Predicting acceptance intention of sports wearable smart device based on technology acceptance model and theory of planned behavior : The moderating e

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1 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:

5 Fig. 1. Research model

6 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 Female s s s Over 50s none Once a week Twice a week times a week times a week times a week over 6 times a week Business or Sales Production or Technical Professional Office worker Public officer Student Other 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

7 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) 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) (.869) I find sports wearable smart device easy to use..858(1.016) Using sports wearable smart device can improve my exercise performance..827(1.000) PU 2. Using sports wearable smart device can increase my exercise productivity..908(1.061) Using sports wearable smart device can increase my exercise effectiveness..820(.905) A 4. I find using sports wearable smart device useful..787(.852) Using sports wearable smart device is a good idea..831(1.000) Using sports wearable smart device is a wise idea..820(.992) Using sports wearable smart device is a like idea..868(1.021) Using sports wearable smart device is a pleasant..816(1.005) People important to me supported my using sports wearable smart device.844(1.000) 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) (1.078) People who influence my behavior wanted me to use sports wearable smart device instead of any alternative goods..774(.940) PBC AI 1. I am capable of using sports wearable smart device..879(1.000) Using sports wearable smart device is entirely within my control..772(.908) I have the resources, time and opportunity to use sports wearable smart device..793(.928) I think that sports wearable smart device is required to exercise..777(1.000) I will talk about sports wearable smart device positively to others..850(.939) If I use sports wearable smart device, I will continue to use it..727(.822) I would recommend the sports wearable smart device to others.815(.961)

8 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 Table 6. Results of path analysis H Path Estimate S.E t H1 PEOU A *** H2 PU A *** H3 A AI *** H4 SN AI *** H5 PBC AI

9 Fig. 3. Results of path analysis

10 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 Supported model Rejected Table 8. Results of final model Regression Weights Standardized Regression Weights Male Female Male Female PEOU A.202***.202*** PU A.566***.566*** A AI.746***.489*** SN AI.137*.432*** PBC AI

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