"My Sports Data”: A Privacy Calculus Model Analysis with Mediation Effects of Personal Competence and Perceived Value

"My Sports Data”: A Privacy Calculus Model Analysis with Mediation Effects of Personal Competence and Perceived Value | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article "My Sports Data”: A Privacy Calculus Model Analysis with Mediation Effects of Personal Competence and Perceived Value Young-Jae Kim, E-Sack Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5237195/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study analyzed individuals’ intention to provide their personal information, specifically the “My Sports Data (MSD),” and explored how personal competence and perceived value influence this intention. A privacy calculus model was applied and descriptive statistics, confirmatory factor analysis, correlation analysis, and structural equation modeling were conducted on a sample of 1,000 South Korean adults aged 20–65 years. The results showed that perceived private and public benefits affected perceived value and perceived privacy and security risks. In addition, perceived value significantly affected the intention to provide behavioral and physical information. These findings indicate that by ensuring the protection of personal information and clearly explaining the positive benefits of sharing sports data, people will be more likely to share their sports data so they could access potential benefits. This, in turn, allows for more personalized sports solutions and improvements in sports. Social science/Cultural and media studies Social science/Sociology My Sports Data privacy calculus model intention to provide personal competence perceived value Figures Figure 1 Figure 2 Introduction Technological capability in the era of hyperconnectivity and hyper-convergence, the popularization of mobile devices, and advances in information and communications technology has led to a massive collection of personal data online (Shajilin Loret and Arul, 2024 ). Policies on the use of these data in new industries are being formulated to generate value (Ciuriak, 2023 ). One such concept is the “My Data” project, wherein the data subject retains sovereignty and authority over their own data (Langford et al., 2020 ). Within this human-centric data ecosystem, the data subjects actively manage and control their own data, employ their data in credit, asset, and health management, and exercise sovereignty over data that are challenging to control themselves such as healthcare, communications, and financial data (Poikola et al., 2020 ). The South Korean government aims to strengthen the “My Data” industry through tangible stimulation for start-ups in novel, data service-related sectors (Jo, 2023). Regarding related industries, the demand for personalized sports services, which are closely related to health data, is increasing (Korea Professional Sports Association, 2022 ). Korean sports data services primarily partake in supply system construction (Ministry of Culture, Sports and Tourism, 2019 ). Personal information data utilization in the sports industry can be indiscriminately used by data parties such as behavior, preferences, manipulation of choices, opportunities, and deprivation of benefits. Ethical and legal principles such as protection, consent, transparency, and accountability that can ensure responsible and respectful use of data in sports should be upheld not only by the state but also by businesses (FasterCapital, 2024 ). However, sports data in South Korea have predominantly been used for analysis of athletes’ records, salary negotiations, and sports betting, which do not reflect the full potential of such data (Hwang, 2021 ). The use of sports data in South Korea is gradually gaining popularity; however, the field of sports science, local governments, and the central government have not responded appropriately (Jo, 2022 ). Hence, a personalized sports data service industry that is based on information about people’s needs and wants is needed. Since 2019, South Korea has consistently implemented “My Data” projects focused on healthcare, finance, energy, distribution, transport, small business owners, welfare, lifestyle, and academics. However, sports data projects have lagged behind other sectors because of the commonly held perspective that sports are not essential to our daily lives (Noh, 2021 ). In addition, compared with other fields, the sports, culture, and tourism industries lack a personalized foundation supported by digital technology, with the quality of personalized digital-related products also low (Zhao and Li, 2024 ). Thus, it is vital to initiate various discussions regarding the construction of the “My Sports Data” (MSD) platform in the hope of improving the population’s health and quality of life. Big data are becoming central to our economies, and people’s awareness of social problems related to the protection and safety of personal information is growing. With data in diverse sectors being collected indiscriminately, the threat of infringing upon entities’ rights to information sovereignty is increasing, while measures to improve information sovereignty to protect rights that are infringed upon are severely lacking (Lee, 1999 ). Existing data industry policies are docile regarding the protection and guarantee of data subjects’ rights. As such, the rights to autonomy and decision-making granted to data subjects who generate and provide data must be discussed, and related policies must be established (Jang, 2022 ). In particular, the study by Mittal et al. ( 2024 ) indicated a need for ways to measure the risk of personal information protection, as quantitative methods for confirming personal information protection, regulatory compliance, and fairness are lacking. In 2022, 1,802 data leaks occurred in the United States, exposing the personal information of 422 million people. This led Americans to carefully consider how everyone, including individuals, companies, and the government, uses and protects their personal information (Drenik, 2023 ). Such issues may also arise in the sports data industry. To address these concerns, studies must examine the right to authority over one’s personal sports data in order to protect sports-related personal information. Personal information can be broadly categorized as identifying or non-identifying. Identifying personal information comprises specific data that can be used to single out a person, such as a resident registration number, name, or address. This direct link to privacy concerns raises persistent issues about personal information. By contrast, non-identifying information, while not giving third parties the ability to identify an individual solely with such information (e.g., a user’s tendencies, behaviors, and location history), could predict an individual’s interests and preferences. Further, the sharing and combination of this information can, to some extent, be used to identify individuals. This possibility for non-identifying information to predict individual preferences has made it a subject of intense debate regarding potential privacy concerns (Ioannou et al., 2020 ). Previously, issues related to private information were investigated using Laufer and Wolfe’s ( 1977 ) privacy calculus model. This model uses the calculus of behavior to make decisions regarding privacy by weighing potential costs and benefits in each situation for individuals, which is useful for personal information management (Laufer and Wolfe, 1977 ). This theoretical model provides personal information based on potential risks and benefits (Campbell and Carlson, 2002 ). The underlying privacy theory is an adaptation of social exchange theory, which holds that the potential risks and rewards regarding others may be identified, which allows selecting the interactions that lead to greater rewards (Homans, 1961 ). This is combined with expectancy theory (Vroom, 1964 ), which states that people try to maximize positive results and minimize negative results (Laufer and Wolfe, 1977 ). Applying individuals’ sports-related demands can spur further growth for the sports data industry. However, in industries such as MSD, individuals’ intention to provide data must first be examined given the importance of permissions and authority over personal information. Furthermore, such intention could differ depending on personal competence, and the perceived value may also vary because of the moderation effect of personal competence. Currently, various information technology industries have suggested methods to cope with data provision by examining the risks of providing individual pieces of data. However, even though sports data are becoming vital, limited studies have examined the related risks and benefits. To bridge this literature gap, this study used the privacy calculus model of Laufer and Wolfe ( 1977 ) to investigate the intentionality of data provision based on the risks and benefits of providing individual pieces of sports data, thereby generating fundamental data for the MSD industry in South Korea. Methods This study used Laufer and Wolfe’s ( 1977 ) privacy calculus model to analyze the intention to provide MSD personal information, with individual competence or perceived value as mediators. Participants The participants were South Korean adults aged 20–65 years who completed an online questionnaire administered from March 13 to 22, 2024 via the South Korean professional survey company “Embrain.” Participants were recruited using a non-stochastic convenience sampling method, and survey responses were collected from 1,000 people. This study was approved by the institutional review board at Chung-Ang University (1041078-20231205-HR-321), and the survey was performed following the principles of the 1975 Declaration of Helsinki. Consent was sought from all potential survey participants before conducting the survey, and only those who consented were included in the study. Research instruments The questionnaires used in this study comprised factors used in the conventional privacy calculus model, namely, intention to provide personal information, perceived data control competence, and personal information policy. Each item on the instruments was subjected to content validation by an eight-person panel consisting of four sports sociology experts, three leisure or recreation experts, and one statistics expert. All items were adapted and supplemented to suit the study aims. Perceived benefit To measure perceived benefit, this study adapted the items used in Cheng et al. ( 2006 ) and Tingchi Liu et al. ( 2013 ) to suit our objectives. The instrument comprised two factors: perceived personal benefits (four items) and perceived public benefits (four items). Each item was scored on a 7-point Likert scale ranging from 1 (very strongly disagree) to 7 (very strongly agree). Cronbach’s ⍺ was .943 and .954 for perceived personal and public benefit, respectively, indicating good reliability of the instrument. Perceived risk Perceived risk was divided into privacy risk (three items) and security risk (three items). For these items, we adapted the e-Tail quality (eTailQ: etail) scale developed by Wolfinbarger and Gilly ( 2003 ) to measure perceived risk regarding personal information protection and security online, and used in a study by Nepomuceno et al. ( 2014 ). Cronbach’s ⍺ was .911 and .900 for privacy and security risk, respectively, demonstrating high reliability of the instrument. Perceived value To measure perceived value, this study used three items from a scale used for overall user evaluation of the costs and benefits of using a product or service in studies by Sirdeshmukh et al. ( 2002 ), Dinev and Hart ( 2006 ), and Kim et al. ( 2007 ). Cronbach’s ⍺ was .950 for perceived value, indicating excellent reliability of the instrument. Intention to provide personal information To measure the intention to provide personal information, this study employed factors proposed by Ajzen and Fishbein ( 1973 ) and used by Yu et al. ( 2020 ) to ascertain the intention to make plans to participate in certain behaviors. This was divided into two categories: intention to provide sports behavioral information (four items) and intention to provide sports physical information (four items). Responses were scored on a 7-point Likert scale ranging from 1 (very strongly disagree) to 7 (very strongly agree). Cronbach’s ⍺ was .961 and .967 for the intention to provide sports behavioral and physical information, respectively, indicating that the instrument is reliable. Perceived data control competence and personal information policy Perceived data control competence and personal information policy were divided into two factors: perceived data control competence (four items) and perceived personal information policy (four items). For the perceived personal information policy factors, we selected the items from a study by Cranor et al. ( 2000 ) on Internet user attitudes toward online personal information protection, a study by Milne and Boza ( 1999 ) on database marketing and related personal information protection issues, and a study by Sheehan and Hoy ( 2000 ) on situational dimensions of personal information protection issues. We adapted and supplemented the items to suit our study objectives. Responses were scored on a 7-point Likert scale ranging from 1 (very strongly disagree) to 7 (very strongly agree). Cronbach’s ⍺ was .833 and .888 for perceived data control competence and perceived personal information policy, respectively, indicating good reliability of the instrument. Data sensitivity To measure data sensitivity, we adapted the sensitivity of information and willingness to share information used in the study by Ioannou et al. ( 2020 ), using the factors of sensitivity of behavioral information (three items) and sensitivity of physical information (three items). Cronbach’s ⍺ was .947 and .922 for privacy and security risks, respectively, indicating good reliability of the instrument. Analysis methods After coding and cleaning the data, we used SPSS 28.0 and AMOS 28.0 to perform the following analyses. First, we used frequency analysis to investigate participants’ demographic characteristics and descriptive statistics to verify the normal distribution of the perceived benefit, perceived risk, perceived value, intent to provide personal information, perceived data control competence and personal information policy, and sensitivity of data scales. Skewness and kurtosis were calculated according to the method of West et al. ( 1995 ; skewness < 3, kurtosis < 8). The measured skewness and kurtosis values ranged from − .598 to .225 and from − .340 to .246, respectively, demonstrating normal distributions. Second, among the factors used in this study, we performed item parceling for perceived benefit, perceived risk, perceived value, intention to provide personal information, perceived data control competence and personal information policy, and data sensitivity. Third, we calculated Cronbach’s ɑ to ensure reliability of the research instruments and performed confirmatory factor analysis to test their validity. Fourth, we performed correlation analysis and calculated the average variance extracted (AVE) to investigate the correlations between factors and discriminant validity. Fifth, based on Laufer and Wolfe’s ( 1977 ) privacy calculus model, we performed bootstrapping resampling (2,000 iterations), and using sensitivity of behavioral information and sensitivity of physical information as control variables, we performed structural equation modeling of the relationships among intention to share sports behavioral information, intention to share sports physical information, perceived value, perceived personal benefit, perceived public benefit, privacy risk, security risk, perceived data control competence, perceived personal information policy, sensitivity of behavioral information, and sensitivity of physical information. Subsequently, to verify the moderation effects of personal competence on the relationships of benefit and risk with perceived value, we classified participants according to perceived data control competence and personal information policy. Finally, we set up restricted and unrestricted models to test the moderators’ effect on the relationships between latent factors (Lee and Lim, 2008 ). We derived the X 2 statistic for each model to determine the final moderation effect. Results This study used Laufer and Wolfe’s ( 1977 ) privacy calculus model to derive factors affecting the intention to provide MSD among South Koreans and investigate the benefits perceived by information providers. The following subsections describe the results. Demographic characteristics The participants’ demographic characteristics are shown in Table 1 . There were 511 male (51.1%) and 489 female (48.9%) participants with mean age of 44.0 years (standard deviation [SD]:12.67). Most were married (601 persons, 60.1%); others were unmarried (385, 38.5%) or bereaved or divorced (14, 1.4%). Occupation-wise, office workers (545, 54.5%) ranked first, followed by homemakers (151, 15.1%), others (90, 9.0%), self-employed individuals (83, 8.3%), students (75, 7.5%), and civil servants (56, 5.6%). Educational attainment was primarily college graduate (569, 56.9%). Mean monthly income was typically 3.01–5.00 million KRW (311, 31.1%), 2.01–3.00 million KRW (248, 24.8%), or ≥ 5.01 million KRW (218, 21.8%). Table 1 Demographic characteristics of participants (n = 1,000). Variable Characteristic n % Sex Male 511 51.1 Female 489 48.9 Age, M (SD) 44.04 (12.674) 20s 182 18.2 30s 189 18.9 40s 228 22.8 50s 255 25.5 60–65 years 146 14.6 Marital status Unmarried 385 38.5 Married 601 60.1 Bereaved/divorced 14 1.4 Job Student 75 7.5 Office worker 545 54.5 Self-employed 83 8.3 Civil servant 56 5.6 Homemaker 151 15.1 Other 90 9.0 Educational attainment Middle school 194 19.4 High School 144 14.4 College 569 56.9 Master’s 79 7.9 PhD 14 1.4 Mean monthly income ≤ ₩1,000,000 121 12.1 ₩1,010,000–₩2,000,000 102 10.2 ₩2,010,000–₩3,000,000 248 24.8 ₩3,010,000–₩5,000,000 311 31.1 ≥ ₩5,000,000 218 21.8 Sports participation type Alone 533 53.3 Family 237 23.7 Friends/colleagues 193 19.3 Club 37 3.7 Sports participation duration 1–2 years 404 40.4 3–4 years 257 25.7 5–6 years 111 11.1 ≥ 7 years 228 22.8 My Data experience Yes 740 74.0 No 175 17.5 Not sure 85 8.5 Experience of providing physical activity data Yes 614 61.4 No 335 33.5 Not sure 51 5.1 Regarding types of sports participation, the most frequent response was “alone” (533, 53.3%), followed by “family” (237, 23.7%), “friends or colleagues” (193, 19.3%), and “clubs” (37, 3.7%). Sports participation duration was most frequently 1−2 years (404, 40.4%), followed by 3−4 years (257, 25.7%), ≥ 7 years (228, 22.8%), and 5−6 years (111, 11.1%). A total of 740 persons (74.0%) reported experience with “My data”; 175 persons (17.5%) reported no experience, and 85 persons (8.5%) were unsure. Overall, 614 persons (61.4%) reported previous experience in providing physical activity-related data; 335 persons (33.5%) reported no such experience, and 51 persons (5.1%) were unsure. (Insert Table 1 here.) Item-parceling analysis We used item-parceling analysis because it can alleviate problems with non-normal distributions (Hau and Marsh, 2004 ), enable stable, accurate non-parametric estimations (Nasser and Wisenbaker, 2003 ), and improve model fit (Rogers and Schmitt, 2004 ). Item parcels are constructed by taking the sum or the mean of scores for two or more individual items and using them as variables in a model or as indices for a factor being measured in a factor model (Kishton and Widaman, 1994 ). Although the sum or mean score can be used to form item parcels, using the mean score is recommended (Little et al., 2013 ). The primary assumption when making item parcels is the unidimensionality of the items. After performing exploratory factor analysis and ranking items according to the absolute factor loading value, item parcels were constructed from three to four items each to ensure each had a similar loading value, and the highest mean score was used (Little et al., 2013 ). This is because when the number of items in each parcel differs, the mean and variance can be made similar and can reflect the actual scales, helping to interpret the results. Table 2 provides the parcel size and item numbers for each parcel used. Perceived personal and public benefits as subfactors of perceived benefits; the intention to provide sports behavioral information and the intention to provide sports physical information as subfactors of the intention to provide sports personal information; perceived data control competence; and personal information policy all consisted of four items, which were organized into two item parcels. Meanwhile, privacy risk and security risk as subfactors of perceived risk; perceived value; and sensitivity of behavioral information and sensitivity of physical information as subfactors of sensitivity of data all consisted of three items organized into one item parcel. Table 2 Item parcel size and item numbers. Factor Item parcel Item count Item numbers Perceived benefit Perceived personal benefit Perceived personal benefit 1 2 2, 3 Perceived personal benefit 2 2 1, 4 Perceived public benefit Perceived public benefit 1 2 3, 4 Perceived public benefit 2 2 1, 2 Perceived risk Privacy risk Privacy risk 1 3 1, 2, 3 Security risk Security risk 1 3 1, 2, 3 Perceived value Perceived value 1 3 1, 2, 3 Intention to provide sports physical information Intention to provide sports behavioral information Intention to provide sports behavioral information 1 2 1, 2 Intention to provide sports behavioral information 2 2 3, 4 Intention to provide sports physical information Intention to provide sports physical information 1 2 1, 3 Intention to provide sports physical information 2 2 2, 4 Perceived data control competence and personal information policy Perceived data control competence Perceived data control competence 1 2 1, 3 Perceived data control competence 2 2 2, 4 Personal information policy Personal information policy 1 2 1, 3 Personal information policy 2 2 2, 4 Sensitivity of data Sensitivity of behavioral information Sensitivity of behavioral information 1 3 1, 2, 3 Sensitivity of physical information Sensitivity of physical information 1 3 1, 2, 3 (Insert Table 2 here.) Model fit and exploratory factor analysis As shown in Table 3 , the fit indices for the initial research model were as follows: chi-square (X 2 ) = 1010.805, df = 269, p = 0.000, goodness-of-fit index (GFI) = 0.922, RMR = 0.057, root mean squared error of approximation (RMSEA) = 0.053, incremental fit index (IFI) = 0.977, Tucker-Lewis index (TLI) = 0.969, and comparative fit index (CFI) = 0.977, showing a suitable fit overall. When we investigated the construct reliability, composite reliability, and convergent validity of the measurement model, as shown in Table 4 , the construct reliability values were > 7, demonstrating suitable reliability (Nunnally and Bernstein, 1994 ). The AVE values were > .05, demonstrating the validity of the model, and the AVE values were > .8, showing convergent validity (Fornell and Larcker, 1981 ). Table 3 Model fit. Fit index Absolute fit indices CMIN / Df GFI RMR RMSEA AGFI Value 1010.805 / 269 0.922 0.057 0.053 0.891 Criterion ≥ .05 ≥ .8 ≤ .05 ≤ .1 ≥ .9 Fit index Incremental fit indices NFI RFI IFI TLI CFI Value 0.968 0.959 0.977 0.969 0.977 Criterion ≥ .9 ≥ .9 ≥ .9 ≥ .9 ≥ .9 Table 4 Confirmatory factor analysis results. Item parcel Unstandardized S.E. C.R. Standardized (β) AVE Construct reliability Intention to provide sports behavioral information 1 1 0.955 0.847 0.917 Intention to provide sports behavioral information 2 0.960 0.014 66.682*** 0.956 Intention to provide sports physical information 1 1 0.970 0.901 0.948 Intention to provide sports physical information 2 1.001 0.011 88.940*** 0.977 Perceived value 3 1.013 0.018 57.425*** 0.935 0.763 0.906 Perceived value 2 1 0.938 Perceived value 1 0.983 0.019 52.99*** 0.915 Perceived personal benefit 2 1 0.972 0.773 0.871 Perceived personal benefit 1 0.952 0.018 52.007*** 0.888 Perceived public benefit 2 1 0.956 0.680 0.809 Perceived public benefit 1 2.004 0.033 61.27*** 0.948 Privacy risk 3 0.997 0.028 35.385*** 0.812 Privacy risk 2 1 0.916 0.641 0.842 Privacy risk 1 1.03 0.022 46.149*** 0.922 Security risk 3 1.127 0.035 31.940*** 0.905 Security risk 2 1 0.779 0.595 0.814 Security risk 1 1.111 0.034 32.233*** 0.912 Data control competence 2 1.193 0.037 31.940*** 0.914 0.700 0.823 Data control competence 1 1 0.862 Personal information policy 2 1.041 0.024 43.216*** 0.907 0.808 0.894 Personal information policy 1 1 0.952 Sensitivity of behavioral information 3 1.049 0.022 48.503*** 0.946 0.758 0.904 Sensitivity of behavioral information 2 1.038 0.021 48.489*** 0.946 Sensitivity of behavioral information 1 1 0.887 Sensitivity of physical information 3 1.056 0.024 43.771*** 0.943 0.656 0.851 Sensitivity of physical information 2 0.992 0.027 37.302*** 0.865 Sensitivity of physical information 1 1 0.872 * denotes p < .05; **, p < .01; and ***, p < .001. Correlation analysis and discriminant validity Table 5 provides the results of the correlation analysis between the main factors and discriminant validity. The square roots of the AVE values were typically higher than the correlation coefficients between related variables (Fornell and Larcker, 1981 ), showing the discriminant validity of our study. Table 5 Discriminant validity analysis. 1 2 3 4 5 6 7 8 9 10 11 AVE Intention to provide behavioral information (ρ 2 ) 1 0.847 Intention to provide physical information (ρ 2 ) .922*** (.850) 1 0.901 Perceived value (ρ 2 ) − .207 (.043) .836*** (.699) 1 0.763 Perceived personal benefit (ρ 2 ) .805* (.648) .829** (.687) .939*** (.882) 1 0.773 Perceived public benefit (ρ 2 ) .789* (.623) .807** (.651) .849* (.721) .871*** (.759) 1 0.680 Privacy risk (ρ 2 ) .791* (.626) -141** (.020) − .109* (.012) − .119** (.014) − .076*** (.006) 1 0.641 Security risk (ρ 2 ) -105* (.011) − .259** (.067) − .232* (.054) − .208* (.043) − .182** (.033) .822*** (.676) 1 0.595 Data control competence (ρ 2 ) − .232* (.054) .318* (.101) .317** (.100) .357* (.127) .331* (.110) .087* (.008) .061*** (.004) 1 0.700 Personal information policy (ρ 2 ) .323* (.104) .400* (.160) .441* (.194) .453* (.205) .434* (.188) .0112* (.013) .037 (.001) .761* (.579) 1 0.808 Sensitivity of behavioral information (ρ 2 ) .415* (.172) − .203* (.041) − .159** (.025) − .147* (.022) − .144** (.021) .527** (.278) .590** (.348) .220* (.048) .228** (.052) 1 0.758 Sensitivity of physical information (ρ 2 ) -225*** (.051) − .249* (.062) − .191* (.036) − .187* (.035) − .189** (.036) .495* (.245) .581** (.338) .183** (.033) .183* (.033) .840* (.706) 1 0.656 * denotes p < .05; **, p < .01; and ***, p < .001. (Insert Table 5 here.) Structural equation modeling results Table 6 shows the results of validating our research model using Analysis of Moment Structures (AMOS). First, in terms of how perceived benefit and perceived risk affected perceived value, perceived personal benefit (t = 37.229, p < .001), perceived public benefit (t = 19.557, p < .001), and privacy risk (t = 5.117, p < .001) showed significant positive effects, and security risk (t=-7.055, p < .001) showed a significant negative effect. When we set the sensitivity of behavioral and physical information as control variables for the intention to provide behavioral and physical information, the sensitivity of physical information (t=-4.741, p < .001) was a significant controlling factor explaining the intention to provide physical information. Perceived value showed significant effects on intention to provide sports behavioral information (t = 28.598, p < .001) and intention to provide sports physical information (t = 31.310, p Perceived value 0.728 0.02 37.229*** 0.847 Selected Perceived public benefit -> Perceived value 0.305 0.016 19.557*** 0.346 Selected Privacy risk -> Perceived value 0.079 0.015 5.117*** 0.085 Selected Security risk -> Perceived value -0.102 0.014 -7.055*** -0.119 Selected Perceived value -> Intention to provide behavioral information 0.816 0.029 28.598*** 0.789 Selected Perceived value -> Intention to provide physical information 0.89 0.028 31.310*** 0.830 Selected * denotes p < .05; **, p < .01; and ***, p < .001. (Insert Table 6 and Fig. 1 here.) Moderation effects of data control competence and personal information policy We performed an additional analysis to test the moderation effects of personal competence for MSD on the paths from perceived benefit and risk to perceived value. As shown in Table 7 , the significance level for the restricted model was 1.000. Because this result did not satisfy the criterion of p < .05, the moderation effects of MSD control competence and personal information policy were used. Table 7 Comparison of models. Model df CMIN p NFI Delta-1 IFI Delta-2 RFI rho-1 TLI rho-2 Restricted model 6 .000 1.000 .000 .000 − .003 − .003 (Insert Table 7 here.) We investigated how perceived personal benefit, perceived public benefit, privacy risk, and security risk affected perceived value when MSD personal competence and MSD personal information policy were inserted as moderators. When data control competence was inserted as a moderator, perceived personal benefit (t = 36.875, p < .001), perceived public benefit (t = 19.619, p < .001), and privacy risk (t = 4.959, p < .001) showed significant positive effects, and security risk (t=-7.578, p < .001) showed a significant negative effect (Table 8 ). Table 8 Validation of moderation effects analysis. Moderation effect Path Estimate S.E. C.R. Standardized (β) Selection Data control competence Perceived personal benefit -> Perceived value 0.712 0.019 36.875*** 0.846 Selected Perceived public benefit -> perceived value 0.300 0.015 19.619*** 0.348 Selected Privacy risk -> Perceived value 0.070 0.014 4.959*** 0.082 Selected Security risk -> Perceived value -0.105 0.014 -7.578*** -0.128 Selected Personal information policy Perceived personal benefit -> Perceived value 0.712 0.014 52.149*** 0.846 Selected Perceived public benefit -> perceived value 0.300 0.011 27.745*** 0.348 Selected Privacy risk -> Perceived value 0.07 0.010 7.013*** 0.082 Selected Security risk -> Perceived value − .105 0.010 -10.716*** -0.128 Selected * denotes p < .05; **, p < .01; and ***, p < .001. (Insert Table 8 here.) As shown in Fig. 2 , when MSD personal information policy was inserted as a moderator, perceived personal benefit (t = 52.149, p < .001), perceived public benefit (t = 27.745, p < .001), and privacy risk (t = 7.013, p < .001) showed significant positive effects, and security risk (t=-10.716, p < .001) showed a significant negative effect. (Insert Fig. 2 here.) Discussion, limitations, and future directions Discussion This study aimed to understand the intention to provide MSD based on Laufer and Wolfe’s ( 1977 ) privacy calculus model. Specifically, we analyzed the effects of perceived benefit and risk on perceived value. Further, we analyzed the effects of perceived benefit and risk on intention to provide information when perceived data control competence and personal information policy were included as moderators. The findings are discussed in detail below. First, for this study’s participants, the perceived personal and public benefits of MSD positively affected perceived value. This indicates that participants felt that providing their own sports data could positively affect them financially, in terms of health, or societally by improving the quality of data services. When the moderation effects of data control competence and personal information policy were included in the model, both perceived personal and public benefits showed significant effects. This suggests that knowing how one’s personal information will be managed is essential. Through accurate awareness of the relevant policies of the organization using one’s sports data, one can explore the most beneficial approach for oneself. The perceived personal or public benefits of MSD showed similar trends in previous studies. Wicks et al. ( 2012 ) reported that information sharing between people who had experienced similar symptoms increased individuals’ awareness of their own health condition and could promote more active participation in health management through the learning of effective treatment methods. Additionally, Burstein et al. ( 2005 ) reported that through easier online access, health information has become indispensable in aiding decision-making and personal disease management. Previous studies also showed data control competency and personal information policy-related factors as crucial variables. Lim et al. ( 2018 ) investigated trends in a virtual data leak scenario in a Korean sample to quantitatively analyze the value of personal information. They found that people typically assigned high value to information that could cause immediate and actual damage when leaked; examples are personal information, purchase history, and transaction details. When sharing personal information, one must be cautious about the protection of personal information. Specifically, leakage of personal information can lead to personal and social risk; hence, policies and regulations related to the protection of personal information must be reinforced. This means that when providing sports data, the strength of regulations should differ depending on the level of personal information being provided. FasterCapital ( 2024 ) reported that when done indiscriminately, the use of personal information in the sports industry could cause behavior, preference, or choice manipulation, as well as loss of opportunities or benefits for the data subject. To alleviate the risks associated with the use of data, both countries and companies will have to protect ethical and legal principles such as protection, consent, transparency, and responsibility to guarantee responsible and respectful use of data in sports. According to Zhao and Li ( 2024 ), the implementation of more efficient and friendly data protection technologies and privacy protection strategies could increase user trust and ensure sustainable information security. Second, privacy and security risks also had significant effects on perceived value, with security risks having a negative effect. Specifically, privacy risk and security risk showed opposite effects. This may be because even though sharing sports data could expose one’s private life, the sharing of such data with unspecified masses poses no direct harm, unlike in the case of financial data, where direct repercussions are likely. In other words, although personal information could be leaked, the economic or physical benefits of sharing data is deemed to be greater than the risk. Koreans show meager awareness of personal information protection, as evidenced by the (approximately) 11,000 victims of voice phishing from the leakage of personal information in 2023 (The Financial Supervisory Service, 2024 ). NordVPN ( 2023 ) conducted the “National Privacy Test” on 175 countries, and Koreans recorded an average score of 46 out of 100 points, showing the lowest awareness of online security and personal information protection worldwide. Notably, 6% of the participants knew almost nothing about personal information protection and cybersecurity. These findings align with our study, wherein participants believed that even if they shared their sports information, it would not be leaked. This suggests that in addition to measures to protect Korean individuals’ MSD personal information, education regarding data leaks is crucial. When we analyzed a model that included the moderation effects of data control competence and personal information policy, privacy and security risks showed significant effects. Specifically, while security risk showed a negative effect, privacy risk showed a positive effect. This indicates that although participants were aware that their sports data could be exposed, they believed that their personal information would be protected, and they could take action using their own personal methods, not suffer economic or social injury, and manage their data themselves. Udesky et al. ( 2020 ) reported that individuals often do not feel that they control their data in online environments, which suggests that they may overlook or ignore the risks associated with sharing their data. This lack of awareness underscores the importance of protecting personal information. Therefore, measures must be developed to help individuals understand the risks of sharing data and improve their ability to manage their data. The development of effective personal information policies and data protection methods for sports data is crucial to ensure an environment where individuals can safely manage their own sports data. Third, the perceived value of the intention to provide MSD showed significant positive effects for the intention to provide behavioral data and the intention to provide physical data. This indicates that although data provision may have evoked anxiety in individuals, they still intended to share their behavioral and physical data because they believed that the benefits to themselves would be sufficiently large. This may be because of the cost–benefit paradigm, which originates from behavioral decision theory (Johnson and Payne, 1985 ). The core of the cost–benefit paradigm is that all kinds of decisions can be explained by the required effort, outcomes, and one’s perceived value of the outcomes associated with one’s choices and strategies (Davis, 1989 ; Mclean et al., 2018 ). In other words, although the participants in this study felt some anxiety about sharing their sports data, the perceived value was even greater; hence, they remained in favor of providing their data. Turel et al. ( 2010 ) and Wang ( 2014 ) also reported that individuals showed a positive shift in attitude when they believed that sharing their data online would be of personal benefit. This crucial factor determined their behavioral intention toward sharing. Ko et al. ( 2009 ) also reported that perceived value positively affected behavioral intention in a study using mobile phones, and Groß ( 2018 ) reported that beyond perceived value, loyalty could also increase. By contrast, Benson et al. ( 2015 ) reported that as control over personal information increased on social networking sites, the provision of personal information decreased. In other words, when individuals have a better understanding of their personal information or greater certainty that their personal information is protected, they may be more likely to share it. This demonstrates the importance of environments and policies that protect individuals’ personal information. Education regarding the protection of personal information must be provided, and effective policies to protect personal information must be implemented. Anshori et al. ( 2022 ) reported that during electronic transactions, when people felt more strongly about the insufficient protection of their personal information because of inadequate security, their trust in that specific and other platforms from the same company decreased, leading to a decrease in their intention to share personal information. Therefore, by securely protecting people’s personal information and providing evident positive benefits to data subjects, the sharing of sports data by people who want to receive these benefits would be encouraged. In turn, the overall advancement of sports can be improved by protecting data subjects’ personal information and providing more personalized sports solutions. Limitations and future directions This study has some limitations. First, the study used existing “My Data” for the financial and public health sectors. The MSD project, which adopts new academic and social perspectives, must be differentiated from existing health or healthcare data paradigms. Currently, the classification systems used in sports data policies and research are based on past content. Therefore, future MSD studies must use more diverse sources. Second, this was a cross-sectional study with Korean participants. This study did not consider various factors such as ethnicity, environment, region, or culture. The personal information data industry in South Korea is growing, with continual projects aimed at using personal data. Further, many companies also use “My Data” to provide more value and convenience to their customers. If more factors related to sports data are considered, the construction of data for MSD, which represents various goals, interests, and preferences in the new technological age, would have significant practical implications for the sports industry. Declarations Author Contribution Y-JK: Methodology, Validation, Writing—review & editing. E-SK: Writing—review & editing, Conceptualization, Formal analysis, Methodology, Validation, Writing—original draft, Writing—review & editing, Investigation. References Ajzen I, Fishbein M (1973) Attitudinal and normative variables as predictors of specific behavior. 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J Retailing 79(3):183–198. https://doi.org/10.1016/S0022-4359(03)00034-4 Yu L, Li H, He W et al (2020) A meta-analysis to explore privacy cognition and information disclosure of internet users. Int J Inf Manag 51(1):102015. https://doi.org/10.1016/j.ijinfomgt.2019.09.011 Zhao R, Li L (2024) Does digitalization always benefit cultural, sports, and tourism enterprises quality? Unveiling the inverted U-shaped relationship from a resource and capability perspective. Humanit Soc Sci Commun 11:1066. https://doi.org/10.1057/s41599-024-03545-w Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5237195","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":369322736,"identity":"4501bb4f-b9e6-4ab3-9ae4-d77abc057227","order_by":0,"name":"Young-Jae Kim","email":"","orcid":"","institution":"Chung-Ang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Young-Jae","middleName":"","lastName":"Kim","suffix":""},{"id":369322737,"identity":"6e4617dc-fd22-44b2-83e3-c040e6f95180","order_by":1,"name":"E-Sack Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDCC4wwMzAwHGBj4iddyGKpFsoFkLQYHiNXBd5j54eOCM7V2m283P5P4uIdBnl+MgGbJw2zGxjNuHE/edueYmeSMZwyGM2cn4NdicJjBTJrnw7FksxsJxsY8BxgSDG4T1ML+/TdIi/GM9M/Gf4jTwmPGzHOjxs5AIsfwMQMxWiQP8xRL85w5kCBxI6fwYc8BCcJ+4TvevvEzz7E6e/4Z6RsO/DhgI88vTUALFBxObIAwJIhSDgJ19kQrHQWjYBSMgpEHABDHSZ8KuRiyAAAAAElFTkSuQmCC","orcid":"","institution":"Chung-Ang University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"E-Sack","middleName":"","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2024-10-10 07:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5237195/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5237195/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67361605,"identity":"69bda0c5-89f2-4b14-8225-8c59437d5a38","added_by":"auto","created_at":"2024-10-24 06:19:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48108,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model: main validation.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5237195/v1/f0a1d6aaa6f3dd2ee9b05d3e.png"},{"id":67361606,"identity":"9e211457-059b-42ee-92da-9b7ebb37ed98","added_by":"auto","created_at":"2024-10-24 06:19:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65864,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model: testing moderation effects.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5237195/v1/12b57f12540321372b3bae38.png"},{"id":70449876,"identity":"65c2a0fa-2752-484d-b00a-cf3f20c17d76","added_by":"auto","created_at":"2024-12-03 09:33:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1065435,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5237195/v1/ac5898ed-a587-423a-8ff0-408b9b36db6b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\"My Sports Data”: A Privacy Calculus Model Analysis with Mediation Effects of Personal Competence and Perceived Value","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTechnological capability in the era of hyperconnectivity and hyper-convergence, the popularization of mobile devices, and advances in information and communications technology has led to a massive collection of personal data online (Shajilin Loret and Arul, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Policies on the use of these data in new industries are being formulated to generate value (Ciuriak, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). One such concept is the \u0026ldquo;My Data\u0026rdquo; project, wherein the data subject retains sovereignty and authority over their own data (Langford et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Within this human-centric data ecosystem, the data subjects actively manage and control their own data, employ their data in credit, asset, and health management, and exercise sovereignty over data that are challenging to control themselves such as healthcare, communications, and financial data (Poikola et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe South Korean government aims to strengthen the \u0026ldquo;My Data\u0026rdquo; industry through tangible stimulation for start-ups in novel, data service-related sectors (Jo, 2023). Regarding related industries, the demand for personalized sports services, which are closely related to health data, is increasing (Korea Professional Sports Association, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Korean sports data services primarily partake in supply system construction (Ministry of Culture, Sports and Tourism, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Personal information data utilization in the sports industry can be indiscriminately used by data parties such as behavior, preferences, manipulation of choices, opportunities, and deprivation of benefits. Ethical and legal principles such as protection, consent, transparency, and accountability that can ensure responsible and respectful use of data in sports should be upheld not only by the state but also by businesses (FasterCapital, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, sports data in South Korea have predominantly been used for analysis of athletes\u0026rsquo; records, salary negotiations, and sports betting, which do not reflect the full potential of such data (Hwang, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The use of sports data in South Korea is gradually gaining popularity; however, the field of sports science, local governments, and the central government have not responded appropriately (Jo, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hence, a personalized sports data service industry that is based on information about people\u0026rsquo;s needs and wants is needed.\u003c/p\u003e \u003cp\u003eSince 2019, South Korea has consistently implemented \u0026ldquo;My Data\u0026rdquo; projects focused on healthcare, finance, energy, distribution, transport, small business owners, welfare, lifestyle, and academics. However, sports data projects have lagged behind other sectors because of the commonly held perspective that sports are not essential to our daily lives (Noh, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, compared with other fields, the sports, culture, and tourism industries lack a personalized foundation supported by digital technology, with the quality of personalized digital-related products also low (Zhao and Li, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, it is vital to initiate various discussions regarding the construction of the \u0026ldquo;My Sports Data\u0026rdquo; (MSD) platform in the hope of improving the population\u0026rsquo;s health and quality of life.\u003c/p\u003e \u003cp\u003eBig data are becoming central to our economies, and people\u0026rsquo;s awareness of social problems related to the protection and safety of personal information is growing. With data in diverse sectors being collected indiscriminately, the threat of infringing upon entities\u0026rsquo; rights to information sovereignty is increasing, while measures to improve information sovereignty to protect rights that are infringed upon are severely lacking (Lee, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExisting data industry policies are docile regarding the protection and guarantee of data subjects\u0026rsquo; rights. As such, the rights to autonomy and decision-making granted to data subjects who generate and provide data must be discussed, and related policies must be established (Jang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In particular, the study by Mittal et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) indicated a need for ways to measure the risk of personal information protection, as quantitative methods for confirming personal information protection, regulatory compliance, and fairness are lacking.\u003c/p\u003e \u003cp\u003eIn 2022, 1,802 data leaks occurred in the United States, exposing the personal information of 422\u0026nbsp;million people. This led Americans to carefully consider how everyone, including individuals, companies, and the government, uses and protects their personal information (Drenik, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such issues may also arise in the sports data industry. To address these concerns, studies must examine the right to authority over one\u0026rsquo;s personal sports data in order to protect sports-related personal information.\u003c/p\u003e \u003cp\u003ePersonal information can be broadly categorized as identifying or non-identifying. Identifying personal information comprises specific data that can be used to single out a person, such as a resident registration number, name, or address. This direct link to privacy concerns raises persistent issues about personal information. By contrast, non-identifying information, while not giving third parties the ability to identify an individual solely with such information (e.g., a user\u0026rsquo;s tendencies, behaviors, and location history), could predict an individual\u0026rsquo;s interests and preferences. Further, the sharing and combination of this information can, to some extent, be used to identify individuals. This possibility for non-identifying information to predict individual preferences has made it a subject of intense debate regarding potential privacy concerns (Ioannou et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePreviously, issues related to private information were investigated using Laufer and Wolfe\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) privacy calculus model. This model uses the calculus of behavior to make decisions regarding privacy by weighing potential costs and benefits in each situation for individuals, which is useful for personal information management (Laufer and Wolfe, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). This theoretical model provides personal information based on potential risks and benefits (Campbell and Carlson, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The underlying privacy theory is an adaptation of social exchange theory, which holds that the potential risks and rewards regarding others may be identified, which allows selecting the interactions that lead to greater rewards (Homans, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1961\u003c/span\u003e). This is combined with expectancy theory (Vroom, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1964\u003c/span\u003e), which states that people try to maximize positive results and minimize negative results (Laufer and Wolfe, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1977\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApplying individuals\u0026rsquo; sports-related demands can spur further growth for the sports data industry. However, in industries such as MSD, individuals\u0026rsquo; intention to provide data must first be examined given the importance of permissions and authority over personal information. Furthermore, such intention could differ depending on personal competence, and the perceived value may also vary because of the moderation effect of personal competence. Currently, various information technology industries have suggested methods to cope with data provision by examining the risks of providing individual pieces of data. However, even though sports data are becoming vital, limited studies have examined the related risks and benefits. To bridge this literature gap, this study used the privacy calculus model of Laufer and Wolfe (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) to investigate the intentionality of data provision based on the risks and benefits of providing individual pieces of sports data, thereby generating fundamental data for the MSD industry in South Korea.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study used Laufer and Wolfe\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) privacy calculus model to analyze the intention to provide MSD personal information, with individual competence or perceived value as mediators.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe participants were South Korean adults aged 20\u0026ndash;65 years who completed an online questionnaire administered from March 13 to 22, 2024 via the South Korean professional survey company \u0026ldquo;Embrain.\u0026rdquo; Participants were recruited using a non-stochastic convenience sampling method, and survey responses were collected from 1,000 people. This study was approved by the institutional review board at Chung-Ang University (1041078-20231205-HR-321), and the survey was performed following the principles of the 1975 Declaration of Helsinki. Consent was sought from all potential survey participants before conducting the survey, and only those who consented were included in the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch instruments\u003c/h3\u003e\n\u003cp\u003eThe questionnaires used in this study comprised factors used in the conventional privacy calculus model, namely, intention to provide personal information, perceived data control competence, and personal information policy. Each item on the instruments was subjected to content validation by an eight-person panel consisting of four sports sociology experts, three leisure or recreation experts, and one statistics expert. All items were adapted and supplemented to suit the study aims.\u003c/p\u003e\n\u003ch3\u003ePerceived benefit\u003c/h3\u003e\n\u003cp\u003eTo measure perceived benefit, this study adapted the items used in Cheng et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Tingchi Liu et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) to suit our objectives. The instrument comprised two factors: perceived personal benefits (four items) and perceived public benefits (four items). Each item was scored on a 7-point Likert scale ranging from 1 (very strongly disagree) to 7 (very strongly agree). Cronbach\u0026rsquo;s ⍺ was .943 and .954 for perceived personal and public benefit, respectively, indicating good reliability of the instrument.\u003c/p\u003e\n\u003ch3\u003ePerceived risk\u003c/h3\u003e\n\u003cp\u003ePerceived risk was divided into privacy risk (three items) and security risk (three items). For these items, we adapted the e-Tail quality (eTailQ: etail) scale developed by Wolfinbarger and Gilly (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) to measure perceived risk regarding personal information protection and security online, and used in a study by Nepomuceno et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Cronbach\u0026rsquo;s ⍺ was .911 and .900 for privacy and security risk, respectively, demonstrating high reliability of the instrument.\u003c/p\u003e\n\u003ch3\u003ePerceived value\u003c/h3\u003e\n\u003cp\u003eTo measure perceived value, this study used three items from a scale used for overall user evaluation of the costs and benefits of using a product or service in studies by Sirdeshmukh et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), Dinev and Hart (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and Kim et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Cronbach\u0026rsquo;s ⍺ was .950 for perceived value, indicating excellent reliability of the instrument.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIntention to provide personal information\u003c/h2\u003e \u003cp\u003eTo measure the intention to provide personal information, this study employed factors proposed by Ajzen and Fishbein (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1973\u003c/span\u003e) and used by Yu et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to ascertain the intention to make plans to participate in certain behaviors. This was divided into two categories: intention to provide sports behavioral information (four items) and intention to provide sports physical information (four items). Responses were scored on a 7-point Likert scale ranging from 1 (very strongly disagree) to 7 (very strongly agree). Cronbach\u0026rsquo;s ⍺ was .961 and .967 for the intention to provide sports behavioral and physical information, respectively, indicating that the instrument is reliable.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePerceived data control competence and personal information policy\u003c/h3\u003e\n\u003cp\u003ePerceived data control competence and personal information policy were divided into two factors: perceived data control competence (four items) and perceived personal information policy (four items). For the perceived personal information policy factors, we selected the items from a study by Cranor et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) on Internet user attitudes toward online personal information protection, a study by Milne and Boza (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) on database marketing and related personal information protection issues, and a study by Sheehan and Hoy (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) on situational dimensions of personal information protection issues. We adapted and supplemented the items to suit our study objectives. Responses were scored on a 7-point Likert scale ranging from 1 (very strongly disagree) to 7 (very strongly agree). Cronbach\u0026rsquo;s ⍺ was .833 and .888 for perceived data control competence and perceived personal information policy, respectively, indicating good reliability of the instrument.\u003c/p\u003e\n\u003ch3\u003eData sensitivity\u003c/h3\u003e\n\u003cp\u003eTo measure data sensitivity, we adapted the sensitivity of information and willingness to share information used in the study by Ioannou et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), using the factors of sensitivity of behavioral information (three items) and sensitivity of physical information (three items). Cronbach\u0026rsquo;s ⍺ was .947 and .922 for privacy and security risks, respectively, indicating good reliability of the instrument.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis methods\u003c/h2\u003e \u003cp\u003eAfter coding and cleaning the data, we used SPSS 28.0 and AMOS 28.0 to perform the following analyses. First, we used frequency analysis to investigate participants\u0026rsquo; demographic characteristics and descriptive statistics to verify the normal distribution of the perceived benefit, perceived risk, perceived value, intent to provide personal information, perceived data control competence and personal information policy, and sensitivity of data scales. Skewness and kurtosis were calculated according to the method of West et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; skewness\u0026thinsp;\u0026lt;\u0026thinsp;3, kurtosis\u0026thinsp;\u0026lt;\u0026thinsp;8). The measured skewness and kurtosis values ranged from \u0026minus;\u0026thinsp;.598 to .225 and from \u0026minus;\u0026thinsp;.340 to .246, respectively, demonstrating normal distributions. Second, among the factors used in this study, we performed item parceling for perceived benefit, perceived risk, perceived value, intention to provide personal information, perceived data control competence and personal information policy, and data sensitivity. Third, we calculated Cronbach\u0026rsquo;s ɑ to ensure reliability of the research instruments and performed confirmatory factor analysis to test their validity. Fourth, we performed correlation analysis and calculated the average variance extracted (AVE) to investigate the correlations between factors and discriminant validity. Fifth, based on Laufer and Wolfe\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) privacy calculus model, we performed bootstrapping resampling (2,000 iterations), and using sensitivity of behavioral information and sensitivity of physical information as control variables, we performed structural equation modeling of the relationships among intention to share sports behavioral information, intention to share sports physical information, perceived value, perceived personal benefit, perceived public benefit, privacy risk, security risk, perceived data control competence, perceived personal information policy, sensitivity of behavioral information, and sensitivity of physical information.\u003c/p\u003e \u003cp\u003eSubsequently, to verify the moderation effects of personal competence on the relationships of benefit and risk with perceived value, we classified participants according to perceived data control competence and personal information policy. Finally, we set up restricted and unrestricted models to test the moderators\u0026rsquo; effect on the relationships between latent factors (Lee and Lim, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). We derived the X\u003csup\u003e2\u003c/sup\u003e statistic for each model to determine the final moderation effect.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study used Laufer and Wolfe\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) privacy calculus model to derive factors affecting the intention to provide MSD among South Koreans and investigate the benefits perceived by information providers. The following subsections describe the results.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDemographic characteristics\u003c/h2\u003e \u003cp\u003eThe participants\u0026rsquo; demographic characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were 511 male (51.1%) and 489 female (48.9%) participants with mean age of 44.0 years (standard deviation [SD]:12.67). Most were married (601 persons, 60.1%); others were unmarried (385, 38.5%) or bereaved or divorced (14, 1.4%). Occupation-wise, office workers (545, 54.5%) ranked first, followed by homemakers (151, 15.1%), others (90, 9.0%), self-employed individuals (83, 8.3%), students (75, 7.5%), and civil servants (56, 5.6%). Educational attainment was primarily college graduate (569, 56.9%). Mean monthly income was typically 3.01\u0026ndash;5.00\u0026nbsp;million KRW (311, 31.1%), 2.01\u0026ndash;3.00\u0026nbsp;million KRW (248, 24.8%), or \u0026ge;\u0026thinsp;5.01\u0026nbsp;million KRW (218, 21.8%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of participants (n\u0026thinsp;=\u0026thinsp;1,000).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAge, M (SD)\u003c/p\u003e \u003cp\u003e44.04 (12.674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026ndash;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBereaved/divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eJob\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOffice worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCivil servant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomemaker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEducational attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMean monthly income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le; ₩1,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₩1,010,000\u0026ndash;₩2,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₩2,010,000\u0026ndash;₩3,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₩3,010,000\u0026ndash;₩5,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge; ₩5,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSports participation type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFriends/colleagues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSports participation duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;4 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;6 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;7 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMy Data experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot sure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eExperience of providing physical activity data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot sure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding types of sports participation, the most frequent response was \u0026ldquo;alone\u0026rdquo; (533, 53.3%), followed by \u0026ldquo;family\u0026rdquo; (237, 23.7%), \u0026ldquo;friends or colleagues\u0026rdquo; (193, 19.3%), and \u0026ldquo;clubs\u0026rdquo; (37, 3.7%). Sports participation duration was most frequently 1\u0026minus;2 years (404, 40.4%), followed by 3\u0026minus;4 years (257, 25.7%), \u0026ge; 7 years (228, 22.8%), and 5\u0026minus;6 years (111, 11.1%). A total of 740 persons (74.0%) reported experience with \u0026ldquo;My data\u0026rdquo;; 175 persons (17.5%) reported no experience, and 85 persons (8.5%) were unsure. Overall, 614 persons (61.4%) reported previous experience in providing physical activity-related data; 335 persons (33.5%) reported no such experience, and 51 persons (5.1%) were unsure.\u003c/p\u003e \u003cp\u003e(Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eItem-parceling analysis\u003c/h2\u003e \u003cp\u003eWe used item-parceling analysis because it can alleviate problems with non-normal distributions (Hau and Marsh, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), enable stable, accurate non-parametric estimations (Nasser and Wisenbaker, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and improve model fit (Rogers and Schmitt, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Item parcels are constructed by taking the sum or the mean of scores for two or more individual items and using them as variables in a model or as indices for a factor being measured in a factor model (Kishton and Widaman, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Although the sum or mean score can be used to form item parcels, using the mean score is recommended (Little et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The primary assumption when making item parcels is the unidimensionality of the items. After performing exploratory factor analysis and ranking items according to the absolute factor loading value, item parcels were constructed from three to four items each to ensure each had a similar loading value, and the highest mean score was used (Little et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This is because when the number of items in each parcel differs, the mean and variance can be made similar and can reflect the actual scales, helping to interpret the results.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides the parcel size and item numbers for each parcel used. Perceived personal and public benefits as subfactors of perceived benefits; the intention to provide sports behavioral information and the intention to provide sports physical information as subfactors of the intention to provide sports personal information; perceived data control competence; and personal information policy all consisted of four items, which were organized into two item parcels. Meanwhile, privacy risk and security risk as subfactors of perceived risk; perceived value; and sensitivity of behavioral information and sensitivity of physical information as subfactors of sensitivity of data all consisted of three items organized into one item parcel.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eItem parcel size and item numbers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItem parcel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItem count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem numbers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePerceived benefit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerceived personal benefit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived personal benefit 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived personal benefit 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerceived public benefit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived public benefit 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3, 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived public benefit 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerceived risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivacy risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrivacy risk 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 2, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecurity risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecurity risk 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 2, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePerceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived value 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 2, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIntention to provide sports physical information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIntention to provide sports behavioral information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntention to provide sports behavioral information 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntention to provide sports behavioral information 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3, 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIntention to provide sports physical information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntention to provide sports physical information 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntention to provide sports physical information 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePerceived data control competence and personal information policy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerceived data control competence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived data control competence 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived data control competence 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePersonal information policy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePersonal information policy 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePersonal information policy 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSensitivity of data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity of behavioral information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity of behavioral information 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 2, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity of physical information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity of physical information 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 2, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eModel fit and exploratory factor analysis\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the fit indices for the initial research model were as follows: chi-square (X\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;1010.805, df\u0026thinsp;=\u0026thinsp;269, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, goodness-of-fit index (GFI)\u0026thinsp;=\u0026thinsp;0.922, RMR\u0026thinsp;=\u0026thinsp;0.057, root mean squared error of approximation (RMSEA)\u0026thinsp;=\u0026thinsp;0.053, incremental fit index (IFI)\u0026thinsp;=\u0026thinsp;0.977, Tucker-Lewis index (TLI)\u0026thinsp;=\u0026thinsp;0.969, and comparative fit index (CFI)\u0026thinsp;=\u0026thinsp;0.977, showing a suitable fit overall. When we investigated the construct reliability, composite reliability, and convergent validity of the measurement model, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the construct reliability values were \u0026gt;\u0026thinsp;7, demonstrating suitable reliability (Nunnally and Bernstein, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The AVE values were \u0026gt;\u0026thinsp;.05, demonstrating the validity of the model, and the AVE values were \u0026gt;\u0026thinsp;.8, showing convergent validity (Fornell and Larcker, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel fit.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFit index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAbsolute fit indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMIN / Df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAGFI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1010.805 / 269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriterion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFit index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eIncremental fit indices\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriterion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfirmatory factor analysis results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem parcel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConstruct reliability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention to provide sports behavioral information 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention to provide sports behavioral information 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.682***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention to provide sports physical information 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention to provide sports physical information 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.940***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived value 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.425***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived value 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived value 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.99***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived personal benefit 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived personal benefit 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived public benefit 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived public benefit 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.27***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivacy risk 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.385***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivacy risk 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivacy risk 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.149***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecurity risk 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.940***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecurity risk 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecurity risk 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.233***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData control competence 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.940***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData control competence 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal information policy 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.216***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal information policy 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity of behavioral information 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.503***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity of behavioral information 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.489***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity of behavioral information 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity of physical information 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.771***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity of physical information 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.302***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity of physical information 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* denotes \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05; **, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01; and ***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis and discriminant validity\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides the results of the correlation analysis between the main factors and discriminant validity. The square roots of the AVE values were typically higher than the correlation coefficients between related variables (Fornell and Larcker, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), showing the discriminant validity of our study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant validity analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"25\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c24\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c25\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIntention to provide behavioral information (ρ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention to provide physical information (ρ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.922*** (.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c25\" namest=\"c23\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePerceived value (ρ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.207 (.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.836*** (.699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePerceived personal benefit (ρ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.805* (.648)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.829** (.687)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e.939*** (.882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePerceived public benefit (ρ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.789* (.623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.807** (.651)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e.849* (.721)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e.871*** (.759)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrivacy risk (ρ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.791* (.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-141** (.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.109* (.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.119** (.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.076*** (.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSecurity risk (ρ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-105* (.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.259** (.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.232* (.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.208* (.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.182** (.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e.822*** (.676)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eData control competence (ρ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.232* (.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.318* (.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e.317** (.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e.357* (.127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e.331* (.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e.087* (.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e.061*** (.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePersonal information policy (ρ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.323* (.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.400* (.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e.441* (.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e.453* (.205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e.434* (.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e.0112* (.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e.037 (.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e.761* (.579)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSensitivity of behavioral information (ρ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.415* (.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.203* (.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.159** (.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.147* (.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.144** (.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e.527** (.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e.590** (.348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e.220* (.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e.228** (.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSensitivity of physical information (ρ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-225*** (.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.249* (.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.191* (.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.187* (.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.189** (.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e.495* (.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e.581** (.338)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e.183** (.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e.183* (.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e.840* (.706)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"25\"\u003e* denotes \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05; **, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01; and ***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Insert Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e here.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStructural equation modeling results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the results of validating our research model using Analysis of Moment Structures (AMOS). First, in terms of how perceived benefit and perceived risk affected perceived value, perceived personal benefit (t\u0026thinsp;=\u0026thinsp;37.229, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), perceived public benefit (t\u0026thinsp;=\u0026thinsp;19.557, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and privacy risk (t\u0026thinsp;=\u0026thinsp;5.117, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) showed significant positive effects, and security risk (t=-7.055, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) showed a significant negative effect. When we set the sensitivity of behavioral and physical information as control variables for the intention to provide behavioral and physical information, the sensitivity of physical information (t=-4.741, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) was a significant controlling factor explaining the intention to provide physical information. Perceived value showed significant effects on intention to provide sports behavioral information (t\u0026thinsp;=\u0026thinsp;28.598, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and intention to provide sports physical information (t\u0026thinsp;=\u0026thinsp;31.310, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation of the structural equation model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSelection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived personal benefit -\u0026gt; Perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.229***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived public benefit -\u0026gt; Perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.557***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivacy risk -\u0026gt; Perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.117***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecurity risk -\u0026gt; Perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.055***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived value -\u0026gt; Intention to provide behavioral information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.598***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived value -\u0026gt; Intention to provide physical information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.310***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* denotes \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05; **, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01; and ***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Insert Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eModeration effects of data control competence and personal information policy\u003c/h2\u003e \u003cp\u003eWe performed an additional analysis to test the moderation effects of personal competence for MSD on the paths from perceived benefit and risk to perceived value. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the significance level for the restricted model was 1.000. Because this result did not satisfy the criterion of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, the moderation effects of MSD control competence and personal information policy were used.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCMIN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNFI Delta-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIFI Delta-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRFI rho-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTLI rho-2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRestricted model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Insert Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e here.)\u003c/p\u003e \u003cp\u003eWe investigated how perceived personal benefit, perceived public benefit, privacy risk, and security risk affected perceived value when MSD personal competence and MSD personal information policy were inserted as moderators. When data control competence was inserted as a moderator, perceived personal benefit (t\u0026thinsp;=\u0026thinsp;36.875, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), perceived public benefit (t\u0026thinsp;=\u0026thinsp;19.619, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and privacy risk (t\u0026thinsp;=\u0026thinsp;4.959, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) showed significant positive effects, and security risk (t=-7.578, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) showed a significant negative effect (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation of moderation effects analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModeration effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandardized (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eData control competence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived personal benefit -\u0026gt; Perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.875***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived public benefit -\u0026gt; perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.619***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivacy risk -\u0026gt; Perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.959***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecurity risk -\u0026gt; Perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.578***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePersonal information policy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived personal benefit -\u0026gt; Perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.149***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived public benefit -\u0026gt; perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.745***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivacy risk -\u0026gt; Perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.013***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecurity risk -\u0026gt; Perceived value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.716***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* denotes \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05; **, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01; and ***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Insert Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e here.)\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, when MSD personal information policy was inserted as a moderator, perceived personal benefit (t\u0026thinsp;=\u0026thinsp;52.149, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), perceived public benefit (t\u0026thinsp;=\u0026thinsp;27.745, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and privacy risk (t\u0026thinsp;=\u0026thinsp;7.013, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) showed significant positive effects, and security risk (t=-10.716, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) showed a significant negative effect.\u003c/p\u003e \u003cp\u003e(Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here.)\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion, limitations, and future directions","content":"\u003ch2\u003eDiscussion\u003c/h2\u003e\u003cp\u003eThis study aimed to understand the intention to provide MSD based on Laufer and Wolfe\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) privacy calculus model. Specifically, we analyzed the effects of perceived benefit and risk on perceived value. Further, we analyzed the effects of perceived benefit and risk on intention to provide information when perceived data control competence and personal information policy were included as moderators. The findings are discussed in detail below.\u003c/p\u003e \u003cp\u003eFirst, for this study\u0026rsquo;s participants, the perceived personal and public benefits of MSD positively affected perceived value. This indicates that participants felt that providing their own sports data could positively affect them financially, in terms of health, or societally by improving the quality of data services. When the moderation effects of data control competence and personal information policy were included in the model, both perceived personal and public benefits showed significant effects. This suggests that knowing how one\u0026rsquo;s personal information will be managed is essential. Through accurate awareness of the relevant policies of the organization using one\u0026rsquo;s sports data, one can explore the most beneficial approach for oneself.\u003c/p\u003e \u003cp\u003eThe perceived personal or public benefits of MSD showed similar trends in previous studies. Wicks et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported that information sharing between people who had experienced similar symptoms increased individuals\u0026rsquo; awareness of their own health condition and could promote more active participation in health management through the learning of effective treatment methods. Additionally, Burstein et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) reported that through easier online access, health information has become indispensable in aiding decision-making and personal disease management. Previous studies also showed data control competency and personal information policy-related factors as crucial variables. Lim et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) investigated trends in a virtual data leak scenario in a Korean sample to quantitatively analyze the value of personal information. They found that people typically assigned high value to information that could cause immediate and actual damage when leaked; examples are personal information, purchase history, and transaction details. When sharing personal information, one must be cautious about the protection of personal information. Specifically, leakage of personal information can lead to personal and social risk; hence, policies and regulations related to the protection of personal information must be reinforced. This means that when providing sports data, the strength of regulations should differ depending on the level of personal information being provided. FasterCapital (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that when done indiscriminately, the use of personal information in the sports industry could cause behavior, preference, or choice manipulation, as well as loss of opportunities or benefits for the data subject. To alleviate the risks associated with the use of data, both countries and companies will have to protect ethical and legal principles such as protection, consent, transparency, and responsibility to guarantee responsible and respectful use of data in sports. According to Zhao and Li (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the implementation of more efficient and friendly data protection technologies and privacy protection strategies could increase user trust and ensure sustainable information security.\u003c/p\u003e \u003cp\u003eSecond, privacy and security risks also had significant effects on perceived value, with security risks having a negative effect. Specifically, privacy risk and security risk showed opposite effects. This may be because even though sharing sports data could expose one\u0026rsquo;s private life, the sharing of such data with unspecified masses poses no direct harm, unlike in the case of financial data, where direct repercussions are likely. In other words, although personal information could be leaked, the economic or physical benefits of sharing data is deemed to be greater than the risk.\u003c/p\u003e \u003cp\u003eKoreans show meager awareness of personal information protection, as evidenced by the (approximately) 11,000 victims of voice phishing from the leakage of personal information in 2023 (The Financial Supervisory Service, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). NordVPN (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) conducted the \u0026ldquo;National Privacy Test\u0026rdquo; on 175 countries, and Koreans recorded an average score of 46 out of 100 points, showing the lowest awareness of online security and personal information protection worldwide. Notably, 6% of the participants knew almost nothing about personal information protection and cybersecurity. These findings align with our study, wherein participants believed that even if they shared their sports information, it would not be leaked. This suggests that in addition to measures to protect Korean individuals\u0026rsquo; MSD personal information, education regarding data leaks is crucial. When we analyzed a model that included the moderation effects of data control competence and personal information policy, privacy and security risks showed significant effects. Specifically, while security risk showed a negative effect, privacy risk showed a positive effect. This indicates that although participants were aware that their sports data could be exposed, they believed that their personal information would be protected, and they could take action using their own personal methods, not suffer economic or social injury, and manage their data themselves.\u003c/p\u003e \u003cp\u003eUdesky et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported that individuals often do not feel that they control their data in online environments, which suggests that they may overlook or ignore the risks associated with sharing their data. This lack of awareness underscores the importance of protecting personal information. Therefore, measures must be developed to help individuals understand the risks of sharing data and improve their ability to manage their data. The development of effective personal information policies and data protection methods for sports data is crucial to ensure an environment where individuals can safely manage their own sports data.\u003c/p\u003e \u003cp\u003eThird, the perceived value of the intention to provide MSD showed significant positive effects for the intention to provide behavioral data and the intention to provide physical data. This indicates that although data provision may have evoked anxiety in individuals, they still intended to share their behavioral and physical data because they believed that the benefits to themselves would be sufficiently large. This may be because of the cost\u0026ndash;benefit paradigm, which originates from behavioral decision theory (Johnson and Payne, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). The core of the cost\u0026ndash;benefit paradigm is that all kinds of decisions can be explained by the required effort, outcomes, and one\u0026rsquo;s perceived value of the outcomes associated with one\u0026rsquo;s choices and strategies (Davis, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Mclean et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In other words, although the participants in this study felt some anxiety about sharing their sports data, the perceived value was even greater; hence, they remained in favor of providing their data. Turel et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Wang (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) also reported that individuals showed a positive shift in attitude when they believed that sharing their data online would be of personal benefit. This crucial factor determined their behavioral intention toward sharing. Ko et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) also reported that perceived value positively affected behavioral intention in a study using mobile phones, and Gro\u0026szlig; (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) reported that beyond perceived value, loyalty could also increase.\u003c/p\u003e \u003cp\u003eBy contrast, Benson et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported that as control over personal information increased on social networking sites, the provision of personal information decreased. In other words, when individuals have a better understanding of their personal information or greater certainty that their personal information is protected, they may be more likely to share it. This demonstrates the importance of environments and policies that protect individuals\u0026rsquo; personal information. Education regarding the protection of personal information must be provided, and effective policies to protect personal information must be implemented. Anshori et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that during electronic transactions, when people felt more strongly about the insufficient protection of their personal information because of inadequate security, their trust in that specific and other platforms from the same company decreased, leading to a decrease in their intention to share personal information. Therefore, by securely protecting people\u0026rsquo;s personal information and providing evident positive benefits to data subjects, the sharing of sports data by people who want to receive these benefits would be encouraged. In turn, the overall advancement of sports can be improved by protecting data subjects\u0026rsquo; personal information and providing more personalized sports solutions.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future directions\u003c/h2\u003e \u003cp\u003eThis study has some limitations. First, the study used existing \u0026ldquo;My Data\u0026rdquo; for the financial and public health sectors. The MSD project, which adopts new academic and social perspectives, must be differentiated from existing health or healthcare data paradigms. Currently, the classification systems used in sports data policies and research are based on past content. Therefore, future MSD studies must use more diverse sources. Second, this was a cross-sectional study with Korean participants. This study did not consider various factors such as ethnicity, environment, region, or culture. The personal information data industry in South Korea is growing, with continual projects aimed at using personal data. Further, many companies also use \u0026ldquo;My Data\u0026rdquo; to provide more value and convenience to their customers. If more factors related to sports data are considered, the construction of data for MSD, which represents various goals, interests, and preferences in the new technological age, would have significant practical implications for the sports industry.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY-JK: Methodology, Validation, Writing\u0026mdash;review \u0026amp; editing. 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Unveiling the inverted U-shaped relationship from a resource and capability perspective. Humanit Soc Sci Commun 11:1066. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1057/s41599-024-03545-w\u003c/span\u003e\u003cspan address=\"10.1057/s41599-024-03545-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"My Sports Data, privacy calculus model, intention to provide, personal competence, perceived value","lastPublishedDoi":"10.21203/rs.3.rs-5237195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5237195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study analyzed individuals’ intention to provide their personal information, specifically the “My Sports Data (MSD),” and explored how personal competence and perceived value influence this intention. A privacy calculus model was applied and descriptive statistics, confirmatory factor analysis, correlation analysis, and structural equation modeling were conducted on a sample of 1,000 South Korean adults aged 20–65 years. The results showed that perceived private and public benefits affected perceived value and perceived privacy and security risks. In addition, perceived value significantly affected the intention to provide behavioral and physical information. These findings indicate that by ensuring the protection of personal information and clearly explaining the positive benefits of sharing sports data, people will be more likely to share their sports data so they could access potential benefits. This, in turn, allows for more personalized sports solutions and improvements in sports.","manuscriptTitle":"\"My Sports Data”: A Privacy Calculus Model Analysis with Mediation Effects of Personal Competence and Perceived Value","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-24 06:19:09","doi":"10.21203/rs.3.rs-5237195/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05af4e98-6511-4d4d-bd13-866c9422a586","owner":[],"postedDate":"October 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39301168,"name":"Social science/Cultural and media studies"},{"id":39301169,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2024-12-03T09:24:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-24 06:19:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5237195","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5237195","identity":"rs-5237195","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}