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Investigating The Factors Affecting Consumers' Behavioral Intentions Towards Drone Food and Beverage Delivery Service

Yıl 2025, Cilt: 16 Sayı: 2, 378 - 389, 26.11.2025
https://doi.org/10.54558/jiss.1695791

Öz

Aim: The purpose of this study is to examine the impact of hedonic motivation, functional motivation, perceived risk and perceived trust on consumers' positive word-of-mouth intention and intention to use drone food and beverage delivery service.
Method: Data were collected from the target population using social media tools and snowball sampling method. An online survey was used. Data were obtained from 210 people who watched a detailed video about the drone delivery service. The data were analysed using SPSS and Jamovi software.
Results: According to the results of the analyses, hedonic motivation, functional motivation and perceived trust have a significant positive effect on both intention to use and positive word-of-mouth intention. Perceived risk, on the other hand, has a significant negative effect on both intention to use and positive word-of-mouth intention.
Conclusion: As a result of the study, it was determined that the factors affecting consumers' behavioral intentions (intention to use and word-of-mouth intention) towards drone food and beverage delivery service are hedonic motivation, functional motivation, perceived trust and perceived risk.
Originality: The study successfully explains the factors on behavioral intentions towards drone food and beverage delivery service from the consumer perspective

Kaynakça

  • Alfaisal, R., Alhumaid, K., Alnazzawi, N., Abou Samra, R., Salloum, S., Shaalan, K. & Monem, A. A. (2022). Predicting the intention to use Google Glass in the educational projects: A hybrid SEM-ML approach. Academy of Strategic Management Journal, 21(6), 1-13.
  • Aydın, İ. (2022). Hedonik motivasyonun drone ile teslimata yönelik tutum üzerindeki etkisi: Ağızdan ağıza iletişimin aracılık rolü. İşletme Akademisi Dergisi, 3(1), 34-45.
  • Aydın, İ. & Çelik, Z. (2023). Investigation of the mediating roles of perceived risk and word of mouth in the effect of perceived trust in with drone delivery on intention to use. Equinox, Journal of Economics, Business & Political Studies, 10(1), 49-67.
  • Bagozzi, R. P., Yi, Y. & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421-458.
  • Bamburry, D. (2015). Drones: Designed for product delivery. Design Management Review, 26(1), 40-48.
  • Bozkurt, M. (2023). Drone aracılığıyla ürün dağıtımına yönelik tüketici tutumlarının incelenmesi. Mersin Üniversitesi Sosyal Bilimler Enstitüsü İşletme Bilgi Yönetimi Anabilim Dalı. Yüksek Lisans Tezi.
  • Cha, S. S. (2020). Customers’ intention to use robot-serviced restaurants in Korea: relationship of coolness and MCI factors. International Journal of Contemporary Hospitality Management, 32(9), 2947-2968.
  • Chang, V., Chundury, P. & Chetty, M. (2017, May). Spiders in the sky: User perceptions of drones, privacy, and security. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 6765-6776).
  • Çelik, Z. & Aydın, İ. (2021). Perakendecilikte drone ile ürün teslimatının tüketicilerin davranışsal niyetlerine etkisi. Business & Management Studies: An International Journal, 9(4), 1422-1436.
  • DeCarlo, L. T. (1997). On the meaning and use of kurtosis. Psychological methods, 2(3), 292.
  • Dukkanci, O., Kara, B. Y. & Bektaş, T. (2021). Minimizing energy and cost in range-limited drone deliveries with speed optimization. Transportation Research Part C: Emerging Technologies, 125, 102985.
  • Dülek, B. & Çelik, Z. (2022). Factors affecting positive word-of-mouth communication intention for drone-based delivery service in retailing. Akademik Yaklaşımlar Dergisi, 13(1), 40-55.
  • Erdem, M. A. (2024). Son adım teslimatta otonom drone kullanımının kullanıcı kabulü. İstanbul Teknik Üniversitesi Lisansüstü Eğitim Enstitüsü İşletme Mühendisliği Anabilim Dalı. Yüksek Lisans Tezi.
  • Feng, J. (2010). Three essays of online word of mouth. The University of Wisconsin-Milwaukee. Dissertations & Theses.
  • Field, A. P. (2024). Discovering statistics using IBM SPSS statistics (6th Edition). Sage Publications, Inc.
  • Forsythe, S. M. & Shi, B. (2003). Consumer patronage and risk perceptions in Internet shopping. Journal of Business Research, 56(11), 867-875.
  • Furukawa, H., Matsumura, K. & Harada, S. (2019). Effect of consumption values on consumer satisfaction and brand commitment: Investigating functional, emotional, social, and epistemic values in the running shoes market. International Review of Management and Marketing, 9(6), 158.
  • Gupta, S. & Kim, H. W. (2007). The moderating effect of transaction experience on the decision calculus in on-line repurchase. International Journal of Electronic Commerce, 12(1), 127-158.
  • Hair, J. F., Jr., Black, W. C., Babin, B. J. & Anderson, R. E. (2009). Multivariate data analysis (Seventh Edition). Upper Saddle River, NJ: Pearson Prentice Hall.
  • Hirschman, E. C. & Holbrook, M. B. Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing, 46(3), 1982, 92-101.
  • Hossain, R. (2022). A short review of the drone technology. International Journal of Mechatronics and Manufacturing Technology, 7(2), 53-68.
  • Hwang, J., Kim, J. J. & Lee, K. W. (2021). Investigating consumer innovativeness in the context of drone food delivery services: Its impact on attitude and behavioral intentions. Technological Forecasting and Social Change, 163, 120433.
  • Hwang, J. & Choe, J. Y. (2019). Exploring perceived risk in building successful drone food delivery services. International Journal of Contemporary Hospitality Management, 31(8), 3249-3269.
  • Hwang, J., Kim, H. & Kim, W. (2019). Investigating motivated consumer innovativeness in the context of drone food delivery services. Journal of Hospitality and Tourism Management, 38, 102-110.
  • Hwang, J., Lee, J. S. & Kim, H. (2019). Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: The moderating role of gender and age. International Journal of Hospitality Management, 81, 94-103.
  • Ismagilova, E., Rana, N. P., Slade, E. L. & Dwivedi, Y. K. (2021). A meta-analysis of the factors affecting eWOM providing behaviour. European Journal of Marketing, 55(4), 1067-1102.
  • Kapser, S. & Abdelrahman, M. (2020). Acceptance of autonomous delivery vehicles for last-mile delivery in Germany-Extending UTAUT2 with risk perceptions. Transportation Research Part C: Emerging Technologies, 111, 210-225.
  • Kavak, A. & Odabaş, H. (2023). Üniversite kütüphanelerinde teknolojik ve kurumsal bilgi güvenliği önlemlerinin uygulanma yeterliliği. Çankırı Karatekin Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 14(2), 293-332. https://doi.org/10.54558/jiss.1321640
  • Khan, U., Dhar, R. & Wertenbroch, K. (2005). A behavioral decision theory perspective on hedonic and utilitarian choice. In Inside Consumption (1st Edition), 144-165.
  • Khan, R., Tausif, S. & Javed Malik, A. (2019). Consumer acceptance of delivery drones in urban areas. International Journal of Consumer Studies, 43(1), 87-101.
  • Kim, J. J. & Hwang, J. (2020). Merging the norm activation model and the theory of planned behavior in the context of drone food delivery services: Does the level of product knowledge really matter?. Journal of Hospitality and Tourism Management, 42, 1-11.
  • Klein, J. F., Falk, T., Esch, F. R. & Gloukhovtsev, A. (2016). Linking pop-up brand stores to brand experience and word of mouth: The case of luxury retail. Journal of Business Research, 69(12), 5761-5767.
  • Lee, S. (2016). Consumers' Motivation and Active Participation on Fashion Brand's Social Networking Sites: Moderating Effect of General SNS Usage. In International Textile and Apparel Association Annual Conference Proceedings (Vol. 73, No. 1). November, Iowa State University Digital Press.
  • Leon, S., Chen, C. & Ratcliffe, A. (2023). Consumers’ perceptions of last mile drone delivery. International Journal of Logistics Research and Applications, 26(3), 345-364.
  • Li, C. (2021). Artificial intelligence technology in UAV equipment. In 2021 IEEE/ACIS 20th International Fall Conference on Computer and Information Science (ICIS Fall) (pp. 299-302). IEEE.
  • Marsden, N., Bernecker, T., Zöllner, R., Sußmann, N. & Kapser, S. (2018). BUGA: log–A real-world laboratory approach to designing an automated transport system for goods in Urban Areas. In 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1-9). June. IEEE.
  • Miles, J. (2014). Tolerance and variance inflation factor. Wiley Statsref: Statistics Reference Online.
  • Millan, E. S. & Howard, E. (2007). Shopping for pleasure? Shopping experiences of Hungarian consumers. International Journal of Retail & Distribution Management, 35(6), 474-487.
  • 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.
  • Mittendorf, C., Franzmann, D. & Ostermann, U. (2017). Why would customers engage in drone deliveries?. In AMCIS 2017 Proceedings, 1-10.
  • Moore, S. G. (2009). Some things are better left unsaid: How word of mouth influences the speaker. Duke University.
  • Nakiboğlu, G. (2020). Drone taşımacılığı ve son-adım teslimatta kullanımı. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 24(2), 285-298.
  • Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric theory, (Third Edition). New York, McGraw-Hill.
  • Odabaşı Y. & Oyman M. (2019). Pazarlama iletişimi yönetimi. (3. Baskı). Mediacat Yayınları.
  • Ramadan, Z. B., Farah, M. F. & Mrad, M. (2017). An adapted TPB approach to consumers’ acceptance of service-delivery drones. Technology Analysis & Strategic Management, 29(7), 817-828.
  • Rini, G. P. & Ferdinand, A. T. (2024). How Does Ergo-Functional Value Resonance Enhance Intention to Use? An SDL Perspective. International Journal of Innovation and Technology Management, 21(01), 2450003.
  • Stevens, J. (2009). Applied multivariate statistics for the social sciences, (5th Edition) Routledge Academic.
  • Sukhu, A. & Bilgihan, A. (2021). The impact of hedonic dining experiences on word of mouth, switching intentions and willingness to pay. British Food Journal, 123(12), 3954-3969.
  • Tabachnick, B. G., Fidell, L. S. & Ullman, J. B. (2013). Using multivariate statistics (Vol. 6, pp. 497-516). Boston, MA: Pearson.
  • Tom, N. M. F. (2020). Crashed! Why drone delivery is another tech idea not ready to take off. International Business Research, 13(7), 251-251.
  • Turğut, M. & Şeker, B. (2022). İnsansız hava araçlarının (İHA) taşımacılıkta kullanımına yönelik keşfedici bir araştırma: drone taşımacılığı ve uygulamaları. Journal of Intelligent Transportation Systems & Applications, 5(2), 169-187.
  • Uygun, M, Taner, Ö. Ö. & Özbay, S. (2011). Tüketicilerin hizmet deneyimleri ile ağızdan ağıza iletişim davranışları arasındaki ilişkiler. Organizasyon ve Yönetim Bilimleri Dergisi, 3(2), 331-342.
  • Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704.
  • Wangenheim, F. V. & Bayón, T. (2004). The effect of word of mouth on services switching: Measurement and moderating variables. European Journal of Marketing, 38(9/10), 1173-1185.
  • Widjaja, E. (2010). Motivation behind volunteerism.
  • Wu, S., Ren, M., Pitafi, A. H. & Islam, T. (2020). Self‐i congruence, functional congruence, and mobile app intention to use. Mobile Information Systems, 2020(1), 5125238.
  • VanVoorhis, C. W. & Morgan, B. L. (2007). Understanding power and rules of thumb for determining sample sizes. Tutorials in Quantitative Methods for Psychology, 3(2), 43-50.
  • Yap, K. B., Soetarto, B. & Sweeney, J. C. (2013). The relationship between electronic word-of-mouth motivations and message characteristics: The sender's perspective. Australasian Marketing Journal, 21(1), 66-74.
  • Yoo, W., Yu, E. & Jung, J. (2018). Drone delivery: Factors affecting the public’s attitude and intention to adopt. Telematics and Informatics, 35(6), 1687-1700.
  • Zhang, Y. &Lv, T. (2010, September). Analysis of the relationship between involvement and the internet word-of-mouth. In 2010 2nd IEEE InternationalConference on Network Infrastructure and Digital Content (pp. 1018-1024). IEEE.
  • Zwickle, A., Farber, H. B. & Hamm, J. A. (2019). Comparing public concern and support for drone regulation to the current legal framework. Behavioral Sciences & the Law, 37(1), 109-124.

Tüketicilerin Drone ile Yiyecek ve İçecek Teslimatı Hizmetine Yönelik Davranışsal Niyetlerini Etkileyen Faktörlerin Araştırılması

Yıl 2025, Cilt: 16 Sayı: 2, 378 - 389, 26.11.2025
https://doi.org/10.54558/jiss.1695791

Öz

Amaç: Bu çalışmanın amacı, hedonik motivasyon, fonksiyonel motivasyon, algılanan güven ve algılanan riskin tüketicilerin drone ile yiyecek ve içecek teslimatı hizmetine yönelik olumlu ağızdan ağıza iletişim niyeti ve kullanma niyeti üzerindeki etkisini incelemektir.
Yöntem: Sosyal medya araçları ve kartopu örnekleme yöntemi kullanılarak hedef kitleden veri toplanmıştır. Çevrimiçi bir anket kullanılmıştır. Drone ile teslimat hizmeti hakkındaki detaylı videoyu izleyen 210 kişiden veri elde edilmiştir. SPSS ve Jamovi yazılımları kullanılarak verilerin analizi yapılmıştır.
Bulgular Analiz sonuçlarına göre, hedonik motivasyon, fonksiyonel motivasyon ve algılanan güven hem kullanma niyetini hem de olumlu ağızdan ağıza iletişim niyetini olumlu yönde anlamlı olarak etkilemektedir. Algılanan risk ise hem kullanma niyetini hem de olumlu ağızdan ağıza iletişim niyetini olumsuz yönde anlamlı olarak etkilemektedir.
Sonuç: Çalışma sonucunda, tüketicilerin drone ile yiyecek ve içecek teslimatı hizmetine yönelik davranışsal niyetlerini (kullanma niyeti ve ağızdan ağıza iletişim niyeti) etkileyen faktörlerin hedonik motivasyon, fonksiyonel motivasyon, algılanan güven ve algılanan risk olduğu belirlenmiştir.
Özgünlük: Çalışma, tüketici perspektifinden drone ile yiyecek ve içecek teslimat hizmetine yönelik davranışsal niyetleri etkileyen faktörleri başarılı bir şekilde açıklamaktadır.

Kaynakça

  • Alfaisal, R., Alhumaid, K., Alnazzawi, N., Abou Samra, R., Salloum, S., Shaalan, K. & Monem, A. A. (2022). Predicting the intention to use Google Glass in the educational projects: A hybrid SEM-ML approach. Academy of Strategic Management Journal, 21(6), 1-13.
  • Aydın, İ. (2022). Hedonik motivasyonun drone ile teslimata yönelik tutum üzerindeki etkisi: Ağızdan ağıza iletişimin aracılık rolü. İşletme Akademisi Dergisi, 3(1), 34-45.
  • Aydın, İ. & Çelik, Z. (2023). Investigation of the mediating roles of perceived risk and word of mouth in the effect of perceived trust in with drone delivery on intention to use. Equinox, Journal of Economics, Business & Political Studies, 10(1), 49-67.
  • Bagozzi, R. P., Yi, Y. & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421-458.
  • Bamburry, D. (2015). Drones: Designed for product delivery. Design Management Review, 26(1), 40-48.
  • Bozkurt, M. (2023). Drone aracılığıyla ürün dağıtımına yönelik tüketici tutumlarının incelenmesi. Mersin Üniversitesi Sosyal Bilimler Enstitüsü İşletme Bilgi Yönetimi Anabilim Dalı. Yüksek Lisans Tezi.
  • Cha, S. S. (2020). Customers’ intention to use robot-serviced restaurants in Korea: relationship of coolness and MCI factors. International Journal of Contemporary Hospitality Management, 32(9), 2947-2968.
  • Chang, V., Chundury, P. & Chetty, M. (2017, May). Spiders in the sky: User perceptions of drones, privacy, and security. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 6765-6776).
  • Çelik, Z. & Aydın, İ. (2021). Perakendecilikte drone ile ürün teslimatının tüketicilerin davranışsal niyetlerine etkisi. Business & Management Studies: An International Journal, 9(4), 1422-1436.
  • DeCarlo, L. T. (1997). On the meaning and use of kurtosis. Psychological methods, 2(3), 292.
  • Dukkanci, O., Kara, B. Y. & Bektaş, T. (2021). Minimizing energy and cost in range-limited drone deliveries with speed optimization. Transportation Research Part C: Emerging Technologies, 125, 102985.
  • Dülek, B. & Çelik, Z. (2022). Factors affecting positive word-of-mouth communication intention for drone-based delivery service in retailing. Akademik Yaklaşımlar Dergisi, 13(1), 40-55.
  • Erdem, M. A. (2024). Son adım teslimatta otonom drone kullanımının kullanıcı kabulü. İstanbul Teknik Üniversitesi Lisansüstü Eğitim Enstitüsü İşletme Mühendisliği Anabilim Dalı. Yüksek Lisans Tezi.
  • Feng, J. (2010). Three essays of online word of mouth. The University of Wisconsin-Milwaukee. Dissertations & Theses.
  • Field, A. P. (2024). Discovering statistics using IBM SPSS statistics (6th Edition). Sage Publications, Inc.
  • Forsythe, S. M. & Shi, B. (2003). Consumer patronage and risk perceptions in Internet shopping. Journal of Business Research, 56(11), 867-875.
  • Furukawa, H., Matsumura, K. & Harada, S. (2019). Effect of consumption values on consumer satisfaction and brand commitment: Investigating functional, emotional, social, and epistemic values in the running shoes market. International Review of Management and Marketing, 9(6), 158.
  • Gupta, S. & Kim, H. W. (2007). The moderating effect of transaction experience on the decision calculus in on-line repurchase. International Journal of Electronic Commerce, 12(1), 127-158.
  • Hair, J. F., Jr., Black, W. C., Babin, B. J. & Anderson, R. E. (2009). Multivariate data analysis (Seventh Edition). Upper Saddle River, NJ: Pearson Prentice Hall.
  • Hirschman, E. C. & Holbrook, M. B. Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing, 46(3), 1982, 92-101.
  • Hossain, R. (2022). A short review of the drone technology. International Journal of Mechatronics and Manufacturing Technology, 7(2), 53-68.
  • Hwang, J., Kim, J. J. & Lee, K. W. (2021). Investigating consumer innovativeness in the context of drone food delivery services: Its impact on attitude and behavioral intentions. Technological Forecasting and Social Change, 163, 120433.
  • Hwang, J. & Choe, J. Y. (2019). Exploring perceived risk in building successful drone food delivery services. International Journal of Contemporary Hospitality Management, 31(8), 3249-3269.
  • Hwang, J., Kim, H. & Kim, W. (2019). Investigating motivated consumer innovativeness in the context of drone food delivery services. Journal of Hospitality and Tourism Management, 38, 102-110.
  • Hwang, J., Lee, J. S. & Kim, H. (2019). Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: The moderating role of gender and age. International Journal of Hospitality Management, 81, 94-103.
  • Ismagilova, E., Rana, N. P., Slade, E. L. & Dwivedi, Y. K. (2021). A meta-analysis of the factors affecting eWOM providing behaviour. European Journal of Marketing, 55(4), 1067-1102.
  • Kapser, S. & Abdelrahman, M. (2020). Acceptance of autonomous delivery vehicles for last-mile delivery in Germany-Extending UTAUT2 with risk perceptions. Transportation Research Part C: Emerging Technologies, 111, 210-225.
  • Kavak, A. & Odabaş, H. (2023). Üniversite kütüphanelerinde teknolojik ve kurumsal bilgi güvenliği önlemlerinin uygulanma yeterliliği. Çankırı Karatekin Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 14(2), 293-332. https://doi.org/10.54558/jiss.1321640
  • Khan, U., Dhar, R. & Wertenbroch, K. (2005). A behavioral decision theory perspective on hedonic and utilitarian choice. In Inside Consumption (1st Edition), 144-165.
  • Khan, R., Tausif, S. & Javed Malik, A. (2019). Consumer acceptance of delivery drones in urban areas. International Journal of Consumer Studies, 43(1), 87-101.
  • Kim, J. J. & Hwang, J. (2020). Merging the norm activation model and the theory of planned behavior in the context of drone food delivery services: Does the level of product knowledge really matter?. Journal of Hospitality and Tourism Management, 42, 1-11.
  • Klein, J. F., Falk, T., Esch, F. R. & Gloukhovtsev, A. (2016). Linking pop-up brand stores to brand experience and word of mouth: The case of luxury retail. Journal of Business Research, 69(12), 5761-5767.
  • Lee, S. (2016). Consumers' Motivation and Active Participation on Fashion Brand's Social Networking Sites: Moderating Effect of General SNS Usage. In International Textile and Apparel Association Annual Conference Proceedings (Vol. 73, No. 1). November, Iowa State University Digital Press.
  • Leon, S., Chen, C. & Ratcliffe, A. (2023). Consumers’ perceptions of last mile drone delivery. International Journal of Logistics Research and Applications, 26(3), 345-364.
  • Li, C. (2021). Artificial intelligence technology in UAV equipment. In 2021 IEEE/ACIS 20th International Fall Conference on Computer and Information Science (ICIS Fall) (pp. 299-302). IEEE.
  • Marsden, N., Bernecker, T., Zöllner, R., Sußmann, N. & Kapser, S. (2018). BUGA: log–A real-world laboratory approach to designing an automated transport system for goods in Urban Areas. In 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1-9). June. IEEE.
  • Miles, J. (2014). Tolerance and variance inflation factor. Wiley Statsref: Statistics Reference Online.
  • Millan, E. S. & Howard, E. (2007). Shopping for pleasure? Shopping experiences of Hungarian consumers. International Journal of Retail & Distribution Management, 35(6), 474-487.
  • 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.
  • Mittendorf, C., Franzmann, D. & Ostermann, U. (2017). Why would customers engage in drone deliveries?. In AMCIS 2017 Proceedings, 1-10.
  • Moore, S. G. (2009). Some things are better left unsaid: How word of mouth influences the speaker. Duke University.
  • Nakiboğlu, G. (2020). Drone taşımacılığı ve son-adım teslimatta kullanımı. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 24(2), 285-298.
  • Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric theory, (Third Edition). New York, McGraw-Hill.
  • Odabaşı Y. & Oyman M. (2019). Pazarlama iletişimi yönetimi. (3. Baskı). Mediacat Yayınları.
  • Ramadan, Z. B., Farah, M. F. & Mrad, M. (2017). An adapted TPB approach to consumers’ acceptance of service-delivery drones. Technology Analysis & Strategic Management, 29(7), 817-828.
  • Rini, G. P. & Ferdinand, A. T. (2024). How Does Ergo-Functional Value Resonance Enhance Intention to Use? An SDL Perspective. International Journal of Innovation and Technology Management, 21(01), 2450003.
  • Stevens, J. (2009). Applied multivariate statistics for the social sciences, (5th Edition) Routledge Academic.
  • Sukhu, A. & Bilgihan, A. (2021). The impact of hedonic dining experiences on word of mouth, switching intentions and willingness to pay. British Food Journal, 123(12), 3954-3969.
  • Tabachnick, B. G., Fidell, L. S. & Ullman, J. B. (2013). Using multivariate statistics (Vol. 6, pp. 497-516). Boston, MA: Pearson.
  • Tom, N. M. F. (2020). Crashed! Why drone delivery is another tech idea not ready to take off. International Business Research, 13(7), 251-251.
  • Turğut, M. & Şeker, B. (2022). İnsansız hava araçlarının (İHA) taşımacılıkta kullanımına yönelik keşfedici bir araştırma: drone taşımacılığı ve uygulamaları. Journal of Intelligent Transportation Systems & Applications, 5(2), 169-187.
  • Uygun, M, Taner, Ö. Ö. & Özbay, S. (2011). Tüketicilerin hizmet deneyimleri ile ağızdan ağıza iletişim davranışları arasındaki ilişkiler. Organizasyon ve Yönetim Bilimleri Dergisi, 3(2), 331-342.
  • Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704.
  • Wangenheim, F. V. & Bayón, T. (2004). The effect of word of mouth on services switching: Measurement and moderating variables. European Journal of Marketing, 38(9/10), 1173-1185.
  • Widjaja, E. (2010). Motivation behind volunteerism.
  • Wu, S., Ren, M., Pitafi, A. H. & Islam, T. (2020). Self‐i congruence, functional congruence, and mobile app intention to use. Mobile Information Systems, 2020(1), 5125238.
  • VanVoorhis, C. W. & Morgan, B. L. (2007). Understanding power and rules of thumb for determining sample sizes. Tutorials in Quantitative Methods for Psychology, 3(2), 43-50.
  • Yap, K. B., Soetarto, B. & Sweeney, J. C. (2013). The relationship between electronic word-of-mouth motivations and message characteristics: The sender's perspective. Australasian Marketing Journal, 21(1), 66-74.
  • Yoo, W., Yu, E. & Jung, J. (2018). Drone delivery: Factors affecting the public’s attitude and intention to adopt. Telematics and Informatics, 35(6), 1687-1700.
  • Zhang, Y. &Lv, T. (2010, September). Analysis of the relationship between involvement and the internet word-of-mouth. In 2010 2nd IEEE InternationalConference on Network Infrastructure and Digital Content (pp. 1018-1024). IEEE.
  • Zwickle, A., Farber, H. B. & Hamm, J. A. (2019). Comparing public concern and support for drone regulation to the current legal framework. Behavioral Sciences & the Law, 37(1), 109-124.
Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Tüketici Davranışı
Bölüm Araştırma Makalesi
Yazarlar

Can Dağdalan 0009-0001-5189-7175

Zübeyir Çelik 0000-0003-1692-9378

Yayımlanma Tarihi 26 Kasım 2025
Gönderilme Tarihi 9 Mayıs 2025
Kabul Tarihi 8 Eylül 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 16 Sayı: 2

Kaynak Göster

APA Dağdalan, C., & Çelik, Z. (2025). Tüketicilerin Drone ile Yiyecek ve İçecek Teslimatı Hizmetine Yönelik Davranışsal Niyetlerini Etkileyen Faktörlerin Araştırılması. Çankırı Karatekin Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 16(2), 378-389. https://doi.org/10.54558/jiss.1695791