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TÜKETİCİLERİN SESLİ ASİSTAN ALIŞKANLIKLARINDA ALGILANAN ÜRKÜTÜCÜLÜK VE TÜKETİCİ DİRENCİNİN ROLÜ: DAVRANIŞSAL NEDEN PERSPEKTİFİ

Year 2025, Issue: 47, 87 - 102, 06.05.2025
https://doi.org/10.18092/ulikidince.1548247

Abstract

Bu çalışmanın amacı, tüketicilerin çevrimiçi sesli asistan kullanım alışkanlıklarına algılanan ürkütücülük ve tüketici direncinin etkilerini incelemektir. Çalışmanın evrenini Türkiye’deki sesli asistan kullanıcıları oluşturmaktadır. Kolayda örnekleme yöntemi ile çevrimiçi anket tekniği kullanılarak, çevrimiçi kanallar vasıtası ile 18 yaş üstü 252 sesli asistan kullanıcısından veri toplanmıştır. Çalışmada nicel araştırma yönteminden faydalanılmış olup veriler ve en küçük kareler yapısal eşitlik modellemesi (PLS-SEM) ile analiz edilmiştir. Yapılan analiz sonuçları, algılanan ürkütücülüğün tüketicilerin sesli asistanları online alışveriş amacıyla kullanımında tüketici direncini artırdığını göstermiştir. Ayrıca, tüketici direncinin tüketicilerin sesli asistanlara karşı tutumu üzerinde olumsuz bir etkiye sahipken, tutumun sesli asistan kullanma alışkanlığı üzerinde olumlu bir etkiye sahip olduğu ortaya konmuştur. Bu çalışma algılanan ürkütücülüğü sesli asistanların kullanımının alışkanlık haline gelmesinde bir inhibitör olarak ele alarak tüketici davranışları alan yazınlarına özgün katkı sağlamaktadır.

Ethical Statement

Bu makalede etik kurallara uyduğumu beyan ederim.

References

  • Acikgoz, F., & Vega, R. P. (2022). The Role of Privacy Cynicism in Consumer Habits with Voice Assistants: A Technology Acceptance Model Perspective. International Journal of Human-Computer Interaction, 38(12), 1138-1152.
  • Ajzen, I., 1985. From Intentions to Actions: A Theory of Planned Behaviour. Berlin: Springer
  • Al-Fraihat, D., Alzaidi, M. & Joy, M. (2023). Why Do Consumers Adopt Smart Voice Assistants for Shopping Purposes? A Perspective from Complexity Theory. Intelligent Systems with Applications, 18, 200230.
  • Amoroso, D., & Lim, R. (2017). The Mediating Effects of Habit on Continuance Intention. International Journal of Information Management, 37(6), 693–702.
  • Bagozzi, R. P., & Yi, Y. (1988). On The Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16, 74-94.
  • Bentler, P. M., & Chou, C. P. (1987). Practical Issues in Structural Modelling. Sociological Methods & Research, 16(1), 78–117.
  • Campbell, J. Y., & Cochrane, J. H. (1999). By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behaviour. Journal of Political Economy, 107(2), 205-251.
  • Cao, D., Sun, Y., Goh, E., Wang, R., & Kuiavska, K. (2022). Adoption of Smart Voice Assistants Technology among Airbnb Guests: A Revised Self-Efficacy-Based Value Adoption Model (SVAM). International Journal of Hospitality Management, 101, 103124.
  • Chattaraman, V., Kwon, W. S., Gilbert, J. E. & Ross, K. (2019). Should AI-Based, Conversational Digital Assistants Employ Social or Task-Oriented Interaction Style? A Task-Competency and Reciprocity Perspective for Older Adults. Computers in Human Behaviour, 90, 315-330.
  • Choudhary, S., Kaushik, N., Sivathanu, B., & Rana, N. P. (2024). Assessing Factors Influencing Customers’ Adoption of AI-Based Voice Assistants. Journal of Computer Information Systems, 1-18.
  • Claudy, M. C., Garcia, R., & O’Driscoll, A. (2015). Consumer Resistance to Innovation: A Behavioural Reasoning Perspective. Journal of the Academy of Marketing Science, 43, 528-544.
  • Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 319-340.
  • Diddi, S., Yan, R. N., Bloodhart, B., Bajtelsmit, V., & McShane, K. (2019). Exploring Young Adult Consumers’ Sustainable Clothing Consumption Intention-Behavior Gap: A Behavioral Reasoning Theory Perspective. Sustainable Production and Consumption, 18, 200-209.
  • DuHadway, S., & Dreyfus, D. (2017). A Simulation for Managing Complexity in Sales and Operations Planning Decisions. Decision Sciences Journal of Innovative Education, 15(4), 330-348.
  • Eagly, A. H. (1993). The Psychology of Attitudes. New York: Fort Worth/Harcout Brace Jovanovich College Publishers.
  • Fernandes, T. & Oliveira, E. (2021). Understanding Consumers’ Acceptance of Automated Technologies in Service Encounters: Drivers of Digital Voice Assistants’ Adoption. Journal of Business Research, 122, 180-191.
  • Fishbein, M., Ajzen, I., (1975). Belief, Attitudes, Intention, and Behaviour: An Introduction to Theory and Research. Addison-Wesley: Reading, MA.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.
  • Future Market Insights, (2022). With 15.6% CAGR, Conversational Commerce Market Size to Hit US$ 26,301.8 Million by 2032. Accessed 23 August 2024 from https://www.globenewswire.com/en/news-release/2022/08/22/2502010/0/en/With-15-6-CAGR-Conversational-CommerceMarket-Size-to-Hit-US-26-301-8-Million-by-2032-Future-Market-Insights-Inc.html
  • Hair, J. F., Sarstedt, M., Ringle, C. M. & Gudergan, S. P. (2018). Advanced Issues in Partial Least Squares Structural Equation Modelling (PLS-SEM). Thousand Oaks, CA: Sage.
  • Handrich, M. (2021). Alexa, You Freak Me Out - Identifying Drivers of Innovation Resistance and Adoption of Intelligent Personal Assistants [Paper presentation]. ICIS 2021 Proceedings.
  • Henseler, J., Ringle, C. M. & Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modelling. Journal of The Academy of Marketing Science, 43(1), 115-135.
  • Hernández-Ortega, B., Ferreira, I., & Lapresta-Romero, S. (2024). Long-Term Relationships Between Users and Smart Voice Assistants: The Roles of Experience and Love. Online Information Review.
  • Jacucci, G., Spagnolli, A., Freeman, J., & Gamberini, L. (2014, October). Symbiotic Interaction: A Critical Definition and Comparison to Other Human-Computer Paradigms. In: G. G., Jacucci, L., Gamberini, J., Freeman, & A., Spagnolli eds. in Symbiotic Interaction. Symbiotic 2015. Lecture Notes in Computer Science, 8820 (pp. 3–20). Cham: Springer.
  • Jan, I. U., Ji, S., & Kim, C. (2023). What (De) Motivates Customers to Use AI-Powered Conversational Agents for Shopping? The Extended Behavioural Reasoning Perspective. Journal of Retailing and Consumer Services, 75, 103440.
  • Kaplan, A. M., & Haenlein, M. (2019). Siri, Siri, in My Hand: Who’s the Fairest in The Land? On the Interpretations, Illustrations, and Implications of Artificial Intelligence. Business Horizons, 62(1), 15–25.
  • Kasilingam, D. L. (2020). Understanding The Attitude and Intention to Use Smartphone Chatbots for Shopping. Technology in Society, 62, 101280.
  • Kock, N. (2015). Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach. International Journal of e-Collaboration (ijec), 11(4), 1-10.
  • Kock, N., & Lynn, G. (2012). Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations. Journal of the Association for Information Systems, 13(7).
  • Kowalczuk, P. (2018). Consumer Acceptance of Smart Speakers: A Mixed Methods Approach. Journal of Research in Interactive Marketing, 12 (4), 418-431.
  • Lee, G., & Kim, Y. (2022). Effects of Resistance Barriers to Service Robots on Alternative Attractiveness and Intention to Use. Sage Open, 12(2), 21582440221099293.
  • Lefever, S., Dal, M. & Matthíasdóttir, Á. (2007). Online Data Collection in Academic Research: Advantages and Limitations. British Journal of Educational Technology, 38(4), 574-582.
  • Liao, C., Palvia, P., & Lin, H. N. (2006). The Roles of Habit and Website Quality in E-Commerce. International Journal of Information Management, 26(6), 469–483.
  • Mani, Z., & Chouk, I. (2018). Consumer Resistance to Innovation in Services: Challenges and Barriers in the Internet of Things Era. Journal of Product Innovation Management, 35(5), 780-807.
  • McCarthy, M. B., Collins, A. M., Flaherty, S. J., & McCarthy, S. N. (2017). Healthy Eating Habit: A Role for Goals, Identity, and Self-Control? Psychology & Marketing, 34(8), 772–785.
  • McAndrew, F. T., & Koehnke, S. S. (2016). On the Nature of Creepiness. New Ideas in Psychology, 43, 10–15.
  • McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa… Examine the Variables Influencing the Use of Artificial Intelligent in-Home Voice Assistants. Computers in Human Behaviour, 99, 28-37.
  • Mishra, A., Shukla, A., & Sharma, S. K. (2022). Psychological Determinants of Users’ Adoption and Word-Of-Mouth Recommendations of Smart Voice Assistants. International Journal of Information Management, 67, 102413.
  • Moriuchi, E. (2019). Okay, Google! An Empirical Study on Voice Assistants on Consumer Engagement and Loyalty. Psychology & Marketing,36(5), 489–501.
  • Mou, Y., & Meng, X. (2024). Alexa, It is Creeping Over Me-Exploring the Impact of Privacy Concerns on Consumer Resistance to Intelligent Voice Assistants. Asia Pacific Journal of Marketing and Logistics, 36(2), 261-292.
  • Pennington, N., & Hastie, R. (1988). Explanation-Based Decision Making: Effects of Memory Structure on Judgment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(3), 521.
  • Phillips, M. (2020). Horror Masks are Never Just About the Monster: These Cinematic Mainstays Continue to Terrify. Accessed 10 January 2025 from https://www.nytimes.com/2020/10/23/movies/halloween-horror-masks.html
  • Raff, S., Rose, S., & Huynh, T. (2024). Perceived Creepiness in Response to Smart Home Assistants: A Multi-Method Study. International Journal of Information Management, 74, 102720.
  • Rese, A., Ganster, L., & Baier, D. (2020). Chatbots in Retailers’ Customer Communication: How to Measure Their Acceptance? Journal of Retailing and Consumer Services, 56, 102176.
  • Sahu, A. K., Padhy, R. K., & Dhir, A. (2020). Envisioning The Future of Behavioural Decision-Making: A Systematic Literature Review of Behavioural Reasoning Theory. Australasian Marketing Journal, 28(4), 145-159.
  • Singh, C., Dash, M. K., Sahu, R., & Kumar, A. (2024). Investigating The Acceptance Intentions of Online Shopping Assistants in E-Commerce Interactions: Mediating Role of Trust and Effects of Consumer Demographics. Heliyon, 10(3).
  • Sivathanu, B. (2021). Adoption of Online Subscription Beauty Boxes: A Behavioural Reasoning Theory (BRT) Perspective. Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business, 958-983.
  • Sohn, K., & Kwon, O. (2020). Technology Acceptance Theories and Factors Influencing Artificial Intelligence-Based Intelligent Products. Telematics and Informatics, 47, 101324.
  • Stevens, A. M., 2016. Antecedents and Outcomes of Perceived Creepiness in Online Personalized Communications [Published doctoral dissertation]. Cleveland, Ohio: Case Western Reserve University.
  • Talke, K., & Heidenreich, S. (2014). How to Overcome Pro‐Change Bias: Incorporating Passive and Active Innovation Resistance in Innovation Decision Models. Journal of Product Innovation Management, 31(5), 894-907.
  • Tandon, A., Dhir, A., Kaur, P., Kushwah, S., & Salo, J. (2020). Behavioral Reasoning Perspectives on Organic Food Purchase. Appetite, 154, 104786.
  • Tene, O., & Polonetsky, J. (2013). A Theory of Creepy: Technology, Privacy and Shifting Social Norms. Yale JL & Tech., 16, 59.
  • Torkamaan, H., Barbu, C. M., & Ziegler, J. (2019, September). How Can They Know That? A Study of Factors Affecting the Creepiness of Recommendations. [Paper presentation]. 13th ACM Conference on Recommender Systems.
  • Uddin, S. F., Sabir, L. B., Kirmani, M. D., Kautish, P., Roubaud, D., & Grebinevych, O. (2024). Driving Change: Understanding Consumers’ Reasons Influencing Electric Vehicle Adoption from The Lens of Behavioural Reasoning Theory. Journal of Environmental Management, 369, 122277.
  • 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.
  • Vimalkumar, M., Sharma, S. K., Singh, J. B.& Dwivedi, Y. K. (2021). Okay Google, What About My Privacy?’: User's Privacy Perceptions and Acceptance of Voice-Based Digital Assistants. Computers in Human Behaviour, 120, 106763.
  • Watt, M. C., Maitland, R. A., & Gallagher, C. E. (2017). A Case of the “Heeby Jeebies”: An Examination of Intuitive Judgements of “Creepiness”. Canadian Journal of Behavioural Science / Revue Canadienne Des Sciences Délután Comportement, 49(1), 58–69.
  • Westaby, J. D. (2005). Behavioural Reasoning Theory: Identifying New Linkages Underlying Intentions and Behaviour. Organizational Behaviour and Human Decision Processes, 98(2), 97-120.
  • Wu, B., & Chen, X. (2017). Continuance Intention to Use Moocs: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model. Computers in Human Behaviour, 67, 221–232.
  • Zafar, A. U., Shahzad, M., Shahzad, K., Appolloni, A., & Elgammal, I. (2024). Gamification and Sustainable Development: Role of Gamified Learning in Sustainable Purchasing. Technological Forecasting and Social Change, 198, 122968.

THE ROLE OF PERCEIVED CREEPINESS AND CONSUMER RESISTANCE IN CONSUMER HABITS WITH VOICE ASSISTANTS: A BEHAVIORAL REASONING PERSPECTIVE

Year 2025, Issue: 47, 87 - 102, 06.05.2025
https://doi.org/10.18092/ulikidince.1548247

Abstract

The purpose of this study is examining the effects of perceived creepiness and consumer resistance on consumer habits with Smart voice assistants in online shopping based on Behavioural Reasoning Theory. The population of the study consists of voice assistant users in Turkey. Data were collected from 252 voice assistant users aged 18 and above through an online survey technique, using online channels, employing a convenience sampling method. Quantitative research methods were employed in the study and the data were analyzed with PLS-SEM. Results showed that perceived creepiness increases consumers' resistance to SVAs. Also, consumer resistance has a negative effect on attitude, while attitude has a positive effect on the habit of using voice assistants. Results were discussed based on Behavioural Reasoning perspective and recommendations were offered for theoreticians and practitioners. This study makes a unique contribution to the literature on voice assistants, behavioral reasoning theory and consumer behavior by considering perceived creepiness as an inhibitor in the habitual use of voice assistants.

References

  • Acikgoz, F., & Vega, R. P. (2022). The Role of Privacy Cynicism in Consumer Habits with Voice Assistants: A Technology Acceptance Model Perspective. International Journal of Human-Computer Interaction, 38(12), 1138-1152.
  • Ajzen, I., 1985. From Intentions to Actions: A Theory of Planned Behaviour. Berlin: Springer
  • Al-Fraihat, D., Alzaidi, M. & Joy, M. (2023). Why Do Consumers Adopt Smart Voice Assistants for Shopping Purposes? A Perspective from Complexity Theory. Intelligent Systems with Applications, 18, 200230.
  • Amoroso, D., & Lim, R. (2017). The Mediating Effects of Habit on Continuance Intention. International Journal of Information Management, 37(6), 693–702.
  • Bagozzi, R. P., & Yi, Y. (1988). On The Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16, 74-94.
  • Bentler, P. M., & Chou, C. P. (1987). Practical Issues in Structural Modelling. Sociological Methods & Research, 16(1), 78–117.
  • Campbell, J. Y., & Cochrane, J. H. (1999). By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behaviour. Journal of Political Economy, 107(2), 205-251.
  • Cao, D., Sun, Y., Goh, E., Wang, R., & Kuiavska, K. (2022). Adoption of Smart Voice Assistants Technology among Airbnb Guests: A Revised Self-Efficacy-Based Value Adoption Model (SVAM). International Journal of Hospitality Management, 101, 103124.
  • Chattaraman, V., Kwon, W. S., Gilbert, J. E. & Ross, K. (2019). Should AI-Based, Conversational Digital Assistants Employ Social or Task-Oriented Interaction Style? A Task-Competency and Reciprocity Perspective for Older Adults. Computers in Human Behaviour, 90, 315-330.
  • Choudhary, S., Kaushik, N., Sivathanu, B., & Rana, N. P. (2024). Assessing Factors Influencing Customers’ Adoption of AI-Based Voice Assistants. Journal of Computer Information Systems, 1-18.
  • Claudy, M. C., Garcia, R., & O’Driscoll, A. (2015). Consumer Resistance to Innovation: A Behavioural Reasoning Perspective. Journal of the Academy of Marketing Science, 43, 528-544.
  • Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 319-340.
  • Diddi, S., Yan, R. N., Bloodhart, B., Bajtelsmit, V., & McShane, K. (2019). Exploring Young Adult Consumers’ Sustainable Clothing Consumption Intention-Behavior Gap: A Behavioral Reasoning Theory Perspective. Sustainable Production and Consumption, 18, 200-209.
  • DuHadway, S., & Dreyfus, D. (2017). A Simulation for Managing Complexity in Sales and Operations Planning Decisions. Decision Sciences Journal of Innovative Education, 15(4), 330-348.
  • Eagly, A. H. (1993). The Psychology of Attitudes. New York: Fort Worth/Harcout Brace Jovanovich College Publishers.
  • Fernandes, T. & Oliveira, E. (2021). Understanding Consumers’ Acceptance of Automated Technologies in Service Encounters: Drivers of Digital Voice Assistants’ Adoption. Journal of Business Research, 122, 180-191.
  • Fishbein, M., Ajzen, I., (1975). Belief, Attitudes, Intention, and Behaviour: An Introduction to Theory and Research. Addison-Wesley: Reading, MA.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.
  • Future Market Insights, (2022). With 15.6% CAGR, Conversational Commerce Market Size to Hit US$ 26,301.8 Million by 2032. Accessed 23 August 2024 from https://www.globenewswire.com/en/news-release/2022/08/22/2502010/0/en/With-15-6-CAGR-Conversational-CommerceMarket-Size-to-Hit-US-26-301-8-Million-by-2032-Future-Market-Insights-Inc.html
  • Hair, J. F., Sarstedt, M., Ringle, C. M. & Gudergan, S. P. (2018). Advanced Issues in Partial Least Squares Structural Equation Modelling (PLS-SEM). Thousand Oaks, CA: Sage.
  • Handrich, M. (2021). Alexa, You Freak Me Out - Identifying Drivers of Innovation Resistance and Adoption of Intelligent Personal Assistants [Paper presentation]. ICIS 2021 Proceedings.
  • Henseler, J., Ringle, C. M. & Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modelling. Journal of The Academy of Marketing Science, 43(1), 115-135.
  • Hernández-Ortega, B., Ferreira, I., & Lapresta-Romero, S. (2024). Long-Term Relationships Between Users and Smart Voice Assistants: The Roles of Experience and Love. Online Information Review.
  • Jacucci, G., Spagnolli, A., Freeman, J., & Gamberini, L. (2014, October). Symbiotic Interaction: A Critical Definition and Comparison to Other Human-Computer Paradigms. In: G. G., Jacucci, L., Gamberini, J., Freeman, & A., Spagnolli eds. in Symbiotic Interaction. Symbiotic 2015. Lecture Notes in Computer Science, 8820 (pp. 3–20). Cham: Springer.
  • Jan, I. U., Ji, S., & Kim, C. (2023). What (De) Motivates Customers to Use AI-Powered Conversational Agents for Shopping? The Extended Behavioural Reasoning Perspective. Journal of Retailing and Consumer Services, 75, 103440.
  • Kaplan, A. M., & Haenlein, M. (2019). Siri, Siri, in My Hand: Who’s the Fairest in The Land? On the Interpretations, Illustrations, and Implications of Artificial Intelligence. Business Horizons, 62(1), 15–25.
  • Kasilingam, D. L. (2020). Understanding The Attitude and Intention to Use Smartphone Chatbots for Shopping. Technology in Society, 62, 101280.
  • Kock, N. (2015). Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach. International Journal of e-Collaboration (ijec), 11(4), 1-10.
  • Kock, N., & Lynn, G. (2012). Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations. Journal of the Association for Information Systems, 13(7).
  • Kowalczuk, P. (2018). Consumer Acceptance of Smart Speakers: A Mixed Methods Approach. Journal of Research in Interactive Marketing, 12 (4), 418-431.
  • Lee, G., & Kim, Y. (2022). Effects of Resistance Barriers to Service Robots on Alternative Attractiveness and Intention to Use. Sage Open, 12(2), 21582440221099293.
  • Lefever, S., Dal, M. & Matthíasdóttir, Á. (2007). Online Data Collection in Academic Research: Advantages and Limitations. British Journal of Educational Technology, 38(4), 574-582.
  • Liao, C., Palvia, P., & Lin, H. N. (2006). The Roles of Habit and Website Quality in E-Commerce. International Journal of Information Management, 26(6), 469–483.
  • Mani, Z., & Chouk, I. (2018). Consumer Resistance to Innovation in Services: Challenges and Barriers in the Internet of Things Era. Journal of Product Innovation Management, 35(5), 780-807.
  • McCarthy, M. B., Collins, A. M., Flaherty, S. J., & McCarthy, S. N. (2017). Healthy Eating Habit: A Role for Goals, Identity, and Self-Control? Psychology & Marketing, 34(8), 772–785.
  • McAndrew, F. T., & Koehnke, S. S. (2016). On the Nature of Creepiness. New Ideas in Psychology, 43, 10–15.
  • McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa… Examine the Variables Influencing the Use of Artificial Intelligent in-Home Voice Assistants. Computers in Human Behaviour, 99, 28-37.
  • Mishra, A., Shukla, A., & Sharma, S. K. (2022). Psychological Determinants of Users’ Adoption and Word-Of-Mouth Recommendations of Smart Voice Assistants. International Journal of Information Management, 67, 102413.
  • Moriuchi, E. (2019). Okay, Google! An Empirical Study on Voice Assistants on Consumer Engagement and Loyalty. Psychology & Marketing,36(5), 489–501.
  • Mou, Y., & Meng, X. (2024). Alexa, It is Creeping Over Me-Exploring the Impact of Privacy Concerns on Consumer Resistance to Intelligent Voice Assistants. Asia Pacific Journal of Marketing and Logistics, 36(2), 261-292.
  • Pennington, N., & Hastie, R. (1988). Explanation-Based Decision Making: Effects of Memory Structure on Judgment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(3), 521.
  • Phillips, M. (2020). Horror Masks are Never Just About the Monster: These Cinematic Mainstays Continue to Terrify. Accessed 10 January 2025 from https://www.nytimes.com/2020/10/23/movies/halloween-horror-masks.html
  • Raff, S., Rose, S., & Huynh, T. (2024). Perceived Creepiness in Response to Smart Home Assistants: A Multi-Method Study. International Journal of Information Management, 74, 102720.
  • Rese, A., Ganster, L., & Baier, D. (2020). Chatbots in Retailers’ Customer Communication: How to Measure Their Acceptance? Journal of Retailing and Consumer Services, 56, 102176.
  • Sahu, A. K., Padhy, R. K., & Dhir, A. (2020). Envisioning The Future of Behavioural Decision-Making: A Systematic Literature Review of Behavioural Reasoning Theory. Australasian Marketing Journal, 28(4), 145-159.
  • Singh, C., Dash, M. K., Sahu, R., & Kumar, A. (2024). Investigating The Acceptance Intentions of Online Shopping Assistants in E-Commerce Interactions: Mediating Role of Trust and Effects of Consumer Demographics. Heliyon, 10(3).
  • Sivathanu, B. (2021). Adoption of Online Subscription Beauty Boxes: A Behavioural Reasoning Theory (BRT) Perspective. Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business, 958-983.
  • Sohn, K., & Kwon, O. (2020). Technology Acceptance Theories and Factors Influencing Artificial Intelligence-Based Intelligent Products. Telematics and Informatics, 47, 101324.
  • Stevens, A. M., 2016. Antecedents and Outcomes of Perceived Creepiness in Online Personalized Communications [Published doctoral dissertation]. Cleveland, Ohio: Case Western Reserve University.
  • Talke, K., & Heidenreich, S. (2014). How to Overcome Pro‐Change Bias: Incorporating Passive and Active Innovation Resistance in Innovation Decision Models. Journal of Product Innovation Management, 31(5), 894-907.
  • Tandon, A., Dhir, A., Kaur, P., Kushwah, S., & Salo, J. (2020). Behavioral Reasoning Perspectives on Organic Food Purchase. Appetite, 154, 104786.
  • Tene, O., & Polonetsky, J. (2013). A Theory of Creepy: Technology, Privacy and Shifting Social Norms. Yale JL & Tech., 16, 59.
  • Torkamaan, H., Barbu, C. M., & Ziegler, J. (2019, September). How Can They Know That? A Study of Factors Affecting the Creepiness of Recommendations. [Paper presentation]. 13th ACM Conference on Recommender Systems.
  • Uddin, S. F., Sabir, L. B., Kirmani, M. D., Kautish, P., Roubaud, D., & Grebinevych, O. (2024). Driving Change: Understanding Consumers’ Reasons Influencing Electric Vehicle Adoption from The Lens of Behavioural Reasoning Theory. Journal of Environmental Management, 369, 122277.
  • 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.
  • Vimalkumar, M., Sharma, S. K., Singh, J. B.& Dwivedi, Y. K. (2021). Okay Google, What About My Privacy?’: User's Privacy Perceptions and Acceptance of Voice-Based Digital Assistants. Computers in Human Behaviour, 120, 106763.
  • Watt, M. C., Maitland, R. A., & Gallagher, C. E. (2017). A Case of the “Heeby Jeebies”: An Examination of Intuitive Judgements of “Creepiness”. Canadian Journal of Behavioural Science / Revue Canadienne Des Sciences Délután Comportement, 49(1), 58–69.
  • Westaby, J. D. (2005). Behavioural Reasoning Theory: Identifying New Linkages Underlying Intentions and Behaviour. Organizational Behaviour and Human Decision Processes, 98(2), 97-120.
  • Wu, B., & Chen, X. (2017). Continuance Intention to Use Moocs: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model. Computers in Human Behaviour, 67, 221–232.
  • Zafar, A. U., Shahzad, M., Shahzad, K., Appolloni, A., & Elgammal, I. (2024). Gamification and Sustainable Development: Role of Gamified Learning in Sustainable Purchasing. Technological Forecasting and Social Change, 198, 122968.
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Details

Primary Language English
Subjects Integrated Marketing Communication, Digital Marketing
Journal Section Articles
Authors

Müzeyyen Gelibolu 0000-0002-9852-7243

Publication Date May 6, 2025
Submission Date September 11, 2024
Acceptance Date March 21, 2025
Published in Issue Year 2025 Issue: 47

Cite

APA Gelibolu, M. (2025). THE ROLE OF PERCEIVED CREEPINESS AND CONSUMER RESISTANCE IN CONSUMER HABITS WITH VOICE ASSISTANTS: A BEHAVIORAL REASONING PERSPECTIVE. Uluslararası İktisadi Ve İdari İncelemeler Dergisi(47), 87-102. https://doi.org/10.18092/ulikidince.1548247

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