Araştırma Makalesi
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Ne Kadar Hazırız? Nesnelerin İnterneti Teknolojilerinin (IoT) Tüketiciler Tarafından Kabulü

Yıl 2021, , 401 - 426, 01.08.2021

Haldun Çolak , Celal Hakan Kağnicioğlu

https://doi.org/10.17153/oguiibf.877372

Öz

Bu çalışmanın amacı Nesnelerin İnterneti teknolojilerinin (IoT) tüketiciler tarafından kabulünde önemli olan faktörlerin belirlenmesi ve kabul davranışını açıklayan bir modelin kurulmasıdır. 359 katılımcıdan toplanan veriler kısmi en küçük kareler yapısal eşitlik modellemesi ile test edilmektedir. Buna göre kabul davranışında belirleyici olan davranışsal niyetin %62’si açıklanmaktadır. Bu bağlamda, birçok ilişki mevcut araştırma kapsamında ilk kez tanımlanmış ve literatüre sunulmuştur. Sonuçlar, IoT teknolojilerinin kabulü noktasında tüketici niyet yapısının ne kadar karmaşık olduğunu göstermektedir.

Anahtar Kelimeler

IoT Tüketici Kabulü UTAUT 2 PLS YEM Teknoloji Kabulü

Kaynakça

  • Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. The handbook of attitudes, 173(221), 31.
  • Ajzen, I. (2002). Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior. Journal of applied social psychology, 32(4), 665-683.
  • Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100-110.
  • Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., Lal, B., & Williams, M. D. (2015). Consumer adoption of Internet banking in Jordan: Examining the role of hedonic motivation, habit, self-efficacy and trust. Journal of Financial Services Marketing, 20(2), 145-157.
  • Aldossari, M. Q., & Sidorova, A. (2018). Consumer Acceptance of Internet of Things (IoT): Smart Home Context. Journal of Computer Information Systems, 1-11.
  • AlHogail, A. (2018). Improving IoT Technology Adoption through Improving Consumer Trust. Technologies, 6(3), 64.
  • Ande, R., Adebisi, B., Hammoudeh, M., & Saleem, J. (2020). Internet of Things: Evolution and technologies from a security perspective. Sustainable Cities and Society, 54, 101728.
  • Atzori, L., Iera, A., & Morabito, G. (2017). Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Networks, 56, 122-140.
  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94.
  • Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173.
  • Baudier, P., Ammi, C., & Deboeuf-Rouchon, M. (2018). Smart home: Highly-educated students' acceptance. Technological Forecasting and Social Change, 119355.
  • Beh, P. K., Ganesan, Y., Iranmanesh, M., & Foroughi, B. (2019). Using smartwatches for fitness and health monitoring: the UTAUT2 combined with threat appraisal as moderators. Behaviour & Information Technology, 1-18.
  • Bölen, M. C. (2020). Exploring the determinants of users’ continuance intention in smartwatches. Technology in Society, 60, 101209.
  • Brauner, P., Van Heek, J., & Ziefle, M. (2017). Age, gender, and technology attitude as factors for acceptance of smart interactive textiles in home environments. In Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AgingWell.
  • Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 29(3).
  • Casola, V., De Benedictis, A., Riccio, A., Rivera, D., Mallouli, W., & de Oca, E. M. (2019). A security monitoring system for internet of things. Internet of Things, 7, 100080.
  • Celic, L., & Magjarevic, R. (2020). Seamless connectivity architecture and methods for IoT and wearable devices. Automatika, 61(1), 21-34.
  • Chin, W. W., Peterson, R. A., & Brown, S. P. (2008). Structural Equation Modeling in Marketing: Some Practical Reminders. Journal of Marketing Theory and Practice, 16(4), 287‐298.
  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
  • Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of marketing research, 16(1), 64-73.
  • Chipeva, P., Cruz-Jesus, F., Oliveira, T., & Irani, Z. (2018). Digital divide at individual level: Evidence for Eastern and Western European countries. Government Information Quarterly, 35(3), 460-479.
  • Chong, A. Y. L., & Chan, F. T. (2012). Structural equation modeling for multi-stage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry. Expert Systems with Applications, 39(10), 8645-8654.
  • Cohen, J. (1988). Statistical power analysis for the behaviors science. (2nd). New Jersey: Laurence Erlbaum Associates, Publishers, Hillsdale.
  • 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.
  • Dhaggara, D., Goswami, M., & Kumar, G. (2020). Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective. International Journal of Medical Informatics, 104164.
  • Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263-282.
  • Dutot, V., Bhatiasevi, V., & Bellallahom, N. (2019). Applying the technology acceptance model in a three-countries study of smartwatch adoption. The Journal of High Technology Management Research, 30(1), 1-14.
  • El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743-763.
  • Ferreira, J. B., da Rocha, A., & da Silva, J. F. (2014). Impacts of technology readiness on emotions and cognition in Brazil. Journal of Business Research, 67(5), 865-873.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
  • Gaitán, J., Peral Peral, B., & Ramón Jerónimo, M. (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet Banking and Commerce, 20 (1), 1-23.
  • Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704-1723.
  • Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2), 211-231.
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  • Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems.
  • Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565-580.
  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (pp. 277-319). Emerald Group Publishing Limited.
  • Hsu, C. L., & Lin, J. C. C. (2016). An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives. Computers in Human Behavior, 62, 516-527.
  • Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, 79, 111-122.
  • Khattak, H. A., Shah, M. A., Khan, S., Ali, I., & Imran, M. (2019). Perception layer security in Internet of Things. Future Generation Computer Systems, 100, 144-164.
  • Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Research note—two competing perspectives on automatic use: A theoretical and empirical comparison. Information systems research, 16(4), 418-432.
  • Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management science, 51(5), 741-755.
  • Kim, J., & Park, E. (2019). Beyond coolness: Predicting the technology adoption of interactive wearable devices. Journal of Retailing and Consumer Services, 49, 114-119.
  • Kuo, K. M., Liu, C. F., & Ma, C. C. (2013). An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems. BMC medical informatics and decision making, 13(1), 88.
  • Lee, I. (2019). The Internet of Things for enterprises: An ecosystem, architecture, and IoT service business model. Internet of Things, 7, 100078.
  • Lee, W., & Shin, S. (2019). An empirical study of consumer adoption of Internet of Things services. International Journal of Engineering and Technology Innovation, 9(1), 1.
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  • Manzano, J., Lassala-Navarré, C., Ruiz-Mafé, C., & Sanz-Blas, S. (2009). The role of consumer innovativeness and perceived risk in online banking usage. International Journal of Bank Marketing, 27(1), 53-75.
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  • Midgley, D. F., & Dowling, G. R. (1978). Innovativeness: The concept and its measurement. Journal of consumer research, 4(4), 229-242.
  • Morrison, D. E., & Firmstone, J. (2000). The social function of trust and implications for e-commerce. International Journal of Advertising, 19(5), 599-623.
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  • Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404-414.
  • Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of service research, 18(1), 59-74.
  • Park, E. (2020). User acceptance of smart wearable devices: An expectation-confirmation model approach. Telematics and Informatics, 47, 101318.
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How Ready Are We? Acceptance of Internet of Things (IoT) Technologies by Consumers

Yıl 2021, , 401 - 426, 01.08.2021

Haldun Çolak , Celal Hakan Kağnicioğlu

https://doi.org/10.17153/oguiibf.877372

Öz

The aim of this study is to determine the factors that are important in the acceptance of Internet of Things technologies by consumers and to establish a model that explains the acceptance behavior. With data collected from 359 participants, the model is tested using partial least square structural equation model. Accordingly, 62% of behavioral intention is explained. In this context, some relationships are defined for the first time within the scope of the present study and presented to the literature. Results show how complex the formation of consumer intention is for the adoption of IoT technologies.

Anahtar Kelimeler

IoT Consumer Adoption UTAUT 2 PLS SEM Technology Acceptance

Kaynakça

  • Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. The handbook of attitudes, 173(221), 31.
  • Ajzen, I. (2002). Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior. Journal of applied social psychology, 32(4), 665-683.
  • Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100-110.
  • Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., Lal, B., & Williams, M. D. (2015). Consumer adoption of Internet banking in Jordan: Examining the role of hedonic motivation, habit, self-efficacy and trust. Journal of Financial Services Marketing, 20(2), 145-157.
  • Aldossari, M. Q., & Sidorova, A. (2018). Consumer Acceptance of Internet of Things (IoT): Smart Home Context. Journal of Computer Information Systems, 1-11.
  • AlHogail, A. (2018). Improving IoT Technology Adoption through Improving Consumer Trust. Technologies, 6(3), 64.
  • Ande, R., Adebisi, B., Hammoudeh, M., & Saleem, J. (2020). Internet of Things: Evolution and technologies from a security perspective. Sustainable Cities and Society, 54, 101728.
  • Atzori, L., Iera, A., & Morabito, G. (2017). Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Networks, 56, 122-140.
  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94.
  • Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173.
  • Baudier, P., Ammi, C., & Deboeuf-Rouchon, M. (2018). Smart home: Highly-educated students' acceptance. Technological Forecasting and Social Change, 119355.
  • Beh, P. K., Ganesan, Y., Iranmanesh, M., & Foroughi, B. (2019). Using smartwatches for fitness and health monitoring: the UTAUT2 combined with threat appraisal as moderators. Behaviour & Information Technology, 1-18.
  • Bölen, M. C. (2020). Exploring the determinants of users’ continuance intention in smartwatches. Technology in Society, 60, 101209.
  • Brauner, P., Van Heek, J., & Ziefle, M. (2017). Age, gender, and technology attitude as factors for acceptance of smart interactive textiles in home environments. In Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AgingWell.
  • Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 29(3).
  • Casola, V., De Benedictis, A., Riccio, A., Rivera, D., Mallouli, W., & de Oca, E. M. (2019). A security monitoring system for internet of things. Internet of Things, 7, 100080.
  • Celic, L., & Magjarevic, R. (2020). Seamless connectivity architecture and methods for IoT and wearable devices. Automatika, 61(1), 21-34.
  • Chin, W. W., Peterson, R. A., & Brown, S. P. (2008). Structural Equation Modeling in Marketing: Some Practical Reminders. Journal of Marketing Theory and Practice, 16(4), 287‐298.
  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
  • Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of marketing research, 16(1), 64-73.
  • Chipeva, P., Cruz-Jesus, F., Oliveira, T., & Irani, Z. (2018). Digital divide at individual level: Evidence for Eastern and Western European countries. Government Information Quarterly, 35(3), 460-479.
  • Chong, A. Y. L., & Chan, F. T. (2012). Structural equation modeling for multi-stage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry. Expert Systems with Applications, 39(10), 8645-8654.
  • Cohen, J. (1988). Statistical power analysis for the behaviors science. (2nd). New Jersey: Laurence Erlbaum Associates, Publishers, Hillsdale.
  • 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.
  • Dhaggara, D., Goswami, M., & Kumar, G. (2020). Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective. International Journal of Medical Informatics, 104164.
  • Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263-282.
  • Dutot, V., Bhatiasevi, V., & Bellallahom, N. (2019). Applying the technology acceptance model in a three-countries study of smartwatch adoption. The Journal of High Technology Management Research, 30(1), 1-14.
  • El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743-763.
  • Ferreira, J. B., da Rocha, A., & da Silva, J. F. (2014). Impacts of technology readiness on emotions and cognition in Brazil. Journal of Business Research, 67(5), 865-873.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
  • Gaitán, J., Peral Peral, B., & Ramón Jerónimo, M. (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet Banking and Commerce, 20 (1), 1-23.
  • Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704-1723.
  • Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2), 211-231.
  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an integrated model. MIS quarterly, 27(1), 51-90.
  • Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems.
  • Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565-580.
  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (pp. 277-319). Emerald Group Publishing Limited.
  • Hsu, C. L., & Lin, J. C. C. (2016). An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives. Computers in Human Behavior, 62, 516-527.
  • Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, 79, 111-122.
  • Khattak, H. A., Shah, M. A., Khan, S., Ali, I., & Imran, M. (2019). Perception layer security in Internet of Things. Future Generation Computer Systems, 100, 144-164.
  • Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Research note—two competing perspectives on automatic use: A theoretical and empirical comparison. Information systems research, 16(4), 418-432.
  • Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management science, 51(5), 741-755.
  • Kim, J., & Park, E. (2019). Beyond coolness: Predicting the technology adoption of interactive wearable devices. Journal of Retailing and Consumer Services, 49, 114-119.
  • Kuo, K. M., Liu, C. F., & Ma, C. C. (2013). An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems. BMC medical informatics and decision making, 13(1), 88.
  • Lee, I. (2019). The Internet of Things for enterprises: An ecosystem, architecture, and IoT service business model. Internet of Things, 7, 100078.
  • Lee, W., & Shin, S. (2019). An empirical study of consumer adoption of Internet of Things services. International Journal of Engineering and Technology Innovation, 9(1), 1.
  • Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 31(4).
  • Luhmann, N. (2000). Familiarity, confidence, trust: Problems and alternatives. Trust: Making and breaking cooperative relations, 6(1), 94-107.
  • Manzano, J., Lassala-Navarré, C., Ruiz-Mafé, C., & Sanz-Blas, S. (2009). The role of consumer innovativeness and perceived risk in online banking usage. International Journal of Bank Marketing, 27(1), 53-75.
  • Martino, B., Rak, M., Ficco, M., Esposito, A., Maisto, S. A., & Nacchia, S. (2018). Internet of things reference architectures, security and interoperability: A survey. Internet of Things, 1, 99-112.
  • Midgley, D. F., & Dowling, G. R. (1978). Innovativeness: The concept and its measurement. Journal of consumer research, 4(4), 229-242.
  • Morrison, D. E., & Firmstone, J. (2000). The social function of trust and implications for e-commerce. International Journal of Advertising, 19(5), 599-623.
  • Nascimento, B., Oliveira, T., & Tam, C. (2018). Wearable technology: What explains continuance intention in smartwatches?. Journal of Retailing and Consumer Services, 43, 157-169.
  • Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404-414.
  • Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of service research, 18(1), 59-74.
  • Park, E. (2020). User acceptance of smart wearable devices: An expectation-confirmation model approach. Telematics and Informatics, 47, 101318.
  • Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467-480.
  • Rahman, S. A., Taghizadeh, S. K., Ramayah, T., & Alam, M. M. D. (2017). Technology acceptance among micro-entrepreneurs in marginalized social strata: The case of social innovation in Bangladesh. Technological Forecasting and Social Change, 118, 236-245.
  • Ramantoko, G., Putra, G., Ariyanti, M., & Sianturi, N. V. (2016, March). Early Adoption Characteristic of Consumers' Behavioral Intention to Use Home Digital Services in Indonesia. In 3rd International Seminar and Conference on Learning Organization. Atlantis Press.
  • Roy, S., & Moorthi, Y. L. R. (2017). Technology readiness, perceived ubiquity and M-commerce adoption: The moderating role of privacy. Journal of Research in Interactive Marketing, 11(3), 268-295.
  • Sanguinetti, A., Karlin, B., & Ford, R. (2018). Understanding the path to smart home adoption: Segmenting and describing consumers across the innovation-decision process. Energy research & social science, 46, 274-283.
  • Schill, M., Godefroit-Winkel, D., Diallo, M. F., & Barbarossa, C. (2019). Consumers’ intentions to purchase smart home objects: Do environmental issues matter?. Ecological Economics, 161, 176-185.
  • Seol, S., Ko, D., & Yeo, I. (2017). UX Analysis based on TR and UTAUT of Sports Smart Wearable Devices. KSII Transactions on Internet & Information Systems, 11(8).
  • Sevim, N., Yüncü, D., & HALL, E. E. (2017). Online seyahat ürünlerinde genişletilmiş teknoloji kabul modelinin analizi. İnternet Uygulamaları ve Yönetimi Dergisi, 8(2), 45-61.
  • Shin, D. H. (2010). The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption. Interacting with computers, 22(5), 428-438.
  • 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, 30(6), 1615.
  • Silva, B. N., Khan, M., & Han, K. (2018). Internet of things: A comprehensive review of enabling technologies, architecture, and challenges. IETE Technical review, 35(2), 205-220.
  • Sułkowski, Ł., & Kaczorowska-Spychalska, D. (2017, July). Consumer Perception of Internet of Things. In International Conference on Applied Human Factors and Ergonomics (pp. 247-258). Springer, Cham.
  • Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational statistics & data analysis, 48(1), 159-205.
  • Tewari, A., & Gupta, B. B. (2020). Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework. Future generation computer systems, 108, 909-920.
  • Thompson, R. L., Higgins, C. A., & Howell, J. M. (1994). Influence of experience on personal computer utilization: testing a conceptual model. Journal of management information systems, 11(1), 167-187.
  • Tsai, J. Y., Egelman, S., Cranor, L., & Acquisti, A. (2011). The effect of online privacy information on purchasing behavior: An experimental study. Information Systems Research, 22(2), 254-268.
  • Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), 157-178.
  • 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.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. Wei, T., Marthandan, G., Yee-Loong Chong, A., Ooi, K. B., & Arumugam, S. (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370-388.
  • Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS quarterly, 177-195.
  • Wu, L. H., Wu, L. C., & Chang, S. C. (2016). Exploring consumers’ intention to accept smartwatch. Computers in Human Behavior, 64, 383-392.
  • Yan, Z., Zhang, P., & Vasilakos, A. V. (2014). A survey on trust management for Internet of Things. Journal of network and computer applications, 42, 120-134.
  • Zhang, M., Luo, M., Nie, R., & Zhang, Y. (2017). Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology. International journal of medical informatics, 108, 97-109.
  • https://www.statista.com/statistics/471264/iot- number- of- connected- devicesworldwide.
Toplam 81 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Haldun Çolak 0000-0003-4369-6063

Celal Hakan Kağnicioğlu 0000-0001-7164-3538

Yayımlanma Tarihi 1 Ağustos 2021
Gönderilme Tarihi 9 Şubat 2021
Yayımlandığı Sayı Yıl 2021

Kaynak Göster

APA Çolak, H., & Kağnicioğlu, C. H. (2021). How Ready Are We? Acceptance of Internet of Things (IoT) Technologies by Consumers. Eskişehir Osmangazi Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 16(2), 401-426. https://doi.org/10.17153/oguiibf.877372