Abstract
This work aims to examine the underlying factors associated with the continuous usage of smartwatches and propose a relevant theoretical framework. In order to understand and correlate the exact user motivations and expectations before and after using a smartwatch, a dual approach is taken comprising of a detailed literature review with thematic analysis of the data obtained from an ethnographic study involving 42 participants. Nine key determinants of continuous usage of smartwatches are identified, with four of them being introduced for the first time (perceived comfort, self-socio motivation, battery-life concern, and perceived accuracy and functional limitations) in the wearable context. Thereafter, a research model is developed based upon the expectation-confirmation model (ECM) and empirically tested using a partial least square structural equation modeling approach (PLS-SEM) on data obtained from 312 long-term smartwatch users across four Asian countries. Perceived usefulness, hedonic motivation, perceived comfort and self-socio motivation have a positive impact on the continuous usage. However, perceived privacy, battery-life concern, and perceived accuracy and functional limitations have a negative impact on the continuous usage of smartwatches, the last one being the greatest predictor. The effect of hedonic motivation on perceived usefulness is non-significant. The model explains 64.8% of the variance in the final dependable construct, i.e. continuous usage. The insights provided by this work can help the smartwatch stakeholders to mitigate the existing drawbacks and formulate better growth strategies along with the directions for further development with an aim to increase the customers’ continuous usage of smartwatches.
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References
Statista: Wearable technology—statistics and facts. http://www.statista.com/topics/1556/wearable-technology/ (2017). Accessed 22 Feb 2018
Cecchinato, M.E., Cox, A.L., Bird, J.: (2015) Smartwatches: the good, the bad, and the ugly?. In: Proceedings 33rd Annual ACM Conference Extended Abstracts on Human factors in Computing Systems, pp 2133–2138
Vandrico: The wearables database. https://vandrico.com/wearables/ (2017). Accesses 24 Feb 2018
Wu, L.H., Wu, L.C., Chang, S.C.: Exploring consumer’s intention to accept smartwatch. Comput. Hum. Behav. 64, 383–392 (2016)
Chang, Y., Kim, S., Lee, H., Park, M.C.: A study on the resistance behavior of long-term subscribers to switch from mobile network operators. Entrue J. Inf. Technol. 13(2), 77–91 (2014)
Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340 (1989)
Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478 (2003)
Bhattacherjee, A.: Understanding information systems continuance: an expectation-confirmation model. MIS Q. 25, 351–370 (2001)
Bhattacherjee, A., Hikmet, N.: Reconceptualizing organizational support and its effect on information technology usage: evidence from the healthcare sector. J. Comput. Inf. Syst. 48, 69–76 (2008)
Venkatesh, V., Thong, J.Y.L., Chan, F.K.Y., Hu, P.J.H., Brown, S.A.: Extending the two-stage information systems continuance model: incorporating UTAUT predictors and the role of context. Inf. Syst. J. 21, 527–555 (2011)
Ha, T., Beijnon, B., Kim, S., Lee, S., Kim, J.H.: Examining user perceptions of smartwatch through dynamic topic modelling. Telemat. Inform. 34, 1262–1273 (2017)
Kim, K.J., Shin, D.H.: An acceptance model for smartwatches. Internet Res. 25(4), 527–541 (2015)
Chuah, S.H.W., Rauschnabel, P.A., Krey, N., Nguyen, B., Ramayah, T., Lade, S.: Wearable technologies: the role of usefulness and visibility in smartwatch adoption. Comput. Hum. Behav. 65, 276–284 (2016)
Hsiao, K.L.: What drives smartwatch adoption intention? Comparing Apple and non-Apple watches. Library Hi Tech 35(1), 186–206 (2017)
Raskovic, D., Martin, T., Jovanov, E.: Medical monitoring applications for wearable computing. Comput. J. 47(4), 495–504 (2004)
Chatterjee, A., Aceves, A., Dungca, R., Flores, H., Giddens, K.: (2016) Classification of wearable computing: a survey of electronic assistive technology and future design. In: Proceedings 2nd International Conference on Research in Computational Intelligence and Communication Networks, pp 22–27
Mardonova, M., Choi, Y.: Review of wearable device technology and its applications to the mining industry. Energies 11(3), 547 (2018)
Chan, M., Estève, D., Fourniols, J.Y., Escriba, C., Campo, E.: Smart wearable systems: current status and future challenges. Artif. Intell. Med. 56(3), 137–156 (2012)
Kalantari, M.: Consumers’ adoption of wearable technologies: literature review, synthesis, and future research agenda. Int. J. Technol. Mark. 12(3), 274–307 (2017)
Dieck, M., Jung, T.: A theoretical model of mobile augmented reality acceptance in urban heritage tourism. Curr. Issues Tour. 21(2), 154–174 (2015)
Rauschnabel, P.: Virtually enhancing the real world with holograms: an exploration of expected gratifications of using augmented reality smart glasses. Psychol. Mark. 35(8), 1–16 (2018)
Rauschnabel, P., Ro, Y.K.: Augmented reality smart glasses: an investigation of technology acceptance drivers. Int. J. Technol. Mark. 11(2), 123–148 (2016)
Rauschnabel, P., Brem, A., Ivens, B.: Who will buy smart glasses? Empirical results of two pre-market-entry studies on the role of personality in individual awareness and intended adoption of Google Glass wearables. Comput. Hum. Behav. 49, 635–647 (2015)
Rauschnabel, P., Hein, D.W., He, J., Ro, Y.K., Rawashdeh, S., Krulikowski, B.: Fashion or technology? A fashion perspective on the perception and adoption of augmented reality smart glasses. i-com J. Interact. Media 15(2), 179–194 (2016)
Fang, Y.M., Chang, C.C.: User’s psychological perception and perceived readability of wearable devices for elderly people. Behav. Inf. Technol. 35(3), 225–232 (2016)
Canhoto, A.I., Arp, S.: Exploring the factors that support adoption and sustained use of health and fitness wearables. J. Mark. Manag. 33(1–2), 32–60 (2017)
Turhan, G.: An assessment towards the acceptance of wearable technology to consumers in Turkey: the application to smart bra and t-shirt products. J. Text. Inst. 104(4), 375–395 (2013)
Wu, L., Fan, A.A., Mattila, A.S.: Wearable technology in service delivery processes: the gender-moderated technology objectification effect. Int. J. Hosp. Manag. 51, 1–7 (2015)
Zhang, M., Luo, M., Nie, R., Zhang, Y.: Technical attributes, health attribute, consumer attributes, and their roles in adoption intention of healthcare wearable technology. Int. J. Med. Inform. 108, 97–109 (2017)
Basoglu, N., Ok, A.. E, Daim, T.U.: Technology adoption: case of smart glasses. Technol. Soc. 50, 50–56 (2017)
Bodine, K., Gemperle, F.: (2005) Effects of functionality on perceived comfort of wearables. In: Proceedings 7th IEEE International Symposium on Wearable Computers, pp 57–60
Nasir, S., Yurder, Y.: Consumers’ and physicians’ perceptions about high tech wearable health products. Procedia Soc. Behav. Sci. 195, 1261–1267 (2015)
Yang, H., Yu, J., Zo, H., Choi, M.: User acceptance of wearable devices: an extended perspective of perceived value. Telemat. Inform. 33, 256–269 (2016)
Gu, Z., Wei, J., Xu, F.: An empirical study on factors influencing consumer’s initial trust in wearable commerce. J. Comput. Inf. Syst. 56(1), 79–85 (2016)
Choi, J., Kim, S.: Is the smartwatch an IT product or a fashion product? A study on factors affecting the intention to use smartwatches. Comput. Hum. Behav. 63, 777–786 (2016)
Potnis, D., Demissie, D., Deosthali, K.: Student’s intention to adopt internet-based personal safety wearable devices: extending UTAUT with trusting belief. First Monday 22(9), 1–14 (2017)
Kim, K.J.: Round or square? How screen shape affects utilitarian and hedonic motivations for smartwatch adoption. Cyberpsychol. Behav. Soc. Netw. 19(12), 733–739 (2016)
Kim, K.J.: Shape and size matter for smartwatches: effects of screen shape, screen size, and presentation mode in wearable communication. J. Comput. Mediated Commun. 22, 124–140 (2017)
Lunney, A., Cunningham, N.. R, Eastin, M.S.: Wearable fitness technology: a structural investigation into acceptance and perceived fitness outcomes. Comput. Hum. Behav. 65, 114–120 (2016)
Jeong, S.C., Kim, S.H., Park, J.Y., Choi, B.: Domain-specific innovativeness and new product adoption: a case of wearable devices. Telemat. Inform. 34, 399–412 (2017)
Dehghani, M.: Exploring the motivational factors on continuous usage intention of smartwatches among actual users. Behav. Inf. Technol. 37(2), 145–158 (2018)
Rauschnabel, P., Brem, A., Ro, Y.K.: (2015) Augmented reality smart glasses: definition, conceptual insights, and managerial importance. The University of Michigan-Dearborn. Working paper
Oliver, R.L.: A cognitive model for the antecedents and consequences of satisfaction. J. Mark. Res. 17, 460–469 (1980)
Oghuma, A.P., Saenz, C.L., Wong, S.F., Chang, Y.: An expectation-confirmation model of continuance intention to use mobile instant messaging. Telemat. Inf. 33, 34–47 (2016)
Lee, M.C.: Explaining and predicting users’ continuance intention toward e learning: an extension of the expectation-confirmation model. Comput. Educ. 54, 506–516 (2010)
Susanto, A., Chang, Y., Ha, Y.: Determinants of continuance intention to use the smartphone banking services. Ind. Manag. Data Syst. 116(3), 508–525 (2016)
Eveleth, L.B., Stone, R.W.: Usability, expectation, confirmation, and continuance intentions to use electronic textbooks. Behav. Inf. Technol. 34(10), 992–1004 (2015)
Mingmuang, C., Chongsuphajaisiddhi, V., Papasratorn, B.: (2015) Factors influencing continuance intention to use PSTN: a pilot study of an extended expectation confirmation model for legacy technology. In: Proceedings 7th International Conference on Ubiquitous and Future Networks, pp 874–878
Joo, Y.J., Park, S., Shin, E.K.: Students’ expectation, satisfaction, and continuance intention to use digital textbooks. Comput. Hum. Behav. 69, 83–90 (2017)
Shang, D., Wu, W.: Understanding mobile shopping consumers’ continuance intention. Ind. Manag. Data Syst. 117(1), 213–227 (2017)
Lin, X., Featherman, M., Sarker, S.: Understanding factors affecting users’ social networking site continuance: a gender difference perspective. Inf. Manag. 54(3), 383–395 (2017)
Mouakket, S.: Factors influencing continuance intention to use social networking sites: the Facebook case. Comput. Hum. Behav. 53, 102–110 (2015)
Shen, X.L., Li, Y.J., Sun, Y.: (2018) Wearable health information systems intermittent discontinuance: a revised expectation-disconfirmation model. Ind. Manag. Data Syst. https://doi.org/10.1108/IMDS-05-2017-0222. (accepted)
Harviainen, J.T., Rapp, A.: (2018) Multiplayer online role-playing as information retrieval and system use: an ethnographic study. J. Doc. https://doi.org/10.1108/JD-07-2017-0100
Jamshed, S.: Qualitative research method: interviewing and observation. J. Basic Clin. Pharm. 5(4), 87–88 (2014)
Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006)
Venkatesh, V.: Creation of favorable user perceptions: exploring the role of intrinsic motivations. MIS Q. 23(2), 239–260 (1999)
Kim, H.W., Chan, H.C., Gupta, S.: Value-based adoption of mobile internet: an empirical investigation. Decis. Support Syst. 43(1), 111–126 (2007)
Marangunić, N., Granić, A.: Technology acceptance model: a literature review from 1986 to 2013. Univ. Access Inf. Soc. 14(1), 81–95 (2015)
Tarhini, A., Arachchilage, N.A.G., Masa’deh, R., Abbasi, M.S.: A critical review of theories and models of technology adoption and acceptance in information system research. Int. J. Technol. Diffus. 6(4), 1–20 (2015)
Park, Y., Chen, J.V.: Acceptance and adoption of the innovative use of smartphone. Ind. Manag. Data Syst. 107(9), 1349–1365 (2007)
Kulviwat, S., Bruner, G.C., Kumar, A., Nasco, S.A., Clark, T.: Toward a unified theory of consumer acceptance technology. Psychol. Mark. 24(12), 1059–1084 (2007)
Wixom, B.H., Todd, P.A.: A theoretical integration of user satisfaction and technology acceptance. Inf. Syst. Res. 16(1), 85–102 (2005)
Bhattacherjee, A., Premkumar, G.: Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test. MIS Q. 28(2), 229–254 (2004)
Taherdoost, H.: Development of an adoption model to assess user acceptance of e-service technology: e-service technology acceptance model. Behav. Inf. Technol. 37(2), 173–197 (2018)
Kang, Y.S., Lee, H.: Understanding the role of an IT artifact in online service continuance: an extended perspective of user-satisfaction. Comput. Hum. Behav. 26(3), 353–364 (2010)
Chiu, C.M., Hsu, M.H., Sun, S.Y., Lin, T.C., Sun, P.C.: Usability, quality, value and e-learning continuance decisions. Comput. Educ. 45(4), 399–416 (2005)
Roca, J.C., Chiu, C.M., Martinez, F.J.: Understanding e-learning continuance intention: an extension of the technology acceptance model. Int. J. Hum. Comput. Stud. 64(8), 683–696 (2006)
Venkatesh, V., Thong, J.Y., Xu, X.: Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36(1), 157–178 (2012)
Datta, H., Knox, G., Bronnenberg, B.J.: Changing their tune: how consumers’ adoption of online streaming affects music consumption and discovery. Mark. Sci. 37(1), 5–21 (2017)
Armenteros, M., Liaw, S.S., Fernandez, M., Diaz, R.F., Sanchez, R.A.: Surveying FIFA instructors’ behavioral intention toward the multimedia teaching materials. Comput. Educ. 61, 91–104 (2013)
Ayeh, J.K., Au, N., Law, R.: Predicting the intention to use consumer generated media for travel planning. Tour. Manag. 35, 132–143 (2013)
Kakar, A.K.: How to perceived enjoyment and perceived usefulness of a software product interact over time to impact technology acceptance? Interact. Comput. 29(4), 467–480 (2017)
Kim, Y.H., Kim, D.J., Wachter, K.: A study of mobile user engagement (MoEN): engagement motivations, perceived value, satisfaction, and continued engagement intention. Decis. Support Syst. 56, 361–370 (2013)
Yang, H.E., Wu, C.C., Wang, K.C.: An empirical analysis of online game service satisfaction and loyalty. Expert Syst. Appl. 36(2), 1816–1825 (2009)
Wang, W., Ngai, E.W.T., Wei, H.: Explaining Instant Messaging continuance intention: the role of personality. Int. J. Hum. Comput. Interact. 28(8), 500–510 (2012)
Heijden, H.V.D.: User acceptance of hedonic information systems. MIS Q. 28(4), 695–704 (2004)
Rauniar, R., Rawski, G., Johnson, B.: Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. J. Enterp. Inf. Manag. 27(1), 6–30 (2014)
Lu, J., Liu, C., Wei, J.: How important are enjoyment and mobility for mobile applications? J. Comput. Inf. Syst. 57(1), 1–12 (2016)
Ryan, R.M., Deci, E.L.: Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55(1), 68–78 (2000)
Decharms, R.: Personal causation training in the schools. J. Appl. Soc. Psychol. 2(2), 95–113 (1972)
Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process 50(2), 179–211 (1991)
Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46(2), 186–204 (2000)
Pal, D., Funilkul, S., Charoenkitkarn, N., Kanthamanon, P.: Internet-of-things and smart-homes for elderly healthcare: an end user perspective. IEEE Access 6, 10483–10496 (2018)
Chang, S.E., Shen, W.C., Yeh, C.H.: A comparative study of user intention to recommend content on mobile social networks. Multimedia Tools Appl. 76(4), 5399–5417 (2017)
Pavlou, P.A., Liang, H.G., Xue, Y.J.: Understanding and mitigating uncertainty in online exchange relationships: a principal-agent perspective. MIS Q. 31(1), 105–136 (2007)
Mani, Z., Chouk, I.: Drivers of consumers’ resistance to smart products. J. Mark. Manag. 33(1–2), 76–97 (2016)
Yoon, H.K., Occeña, L.: Impacts of customers’ perceptions on internet banking use with a smartphone. J. Comput. Inf. Syst. 54(3), 1–9 (2014)
Wilson, C., Hargreaves, T., Baldwin, R.H.: Benefits and risks of smart home technologies. Energy Policy 103, 72–83 (2017)
Khalilzadeh, J., Ozturk, A.B., Bilgihan, A.: Security related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Comput. Hum. Behav. 70(2017), 460–474 (2017)
Hou, J.N.Y., Peng, W., Lee, K.M.: Effects of screen size, viewing angle, and players’ immersion tendencies on game experience. Comput. Hum. Behav. 28(2), 617–623 (2012)
Kim, K.J., Sundar, S.S.: Mobile persuasion: can screen size and presentation mode make a difference to trust? Hum. Commun. Res. 42(1), 45–70 (2016)
Hsiao, K.L., Chen, C.C.: What drives smartwatch purchase intention? Perspectives from hardware, software, design, and value. Telemat. Inform. 35(1), 103–113 (2018)
Adapa, A., Nah, F.F.H., Siau, K., Smith, S.N.: Factors influencing the adoption of smart-wearable devices. Int. J. Hum. Comput. Interact. 34(5), 399–409 (2017)
Jegethesan, K., Sneddon, J.N., Soutar, G.N.: Young Australian consumer’s preferences for fashion apparel attributes. J. Fash. Mark. Manag. Int. J. 16(3), 275–289 (2012)
Watson, Z.M., Yan, R.N.: An exploratory study of the decision-processes of fast versus slow fashion consumers. J. Fash. Mark. Manag. Int. J. 17(2), 141–159 (2013)
Ferreira, D.F., Dey, A.K., Kostakos, V.: (2011) Understanding human–smartphone concerns: a study of battery life. In: Proceedings International Conference on Pervasive Computing, pp 19–33
Rawassizadeh, R., Price, B.A., Petre, M.: Wearables: has the age of smartwatches finally arrived? Commun. ACM 58(1), 45–47 (2015)
Spreer, P., Rauschnabel, P.A.: Selling with technology: understanding the resistance to mobile sales assistant use in retailing. J. Pers. Sell. Sales Manag. 36(3), 240–263 (2016)
Park, E., Ohm, J.: Factors influencing users’ employment of mobile map services. Telemat. Inform. 31(2), 253–265 (2014)
Zhu, D.H.: How the content of location-based advertisings influences consumers’ store patronage intention. J. Consum. Mark. 34(7), 603–611 (2017)
Lee, Y.E., Benbasat, I.: The influence of tradeoff-difficulty caused by preference elicitation methods on user acceptance of recommendation agents across loss and gain conditions. Inf. Syst. Res. 22(4), 867–884 (2011)
Zhu, D.H., Chang, Y.P., Luo, J.J., Li, X.: Understanding the adoption of location-based recommendation agents among active users’ of social networking sites. Inf. Process. Manag. 50(5), 675–682 (2014)
Pu, P., Chen, L., Hu, R.: (2011) A user-centric evaluation framework for recommender systems, In: Proceedings 5th ACM Conference on Recommender Systems, pp 157–164
Liao, Z., Cheung, M.T.: Internet based e-banking and consumer attitudes: an empirical study. Inf. Manag. 39(4), 283–295 (2002)
Bhattacherjee, A.: An empirical analysis of the antecedents of electronic commerce service continuation. Decis. Support Syst. 32(2), 201–214 (2001)
Thong, J.Y., Hong, S.J., Tam, K.Y.: The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. Int. J. Hum. Comput. Stud. 64(9), 799–810 (2006)
Taylor, S., Todd, P.A.: Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6(2), 144–176 (1995)
Verkasalo, H., Lopez, N.C., Molina, C.F.J., Bouwman, H.: Analysis of users and non-users of smartphone applications. Telemat. Inform. 27(3), 242–255 (2010)
Malhotra, N., Kim, S., Agarwal, J.: Internet users’ information privacy concerns: the construct, the scale, and a causal model. Inf. Syst. Res. 15(4), 336–355 (2004)
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E.: Multivariate Data Analysis, 7th edn. Prentice Hall Higher Education, Upper Saddle River (2010)
Anderson, J., Gerbing, D.: Structural equation modelling in practice: a review and recommended two-step approach. Psychol. Bull. 103(3), 411–423 (1988)
Claes, F., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981)
Chin, W.W.: The partial least squares approach to structural equation modelling. In: Marcoulides, G.A. (ed.) Modern Methods for Business Research, pp. 22295–22336. Lawrence Erlbaum Associates, New Jersey (1998)
Hair, J., Hult, G., Ringle, C., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modelling (PLS-SEM). Sage Publications, Thousand Oaks (2016)
Tenehaus, M., Vinzi, V., Chatelin, Y., Lauro, C.: PLS path modelling. Comput. Stat. Data Anal. 48(1), 159–205 (2005)
Wetzels, M.: Using PLS path modelling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Q. 33(1), 177–195 (2009)
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Pal, D., Funilkul, S. & Vanijja, V. The future of smartwatches: assessing the end-users’ continuous usage using an extended expectation-confirmation model. Univ Access Inf Soc 19, 261–281 (2020). https://doi.org/10.1007/s10209-018-0639-z
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DOI: https://doi.org/10.1007/s10209-018-0639-z