Predicting acceptance intention of sports wearable smart device based on technology acceptance model and theory of planned behavior : The moderating effects of gender Taejung Kim 1, Ji-myung Jung 2, & Lee, Seung-Lo 3 * 1 Yonsei University, 2 Korea Institute of Sports Science, & 3 Hansei University [Purpose] [Methods] [Results] [Conclusion] Key words:
Fig. 1. Research model
Table 2. Summary of the scales uesd Fig. 2. sports wearable smart device image Table 1. General characteristics of the subjects(n=357) Gender Age Frequency of spotrs activity participation Occupation Demographic n % Male 183 51.3 Female 174 48.7 20s 212 59.4 30s 96 26.9 40s 37 10.4 Over 50s 12 3.4 none 91 25.5 Once a week 82 23.0 Twice a week 61 17.1 3 times a week 63 17.6 4 times a week 25 7.0 5 times a week 18 5.0 over 6 times a week 17 4.8 Business or Sales 20 5.6 Production or Technical 21 5.9 Professional 39 10.9 Office worker 50 14.0 Public officer 15 4.2 Student 177 49.6 Other 35 9.8 Variable Definition of Terms Q PEOU PU A SN PBC AI General characteristics of the subjects a person believes that using a particular system would be free from effort (Davis, 1989) a person believes that using a particular system would enhance his or her job performance(davis, 1989) a person's general feeling of favorableness or unfavorableness for that behavior (Ajzen & Fishbein, 1980) social norm or regulation, and is at times expressed as social influence or social pressure (Ajzen & Fishbein, 1980) a person's perception of the easy or difficult of performing behavior of interest(ajzen, 1985, 1991) individual s will and belief to perform the future behavior(engel & Blackwell, 1982) Gender, Age, Frequency of spotrs activity participation, Occupation, Total 27 4 4 4 4 3 4 4
Table 3. Results of confirmatory factor analysis and reliability test Variable Measures Estimate S.E t CR AVE α 1. Learning to use sports wearable smart device would be easy for me..862(1.000).180 - PE OU 2. My interaction with sports wearable smart device is clear and understandable. 3. It would be easy for me to become skillful at using sports wearable smart device..890(1.083).158 21.992.799(.869).221 18.465.939.795.914 4. I find sports wearable smart device easy to use..858(1.016).191 20.745 1. Using sports wearable smart device can improve my exercise performance..827(1.000).287 - PU 2. Using sports wearable smart device can increase my exercise productivity..908(1.061).148 20.935.921.744.901 3. Using sports wearable smart device can increase my exercise effectiveness..820(.905).249 18.157 A 4. I find using sports wearable smart device useful..787(.852).278 17.128 1. Using sports wearable smart device is a good idea..831(1.000).216-2. Using sports wearable smart device is a wise idea..820(.992).231 18.280 3. Using sports wearable smart device is a like idea..868(1.021).164 19.896 4. Using sports wearable smart device is a pleasant..816(1.005).245 18.138.928.765.901 1. People important to me supported my using sports wearable smart device.844(1.000).211 - SN 2. People who are important to me would think that using sports wearable smart device is a like idea. 3. People who are important to me would think that using sports wearable smart device is a wise idea..881(1.044).165 20.921.876(1.078).184 20.767.929.767.908 4. People who influence my behavior wanted me to use sports wearable smart device instead of any alternative goods..774(.940).309 17.165 PBC AI 1. I am capable of using sports wearable smart device..879(1.000).248-2. Using sports wearable smart device is entirely within my control..772(.908).470 15.654 3. I have the resources, time and opportunity to use sports wearable smart device..793(.928).428 16.053 1. I think that sports wearable smart device is required to exercise..777(1.000).339-2. I will talk about sports wearable smart device positively to others..850(.939).175 16.963 3. If I use sports wearable smart device, I will continue to use it..727(.822).313 14.135 4. I would recommend the sports wearable smart device to others.815(.961).243 16.160.839.635.854.904.702.867
α Table 4. Results of correlation analysis Variable PEOU PU A SN PBC AI PEOU 1 PU A SN PBC AI.258*** (.066).382*** (.146).261*** (.068).249*** (.062).267*** (.071) 1.668*** (.446).431*** (.186).186** (.034).665*** (.442) 1.654*** (.428).402*** (.161).797*** (.635) 1.392*** (.154).645*** (.416) Table 5. Model fit 1.385*** (.148) Model df TLI CFI RMSEA Research model 483.3 219.947.954.058 Table 6. Results of path analysis H Path Estimate S.E t H1 PEOU A.218.044 4.752*** H2 PU A.647.048 11.821*** H3 A AI.659.057 11.349*** H4 SN AI.259.048 5.156*** H5 PBC AI.060.035 1.295 1
Fig. 3. Results of path analysis
Table 7. Comparison of models through invariance test Model χ² df χ² TLI CFI RMSEA Result Male (n=183) Female (n=174) Baseline model Metric invariance model Structural invariance model(1) Structural invariance model(2) Structural invariance model(3) Structural invariance model(4) Path 431.1 219 -.922.932.073 434.7 219 -.909.922.075 865.8 438 -.916.927.052 894.1 455 28.3.917.925.052 894.2 456.1.917.925.052 894.2 457 0.918.926.052 895.2 458 1.918.926.052 Supported model 903.0 459 7.8.917.924.052 Rejected Table 8. Results of final model Regression Weights Standardized Regression Weights Male Female Male Female PEOU A.202***.202***.212.202 PU A.566***.566***.684.593 A AI.746***.489***.774.474 SN AI.137*.432***.144.446 PBC AI.023.023.028.031
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