established by calculating the construct reliability and average variance extracted for all factors, with construct reliability values exceeding .60 and average variance extracted surpassing .50 (Bagozzi & Yi, 1988). The detailed analysis results are presented in Table 2.
Table 2. Results of the Confirmatory Factor and Reliability Analyses
Note. AI = artificial intelligence; CR = construct reliability; AVE = average variance extracted; TLI = Tucker–Lewis index; CFI = comparative fit index; RMSEA = root-mean-square error of approximation.
Data Processing
This research employed SPSS 24.0 and Amos 22.0 for data analysis. We began with a frequency analysis to delineate the general characteristics of the data, followed by a confirmatory factor analysis to establish the reliability and validity of the survey tool. The internal consistency of the questionnaire was assessed using Cronbach’s alpha. Subsequently, we conducted a correlation analysis to examine the interrelationships among variables such as usefulness, enjoyment, complexity, cost, perceived value, and the intention to continue the use of AI coaching services in sports. Last, we tested the research hypotheses through path analysis using structural equation modeling.
Results
Correlation Analysis of Factors
The correlation analysis results detailed in Table 3 show that the Pearson product-moment correlation coefficients between the latent variables were significant and did not exceed .80.
Table 3. Correlation Analysis Results
Note. * p < .05. ** p < .01.
Model Fit
The structural model of this study was analyzed using maximum likelihood estimation for the model’s parameters. The model fit the data well, χ2 = 731.943, df = 307, CFI = .90 (> .90), TLI = .91 (> .90), RMSEA = .080 (< .10).
Hypothesis Testing
Our results revealed the importance of perceived value in influencing continued usage intention for AI coaching services. Hypothesis 1, which suggested that the perceived usefulness of AI coaching services would positively predict perceived value, was supported by our findings. Hypothesis 2, asserting that enjoyment would similarly predict perceived value, was also supported. Hypothesis 3, which anticipated a negative impact of cost on perceived value, was supported. Hypothesis 4, which predicted a negative relationship between complexity and perceived value, was not supported by our data. On the other hand, Hypothesis 5 was strongly supported, revealing that perceived value greatly influenced the intention to continue using AI coaching services.
The detailed statistical evidence for these findings is presented in Table 4 and Figure 2. The results highlight the critical role that both positive aspects, such as usefulness and enjoyment, and negative aspects, like cost, appeared to play in shaping users’ perceived value of AI coaching services. Despite the lack of significant contribution from complexity, the overall pattern suggests the pivotal influence of perceived value in participants’ continued usage intention, offering actionable insights for service providers aiming to bolster user engagement in leisure sports through AI coaching.
Table 4. Path Analysis Results
Note. * p < .05. *** p < .001.
Figure 2. Path Analysis Results
Note. Solid lines denote significant relationships. The dashed line denotes nonsignificance.
Discussion
This study analyzed the intention to continue using AI coaching services among sports participants. The results indicate that perceived benefits, namely usefulness and enjoyment, had a positive and significant impact on the perceived value of these services. Perceived sacrifice, in the form of cost, had a significant negative impact on perceived value, whereas complexity did not have a significant impact. Finally, perceived value significantly influenced continued usage intention.
The Impact of Perceived Benefits on Perceived Value
The usefulness and enjoyment of AI sports coaching services were found to predict perceived value, supporting Hypotheses 1 and 2. Prior research has suggested that when users experience more enjoyment using information technology systems, their motivation to interact with these systems is enhanced (M. M. Luo & Remus, 2014). Therefore, related technological services should be designed to provide users with enjoyable experiences, interesting learning methods, and attractive technology (Balog & Pribeanu, 2010). Han and Park (2021) emphasized that AI services provide both useful aspects, such as motion analysis and feedback, and enjoyable experiences. Further, H.-W. Kim et al. (2007) observed significant effects of the usefulness and enjoyment of mobile internet use on perceived value. Additionally, the benefits obtainable from predicting e-learning service consumers’ perceived value based on the positive impacts of usefulness and enjoyment allow providers to maximize customers’ perceived value (Liao et al., 2022). Liao et al. (2022) explained that in e-learning contexts, usefulness has a greater impact on perceived value than does enjoyment; thus, offering rich learning content and focusing on learners’ experiences can increase perceived value. The greater the perceived benefits, the more likely consumers are to evaluate a product or service positively, which can, in turn, increase perceived value.
The Impact of Perceived Sacrifices on Perceived Value
We found that the perceived sacrifice of cost negatively predicted the perceived value of AI coaching services for sports participants, supporting Hypothesis 3. However, complexity was not a significant predictor of perceived value, so Hypothesis 4 was not supported.
Users are more likely to adopt technology when the benefits it provides justify the cost. Here, cost includes not only monetary expenses but also factors such as time, effort, and the learning curve. Users evaluate the costs associated with adopting and using technology in comparison to potential benefits, and such comparisons play a crucial role in determining the adoption of technology. Our result that cost negatively predicts perceived value is consistent with the findings of numerous previous studies. For instance, H.-Y. Wang and Wang (2010), in their research on the intention to use mobile hotel reservation apps, found that the subfactor of perceived cost, which is a component of sacrifice, negatively affected perceived value. Similarly, C. Luo (2014) studied the intention to use mobile commerce services and found that perceived cost and perceived effort negatively influenced users’ perceived value. Furthermore, Li et al. (2022) examined the predictors of consumer behavior in online-to-offline services and found that perceived cost significantly influenced perceived value, supporting the results of this study.
On the other hand, our result that complexity did not significant predict perceived value differs from the findings of previous studies (Jeong & Kim, 2022; Y. Kim et al., 2017). According to J.-H. Lee and Kim (2021), even when technology is perceived as complex, this does not significantly affect perceived value. They explained that this phenomenon is due to the continuous development of information and communication technology, leading to greater familiarity and proficiency among consumers. In other words, even if consumers perceive a new technology as highly complex, familiarity and proficiency gained from their use of previous versions of related technologies can help overcome this barrier. Initially, consumers may find a new technology or service challenging, but this challenge may not be a significant factor in evaluating the overall value of the service. The aforementioned studies suggested, in particular, that complexity may not serve as a decisive criterion for assessing value, especially when users have the opportunity to gain familiarity and proficiency over time. Moreover, our study highlights that the demographic characteristics of the participants, especially the age group of individuals in their 20s and 30s, have a notable influence on the perceived value of AI coaching services. This influence appears to be consistent across different levels of service complexity, which suggests that younger users may place a different level of importance on certain attributes of AI coaching services, which, in turn, affects their overall valuation of the service. This implies that users’ perceived sacrifices, as identified in various studies, act as critical barriers to information technology adoption (Cho & Jeon, 2020; H.-W. Kim et al., 2007; C. Luo, 2014).
Impact of Perceived Value of Continued Usage Intention
We found that the perceived value of AI coaching services significantly predicted continued usage intention, supporting Hypothesis 5. The core concept of the VAM is that consumers’ behavioral intentions are primarily determined by the relative value they perceive in benefits and losses (H.-W. Kim et al., 2007). Perceived value has long been emphasized as a crucial factor in consumer behavior research that determines actual consumption behavior. This study supports the importance of perceived value in the acceptance of new technology and services, aligning with numerous previous studies (Roostika, 2012; Shelvia et al., 2020).
Perceived value represents how consumers evaluate and perceive the inherent characteristics of a product or service. J. Kim (2020) and Woodruff (1997) suggested that consumers consider perceived value when deciding to purchase or use a product or service, a viewpoint similar to that of Sweeney and Soutar (2001). Accordingly, when consumers assign high value to a new technology or service it can be expected that they will have a higher continued usage intention. This perspective has been empirically supported in various studies related to the intention to use new technology or services. For example, Roostika (2012) found that consumers who perceive high value in new mobile internet information and communications technology are more likely to form positive intentions to use it. Mathavan et al. (2024) conducted research on the acceptance of fitness wearable products and found that perceived value significantly influences usage intention.
These research findings emphasize the significant role of perceived value in the successful market entry of products or services and increasing consumer acceptance. In the case of AI coaching services, the additional cost associated with the need for sensors or hardware, unlike traditional video games or home training programs, can be a barrier to users’ continuous use. However, the findings of this study suggest that if consumers have a high evaluation of the value of AI coaching services, their continued usage intention can be strengthened. This underscores the need for consideration of how to effectively communicate the value of AI coaching services to consumers and persuade them to continue using the service.
Practical Implications
This study provides an in-depth examination of AI coaching services utilized by leisure sports participants, accentuating the positive perceptions of the utility of these services and the enjoyment they offer. Our findings underscore the importance of developing strategies that highlight service benefits to enhance users’ adoption and engagement. We recommend the use of promotional strategies, particularly those involving immersive wearable devices and interactive virtual games, to amplify utility and enjoyment perceptions.
The research delineates that perceived costs (sacrifices) negatively predict the perceived value of AI coaching services, whereas the complexity of these services does not significantly alter this perception. These findings fill gaps in the existing literature by incorporating emotional responses, costs, and sacrifices into the evaluation of technological utility through the lens of the VAM.
From a health psychology perspective, the implications of these findings are significant. In light of the escalating global health challenges, such as the rise of chronic diseases, there is a pressing need to embed technology in health promotion (Kaplan & Stone, 2013). AI coaching services, through their simulation of personal coaching experiences, show promise in increasing physical activity engagement, thereby aiding in the overarching objective of enhancing public health.
The significance of our study extends to the assimilation of innovative technology into day-to-day life, with the younger population displaying a particular inclination toward using these services (Olson et al., 2011). This suggests that the future of AI coaching may be closely aligned with meeting the expectations and lifestyles of this demographic.
Moreover, we found that perceived value is a predominant factor influencing the intention to use AI coaching services. Bolstering this perceived value has the potential to grow service usage and expand participation in leisure sports. Thus, we advocate for a focus on amplifying benefits and minimizing sacrifices to encourage sustained use of these services. Overall, the study provides valuable insights into how leisure sports participants weigh the advantages against the sacrificial aspects when deciding whether to use AI services.
Limitations and Future Research Directions
This study has certain limitations. Specifically, the intensity and duration of engagement with AI coaching services can differ significantly between individuals, even within the same experience, which has implications for their intention to continue using such services. Given these differences, future research could explore broader cognitive value factors and generational variations in value perception and continued usage intention. We also recommend considering regional differences in internet environments and device adoption in future research, as these factors could lead to different outcomes in other areas. By examining these diverse aspects, future studies can provide a more nuanced understanding of AI service adoption in leisure sports and will contribute to the development of more effective health-promotion interventions.
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Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
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Wang, H.-Y., & Wang, S.-H. (2010). Predicting mobile hotel reservation adoption: Insight from a perceived value standpoint. International Journal of Hospitality Management, 29(4), 598–608.
Wang, J., Qiu, K., Peng, H., Fu, J., & Zhu, J. (2019). AI coach: Deep human pose estimation and analysis for personalized athletic training assistance. Proceedings of the 27th ACM International Conference on Multimedia, 2019, 374–382.
Wang, Y., Lo, H.-P., & Yang, Y. (2004). An integrated framework for service quality, customer value, satisfaction: Evidence from China’s telecommunication industry. Information Systems Frontiers, 6(4), 325–340.
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Woodruff, R. B. (1997). Customer value: The next source for competitive advantage. Journal of the Academy of Marketing Science, 25(2), 139–153.
Yi, Y., & La, S. (2003). The relative effects of three dimensions of service quality on customer service—A comparative study of existing vs. potential customers [In Korean]. Korean Marketing Association, 18(4), 67–97.
Yu, H., Seo, I., & Choi, J. (2019). A study of critical factors affecting adoption of self-customisation service – Focused on value-based adoption model. Total Quality Management and Business Excellence, 30, S98–S113.
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22.