Next Article in Journal
Neurobehavioral Alterations from Noise Exposure in Animals: A Systematic Review
Next Article in Special Issue
Travelers’ Subjective Well-Being as an Environmental Practice: Do Airport Buildings’ Eco-Design, Brand Engagement, and Brand Experience Matter?
Previous Article in Journal
Global Research Trends and Hotspots on Mitochondria in Acute Lung Injury from 2012–2021: A Bibliometric Analysis
Previous Article in Special Issue
The Effect of Airbnb Users’ Regret on Dissatisfaction and Negative Behavioral Intention
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 20
Issue 1
10.3390/ijerph20010592
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Decision-Making in Virtual Reality Sports Games Explained via the Lens of Extended Planned Behavior Theory

by
Bo-Hyun Seong
1 and
Chang-Yu Hong
2,*
1
Chungbuk Research Institute, Cheongju-si 28517, Republic of Korea
2
Division of Global and Interdisciplinary Studies, Pukyong National University, Busan 48513, Republic of Korea
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(1), 592; https://doi.org/10.3390/ijerph20010592
Submission received: 12 December 2022 / Revised: 26 December 2022 / Accepted: 27 December 2022 / Published: 29 December 2022
(This article belongs to the Special Issue Satisfaction, Stress, and Mental Health in the Tourism/Hospitality Industry)
Browse Figures
Versions Notes

Abstract

:
This study aims to determine whether the effect of interactivity on participation in virtual sports has risen because of the COVID-19 pandemic and if there is a difference in decision-making. The significance of social factors may be highlighted even more as a rationale for using virtual reality (VR) sports apps throughout the prolonged COVID-19 epidemic. A model was built based on the literature to describe the intention to participate in VR sports, and eight associated hypotheses were established. A sample of 301 submissions from a user poll on Korea’s cycling information sharing website was used for our analysis. SPSS 23.0 (IBM, Armonk, NY, USA) and AMOS 18.0 (IBM, Armonk, NY, USA) were used to validate Hypotheses 1–8 using a multigroup structural equation model (SEM) analysis and multigroup analysis. Although some hypotheses were not validated, the impact of perceived interaction presented as an extra variable altered based on the group participating before and after the COVID-19 epidemic, and the study’s goal was achieved. Given that information technology has evolved by overcoming physical space and socio-cultural constraints to create a society that connects people, the importance of online interaction, such as networking and competition between users, will be emphasized in the VR sports field in the future.

1. Introduction

The worldwide pandemic of COVID-19 has caused a scarcity of in-person visits and cultural exchanges between nations since early 2020, and most governments have established social distancing prevention laws [1,2]. People are spending more time at home and are embracing new lifestyle trends such as telecommuting, online learning, non-face-to-face meetings, and online shopping. Behavior that has led to the enjoyment of leisure activities via engagement with other people has also reduced the amount of interpersonal contact maintained through online platforms and smart devices. Not long ago, digital technology, which has been used primarily to measure, analyze, or broadcast athletes’ performance in the real world [3], has led to commercial success in sports applications seeking a more holistic approach to health and play by providing several functions to the general user [4,5].
Most previous studies stressed that training effectiveness has a significant impact on the usage of sports applications. Consequently, sports applications on smart devices have been recognized as helpful training instruments for increasing physical performance [6]. Although mechanisms can be likened to health benefits, the goal of exercise is to improve performance. In this context, customers appreciate sports applications that enable them to tailor their training to their specific needs [7]. Virtual reality (VR) apps are beneficial in training settings because they enable users to “receive feedback on performance and practice specific skills” [8]. VR systems provide more satisfaction and more consistent fitness routines during exercise [9], which is why they are often associated with healthier living.
Nevertheless, the significance of social factors may be highlighted even more as a rationale for using VR sports apps throughout the extended COVID-19 epidemic. Although a person fundamentally desires to know the extent of their abilities, the measures caused by social distancing fundamentally block the comparison of exercise performance through interaction with others offline. The VR setting can overcome limitations imposed by physical space, and it is a powerful component that maintains the continuity of involvement by encouraging users to communicate and collaborate [8,10]. Competitive factors, such as ranking, improve users’ athletic ability [11]. This strategy is vital for promoting the use of sports applications [12].
Sports digitization is about the connection between players. Zwift, an online cycling game with 16 million members worldwide as of early 2020 [5,13], is a good illustration of this. Although the primary goal of using a VR sports application in a general context may be to improve performance, in the wake of the COVID-19 epidemic, it is also essential to consider the benefits of using a VR sports app to connect with others. There are several benefits to participating in an activity with others rather than engaging in it alone, such as fostering happy feelings and a sense of absorption [14,15]. Because human behavior has been thoroughly studied in numerous sectors, this study attempts to broaden the idea of planned behavior to examine the impact of interaction on VR sports participation [16].
The VR sports industry and community have recently focused on golf, baseball, and cycling. Cycling is a representative sport that attracts many fans worldwide. Bicycles were Korea’s fifth most popular sport, behind trail walking, mountain climbing, bodybuilding, yoga, pilates, and golf [17]. We want to examine why so many individuals enjoy cycling. It might be relatively easy to enjoy in Korean society because of the expansion of infrastructure, such as the creation of bicycle roads, which began in earnest in the early 2000s. It could also be a reason to keep encouraging growth because it fits in with the global eco-friendly mindset by reducing traffic and carbon emissions from daily travel and securing fun activities.
This research topic has been conducted to provide an academic explanation for the decision-making process that leads to participation in VR sports [4,16,18,19,20,21]. Related research is scarce because few researchers have attempted it to date. Thus, we endeavor to create the first model to our knowledge that describes the decision-making path of participation in virtual bicycle sports as leisure activities by expanding the theory of planned behavior (TPB) with perceived interactivity as an additional factor.
Furthermore, just as social media usage for communication has expanded fast because of the COVID-19 worldwide pandemic, VR sports produced using modern innovative information technology may offer a greater incentive to pursue online interaction such as connection and competitiveness. Consequently, we want to determine whether there is a difference in the decision-making process of engaging in VR sports by splitting the participants into two groups: those who used Zwift before and after the outbreak of COVID-19. Given that information technology has progressed by transcending physical space and socio-cultural limits and aiming for a society that links people, online conversations on user engagement through mobile networks can explain the decision-making process in VR sports.

2. Conceptual Note and Literature Review

2.1. VR Sports

Modern society’s production and consumption patterns have shifted because of technological development and the rapid rise of the information and communications technology (ICT) industry [22]. For instance, digitization alters how individuals participate in sports [23]. In sports, digital technology has primarily been used to measure, analyze, or broadcast the performance of professional athletes [3], and recently numerous sophisticated VR sports applications have been developed for baseball, cycling, fishing, tennis, badminton, swimming, and climbing [4]. Despite the necessity for digitizing sports to remain a crucial feature of life in the event of contagious diseases, few sports have attained widespread popularity [24].
The ultimate goal of sports digitization is to combine online and offline experiences. When the online and offline markets are mutually interdependent and complementary, they can contribute to the growth of the sports event industry. As of early 2020, relevant representative examples include the online cycling game “Zwift” [13], which has 16 million users worldwide, and screen golf, which has been commercially successful in Korea [4]. Because screen golf is widely recognized for its optimistic outlook on growth sustainability and the social and economic importance of related corporations entering the Northeast Asian market, many academic discussions on the decision-making process leading to participation have taken place [4,16,19,20].
However, screen golf has a limitation as a sports activity because it does not meet the sports standard [5,25] of having a large following that is not limited to a specific region. Screen golf is a tool for improving personal skills. However, it is insufficient as a sports platform that enables users worldwide to interact in real-time in VR anytime by overcoming space and time constraints. Zwift, in contrast, meets the sports standards proposed by previous researchers because it has a large number of users who voluntarily participate in various countries [25], and competition is governed by institutionalized rules on physical technology [26].
The reigning Olympic champion and three Tour de France winners took part in the first “Virtual Tour de France” in July 2020, and the race was broadcast in over 130 countries, demonstrating that virtual cycling platforms such as Zwift have reached the pinnacle of sport [27]. Non-professionals participated in the event through mass participation races held on the same virtual courses as professionals [27].
Zwift’s unique advantage as a VR sport is that it has achieved technological development that satisfies user technology improvement and enabled interactions that can be connected in real-time to compete with users worldwide. As the saying goes, “A universal case of interesting activity is being with others” [28]. Experiencing and interacting with others rather than participating alone in a particular activity helps positive emotions and immersion [14,15]. Because of the COVID-19 epidemic, people appear to enjoy spending time alone at home. However, in VR, people seek pleasure through interactions that enable them to experience and compete with other individuals pursuing public goals. Accordingly, Zwift is a powerful platform because it enables users to conduct previously unexplored research in the field of VR sports from the perspective of user interaction.

2.2. Extended TPB

Social psychologists have long been fascinated by the process of human behavior. A person’s attitude has been discussed extensively among the various psychological variables because it is a reliable indicator of how humans behave [29]. Based on empirical studies, various factors can affect the strength and stability of attitudes. Research to add variables that can increase the accuracy of behavior prediction in addition to attitudes has begun in earnest. Among the proposed theories in this context, rational behavior theory [30,31] and theory of planned behavior (TPB) [32,33] are widely accepted and used (Figure 1).
Behavior is explained by rational behavior theory through behavioral intention. The intention to perform a specific action can be explained as the direct influencing factor of behavior [34]. Consequently, behavioral intention is defined as a medium that mediates the effects of attitudes and subjective norms on behavior [35]. However, rational behavior theory is still criticized for its limitations because it is based on the fundamental premise that all actions can be fully controlled by the individual [36,37]. Thus, planning behavior theory has been proposed to add perceived control to the extent to which specific behavioral performance is under individual willful control [38] and is used as a practical theoretical framework in various fields to explain how human behavior occurs [39,40,41,42,43,44,45,46].
However, because of the low explanatory power of three independent variables—attitude of planned behavior theory, subjective norms, and perceived behavior control—many researchers agree that a continuous search for additional variables affecting behavior should be performed [36,37,47,48]. Ref. [33], who proposed the TPB, also explained that it was fundamentally an open theory by extending the path to explain behavioral intent via additional variables. Accordingly, until recently, numerous studies were deemed to have successfully expanded the TPB by adding variables deemed crucial to a particular setting [16,49,50,51,52,53].
The following explains why the debate over the expansion of the TPB continues. From the standpoint of activism, human behavior is influenced by the ever-changing social and physical environment. The recent COVID-19 pandemic is an example of a social environment change that significantly impacts human behavior. Most countries have implemented social distancing as a major action immunity strategy for epidemic control [54,55,56,57]. Various events and gatherings where a large number of people gathered were controlled, and intensive social distancing measures were implemented to encourage people not to go out. However, humans are social animals. It is human nature to want to connect with and communicate with others. Human instincts are being expressed in search of some possible alternative, just as the use of social media for communication has rapidly increased because it has become impossible to face others in everyday life.
Furthermore, Web 2.0 platforms are enabling online interaction beyond communication. For example, VR sports based on information technology have a higher proclivity to participate in online interactions such as user connection and competition—experiencing and interacting with others, rather than participating alone in a specific activity, promotes positive emotions and immersion [14,15]. It is possible that using VR sports applications in a general setting is intended to improve performance. Consequently, it is important to discuss how participation in VR sports applications can be used as a tool for interacting with other people during a massive shift in the surrounding environment brought on by the COVID-19 pandemic.
Until date, this study team has used a variety of models to describe the process of adopting digital sports. The process of using screen golf was explained using the technology readiness and acceptance model (TRAM) [4], and the difference in the decision-making process between screen golf and screen baseball users was explained using the technology acceptance model (TAM) [57]. Based on the findings of this study, the research team intends to improve and maximize the usage of the extended theory of planned behavior model (ETPB) as part of a research series on digital sports.

2.3. Perceived Interactivity in VR Sports

Social interactions take place primarily within social organizations that adhere to particular norms. However, they can also occur in free and transient social relationships (communita) formed through participation in festivals, leisure, tourism, and sports [58]. Emotional expression and behavior formed in play-for-play temporary communities [59] create an atmosphere that can be fused and assimilated into the collective environment [58,60]. Individuals with similar interests gather simultaneously and space and form a consensus by doing specific activities suggested by service providers, which is why exchanges between spectators can be active in performance venues, sports stadiums, and festivals [58,61,62,63,64].
Extensive interaction-related research has been conducted in the new media sector, as evidenced by the digital convergence phenomena [65]. Some researchers [65,66] found that interaction between users is one of the most important ways to distinguish new media from old media. increasing interaction enhances the efficacy of most web-based formats and content [67]. The idea that it is critical to quantify the degree to which a particular environment is seen as interactive has gained widespread favor [67,68,69,70]. The concept of perceived interaction in the sphere of new media has significant ramifications for the subject of sports digitization.
Factors such as competitiveness and cooperation, notions related to interaction in sports, have a favorable effect on participants in both online and offline settings [71]. Rankings are one of the primary tactics for promoting sports applications [11] and can enhance users’ athletic skills [12]. People compare their skills to others through objective or subjective evaluations; this social comparison drives them to do harder and better, close the performance gap with others, and outperform others [72].
Sports games should provide opportunities for players to compete, collaborate, and interconnect to foster interaction, and they should be designed to increase enjoyment through engagement with others [73,74]. Sanz, Multon and Lécuyer [75] highlighted the importance of discussing interactions with other users during the training process of athletes using VR platforms. Furthermore, during the COVID-19 epidemic, ref. [5] interviewed pro-cyclists using the VR sports platform Zwift, indicating that users valued the ability to engage with other digitally mediated users (Figure 2).
Interaction between users should be considered when discussing the enjoyment of VR sports, although relevant research is scarce. Previous research has concentrated on the effectiveness of training because of the usage of sports applications, primarily highlighting its significance as an effective, tailored training tool for enhancing physical performance [6,7]. The COVID-19 pandemic’s quarantine strategy has limited outdoor sporting activities and communication. However, by facilitating interaction between users, VR environments can overcome physical space constraints and be highly influential in sustaining participation in particular activities [8,10], i.e., the role of decision-making in using VR sports applications during the COVID-19 pandemic.

3. Research Design and Methodology

3.1. Research Model and Hypotheses

A model was built based on literature research to describe the intention to participate in VR sports, and eight associated hypotheses were established (Figure 3). Hypotheses 1–4 describe the influence link between independent factors and participation intention, stating that personal attitude, subjective norms, perceived behavioral control, and perceived interaction all impact participation intention. Hypotheses 1–3 are based on the overall findings of the previously stated theory of planned behavior (TPB) Study [33,39,41,43,46,53,76,77].
Hypothesis 1. 
Attitudes will positively affect the intention to use VR sports.
Hypothesis 2. 
Subjective norms will positively affect the intention to use VR sports.
Hypothesis 3. 
Perceived behavioral controls will positively affect the intention to use VR sports.
Hypothesis 4 describes how perceived interaction, when used as an additional variable, has a positive effect on participation intention and establishes a previously discussed debate [5,11,71,75].
Hypothesis 4. 
Perceived interactivity will positively affect the intention to use VR sports.
Hypotheses 5–8 were developed to explain how the COVID-19 epidemic influences the interaction between attitudes, subjective norms, perceived behavioral control, structural tourism restrictions, and participation intentions. No study directly verified the influence relationship between variables set in the hypothesis. Nevertheless, after COVID-19, the relevance of social factors as a rationale for using VR sports applications is further emphasized because social distancing methods prevent comparison of exercise accomplishment through offline connections with others. VR environments can transcend physical space limits and have a favorable effect on participation in specific activities by increasing user interaction [8,10]; their influence would have increased after COVID-19. For Hypotheses 5–8, the influence relationship between variables varies depending on whether the group participated before or during COVID-19.
Hypothesis 5. 
The effect of attitude on intention varies depending on whether the group participated before or during COVID-19.
Hypothesis 6. 
The effect of subjective norms on intention varies depending on whether the group participated before or during COVID-19.
Hypothesis 7. 
The effect of perceived behavioral control on intention varies depending on whether the group participated before or during COVID-19.
Hypothesis 8. 
The effect of perceived interactivity on intention varies depending on whether the group participated before or during COVID-19.

3.2. Data Collection and Analytical Design

A questionnaire with 30 questions was used to measure six variables from the hypotheses and demographic information about respondents. Previous research was used to develop the measurement items. Previous studies on planning behavior theory were used to construct assessments of attitudes, subjective norms, perceived behavioral control, and participation intention [33,39,41,46,76,77]. Regarding perceived interaction, a theoretical survey in a behavior-related study was conducted on members of DOSSA (https://corearoadbike.com/) (accessed on 10 March 2022), where around 250,000 online bikers in Korea joined and shared information. The participants were chosen from among the members who use Zwift, and the test occurred between 1 and 15 September of the following year (2021).
Based on the survey, 301 valid samples were selected for the final analysis, omitting questionnaires determined to have been answered insincerely, and data analysis was conducted using SPSS 23 and AMOS 18.0. Frequency analysis and K-means clustering analysis were used to determine the general features of the respondents, and the validity of each measurement variable was validated using confirmatory factor analysis, correlation analysis, and reliability analysis tools. Finally, a structural equation model (SEM) is used to verify Hypotheses 1–4, and multigroup analysis with SEM is used to verify Hypotheses 5–8.

4. Results

4.1. Respondents’ Demographic Characteristics

A total of 301 valid samples were analyzed to identify demographic variables such as gender, residence, age, occupation, average household income, and academic background (Table 1). Males (52.2%) outnumbered females (47.8%), and those aged 30s (76.4%) outnumbered those in their 20s (14.6%) and 40s (6.0%) and 50s (2.0%). In addition, 84.4% answered that households earn more than 5 million won a month. Seoul (24.3%), which was one of the survey sites, showed the highest rate of residence and relatively large percentages of people residents from nearby area such as Gyeonggi-do (17.9%), Incheon (10.6%), Busan (10.0%).
The results of the survey judged that there was no problem in representing the gender and regional population distribution presented in the demographics released by the Ministry of Public Administration and Security (https://www.mois.go.kr/eng/a01/engMain.do) (accessed on 25 January 2022) as of September 2021. The fact that college graduates or higher, office worker, monthly income per household of more than 5 million won, and the high proportion of people in their 30s were also judged to be acceptable as characteristics of the demand group that uses both cycle and VR Sports Zwift.

4.2. Confirmatory Factor Analysis

The fit index of the measurement model was χ2/df = 1.410 (χ2 = 332.653, df = 236), RMR = 0.028, RMSEA = 0.037, GFI = 0.917, NFI = 0.905, RFI = 0.889, IFI = 0.970, TLI = 0.965, and CFI = 0.970, based on the confirmatory factor analysis for the validity verification of the measurement items. The recommended fit index criteria are χ2/df ≤ 3, RMSEA ≤ 0.08, RMR ≤ 0.05, and GFI, NFI, TLI, CFI = 0.9 or higher. However, one of the survey items used to calculate interactivity was 0.390, which was removed because it did not fulfill the requirement (0.5–0.95).
After removing survey questions that were not applicable, a new round of confirmatory factor analysis was conducted. Based on the findings, the majority of the fit indices of the measurement model belonged to the category comprised of the recommended criteria (χ2/df = 1.394 (χ2 = 299.627, df = 215), RMR = 0.026, RMSEA = 0.036, GFI = 0.921, NFI = 0.912, RFI = 0.896, IFI = 0.973, TLI = 0.968, and CFI = 0.973). Convergent validity was demonstrated because the scores on each measurement item of construct reliability (CR) (higher than 1.965, p < 0.05), standardized factor loading (0.5–0.95), CR (recommended range—higher than 0.7), and average variance extracted (AVE) (recommended range—higher than 0.5) were all higher than the reference value (Table 2).
In contrast, based on the correlation analysis between measurement items, the correlation coefficient between measurement items in Table 3 appeared to be between 0.122 and 0.789, which is within the recommended level of 0.85 [78]. The average dispersion extraction index (AVE) of each measurement item was more valid than the squared value of the correlation coefficient [79].

4.3. Hypothesis Testing

(1) Verification of Research Hypotheses 1–4
We examined the parameter estimates and the goodness-of-fit of the SEM to determine whether the hypothesized model was a good fit for the collected data. This good fit was confirmed by the SEM’s fit indices, as follows: 2/df = 1.394 (2 = 299.627, df = 215), RMR = 0.026, RMSEA = 0.036, GFI = 0.921, NFI = 0.912, RFI = 0.896, IFI = 0.973, TLI = 0.968, and CFI = 0.973. Therefore, the relationships between the five conceptions were investigated to test the four hypotheses (Table 4).
Following hypothesis verification, attitude, perceived behavioral control, and perceived interaction all had a substantial effect on the intention to use, validating Hypotheses 1, 3, and 4. In contrast, the association between subjective norms and intention to use was not significant. Hence, Hypothesis 2 was not supported. The results of the hypothesis verification revealed that the individual’s attitude has a relatively significant effect, consistent with the findings of earlier studies [80,81] that explain the first key drivers of behavioral intent on attitude.
The Hypothesis 4 that a relationship exists between perceived interaction and intention to use is supported, and the significance of the hypothesis verification result can be emphasized as a variable for the planned behavior theory’s extension. The influence of perceived behavioral control, in contrast, was the lowest among the influence relationships indicated by the hypothesis, which is significant because of specific changes from previous studies [53,76,82,83].
(2) Verification of Hypotheses 5–8
Hypotheses 5–8 investigate whether the influence relationship between the independent variables (subjective norm, perceived behavioral control, and perceived interactivity) and the dependent variable (intention to use) varies depending on the COVID-19 component. For this hypothesis verification, a multigroup study was performed, dividing the group that used Zwift (n = 140) during the COVID-19 pandemic from the group that used it (n = 161) before the COVID-19 pandemic. A multigroup analysis is a three-step procedure that starts with a multigroup confirmatory factor analysis and concludes with a multigroup SEM analysis. The multigroup confirmatory factor analysis was intended to verify that the measurement scales in this study had measurement uniformity in two groups, which was determined by comparing the difference value between non-constrained and constrained models (χ2, df) with a chi-square distribution table.
In the context of this study, it was necessary to verify that each group—users who started during the COVID-19 pandemic and users who started before the COVID-19 pandemic—understood the concepts of composition in the survey similarly, a pre-validation process for multigroup path analysis. The results of the measurement homogeneity analysis reveal a difference between the non-constrained model and the constrained model of Δχ2 = 13.27 and Δdf = 18, which are presented as Δχ2 = 28.87 when p = 0.5 and df = 18. The difference of χ2 between non-constrained and constrained models is not statistically significant because they are smaller than the values presented in the χ2 distribution table. Consequently, there is no issue with measurement tools concerning measurement uniformity (Table 5).
Concerning this study’s idea of composition, a multigroup route analysis was conducted because the level of measurement invariance was not problematic. Consequently, differences in the relationships between perceived interaction and intention to use and between perceived behavioral control and intention to use were discovered (Table 6). Only in the group who used Zwift before the COVID-19 epidemic was the relationship between perceived behavioral control and intention to use significant. However, only in the group using Zwift during the COVID-19 pandemic was the relationship between perceived interactivity and intention to use significant.
Even if only one group is a significant path, the difference between paths may be significant, and even if both groups are not significant, a significant difference between paths may exist. A multigroup SEM analysis was used to establish the statistical significance of all pathways. Consequently, the difference in χ2 was greater than 3.84 (p = 0.05), equivalent to df = 1 in the χ2 distribution table, which was statistically significant in the association between perceived interactivity and intention to use. Therefore, based on the modulation effect verification (Table 7), Hypothesis 8 was supported (Figure 4).

5. Discussions

This study used an expanded version of the planned behavior theory to explain the decision-making process of a cyclist who selects the VR sports application Zwift. Based on the literature review, four hypotheses were established concerning the influence of attitude, subjective norm, perceived behavioral control, and perceived interaction on intention to use, and four additional hypotheses concerning the role of the COVID-19 pandemic as a moderating variable. Although some hypotheses were not validated by the analysis, the research goal was achieved based on the effect of perceived interactivity as an additional variable and the verification of differences between groups relative to the COVID-19 pandemic.
Furthermore, our hypothesis set was corroborated using data acquired from a poll of 301 Zwift users. The influence of attitude, perceived behavioral control, and perceived interaction on intention to use were significant across all eight hypotheses, while the moderating effect of the COVID-19 pandemic was significant only in the link between perceived interaction and intention to use.
First, the variable with the most significant influence on the intention to use Zwift was “attitude”, with a significant influence when compared with other variables. The more positive cycling users are, the higher the involvement, so technology-oriented promotional professionals must promote the benefits of VR applications. Zwift, as a VR sports application, does not fall short as a tool for improving cycling skills, and it has the added benefit of saving time and money because it can be enjoyed at any time. Furthermore, discontent and concern about sports activity limitations caused by weather circumstances such as summer heat, winter cold, rain, and snow might be alleviated.
That users can work out with other bikers and experience competitive interaction, as validated in this study, is an advantage that may also be emphasized more in the era of the untapped New Normal because they can do it together. Efforts by business marketers to emphasize the benefits of motivation to increase involvement are required.
Existing studies had a substantially lesser influence on subjective standards than on other factors. The second hypothesis that subjective norms have a favorable influence on intention to use was not validated. The opinions of critical persons influencing decision-making can be expressed in a weighted value at the initial start of cycling. However, it can be deduced that aspects that might affect an individual’s will, such as attitude, perceived behavioral control, and perceived interaction, are reflected in the process of selecting VR sports applications, which can be called training assistance, after the start of the cycle. According to several Planned Behavior Theory studies [33,40,47,84], subjective standards have a relatively low influence on intention when compared with other variables. Regarding “participation”, the relationship between subjective norm and intention is not relevant when motivation about the purpose and expense of exercise training is accompanied, as in screen golf [4].
The moderating effect, in contrast, verifies that the influence link between the proposed independent and dependent variables in the study model would change depending on the group (the groups using Zwift during and before the COVID-19 pandemic). The group that began using Zwift during the COVID-19 pandemic had a significant association between perceived interaction and intention to use. Statistical significance for the difference between groups in the path was also confirmed. The findings indicate that social interaction can be a significant element in selecting VR sports applications. In contrast, the social distance-making policy resulting from the COVID-19 pandemic has been long-lasting. Furthermore, the association between perceived behavioral control and intention to use was only significant in the group that began using Zwift during the COVID-19 pandemic. However, the statistical significance of the difference between the path groups was not confirmed.
By examining the decision-making process based on the option, it is possible to conclude that the influence of the opinions of those around them has weakened at the stage of selecting the use of VR sports applications for individuals who already enjoy cycling. The opinions of those around them can influence the use of VR sports applications.
Many previous studies have validated the influence relationship between perceived interaction and intention to use. The TPB has concentrated on improving the explanation of the process of specific conduct by adding variables around attitude. Regarding perceived behavioral control, which was stressed by the idea of planned behavior, the theory had a relatively small influence. These findings highlight the importance of the extended planned behavior theory in explaining the process of individual actions by including factors that can reflect specific scenarios. Walker (2010) noted that social flow experienced with others rather than alone has a favorable effect on enjoyment and positive emotions regarding boosting engagement in specific activities. Even though they performed specific tasks with others, they found that interaction commitment with an emotional connection was superior to individual commitment, a condition of emotional separation.
The advancement of information technology has enabled interactivity-oriented communication that transcends spatial and temporal boundaries. Interactivity encompasses all actions of exchange between human individuals, human groups, and things in a particular setting. The extent to which interaction in VR impacts the structure and content of the environment mediated by information technology can be examined.
Humans have an instinctive urge to communicate with others. The topic of the so-called “Fourth Industrial Revolution Industry 4.0”, which is represented by the commercialization of spatial artificial intelligence, human robots, and unmanned vehicles via information technology development, or the hyper-connected society, is the development of new technological achievements and innovative communication methods, and the development of human communication methods. It is the result of increased cognitive ability and wider world recognition.
Because Zwift and screen golf can overcome major obstacles such as weather, time, and accessibility, most individuals who enjoy the event online also actively participate in the event outdoors. Screen golf is propelling the expansion of the overall golf industry by addressing various obstacles such as weather, accessibility, and cost. One of its primary functions will be to improve individual ability through golf training in VR; it will evolve into a platform that can reflect the performance of the actual exercise in real-time in the VR program in the training curriculum. It is feasible to evolve screen golf into a form distinct from sports games or active video games, and it can be popularized because of the benefits consumers seek through sports participation.
Social psychologists have long been interested in describing the process of human action. In empirical studies, individual attitude was once thought to be the most dependable psychological characteristic in predicting behavior. The most generally used theories regarding human action are the theory of reasoned action [85] and the TPB [33]. However, the strength and stability of attitudes are insufficient to predict action because they are impacted by several events [29]. Consequently, various variables to boost behavior prediction explanation were investigated in addition to attitude.
COVID-19 Global Pandemic has prohibited international contact since early 2020, and most countries have enacted social distance prevention policies [1,2]. The amount of time spent at home has increased, as has telecommuting, online learning, non-face-to-face meetings, and internet shopping. Behavior that resulted in the enjoyment of leisure activities through engagement with people also reduced interpersonal contact via online platforms. Not long ago, digital technology, which was primarily used to measure, analyze, or broadcast athletes’ performance in the real world, led to commercial success in sports applications seeking a more holistic approach to health and play by providing several functions to the general user [4,5].
The purpose of using VR sports applications may be to optimize performance. However, at this point, as the world is experiencing significant environmental change because of the COVID-19 pandemic, it is necessary to discuss the benefits of VR sports applications as a tool for interaction with others. Because the COVID-19 global pandemic prevented individuals from interacting in ordinary life, social media use for communication skyrocketed. Information technology-based VR sports may be more likely to facilitate online interactions like user connection. However, similar conversations were held in the study to explain the decision-making process leading to VR sports involvement [4,16,18,19,20,21,42]. Because information technology has advanced by breaking down geographical and cultural barriers to create a society that brings people together, online debates about user engagement can help to make more people aware of how they can participate in VR sports.
Given that there should be a diverse range of participants from all over the world, one of the main characteristics of VR sports, it is critical to secure factors that encourage interaction for the global expansion of screen cycling. One of the motivations for participating in sports was to seek enjoyment through relationships with people with common goals. Regarding user interaction, the financial success of cycling digitization will significantly change the VR sports market in the future.

6. Conclusions

An enhanced version of the planned behavior theory was used in this study to explain the decision-making process of a cyclist who chooses the VR sports application Zwift. The purpose of this study is to examine if the effect of interaction on virtual sports participation has increased because of the COVID-19 pandemic and if there is a difference in decision-making. The importance of social factors may be emphasized even more as a reason for using VR sports apps throughout the protracted COVID-19 outbreak.
Based on literature research, an experimental model was created to characterize the intention to participate in VR sports, and eight associated hypotheses were established. For our analysis, a sample of 301 copies was acquired from a poll of users on Korea’s cycling information sharing website. A multigroup SEM analysis was performed to validate Hypotheses 1–8 using statistical software. Although several hypotheses were not validated, the influence of perceived interaction presented as an extra variable was found to differ depending on the group participating before and after the COVID-19 epidemic, and the study’s goal was achieved.
Only in the group who used Zwift before the COVID-19 outbreak was there a strong influence relationship between perceived behavioral control and intention to use. However, only in the group who used Zwift during the COVID-19 epidemic period was there a significant influence relationship between perceived interactivity and propensity to use. This finding is meaningful for establishing a new trend in VR beyond COVID-19.
This study is significant because it was conducted when the usage of VR sports, which has been identified as a niche market for professional athletes, has been progressively extending to the leisure sports area. Given that information technology has progressed by transcending physical space and socio-cultural barriers to establish a society that links people, the importance of online interaction, such as connection and competitions between users, will be reinforced in VR sports in the future.
This study might highlight some shortcomings in the following areas, insisting the need for more research. For starters, the poll is only available to South Korean consumers. In the future, research that collects data from different nations will yield vital findings in understanding the cultural and national effects of virtual reality sports use. Furthermore, attempts to generalize study findings should be pursued by comparison analysis with high-quality virtual reality sports like screen golf. Second, the study was performed during a period when social distancing measures were tightened as a result of the COVID-19 pandemic, which differs from the present social climate in October 2022. For example, it is required to confirm whether the difference in the influence connection between perceived interactivity and intention to use is still relevant after COVID-19 ends. Third, it is envisaged that additional theories established to describe human behavior would be actively applied in digital sports. The use of the Health Believe Model, 4E (Enable, Encore, Engage, Exemplify), and other models, for example, may lead to a better understanding of the process of adopting digital sports.

Author Contributions

B.-H.S. provided research direction and participated in the research. Both B.-H.S. and C.-Y.H. performed the literature review, gathered relevant data, and wrote the manuscript. The paper was revised by all authors, and the final manuscript was read and confirmed by all authors. B.-H.S. gathered and analyzed the literature for this paper. The published version of the manuscript has been reviewed and approved by all authors. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We appreciate the productive suggestion from editors and anonymous reviewers and would like to give our thanks to them.

Conflicts of Interest

The authors declare no conflict of interests.

References

  1. CDC. COVID-19 and Your Health. 2020. Available online: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/social distancing.html (accessed on 5 March 2022).
  2. World Health Organization. WHO Coronavirus (COVID-19) Dashboard. 2020. Available online: https://covid19.who.int/ (accessed on 5 March 2022).
  3. Xiao, X.; Hedman, J.; Tan, F.T.C.; Tan, C.W.; Lim, E.T.; Clemenson, T.; Henningsson, S.; Mukkamala, R.R.; Vatrapu, R.; Hillegersberg, J.V. Sports digitalization: An overview and A research agenda. In Proceedings of the InInternational Conference on Information (ICIS), Seoul, Republic of Korea, 10–13 December 2017. [Google Scholar]
  4. Seong, B.H.; Hong, C.Y. Corroborating the effect of positive technology readiness on the intention to use the virtual reality sports game “Screen Golf”: Focusing on the technology readiness and acceptance model. Inf. Process. Manag. 2022, 59, 102994. [Google Scholar] [CrossRef]
  5. Westmattelmann, D.; Grotenhermen, J.G.; Sprenger, M.; Rand, W.; Schewe, G. Apart we ride together: The motivations behind users of mixed-reality sports. J. Bus. Res. 2021, 134, 316–328. [Google Scholar] [CrossRef]
  6. Warburton, J.; Terry, D.J. Volunteer decision making by older people: A test of a revised theory of planned behavior. Psychol. Aging. 2000, 22, 245–257. [Google Scholar] [CrossRef]
  7. Higgins, J.P. Smartphone applications for patients’ health and fitness. Am. J. Med. 2016, 129, 11–19. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Neumann, D.L.; Moffitt, R.L.; Thomas, P.R.; Loveday, K.; Watling, D.P.; Lombard, C.L.; Antonova, S.; Tremeer, M.A. A systematic review of the application of interactive virtual reality to sport. Virtual Real. 2018, 22, 183–198. [Google Scholar] [CrossRef] [Green Version]
  9. Banos, R.M.; Escobar, P.; Cebolla, A.; Guixeres, J.; Pitti, A.J.; Lison, J.F.; Botella, C. Using virtual reality to distract overweight children from bodily sensations during exercise. Cyberpsychol. Behav. Soc. Netw. 2016, 19, 115–119. [Google Scholar] [CrossRef]
  10. Irwin, B.C.; Scorniaenchi, J.; Kerr, N.L.; Eisenmann, J.C.; Feltz, D.L. Aerobic exercise is promoted when individual performance affects the group: A test of the Kohler motivation gain effect. Ann. Behav. Med. 2012, 44, 151–159. [Google Scholar] [CrossRef]
  11. Allam, A.; Kostova, Z.; Nakamoto, K.; Schulz, P.J. The effect of social support features and gamification on a Web-based intervention for rheumatoid arthritis patients: Randomized controlled trial. J. Med. Internet Res. 2015, 17, e3510. [Google Scholar] [CrossRef]
  12. Hoffmann, C.P.; Filippeschi, A.; Ruffaldi, E.; Bardy, B.G. Energy management using virtual reality improves 2000-m rowing performance. J. Sports Sci. Med. 2014, 32, 501–509. [Google Scholar] [CrossRef]
  13. Delaney, B.; Bromley, S. Zwift: Your complete guide: What Zwift is, how it works and what equipment you need. Immediate Media Ltd. Retrieved August. 2020, 17, 2020. [Google Scholar]
  14. Rufi, S.; Wlodarczyk, A.; Páez, D.; Javaloy, F. Flow and emotional experience in spirituality: Differences in interactive and coactive collective rituals. J Humanist Psychol. 2016, 56, 373–393. [Google Scholar] [CrossRef]
  15. Walker, C.J. Experiencing flow: Is doing it together better than doing it alone? J. Posit. Psychol. 2010, 5, 3–11. [Google Scholar] [CrossRef]
  16. Han, H.; Hwang, J.; Woods, D.P. Choosing virtual-rather than real-leisure activities: An examination of the decision-making process in screen-golf participants. Asia Pac. J. Tour. Res. 2013, 19, 428–450. [Google Scholar] [CrossRef]
  17. Ministry of Culture, Sports, and Tourism. 2019 Sport White Paper; Korean Ministry of Culture, Sports, and Tourism: Sejong, Republic of Korea, 2021.
  18. Bum, C.H.; Hums, M.A.; Picklesimer, A.; Choi, C. Comparison of the influence of participation in screen golf on self-esteem, loneliness, depression, social isolation, and life satisfaction between people with and without disabilities in Republic of Korea. Phys. Cult. Sport. 2021, 89, 11–21. [Google Scholar] [CrossRef]
  19. Han, H.; Baek, H.; Lee, K.; Huh, B. Perceived benefits, attitude, image, desire, and intention in virtual golf leisure. J. Hosp. Mark. Manag. 2014, 23, 465–486. [Google Scholar] [CrossRef]
  20. Han, H.; Hwang, J. Investigation of the volitional, non-volitional, emotional, motivational and automatic processes in determining golfers’ intention: Impact of screen golf. Int. J. Contemp. Hosp. 2014, 26, 1118–1135. [Google Scholar] [CrossRef]
  21. Lee, J.S. Interactivity: A New Approach. In Proceedings of the 2000 Convention of the Association for Education in Journalism and Mass Communication, Phoenix, AZ, USA, 9–12 August 2000. [Google Scholar]
  22. Stamboulis, Y.; Skayannis, P. Innovation strategies and technology for experience-based tourism. Tour. Manag. 2003, 24, 35–43. [Google Scholar] [CrossRef]
  23. Statista Presents. Ecommerce 2020: Trends And Outlook (The Transformation of eCommerce). 2020. Available online: https://spaces.statista.com/f698ac3e1bf2405190307ce8e6063f47.pdf. (accessed on 1 April 2022).
  24. Hacker, J.; vom Brocke, J.; Handali, J.; Otto, M.; Schneider, J. Virtually in this together–how web-conferencing systems enabled a new virtual togetherness during the COVID-19 crisis. Eur. J. Inf. Syst. 2020, 29, 563–584. [Google Scholar] [CrossRef]
  25. Suits, B. The Elements of Sport. In Ethics in Sport, 2nd ed.; Morgan, W.J., Ed.; Human Kinetics: Champaign, IL, USA, 2007; pp. 33–44. [Google Scholar]
  26. Parry, J. E-sports are not sports. Sport. Ethics Philos. 2019, 13, 3–18. [Google Scholar] [CrossRef]
  27. The New York Times. Gibbon and Tacey Win Opening Stage of Virtual Tour de France. 2020. Available online: https://www.nytimes.com/reuters/2020/07/04/sports/cycling/04reuters-cycling-france-virtual.html (accessed on 5 March 2022).
  28. Csikzentmihalyi, M. Flow: The Psychology of Optimal Experience; Harper and Row: New York, NY, USA, 1990. [Google Scholar]
  29. Mayo, E.J.; Jarvis, L.P. The Psychology of Leisure Travel: Effective Marketing and Selling of Travel Services; CBI Publishing Company, Inc.: Boston, MA, USA, 1981. [Google Scholar]
  30. Ajzen, I.; Fishbein, M. Understanding Attitude and Predicting Social Behavior; Prentice-Hall, Inc.: Englewood, NJ, USA, 1980. [Google Scholar]
  31. Ajzen, I.; Fishbein, M. A Bayesian analysis of attribution processes. Psychol. Bull. 1975, 82, 261. [Google Scholar] [CrossRef]
  32. Ajzen, I. Attitudes, Personality and Behavior; Open University Press: Buckingham, UK, 1988. [Google Scholar]
  33. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  34. Norman, P.; Smith, L. The theory of planned behaviour and exercise: An investigation into the role of prior behaviour, behavioural intentions and attitude variability. Eur. J. Soc. Psychol. 1995, 25, 403–415. [Google Scholar] [CrossRef]
  35. Hagger, M.S.; Chatzisarantis, N.; Biddle, S.J.H. A meta analytic review of the theories of reasoned action and planned behavior in physical activity: Predictive validity and the contribution of additional variables. J. Sport Exerc. Psychol. 2002, 24, 3–32. [Google Scholar] [CrossRef]
  36. Ajzen, I. From intentions to actions: A theory of planned behavior. In Action Control: From Cognition to Behavior; Kuhl, J., Beckman, J., Eds.; Springer: Berlin/Heidelberg, Germany, 1985; p. 11e39. [Google Scholar]
  37. Ajzen, I. The theory of planned behaviour: Reactions and reflections. Psychol. Health 2011, 26, 1113–1127. [Google Scholar] [CrossRef]
  38. Terry, D.J.; O’Leary, J.E. The theory of planned behaviour: The effects of perceived behavioural control and self-efficacy. Br. J. Soc. Psychol. 1995, 34, 199–220. [Google Scholar] [CrossRef]
  39. Courneya, K.S. Understanding readiness for regular physical activity in older individuals: An application of the theory of planned behavior. Health Psychol. 1995, 14, 80. [Google Scholar] [CrossRef]
  40. Hausenblas, H.A.; Carron, A.V.; Mack, D.E. Application of the theories of reasoned action and planned behavior to exercise behavior: A meta-analysis. J. Sport Exerc. Psychol. 1997, 19, 36–51. [Google Scholar] [CrossRef]
  41. Lajunen, T.; Räsänen, M. Can social psychological models be used to promote bicycle helmet use among teenagers? A comparison of the Health Belief Model, Theory of Planned Behavior and the Locus of Control. J. Safety Res. 2004, 35, 115–123. [Google Scholar] [CrossRef]
  42. Lee, H.J.; Jee, Y.S. The impacts of brand asset of domestic screen golf playing systems upon brand trust and brand loyalty. Int. J. Sports Mark. Spons. 2015, 17, 320–332. [Google Scholar] [CrossRef]
  43. Miesen, H.W. Predicting and explaining literary reading: An application of the theory of planned behavior. Poetics. 2003, 31, 189–212. [Google Scholar] [CrossRef]
  44. Quine, L.; Rutter, D.R.; Arnold, L. Comparing the Theory of Planned Behaviour and the Health Belief Model: The Example of Safety Helmet Use Among Schoolboy Cyclists. In Understanding and Changing Health Behaviour; Psychology Press: London, UK, 2013; pp. 89–114. [Google Scholar]
  45. Warburton, D.E.R.; Bredin, S.S.D.; Horita, L.T.L.; Zbogar, D.; Scott, J.M.; Esch, B.T.A.; Rhodes, R.E. The health benefits of interactive video game exercise. Appl. Physiol. Nutr. Metab. 2007, 32, 655–663. [Google Scholar] [CrossRef] [PubMed]
  46. Tonglet, M.; Phillips, P.S.; Read, A.D. Using the Theory of Planned Behaviour to investigate the determinants of recycling behaviour: A case Study from Brixworth, UK. Resources. Conserv. Recycl. 2004, 41, 191–214. [Google Scholar] [CrossRef]
  47. Armitage, D.; Conner, M. Efficacy of the theory of planned behavior. a meta-analysis review. Br. J. Soc. Sci. 2001, 40, 471–499. [Google Scholar]
  48. Sheeran, P. Intention—behavior relations: A conceptual and empirical review. Eur. Rev. Soc. Psychol. 2002, 12, 1–36. [Google Scholar] [CrossRef]
  49. Han, H.; Lee, S.; Lee, C.K. Extending the theory of planned behavior: Visa exemptions and the traveler decision-making process. Tour. Geogr. 2011, 13, 45–74. [Google Scholar] [CrossRef]
  50. Kim, Y.H.; Han, H.S. Intention to pay conventional hotel prices at a green hotel: A modification of the theory of planned behavior. J. Sustain. Tour. 2010, 18, 997–1014. [Google Scholar] [CrossRef]
  51. Oh, H.; Hsu, C. Volitional degrees of gambling behaviors. Ann. Tour. Res. 2001, 28, 618–637. [Google Scholar] [CrossRef]
  52. Perugini, M.; Bagozzi, R.P. The role of desires and anticipated emotions in goal-directed behaviours: Broadening and deepening the theory of planned behaviour. Br. J. Soc. Psychol. 2001, 40, 79–98. [Google Scholar] [CrossRef]
  53. Seong, B.H.; Hong, C.Y. Moderating Effect of Demographic Variables by Analyzing the Motivation and Satisfaction of Visitors to the Former Presidential Vacation Villa: Case Study of Cheongnam-Dae, South Korea. Societies 2021, 11, 104. [Google Scholar] [CrossRef]
  54. Greenstone, M.; Nigam, V. Does social distancing matter? University of Chicago, Becker Friedman Institute for Economics Working Paper. SSRN Electron. J. 2020, 26. [Google Scholar] [CrossRef]
  55. Jefferson, T.; Del Mar, C.B.; Dooley, L.; Ferroni, E.; Al-Ansary, L.A.; Bawazeer, G.A.; Conly, J.M. Physical interventions to interrupt or reduce the spread of respiratory viruses. Cochrane Database Syst. Rev. 2011, 11, CD006207. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Pan, A.; Liu, L.; Wang, C.; Guo, H.; Hao, X.; Wang, Q.; Huang, J.; He, N.; Yu, H.; Lin, X.; et al. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA 2020, 323, 1915–1923. [Google Scholar] [CrossRef] [PubMed]
  57. Seong, B.H.; Hong, C.Y. When It Comes to Screen Golf and Baseball, What Do Participants Think? Int. J. Environ. Res. Public Health 2022, 19, 13671. [Google Scholar] [CrossRef] [PubMed]
  58. Kim, H.; Jamal, T. Touristic quest for existential authenticity. Ann. Tour. Res. 2007, 34, 181–201. [Google Scholar] [CrossRef]
  59. Hatfield, E.; Cacioppo, T.J.; Rapson, R.L. Emotional Contagion; Cambridge University Press: Cambridge, UK, 1994. [Google Scholar]
  60. Packer, J.; Ballantyne, J. The impact of music festival attendance on young people’s psychological and social well-being. Psychol. Music. 2011, 39, 164–181. [Google Scholar] [CrossRef] [Green Version]
  61. Kim, H.S.; Choi, B. The effects of three customer-to-customer interaction quality types on customer experience quality and citizenship behavior in mass service settings. J. Serv. Mark. 2016, 30, 384–397. [Google Scholar] [CrossRef]
  62. Rihova, I.; Buhalis, D.; Gouthro, M.B.; Moital, M. Customer-to-customer cocreation practices in tourism: Lessons from customer-dominant logic. Tour. Manag. 2018, 67, 362–375. [Google Scholar] [CrossRef]
  63. Uhrich, S.; Benkenstein, M. Physical and social atmospheric effects in hedonic service consumption: Customers’ roles at sporting events. Serv. Ind. J. 2012, 32, 1741–1757. [Google Scholar] [CrossRef]
  64. Wu, C.H. The impact of customer-to-customer interaction and customer homogeneity on customer satisfaction in tourism service-The service encounter prospective. Tour. Manag. 2007, 28, 1518–1528. [Google Scholar] [CrossRef]
  65. Cho, C.H.; Leckenby, J.D. Interactivity as a measure of advertising effectiveness: Antecedents and consequences of interactivity in web advertising. In Proceedings of the Conference-American Academy of Advertising, Albuquerque, Mexico, 1–5 June 1999; American Academy of Advertising: New York, NY, USA, 1999; pp. 162–179. [Google Scholar]
  66. McMillan, S.J. A four-part model of cyber-interactivity: Some cyber-places are more interactive than others. New Media Soc. 2002, 4, 271–291. [Google Scholar] [CrossRef]
  67. McMillan, S.J.; Hwang, J.S. Measures of perceived interactivity: An exploration of the role of direction of communication, user control, and time in shaping perceptions of interactivity. J. Advert. 2002, 31, 29–42. [Google Scholar] [CrossRef]
  68. Lee, K.I.; Gould, R. Predicting congregate meal program participation: Applying the extended theory of planned behavior. Int. J. Hosp. Manag. 2012, 31, 828–836. [Google Scholar] [CrossRef]
  69. Liu, Y. Developing a scale to measure the interactivity of websites. J. Advert. Res. 2003, 43, 207–216. [Google Scholar] [CrossRef] [Green Version]
  70. McMillan, S.J. The researchers and the concept: Moving beyond a blind examination of interactivity. Int. J. Advert. 2005, 5, 1–4. [Google Scholar] [CrossRef]
  71. Stapel, D.A.; Koomen, W. Competition, Cooperation and the Effects of Others on Me. J. Pers. Soc. Psychol. 2005, 88, 1029–1038. [Google Scholar] [CrossRef] [PubMed]
  72. Festinger, L. A theory of social comparison processes. Hum. Relat. 1954, 7, 117–140. [Google Scholar] [CrossRef]
  73. Lazzaro, N.; Keeker, K. What’s my method? A game show on games. In Proceedings of the CHI’04 Extended Abstracts on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA, 6 November 2004; pp. 1093–1094. [Google Scholar]
  74. Sweetser, P.; Wyeth, P. GameFlow: A model for evaluating player enjoyment in games. CIE 2005, 3, 3. [Google Scholar] [CrossRef]
  75. Sanz, F.A.; Multon, F.; Lécuyer, A. A methodology for introducing competitive anxiety and pressure in VR sports training. Front. Robot. A. 2015, 2, 10. [Google Scholar]
  76. Ajzen, I.; Driver, B.L. Application of the theory of planned behavior to leisure choice. J. Leis. Res. 1992, 24, 207–224. [Google Scholar] [CrossRef]
  77. Girish, V.G.; Lee, C.K. The relationships of brand experience, sports event image and loyalty: Case of Jeju International Ultramarathon Race. Int. J. Sports Mark. Spons. 2019, 20, 567–582. [Google Scholar] [CrossRef]
  78. Kline, R. Principles And Practice of Structural Equation Modeling; Guilford Press: New York, NY, USA, 2005. [Google Scholar]
  79. Kim, M.C. By Applying Input-Output Analysis, the Economical Ripple Effect Analysis of Sport Event Participants’ Tourist Resort Visit and Tourist Shopping Income. Korean J. Sport Manag. 2007, 12, 27–41. [Google Scholar]
  80. Han, H.S.; Hsu, L.; Sheu, C. Application of the theory of planned behavior to green hotel choice: Testing the effect of environmental friendly activities. Tour. Manag. 2010, 31, 325–334. [Google Scholar] [CrossRef]
  81. Park, S.H.; Hsieh, C.M.; Lee, C.K. Examining Chinese college students’ intention to travel to Japan using the ex-tended theory of planned behavior: Testing destination image and the mediating role of travel constraints. J. Travel Tour. Mark. 2017, 34, 113–131. [Google Scholar] [CrossRef]
  82. Dzewaltowski, D.A.; Noble, J.M.; Shaw, J.M. Physical activity participation: Social cognitive theory versus the theories of reasoned action and planned behavior. J. Sport Exerc Psychol. 1990, 12, 388–405. [Google Scholar] [CrossRef] [PubMed]
  83. Gatch, C.L.; Kendzierski, D. Predicting exercise intentions: The theory of planned behavior. Res. Q. Exerc. Sport 1990, 61, 100–102. [Google Scholar] [CrossRef]
  84. O’Keefe, D.J. Persuasion: Theory & Research; Sage Publications: Thousand Oaks, CA, USA, 2002. [Google Scholar]
  85. Fishbein, M.; Ajzen, I. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research; Addison-Wesley: Boston, MA, USA, 1975. [Google Scholar]
Ijerph 20 00592 g001 550
Figure 1. Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB). Note: Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB). Source: Ajzen & Fishbein (1975). A Bayesian analysis of attribution processes; Ajzen (1988). Attitudes personality and behavior. Buckingham, UK: Open University Press.
Figure 1. Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB). Note: Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB). Source: Ajzen & Fishbein (1975). A Bayesian analysis of attribution processes; Ajzen (1988). Attitudes personality and behavior. Buckingham, UK: Open University Press.
Ijerph 20 00592 g001
Ijerph 20 00592 g002 550
Figure 2. Zwift screen capture (Left); researchers playing Zwift application (Right). Source: https://blog.cyclestore.co.uk/zwift-racing-5-top-tips/ (Accessed on 5 March. 2022) (Left), a researcher’s photograph (Right).
Figure 2. Zwift screen capture (Left); researchers playing Zwift application (Right). Source: https://blog.cyclestore.co.uk/zwift-racing-5-top-tips/ (Accessed on 5 March. 2022) (Left), a researcher’s photograph (Right).
Ijerph 20 00592 g002
Ijerph 20 00592 g003 550
Figure 3. Research model.
Figure 3. Research model.
Ijerph 20 00592 g003
Ijerph 20 00592 g004 550
Figure 4. Research model and hypothesis verification results. Notes: *: <0.05 **: <0.01.
Figure 4. Research model and hypothesis verification results. Notes: *: <0.05 **: <0.01.
Ijerph 20 00592 g004
Table
Table 1. Demographic characteristics of respondents.
Table 1. Demographic characteristics of respondents.
Itemn (%)Itemn (%)
GenderMale157 (52.2)Education
Level		Less than high school4 (1.3)
Female144 (47.8)Attending college7 (2.3)
AgeUnder 203 (1.0)University graduate283 (94.0)
20s44 (14.6)Over Graduate school7 (2.3)
30s230 (76.4)ResidenceSeoul74 (24.6)
40s18 (6.0)Busan30 (10.0)
50s6 (2.0)Daegu12 (4.0)
Monthly
Income		Less than 2 million won1 (0.3)Incheon32 (10.6)
2~3 million won4 (1.3)Gwangju21 (7.0)
3~4 million won27 (9.0)Daejeon21 (7.0)
4~5 million won15 (5.0)Ulsan11 (3.7)
Over than 5 million won254 (84.4)Gyeonggi-do54 (17.9)
OccupationSelf-employed84 (27.9)Gangwon-do1 (0.3)
Professional69 (22.9)Chungnam-do9 (3.0)
Government officer12 (4.0)Chungbuk-do13 (4.3)
Agriculture/Livestock/Fishery1 (0.3)Jeonnam-do4 (1.3)
Student8 (2.7)Jeonbuk-do5 (1.7)
Officer worker126 (41.9)Gyeongnam-do2 (0.7)
Other1 (0.3)Gyeongbuk-do7 (2.3)
Jeju1 (0.3)
Sejong4 (1.3)
Table
Table 2. Results of confirmatory factor analysis.
Table 2. Results of confirmatory factor analysis.
FactorItemsS.E.tStandardized
Coefficients		C.R.AVE
AttitudesTo use Zwift is a valuable.0.08312.490 ***0.7700.9110.671
To use Zwift is significant act0.08611.329 ***0.699
To use Zwift is worthwhile.0.08511.003 ***0.674
To use Zwift is dynamic act.0.08711.878 ***0.729
To use Zwift is attractive act.--0.712
Subjective NormsMy family members think positive that I use a Zwift.0.08710.687 ***0.7310.9130.637
My friends think positive that I use a Zwift.0.06911.771 ***0.696
People around me think positive that I use a Zwift.0.07011.716 ***0.693
My family members support that I use a Zwift.0.08310.120 ***0.608
My friends support that I use a Zwift.--0.739
People around me support that I use a Zwift.0.07712.890 ***0.767
Perceived Behavioral ControlsI can use a Zwift whenever I want.0.0909.576 ***0.6330.8640.560
I have enough resources(money) to use a Zwift.0.0969.962 ***0.662
I have enough time to use a Zwift.0.1029.227 ***0.607
It is easy for me to learn the skills needed to use a Zwift.0.10110.319 ***0.690
I can easily obtain the information I need to enjoy Zwift.--0.687
Perceived InteractivityI feel like I am competing with other cyclists.0.10510.024 ***0.7240.8470.582
I feel like I am working out with other cyclists.0.10210.336 ***0.748
I feel a sense of fellowship with other cyclists.--0.664
I feel like I am in one place with people who are interested in Zwift.0.12310.763 ***0.817
Intention to UseI have a plan to use a Zwift.0.08111.969 ***0.7430.8590.670
I will try to use a Zwift in a long term.0.08612.186 ***0.758
I will certainly invest money and time to have a Zwift.--0.725
Note: *** p < 0.001.
Table
Table 3. Results of correlation analysis.
Table 3. Results of correlation analysis.
FactorAttitudesSubjective NormsPerceived Behavioral ControlsPerceived InteractivityIntention to UseAVE
Attitudes1 0.671
Subjective Norms0.789
(0.622)		1 0.637
Perceived Behavioral Controls0.739
(0.546)		0.693
(0.480)		1 0.560
Perceived Interactivity0.122
(0.015)		0.580
(0.336)		0.652
(0.425)		1 0.582
Intention to Use0.774
(0.599)		0.301
(0.091)		0.318
(0.101)		0.363
(0.132)		10.670
Note: ( ) denotes the square of the correlation coefficient.
Table
Table 4. Summary of tested hypotheses.
Table 4. Summary of tested hypotheses.
Hypothesized PathStandardized
Coefficients		Standard
Error		tResults
H1Attitudes → Intention to use0.5820.1863.452 **supported
H2Subjective Norm → Intention to use0.1300.1510.828not supported
H3Perceived Behavioral Control → Intention to use0.1130.0422.343 *supported
H4Perceived Interactivity → Intention to use0.2520.1072.564 *supported
Notes: *: <0.05 **: <0.01.
Table
Table 5. Measurement uniformity analysis results.
Table 5. Measurement uniformity analysis results.
Modelχ2dfCFIRMSEATLIΔχ2Sig.
Unconstrained *572.794300.9550.0330.947-
Measurement weights **586.064480.9570.0320.951Δχ2(18) = 13.27 ***not Sig.
Note: * Unconstrained model: Models without any constraints. ** Measurement weights model: A model with same constraints on group factor loadings. *** χ2 distribution table (p = 0.05), χ2(18) = 28.87.
Table
Table 6. Multigroup path analysis results.
Table 6. Multigroup path analysis results.
Path PatternsUsers Who Started during
the COVID-19 Pandemic		Users Who Started before
the COVID-19 Pandemic
Standardized
Coefficient		Standard
Error		tStandardized
Coefficient		Standard
Error		t
Attitudes
→ Intention to use		0.5810.2602.603 **0.7200.2612.802 **
Subjective Norm
→ Intention to use		0.1550.2170.7580.0300.2060.126
Perceived Behavioral Control → Intention to use0.1920.2061.1270.2530.1172.060 *
Perceived Interactivity
→ Intention to use		0.2030.0732.726 **0.0040.0500.052
Note: *: <0.05 **: <0.01.
Table
Table 7. Verification results of Hypotheses 5–8 from multigroup SEM analysis.
Table 7. Verification results of Hypotheses 5–8 from multigroup SEM analysis.
Hypothesized Pathχ2dfΔχ2Sig
Unconstrained model572.79430--
H5Attitudes → Intention to use572.81431Δχ2(1) = 0.02not Sig.
H6Subjective Norm → Intention to use572.99431Δχ2(1) = 0.20not Sig.
H7Perceived Behavioral Control → Intention to use572.79431Δχ2(1) = 0.00not Sig.
H8Perceived Interactivity → Intention to use577.94431Δχ2(1) = 5.15Sig.
Note: χ2 difference test: Δχ2(1) = 3.84 (χ2 distribution table, p = 0.05).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Seong, B.-H.; Hong, C.-Y. Decision-Making in Virtual Reality Sports Games Explained via the Lens of Extended Planned Behavior Theory. Int. J. Environ. Res. Public Health 2023, 20, 592. https://doi.org/10.3390/ijerph20010592

AMA Style

Seong B-H, Hong C-Y. Decision-Making in Virtual Reality Sports Games Explained via the Lens of Extended Planned Behavior Theory. International Journal of Environmental Research and Public Health. 2023; 20(1):592. https://doi.org/10.3390/ijerph20010592

Chicago/Turabian Style

Seong, Bo-Hyun, and Chang-Yu Hong. 2023. "Decision-Making in Virtual Reality Sports Games Explained via the Lens of Extended Planned Behavior Theory" International Journal of Environmental Research and Public Health 20, no. 1: 592. https://doi.org/10.3390/ijerph20010592

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop