Investigating the Moderating Role of Age and Gender on the Willingness to use IOT Technology in Sports based on the Technology Acceptance Model (TAM)

محورهای موضوعی : Marketing

Alireza NazemiBidgoli ¹ , ehsan Mohamadi Turkmani ² , Hamidreza Irani ³

1 - PhD student, Sports Management, College of Farabi , University Of Tehran, Qom, Iran
2 - Department of Sport Management, Faculty of Sport Sciences and Health, University of Tehran, Tehran, Iran.
3 - Assistant Professor of Business Administration, University of Tehran, Tehran, Iran.

تاریخ دریافت : 1402/07/05 تاریخ پذیرش : 1402/09/26 تاریخ انتشار : 1402/11/15

کلید واژه: Technology acceptance model new technologies, Sports wearables, Athletes, Sports industry,

چکیده مقاله :

This research aimed to investigate the moderating role of age and gender in the willingness to use IOT technology in sports, based on the Technology Acceptance Theory (TAM). This research utilized a survey of the correlation type, with the statistical population consisting of all athletes who have received sports insurance cards and were active this year. Using Morgan's table, a sample of 394 individuals was selected. Standard questionnaires of willingness to use new technologies were employed as tools. Data analysis was conducted using SPSS version 22 and Warp PLS version 8.The research findings indicated that the variables of perceived usefulness, perceived ease of use, and attitude have an impact on athletes' willingness to use IOT technology in sports. Additionally, in the Technology Acceptance Model, perceived ease of use was found to have a significant effect on perceived usefulness. The study also examined the moderating role of age and gender on the willingness to use IOT devices in sports, revealing that only the relationship between age and perceived usefulness did not reach an acceptable level of significance. Therefore, in order to utilize IOT technology in sports, it is crucial to consider these factors as the use of Internet of Things can lead to highly favorable outcomes.

چکیده انگلیسی:

This research aimed to investigate the moderating role of age and gender in the willingness to use IOT technology in sports, based on the Technology Acceptance Theory (TAM). This research utilized a survey of the correlation type, with the statistical population consisting of all athletes who have received sports insurance cards and were active this year. Using Morgan's table, a sample of 394 individuals was selected. Standard questionnaires of willingness to use new technologies were employed as tools. Data analysis was conducted using SPSS version 22 and Warp PLS version 8.The research findings indicated that the variables of perceived usefulness, perceived ease of use, and attitude have an impact on athletes' willingness to use IOT technology in sports. Additionally, in the Technology Acceptance Model, perceived ease of use was found to have a significant effect on perceived usefulness. The study also examined the moderating role of age and gender on the willingness to use IOT devices in sports, revealing that only the relationship between age and perceived usefulness did not reach an acceptable level of significance. Therefore, in order to utilize IOT technology in sports, it is crucial to consider these factors as the use of Internet of Things can lead to highly favorable outcomes.

منابع و مأخذ:

Ahmad, A., Rasul, T., Yousaf, A., & Zaman, U. (2020). Understanding factors influencing elderly diabetic patients’ continuance intention to use digital health wearables: extending the technology acceptance model (TAM). Journal of Open Innovation: Technology, Market, and Complexity, 6(3), 81. https://doi.org/10.3390/joitmc6030081

Ahmad, B. I. e. (2014). User acceptance of health information technology (HIT) in developing countries: a conceptual model. Procedia Technology, 16, 1287-1296. https://doi.org/https://doi.org/10.1016/j.protcy.2014.10.145

Ahmadi, Y., Nejad, A., & hojat. (2023). Investigating the effect of intra-organizational characteristics on competitive advantage with the mediating role of entrepreneurial marketing in knowledge-based organizations active in the science and technology park of Lorestan province. Productivity management, 71-92. [In persian] https://doi.org/10.30495/QJOPM.2022.1957054.3358

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/https://doi.org/10.1016/0749-5978(91)90020-T

Alsyouf, A., Lutfi, A., Al-Bsheish, M., Jarrar, M. t., Al-Mugheed, K., Almaiah, M. A., Alhazmi, F. N., Masa’deh, R. e., Anshasi, R. J., & Ashour, A. (2022). Exposure Detection Applications Acceptance: The Case of COVID-19. International Journal of Environmental Research and Public Health, 19(12), 7307. https://doi.org/10.3390/ijerph19127307

Amin, M., Rezaei, S., & Tavana, F. S. (2015). Gender differences and consumer’s repurchase intention: the impact of trust propensity, usefulness and ease of use for implication of innovative online retail. International Journal of Innovation and Learning, 17(2), 217-233. https://doi.org/10.1504/IJIL.2015.067409

Anzaldo, D. (2015). Wearable sports technology-Market landscape and compute SoC trends. 2015 International SoC Design Conference (ISOCC),

Ashton, K. (2009). That ‘internet of things’ thing. RFID journal, 22(7), 97-114. https://scirp.org/reference/referencespapers.aspx?referenceid=1578164

Canhoto, A. I., & Arp, S. (2017). Exploring the factors that support adoption and sustained use of health and fitness wearables. Journal of Marketing Management, 33(1-2), 32-60. https://doi.org/10.1080/0267257X.2016.1234505

Chang, C.-J., Yang, S.-C., & Wolzok, E. (2023). Examining the use of fitness apps in sports centers in Taiwan: incorporating task–technology fit into a technology readiness acceptance model. Managing Sport and Leisure, 1-19. https://doi.org/10.1080/23750472.2023.2165532

Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results Massachusetts Institute of Technology]. http://hdl.handle.net/1721.1/15192

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003. https://www.jstor.org/stable/2632151

De Cremer, D., Nguyen, B., & Simkin, L. (2017). The integrity challenge of the Internet-of-Things (IoT): on understanding its dark side. Journal of Marketing Management, 33(1-2), 145-158. https://doi.org/10.1080/0267257X.2016.1247517

Dodson, S. (2003). The internet of things: A tiny microchip is set to replace the barcode on all retail items but opposition is growing to its use. The Guardian, 9, 2003. https://www.theguardian.com/technology/2003/oct/09/shopping.newmedia

Fang, J., Wen, C., George, B., & Prybutok, V. R. (2016). Consumer heterogeneity, perceived value, and repurchase decision-making in online shopping: The role of gender, age, and shopping motives. Journal of Electronic Commerce Research, 17(2), 116. https://www.semanticscholar.org/paper/Consumer-Heterogeneity%2C-Perceived-Value%2C-and-in-The-Fang-Wen/0bca6352595935287ebaa523e96776d6b96e80cf

Felea, M., Bucur, M., Negruţiu, C., Nitu, M., & Stoica, D. A. (2021). Wearable technology adoption among Romanian Students: A structural model based on TAM. Amfiteatru Economic, 23(57), 376-391. https://doi.org/10.24818/EA/2021/57/376

Goldie, J. G. S. (2016). Connectivism: A knowledge learning theory for the digital age? Medical teacher, 38(10), 1064-1069. https://doi.org/10.3109/0142159X.2016.1173661

Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4). hmigninkgibhdckiaphhmbgcghochdjc/pdfjs/web/viewer.html?file=https://jise.org/volume15/n4/JISEv15n4p365.pdf

Holbrook, A. L., Berent, M. K., Krosnick, J. A., Visser, P. S., & Boninger, D. S. (2005). Attitude importance and the accumulation of attitude-relevant knowledge in memory. Journal of personality and social psychology, 88(5), 749. https://doi.org/https://doi.org/10.1037/0022-3514.88.5.749

Karami, M. (2006). Factors influencing adoption of online ticketing. In.

Kastoriano, I., & Halkias, D. (2020). Applying the technology adoption model (TAM) to explore consumers’ perceptions of wearable technology: a multiple case study of an ironman triathlon community: a narrative literature review. Available at SSRN 3637423. https://doi.org/http://dx.doi.org/10.2139/ssrn.3637423

Kehrizsangi, G., butterfly, t., Rahimi, & when. (2017). Investigating factors affecting the adoption of new information technologies in the General Administration of Sports and Youth of Isfahan province based on the TAM model. Journal of sports management, 9(1), 129-144. https://doi.org/https://doi.org/10.22059/jsm.2017.62134

Khorsandi-Fard, M., & Ismaeelzadeh, R. (2018). Green Sport Marketing Mix on Sportswear Consumers Purchasing Behavior. Journal of System Management, 4(1), 45-56. https://sjsm.shiraz.iau.ir/article_537209_72bcc008c0dcc75fdec045f59583d148.pdf

Kim, T., & Chiu, W. (2018). Consumer acceptance of sports wearable technology: The role of technology readiness. International Journal of Sports Marketing and Sponsorship. https://doi.org/10.1108/IJSMS-06-2017-0050

Kim, T. G., Lee, J. H., & Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism management, 29(3), 500-513. https://doi.org/https://doi.org/10.1016/j.tourman.2007.05.016

Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business horizons, 58(4), 431-440. https://doi.org/https://doi.org/10.1016/j.bushor.2015.03.008

Lian, J.-W., & Yen, D. C. (2014). Online shopping drivers and barriers for older adults: Age and gender differences. Computers in Human Behavior, 37, 133-143. https://doi.org/10.1016/j.chb.2014.04.028

Liebana-Cabanillas, F., & Alonso-Dos-Santos, M. (2017). Factors that determine the adoption of Facebook commerce: The moderating effect of age. Journal of Engineering and Technology Management, 44, 1-18. https://doi.org/https://doi.org/10.1016/j.jengtecman.2017.03.001

Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464-478. https://doi.org/10.1016/j.chb.2014.03.022

Lunney, A., Cunningham, N. R., & Eastin, M. S. (2016). Wearable fitness technology: A structural investigation into acceptance and perceived fitness outcomes. Computers in Human Behavior, 65, 114-120. https://doi.org/https://doi.org/10.1016/j.chb.2016.08.007

Lynott, P. P., & McCandless, N. J. (2000). The impact of age vs. life experience on the gender role attitudes of women in different cohorts. Journal of women & aging, 12(1-2), 5-21. https://doi.org/10.1300/J074v12n01_02

Mohammadpour Yaghini, H., Tojari, F., & Aslankhani, M.-A. (2019). Testing the Conceptual Model on the Causal Relationship of Motivation and Consumption Intention. Journal of System Management, 5(1), 115-138. https://sjsm.shiraz.iau.ir/article_545535_8f7254c37f6a924091d667ba4e03697a.pdf

Morales, D., Cotas, A. A., & Esteban-Millat, I. (2023). The effect of flow experience in the adoption of online supermarkets applying the technology acceptance model (TAM). Questiones publicitarias, 6(32), 41-54. https://doi.org/10.5565/rev/qp.387

Papastergiou, M., & Solomonidou, C. (2005). Gender issues in Internet access and favourite Internet activities among Greek high school pupils inside and outside school. Computers & Education, 44(4), 377-393. https://doi.org/10.1016/S0360-1315(04)00053-3

Parayil Iqbal, U., Jose, S. M., & Tahir, M. (2023). Integrating trust with extended UTAUT model: A study on Islamic banking customers’m-banking adoption in the Maldives. Journal of Islamic Marketing, 14(7), 1836-1858. https://doi.org/https://doi.org/10.1108/JIMA-01-2022-0030

Passos, J., Lopes, S. I., Clemente, F. M., Moreira, P. M., Rico-González, M., Bezerra, P., & Rodrigues, L. P. (2021). Wearables and Internet of Things (IoT) technologies for fitness assessment: a systematic review. Sensors, 21(16), 5418. https://doi.org/https://doi.org/10.3390/s21165418

Phillips, L. W., & Sternthal, B. (1977). Age differences in information processing: A perspective on the aged consumer. Journal of Marketing Research, 14(4), 444-457. https://doi.org/https://doi.org/10.1177/002224377701400402

Ramkissoon, H., & Nunkoo, R. (2012). More than just biological sex differences: Examining the structural relationship between gender identity and information search behavior. Journal of Hospitality & Tourism Research, 36(2), 191-215. https://doi.org/https://doi.org/10.1177/1096348010388662

Sardar Donighi, S., & Nour Mohammadi, E. (2015). A Model for Factors Affecting on Online Purchase Intention Raja Company Case Study. Journal of System Management, 1(2), 17-45. https://sjsm.shiraz.iau.ir/article_517103_a9d6dc3457b8531c447b7c6e96e7aeea.pdf

Shakouri, S., khamseh, A., & Radfar, R. (2023). Evaluation of Innovation Components in IT Startups with a Focus on Industry 4.0. Journal of System Management, 9(4), 115-130. https://doi.org/10.30495/jsm.2023.1982523.1795

Shin, S., & Lee, W.-j. (2014). The effects of technology readiness and technology acceptance on NFC mobile payment services in Korea. Journal of Applied Business Research (JABR), 30(6), 1615-1626. https://doi.org/https://doi.org/10.19030/jabr.v30i6.8873

Tan, G. W.-H., Chong, C.-K., Ooi, K.-B., & Chong, A. Y.-L. (2010). The adoption of online banking in Malaysia: an empirical analysis. International Journal of Business and Management Science, 3(2), 169-193. https://www.researchgate.net/publication/237063164_The_adoption_of_online_banking_in_Malaysia_An_empirical_analysis

Tan, G. W.-H., Ooi, K.-B., Chong, S.-C., & Hew, T.-S. (2014). NFC mobile credit card: the next frontier of mobile payment? Telematics and Informatics, 31(2), 292-307. https://doi.org/10.1016/j.tele.2013.06.002

Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163-184. https://doi.org/10.2190/EC.51.2.b

Tholander, J., & Nylander, S. (2015). Snot, sweat, pain, mud, and snow: Performance and experience in the use of sports watches. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems,

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. https://doi.org/https://doi.org/10.2307/30036540

Vidal-Tomás, D. (2022). Blockchain, sport and fan tokens. https://doi.org/https://doi.org/10.1108/JES-02-2023-0094

Whitley Jr, B. E. (1997). Gender differences in computer-related attitudes and behavior: A meta-analysis. Computers in Human Behavior, 13(1), 1-22. https://doi.org/https://doi.org/10.1016/S0747-5632(96)00026-X

Yang, W., Vatsa, P., Ma, W., & Zheng, H. (2023). Does mobile payment adoption really increase online shopping expenditure in China: A gender-differential analysis. Economic Analysis and Policy, 77, 99-110. https://doi.org/10.1016/j.eap.2022.11.001

Zarif Sagheb, M., Nourbakhsh, S. K., & Fallahshams, M. (2019). A Developed Model for Purchase Intention of Foreign Food Products: An Empirical Study in the Iranian Context. Journal of System Management, 5(4), 51-66. https://sjsm.shiraz.iau.ir/article_670192_bf7adbea76a34ad2ca6cd25d65c78e51.pdf

Zhang, X., Wang, Y., & Leung, S. o. (2022). Technology Acceptance Model (TAM) and sports bracelets usage in physical education for freshmen: The role of gender and self-efficacy. Technology, Pedagogy and Education, 1-19. https://doi.org/https://doi.org/10.1080/1475939X.2022.2152861

Zhao, Y., Wang, X., Li, J., Li, W., Sun, Z., Jiang, M., Zhang, W., Wang, Z., Chen, M., & Li, W. J. (2023). Using IoT Smart Basketball and Wristband Motion Data to Quantitatively Evaluate Action Indicators for Basketball Shooting. Advanced Intelligent Systems, 2300239. https://doi.org/https://doi.org/10.1002/aisy.202300239