Sports Participants' Intentions to Use Digital Technology for Sports Participation: A Behavioral Reasoning Theory Perspective

Digital Technology and Sports

Digital Technology, leveraging information communication, data analysis, and other advanced technologies, facilitates the interaction between data, computational, and physical systems. Recognized as an emerging modern technology (Popkova et al., 2022), it encompasses fields like artificial intelligence, big data, the Internet of Things, and blockchain. Beyond being a potential catalyst for future economic growth (Jin et al., 2023), its application in sports is increasingly evident. Numerous studies have explored the integration of digital technology in various aspects of sports, such as sports social media (Yadav et al., 2023), sports training (Tjønndal, 2022), sports education (Keiper et al., 2023), sports participation (Lupton, 2020; Seong & Hong, 2022), smart sports venues (Yang & Cole, 2022), and esports (Hallmann & Giel, 2018). Despite these advancements, there remains a gap in understanding the comprehensive perceptions of consumers and the decision-making processes involved in adopting digital technology within sports participation.

Behavioral Reasoning Theory

BRT, introduced by Westaby in 2005, represents a significant evolution in marketing and behavioral theory, advancing beyond traditional models like the TRA and the TPB. Ryan & Casidy (2018) highlight its distinct advantages over other behavioral theories. Primarily, BRT incorporates a dual-structure, two opposing motivations include: for and against adoption reasons. This approach captures opposing motivations, offering a comprehensive perspective on how these factors jointly influence user intentions and behaviors. By considering both positive and negative aspects, BRT provides a more nuanced understanding of human decision-making. Additionally, BRT places significant emphasis on the role of values and beliefs in shaping reasons and intentions, as noted by Sahu et al. (2020). This aspect underscores the theory's depth in predicting behavioral outcomes. Moreover, Claudy et al. (2015) found that the BRT model more effectively explains dependent variables than other behavioral theories.

The different structures in the BRT model (Westaby, 2005) are defined as follows: Beliefs (values) are defined as a person's cognitive decisions or subjective judgments; Reasons are the rationale influencing the occurrence of behavior, including both supportive and opposing reasons; Global motives relates to three sub-structures: Attitude (ATT), Subjective Norms (SN), and Perceived Behavioral Control (PBC). ATT represents the global choice of performing a behavior, formed through an individual's analysis and evaluation to create a behavioral tendency; SN refers to the perceived social pressure before performing a behavior; PBC is the perceived ease or difficulty of carrying out a specific behavior; Intentions to use (IU) are the intent or adoption intention of individuals to try and strive to perform that behavior.

Attitudes and Global Motives

Behavioral Reasoning Theory, as posited by Westaby(2005), identifies attitude as a crucial factor in shaping consumers’ adoption intentions. Westaby classifies attitude, along with subjective norms and perceived behavioral control, under the umbrella of global Motives. These elements are key in predicting behavioral intentions across various contexts. In consumer behavior research, Ajzen (2001) has underscored attitude as a pivotal predictor of adoption decisions. Empirical studies further reinforce this notion. Wiser (2007) demonstrated that including attitude factors notably improved the prediction accuracy of American households' adoption intention to adopt green energy. Similarly, Songkram et al. (2023) observed in their study on digital learning platforms adoption among students that attitude was the most significant determinant of behavioral intentions. Stockless (2018) also reported a substantial impact of attitude on intentions in their research on learning management system acceptance. Given this consistent emphasis on attitude as a key predictor, this study adopts attitude as the primary indicator of global motives.

Values and Reasons

Individual values significantly influence specific consumer behaviors, like the adoption of new products, as demonstrated by Kamakura & Mazzon (1991). Rooted in Rokeach’s (1973) foundational research, Schwartz (1992) further developed this concept by identifying ten unique values. These values are grouped into four higher-order domains, forming two bipolar dimensions: Openness to change versus conservation, and Self-Transcendence versus Self-Enhancement. The ‘Openness to Change (OTC)’ value, in particular, encourages individuals to embrace novel experiences and uncertainties (Schwartz, 1992). It is characterized by open-mindedness, creativity, an intention to adopt new things, and a propensity for risk-taking. This value encompasses aspects like stimulation, hedonism, and self-direction (Tewari et al., 2023). Given that the integration of digital technology in sports represents a form of innovation, it is pertinent to explore whether and how this ‘new thing’ aligns with individual consumer values. Hence, this study adopts ‘Openness to Change (OTC)’ as a key measure of values to understand the acceptance of digital technology in sports.

Values are a pivotal component in influencing both the acceptance and rejection of new technologies, as well as shaping attitudes, as highlighted by Ashfaq et al. (2021). They are also fundamental to the Behavioral Reasoning Theory (BRT), according to Dhir et al. (2021). Individuals who exhibit a high degree of Openness to Change (OTC) are often more intrigued by new products or services, leading them to explore such innovations (Gupta & Arora, 2017a). For instance, in a study involving 815 respondents on wearable technology in healthcare, Sivathanu (2018a) demonstrated that the values of Openness to Change (OTC) have a positive effect on reasons for acceptance and a negative effect on reasons against adoption. In the context of digital technology application in sports, the influence of participants’ values on their acceptance and rejection of such technology remains an area to be explored. To address this, the current study proposes two hypotheses:

Values and Attitudes

Values can exert a direct influence on attitudes, bypassing the mediation of reasons, as suggested by Sivathanu (2018b). This aligns with Westaby’s (2005) observation that behavior involves diverse, unique, systematic, and complex psychological processes. Consequently, reasoning is not isolated from an individual's beliefs and values (Claudy et al., 2013). This link between values and attitudes is a recognized principle in marketing research (Gupta & Arora, 2017a; Sivathanu, 2018a), indicating a strong correlation between the two. Research by Claudy et al. (2013) supports this, indicating that when a product or service aligns with a consumer’s personal values, it typically leads to more favorable perceptions of that product or service. Given this understanding, it is hypothesized in the context of this study that participants’ values may significantly shape their attitudes towards the use of digital technology in sports participation. Thus, the following hypothesis is proposed:

Reasons for Adoption

In previous research, scholars have argued that perceived usefulness and perceived ease of use together determine attitudes or directly predict behavioral intentions (Davis, 1989). As research has progressed, some scholars have also found that perceived usefulness and perceived ease of use do not fully explain consumers’ behavioral intentions to embrace innovations (Bruner & Kumar, 2005; Jin, 2014; Liao & Tsou, 2009). This is because technology use is influenced by extrinsic motivation (i.e., usefulness) and intrinsic motivation (i.e., enjoyment) (Zhou & Feng, 2017). Furthermore, perceived usefulness, perceived ease of use, and enjoyment are representative of utilitarian motivations, effort expectancy, and hedonic motivations associated with technology use, respectively (Huang & Ren, 2020). In light of these insights, this study will incorporate perceived ease of use, perceived usefulness, and perceived enjoyment as the key reasons for adoption digital technology in sports participation.

Rauniar et al. (2014) assert that perceived ease of use and usefulness are significant in explaining an individual's behavior in adopting new technology. Perceived ease of use refers to the degree to which consumers believe that a particular information technology or system is simple to use and does not require substantial mental or physical effort. Perceived usefulness refers to the extent to which people believe that a particular technology or system helps or enhances their work or daily life (Davis, 1989). These factors have been consistently identified as crucial predictors of positive attitudes in various studies, including the use of Taekwondo scoring technology (Ko et al., 2011), the adoption of E-learning systems by college students (Amin et al., 2017), and the utilization of learning management systems by teachers (Alharbi & Drew, 2014). Additionally, perceived enjoyment, a form of hedonic motivation, is defined as an intrinsic motivation reflecting the pleasure and joy derived from using the system, plays a pivotal role in influencing customer satisfaction and ongoing usage intentions (Akdim et al., 2022). Studies by Hew et al. (2018) and Akdim et al. (2022) found that enjoyable user experiences lead to continued use of mobile applications. However, the research exploring the perceived benefits of digital technology in the context of sports consumers is still limited. Therefore, the impact of these elements on the decision-making process of sports participants utilizing digital technology remains to be fully understood. Consequently, this study proposes the following hypotheses:

Reasons Against Adoption

While the Diffusion of Innovations model primarily focuses on adoption factors, it pays less attention to resistance, which Chen & Kuo (2017) note as a significant limitation in the acceptance of innovations. To address this, the Theory of Innovation Resistance, developed by Ram & Sheth (1989), delves into factors that hinder innovation adoption. They identify functional and psychological barriers as key impediments, further dividing these into usage, value, risk, tradition, and image barriers. Empirical evidence supports the impact of these barriers on innovation promotion. The experience of usage barriers stems from the notion that innovation requires changing consumers’ valuable consumption habits, which are the result of long-term use of the same produc (Sheth, 1981). The development of electric vehicles (EVs) will be limited due to usage barriers (Zhang et al., 2011); Value barriers guide consumers to recognize that the value created by innovative products is higher than that created by existing products (Chen & Kuo, 2017). Laukkanen (2016) found that value barriers are the strongest inhibitory factor on the willingness to innovate and use mobile and internet banking services; The degree of uncertainty and unpredictability associated with innovation is referred to as risk barriers (Sheth, 1981). Risk barriers may make users of online travel agencies feel unsafe, thereby inhibiting their willingness to use online travel agencies Talwar et al. (2020); Traditional barriers occur when innovation forces consumers to accept cultural changes and deviate from established traditions, leading to resistance (Sheth, 1981). Traditional barriers are the reasons why consumers resist mobile commerce applications (Hew et al., 2023); Laukkanen et al. (2009) define image barriers as “obstacles stemming from stereotypes that hinder the adoption of innovations.” Negative image cognition ultimately leads to resistance to innovation (Kleijnen et al., 2009). These findings across different contexts reinforce the relevance of these barriers to innovation resistance. Consequently, this study will consider usage, value, risk, tradition, and image barriers as reasons for resisting the use of digital technology in sports participation. We aim to understand the impact of these barriers on attitudes and adoption Intentions to adopt digital technology. Accordingly, the following hypotheses are posited:

Attitudes and Adoption Intentions

Eagly and Chaiken (1993) describe attitude as a psychological tendency that is expressed by evaluating a specific entity favorably or unfavorably. While consumer intention is recognized as distinct from attitude (Honkanen et al., 2006), positive attitudes towards innovation are often linked to a higher likelihood of adoption (Bagozzi, 1992). The acceptance of technology or innovation by consumers is critical, as technology derives its value from being accepted and used (Oye et al., 2014).Historically, literature has established that attitude positively affects behavioral intention (Davis, 1989; Fishbein & Ajzen, 1975; Westaby, 2005). This correlation is further confirmed in recent studies in various fields, including the adoption of mobile banking (Gupta & Arora, 2017a), mobile shopping (Gupta & Arora, 2017b), and broader innovation adoption (Claudy et al., 2015). In marketing, attitude is particularly recognized as a key determinant of consumer purchasing decisions, thereby serving as a strong predictor of consumer intention and behavior (Casidy et al., 2017; Dilmperi et al., 2017). The influence of consumer attitudes on the intention to use corresponding technologies has been observed in multiple domains, such as mobile technology (McLean & Osei-Frimpong, 2019), green consumption (Verma et al., 2019), smart chatbots (Kasilingam, 2020), AI customer service (Jan et al., 2023), AI voice assistants (Anayat et al., 2023), and autonomous vehicles (Kottasz et al., 2021).Despite extensive discussion in literature on the application and potential of digital technology, studies utilizing Behavioral Reasoning Theory to analyze consumer decision-making processes in this context are limited. Given that the integration of digital technology in sports represents an innovation, understanding the attitudes and intentions of sports participants towards using such technology is crucial. Therefore, the study proposes the following hypothesis:

Values and Adoption Intentions

Openness to Change (OTC) is a value that drives individuals towards intellectual and emotional pursuits in uncertain and unpredictable directions (Schwartz, 1992). Characterized by traits such as open-mindedness, creativity, adoption Intention to experiment, and risk-taking (Kruse et al., 2018), OTC encompasses stimulation, hedonism, and self-direction (Schwartz, 2003). Individuals who highly value OTC often enjoy being early adopters of innovative solutions. Previous studies have shown that consumers who are proactive in health care and inclined to purchase green food products are influenced by this value in their attitude towards green products (Nguyen et al., 2019). In essence, OTC affects consumers’ readiness to embrace unconventional and novel products (Tewari et al., 2022). Given that the integration of digital technology in sports represents a new frontier, it raises the question of whether participants’ preference for new experiences and innovations will lead them to more readily accept and utilize digital technology in sports participation. Therefore, this study proposes the following hypothesis:

Research Subjects

The survey focused on individuals in China who engage in sports activities using digital technology. We conducted random surveys during the Hangzhou Asian Games through various online social platforms, including WeChat, Weibo, Bilibili, Douyin, and others, by distributing online questionnaires. The Hangzhou Asian Games featured a wide array of digital technologies in the field of sports, such as digital twin technology, 3D modeling, intelligent robots, and intelligent monitoring systems. Leveraging the internet platform, which is a direct outcome of digital technology, proved to be an effective means of gathering data from our target population. The initial questionnaire aimed to gauge the frequency of sports participation involving digital technology, with respondents self-assessing their engagement on a scale from 0% to 100%. Respondents who reported not using digital technology for sports activities (0% frequency) were excluded, resulting in 417 eligible questionnaires. After filtering out questionnaires with incomplete or insincere responses (per Zhong et al., 2021), we obtained a total of 374 valid questionnaires, as summarized in Table 1. In terms of demographics, the majority of respondents were male (59.4%) compared to females (40.6%), and the most prevalent age group was 31–40 years, representing 31.6% of the total sample. Bachelor’s degree holders comprised the largest educational background category at 47.9%. Additionally, a significant portion of the respondents (32.9%) reported an income of less than 2000 yuan, with 19.8% indicating a 50% frequency in sports participation using digital technology. Further demographic details are presented in Table 1.

Survey Procedure

To ensure the methodological soundness of our study, our surveyors identified individuals actively engaging in discussions, liking, or bookmarking content on various social media platforms (including WeChat, Weibo, Bilibili, Douyin, etc.) during the Hangzhou Asian Games. These platforms featured short videos, news updates, or WeChat Moments related to the application of digital technology in sports, such as VR venue virtual reality experiences, smart sports, and personalized assessments. This ensured that our respondents were already exposed to content pertaining to the integration of digital technology in sports and were in a suitable state to participate in our survey. It's important to note that the content within these short videos, news updates, or WeChat Moments had to include discussions of digital technology’s role in sports. Following this, our research team provided participants with comprehensive information about the survey’s purpose, procedures, methods, expected time commitment, data confidentiality, and sought their informed consent to participate. This approach guaranteed ethical protection for our participants. To ensure the effectiveness of our survey, the initial section aimed to assess respondents’ adoption intention to engage in sports-related activities, including physical exercise, watching sports events, or specific sports-related consumption. The questionnaire began with an introduction to the application of digital technology in sports, covering aspects like intelligent monitoring devices, VR viewing experiences, smart wearable devices, and big data analytics. This introductory section ensured that respondents had a clear understanding of how digital technology is applied in sports, enabling them to independently complete the survey. Initially, we collected 417 questionnaires, but after excluding those with incomplete or insincere responses (Zhong et al., 2021), we were left with a total of 374 valid questionnaires.

Measurement Tools

In this study, we employed a questionnaire as our primary measurement tool, encompassing latent variables that included openness to change, reasons against adoption, reasons for adoption, attitudes, usage intentions, along with questions regarding demographic characteristics. The values reflecting openness to change and usage barriers and image barriers were adapted from the work of Pillai & Sivathanu (2018). Risk barriers were integrated and applied in this study based on research by Gupta & Arora (2017a) and Sivathanu (2018b). Tradition barriers were adapted from sources such as Pillai & Sivathanu (2018), Sadiq et al. (2021), and Sivathanu (2018a). Perceived ease of use and usefulness were adapted from the study conducted by Alharbi & Drew (2014), while perceived enjoyment drew inspiration from Thong et al. (2006). Attitudes were adapted from Claudy et al. (2015) and Pillai & Sivathanu (2018), and usage intentions were derived from Fishbein & Ajzen (1975).Specific measurement items can be found in Table 2. The observational variables of this study were assessed using a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree).

Data Analysis Method

In this study, data analysis was performed using SPSS 27 and Amos 24. Initially, we conducted descriptive statistical analysis, normality tests on the observational variables, and correlation analysis using SPSS to assess the data's suitability for subsequent statistical analysis. Following this, SPSS was utilized to assess data reliability, ensuring that the survey results met the necessary credibility criteria and demonstrated sufficient reliability and stability. Building on these preliminary steps, we proceeded with confirmatory factor analysis using Amos to evaluate both the convergent and discriminant validity of the data. This step aimed to establish a high level of internal consistency within the latent variables while also confirming sufficient differentiation between these variables, ensuring that the measurement results accurately reflected the nuanced characteristics of the corresponding variables. Lastly, we employed Amos to validate the structural model and hypotheses proposed in this study.

Validation of the Second-Order Factor Structure of Reasons for Adoption and Reasons Against Adoption

The study validated the second-order factor structures of ‘Reason for adoption’ and ‘Reasons against adoption’ separately. The reasons for adoption include three structures: perceived usefulness, perceived ease of use, and perceived enjoyment; the reasons against adoption consist of five structures: usage barriers, risk barriers, value barriers, tradition barriers, and image barriers. The analysis results are as follows: For ‘Reason for adoption’, the model fit indices for the second-order factor structure are: X² = 57.774; DF = 24; X²/DF = 2.407; GFI = .967; TLI = .979; NFI = .977; CFI = .986; RMSEA = .061; SRMR = .0247. The factor loadings for the three factors are: perceived usefulness (.759), perceived ease of use (.750), perceived enjoyment (.678), with all three factors having factor loadings above 0.50. The factor loadings of the observed variables for these three factors are: perceived usefulness (.877~.900), perceived ease of use (.848 ~.886), perceived enjoyment (.835~.892). For ‘Reasons against adoption’, the model fit indices for the second-order factor structure are: X² = 166.626; DF = 85; X²/DF = 1.960; GFI = .944; TLI = .977; NFI = .963; CFI = .981; RMSEA = .051; SRMR = .0336. The factor loadings for the five factors are: usage barriers (.815), risk barriers (.744), value barriers (.797), tradition barriers (.781), image barriers (.731). The factor loadings of the observed variables for these five factors are: usage barriers (.852~.893), risk barriers (.844~.903), value barriers (.850~.874), tradition barriers (.841~.872), image barriers (.863~.900). From the above analysis results, it is clear that both second-order factor models have good fit indices, and the factor loadings are all above 0.5, meeting the standards proposed by Hu and Bentler (1999). It can be concluded that the factors influencing acceptance and rejection reasons demonstrate a stable second-order factor structure. Subsequently, a Confirmatory Factor Analysis (CFA) was conducted based on these second-order factors.

Confirmatory Factor Analysis of All Constructs

Firstly, the validity of the measurement model was tested using SPSS, yielding a KMO value of 0.878, and Bartlett’s test of sphericity was significant (P<0.05). Reliability analysis indicated that the minimum Cronbach’s Alpha coefficient was 0.888, meeting the general standard of 0.7 for internal consistency (Bagozzi & Yi, 1988), as detailed in Table 3. This suggests that the data is suitable for confirmatory factor analysis. Subsequently, confirmatory factor analysis of the measurement model was conducted using Amos 24. The results indicated a good overall fit of the model (X²=826.894; DF=477; X²/DF=1.734; GFI=0.883; TLI=0.969; NFI=0.919; CFI=0.964; RMSEA=0.044; SRMR=0.0543). As shown in Table 3, all standardized factor loadings were statistically significant (p<.001) and exceeded the value of 0.50, aligning with the criteria set by Nunnally and Bernstein (1994) that latent variables’ standardized factor loadings greater than 0.5 are considered to have convergent feasibility. To further verify the validity of the measurement tool, it is necessary to confirm the specific values of Composite Reliability (CR) and Average Variance Extracted (AVE).

To confirm that the measurement tool has good convergent and discriminant validity, the results of the validity analysis are presented in Table 4. Hair et al. (2014) suggest that if the AVE is greater than 0.5 and the CR is greater than 0.7, then the convergent validity is considered acceptable. Additionally, if the square root of each measurement construct’s AVE is greater than the estimated correlations between other measurement constructs (Fornell & Larcker, 1981), the measurement model is deemed to have satisfactory discriminant validity.