PERAKENDECİLİKTE DRONE TABANLI TESLİMAT HİZMETİ İÇİN OLUMLU AĞIZDAN AĞIZA İLETİŞİM NİYETİNİ ETKİLEYEN FAKTÖRLER

Yıl 2022, Cilt: 13 Sayı: 1, 40 - 55, 30.06.2022

Bulut Dülek , Zübeyir Çelik

https://doi.org/10.54688/ayd.1105843

Öz

Son zamanlarda popülaritesi artan ve çeşitli alanlarda sonsuz potansiyel sağlayan drone'lar, eğlenceli bir hobi olmanın yanı sıra faydalı bir ticari araçtır. Bu çalışma, drone tabanlı teslimat hizmetleri için güven, hız ve problem farkındalığının olumlu ağızdan ağza iletişim niyeti üzerindeki doğrudan etkilerini analiz etmeyi amaçlamıştır. Ayrıca, drone tabanlı teslimat hizmetinin olumlu ağızdan ağıza iletişim niyeti üzerindeki etkisi ve tüketicilerin drone tabanlı teslimat hizmeti için olumlu ağızdan ağıza iletişim niyetlerinin cinsiyete ve jenerasyonlara göre önemli ölçüde farklılaşıp farklılaşmadığı da analiz edilmiştir. Çalışma için 433 katılımcıya online anket yöntemi kullanılarak ulaşılmıştır. Elde edilen verileri analiz etmek için birçok farklı istatistiksel analiz kullanılmıştır. Kontrol değişkenlerine göre tüketicilerin ağızdan ağıza iletişim niyetlerindeki farklılıkları test etmek için tek örneklem t testi, bağımsız grup t testi ve ANOVA kullanılmıştır. Çalışma sonucunda drone tabanlı teslimat hizmetinin olumlu ağızdan ağıza iletişim niyeti üzerindeki olumlu etkisinin anlamlı olduğu sonucuna varılmıştır. Ayrıca drone tabanlı teslimat hizmeti için güven, hız ve problem farkındalığının olumlu ağızdan ağıza iletişim niyeti üzerinde doğrudan anlamlı ve olumlu etkileri olduğu sonucuna varılmıştır. Bununla birlikte, tüketicilerin olumlu ağızdan ağıza iletişim niyetlerinde cinsiyete ve jenerasyonlara göre anlamlı bir farklılık bulunamamıştır. Araştırmanın sonuçları tartışılmış ve önerilerde bulunulmuştur.

Anahtar Kelimeler

Drone tabanlı teslimat, Algılanan hız, Algılanan güven, Problem farkındalığı, WOM iletişim niyeti

FACTORS AFFECTING POSITIVE WORD-OF-MOUTH COMMUNICATION INTENTION FOR DRONE-BASED DELIVERY SERVICE IN RETAILING

Öz

Drones, which have recently grown in popularity and provide endless potential in a variety of fields, are a useful commercial tool in addition to being a fun hobby. This study aimed to analyze the direct effects of perceived trust, perceived speed, and problem awareness on positive word-of-mouth communication intention for drone-based delivery services. In addition, the effect of drone-based delivery service on positive word-of-mouth communication intention and whether consumers' positive word-of-mouth communication intentions for drone-based delivery service differ significantly by gender and generation were also analyzed. The online survey method was used to reach 433 people for the study. Many different statistical analyzes were used to analyze the obtained data. One-sample t-test, independent-group t-test and ANOVA were used to test the differences in consumers' WOM communication intentions according to control variables. As a result of the study, it was concluded that the positive effect of the drone-based delivery service on positive word-of-mouth communication intention was significant. It has also been concluded that perceived trust, perceived speed, and problem awareness have direct significant and positive effects on positive word of mouth communication intention for drone-based delivery services. However, no significant difference was found in the positive word of mouth communication intentions of consumers according to gender and generations. The results of the study were discussed and recommendations were provided.

Anahtar Kelimeler

Drone-based delivery, Perceived speed, Perceived trust, Problem awareness, WOM communication intention, Drone-based delivery, Perceived speed, Perceived trust, Problem awareness, WOM communication intention

Kaynakça

• Anderson, E. W. (1998). Customer satisfaction and word-of-mouth. Journal of Service Research, 1(1), 1–14
• Aydın, İ. (2021). Investigation of the effect of robot waiter usage desire on word of mouth communication and robot waiter usage attitude in restaurants. Turkish Business Journal, 2(4), 93-105.
• Belanche, D., Flavián, M., & Pérez-Rueda, A. (2020). Mobile apps use and wom in the food delivery sector: the role of planned behavior, perceived security and customer lifestyle compatibility. Sustainability, 12(10), 4275.
• 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.
• Chiasson, M. A., Shaw, F. S., Humberstone, M., Hirshfield, S., & Hartel, D. (2009). Increased HIV disclosure three months after an online video intervention for men who have sex with men (MSM). AIDS Care, 21(9), 1081-1089.
• Chu, S. C., & Kim, Y. (2011). Determinants of consumer engagement in electronic word-of-mouth (ewom) in social networking site. International Journal of Advertising, 30(1), 47–75.
• Del Rio, A. B., Vazquez, R., & Iglesias, V. (2001). The effects of brand associations on consumer response. Journal of Consumer Marketing, 18(5), 410-425.
• Dichter, E. (1966). How word-of-mouth advertising Works. Harvard Business Review, 44, 147-166.
• Divanoğlu, S. U. (2016). Ağızdan ağıza iletişim ile tüketicilerin alışveriş merkezi tercih etme davranışı arasındaki ilişki. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(1), 97-106.
• Ennew, C. T., Banerjee, A. K., & Li, D. (2000). Managing word of mouth communication: empirical evidence from India. International Journal of Bank Marketing, 18(2), 75-83.
• Floreano, D., and Wood, R. J. (2015). Science, technology and the future of small autonomous drones. Nature 521(7553), 460–466.
• Gao, Y. L., Mattila, A. S., & Lee, S. (2016). A meta-analysis of behavioral ıntentions for environment-friendly ınitiatives in hospitality research. International Journal of Hospitality Management, 54, 107-115.
• Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
• Hair, J. F., Jr., Black, William C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
• Han, H., Al-Ansi, A., Chi, X., Baek, H., & Lee, K. S. (2020). “Impact of environmental csr, service quality, emotional attachment, and price perception on word-of-mouth for full-service airlines. Sustainability, 12(10), 3974.
• Huang, C.-C. (2017). Cognitive factors in predicting continued use of information systems with technology adoption models. Information Research, 22(2), 1-29.
• Hwang, J., & Kim, H. (2019). Consequences of a green image of drone food delivery services: the moderating role of gender and age. Business Strategy and the Environment, 28(5), 872-884.
• Hwang, J., Kim, W., & Kim, J. J. (2020). Application of the value-belief-norm model to environmentally friendly drone food delivery services: the moderating role of product involvement. International Journal of Contemporary Hospitality Management, 32(5), 1775-1794.
• Jalilvand, M. R., Salimipour, S., Elyasi, M., & Mohammadi, M. (2017). Factors influencing word of mouth behaviour in the restaurant industry. Marketing Intelligence & Planning, 35(1), 81-110.
• Jin, S.V., Muqaddam, A., & Ryu, E. (2019). Instafamous and social media influencer marketing. Marketing Intelligence & Planning, 37(5), 567-579.
• Joerss, M., Schröder, J., Neuhaus, F., Klink, C., & Mann, F. (2016). Parcel delivery: the future of last mil. McKinsey&Company, 1-32.
• Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36.
• Kayış, A. (2005). Parametrik hipotez testler, spss uygulamalı çok değişkenli istatistik teknikleri. Ş.Kalaycı(Ed), SPSS uygulamalı çok değişkenli istatistik teknikleri içinde(ss.403-419). Ankara: Asil Yayın Dağıtım.
• Kim, S. H. (2020). Choice model based analysis of consumer preference for drone delivery service. Journal of Air Transport Management, 84, 101785.
• 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.
• Kirschstein, T. (2021). Energy demand of parcel delivery services with a mixed fleet of electric vehicles. Cleaner Engineering and Technology, 5, 100322.
• Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610.
• Kulviwat, S., Bruner Ii, G. C., Kumar, A., Nasco, S. A., & Clark, T. (2007). Toward a unified theory of consumer acceptance technology. Psychology & Marketing, 24(12), 1059-1084.
• Kumar, S., Jain, A., & Hsieh, J. K. (2021). Impact of apps aesthetics on revisit ıntentions of food delivery apps: the mediating role of pleasure and arousal. Journal of Retailing and Consumer Services, 63, 102686.
• Lee, H. L., Chen, Y., Gillai, B., & Rammohan, S. (2016). Technological disruption and ınnovation in last-mile delivery. Stanford Graduate School of Business, 1-26.
• Leon, S., Chen, C., & Ratcliffe, A. (2021). Consumers’ perceptions of last mile drone delivery. International Journal of Logistics Research and Applications, 1-20.
• Lu, L., Zhang, P., & Zhang, T. C. (2021). Leveraging human-likeness of robotic service at restaurants. International Journal of Hospitality Management, 94, 102823.
• Machi, V. (2017). Worldwide military drone production to swell over next decade. National Defense, 102(770), 12-12.
• Matos, C. A., & Rossi, C. A. V. (2008). Word-of-mouth communications in marketing: a meta-analytic review of the antecedents and moderators. Journal of the Academy of Marketing Science, 36(4), 578–596.
• Mayer, R.C., Davis, J. H., & Schoorman, F.D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-34.
• Odabaşı Y. and Oyman M. (2002). Pazarlama iletişimi yönetimi (7.Baskı). İstanbul: Mediacat Yayınları.
• Roca, J. C., García, J. J., & De La Vega, J. J. (2009). The importance of perceived trust, security and privacy in online trading systems. Information Management & Computer Security, 17(2), 96-113.
• Said, H. M., Sukarno, A. F. M., Razak, Z. A., Bayaah, S., & Ahmad, S. R. (2018). The impact of service quality on customers’ positive word of mouth towards food truck business in Malaysia. International Journal of Academic Research in Business and Social Sciences, 8(9), 1919-1940.
• Sampat, B. H., & Sabat, K. C. (2021). What leads consumers to spread ewom for food ordering apps?. Journal of International Technology and Information Management, 29(4), 50-77.
• Stevens, J. (1996). Applied multivariate statistics for the social sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.
• Sun, L. B., & Qu, H. (2011). Is there any gender effect on the relationship between service quality and word-of-mouth?. Journal of Travel & Tourism Marketing, 28(2), 210-224.
• Sundaram, D. S., Mitra, K., & Webster, C. (1998). Word-of-mouth communications: a motivational analysis. Advances in Consumer Research, 25, 527–531.
• WilsonVon Voorhis, C. R., & Morgan, B. L. (2007). Understanding power and rules of thumb for determining sample sizes. tutorial in Quantitative Methods for Psychology, 3(2), 43–50.
• Wu, J. J., Hwang, J. N., Sharkhuu, O., & Tsogt-Ochir, B. (2018). Shopping online and off-line? complementary service quality and ımage congruence. Asia Pacific Management Review, 23(1), 30-36.
• Yazıcı Ayyıldız, A., & Eroğlu, E. (2021). Restoranlarda kullanılan akıllı teknolojiler ve robot restoranlar hakkında tripadvisor’da yapılan yorumların değerlendirilmesi. Journal of Tourism and Gastronomy Studies, 9(2), 1102-1122.
• Yoo, W., Yu, E., & Jung, J. (2018). Drone delivery: factors affecting the public’s attitude and ıntention to adopt. Telematics and Informatics, 35(6), 1687-1700.
• Zhang, T., D. Tao, X. Qu, X. Zhang, R. Lin, and W. Zhang (2019). The roles of ınitial trust and perceived risk in public’s acceptance of automated vehicles. Transportation Research Part C: Emerging Technologies, 98, 207–220.