Planlı Davranış Teorisi Çerçevesinde Kargo Otomat (Kargomat) Sisteminin İncelenmesi: Elektronik Son Adım Teslimat Süreçlerinin E-Ticaret Platformları Özelinde Araştırılması

Year 2022, Volume: 7 Issue: 2, 303 - 320, 16.01.2023

Yavuz Toraman

https://doi.org/10.26650/JTL.2022.1006593

Abstract

Dijitalleşmenin insan hayatına çok farklı noktalarda temas etmesiyle yeniliklerin tüketiciler tarafından algılanması uzmanlar tarafından süreç içerisinde merak konusu olmuştur. Bu bağlamda tüketicilerin yeni teknolojileri kabulünü etkileyen faktörlerin incelenmesi araştırmanın temel problemini oluşturmaktadır. Çalışmada araştırmanın amacına uygun ve literatürde de sıkça değinilen Planlı Davranış Teorisi (PDT) kullanılmıştır. PDT modelinde birden çok ilişki söz konusu olduğundan çalışmada literatüre uygun olarak yapısal eşitlik modellemesi kullanılmıştır. Ayrıca yapısal eşitlik modellemelerinde sıkça kullanılan Smart PLS 3 programı kullanılmıştır. Çalışma verilerinin güvenilirlik ve geçerlilik analizleri sonuçları kabul edilen değerler aralıklarda bulunmasıyla Smart PLS 3 programı yol analizi yapılmıştır. Çalışmaya ait H4 hipotezi reddedilirken diğerleri kabul edilmiştir. H8 hipotezinde ise negatif diğerlerinde ise pozitif bir etki tespit edilmiştir. Çalışmaya dahil edilen ek sorularda katılımcıların %30’undan azı kargomat teknolojisini kullanmadığından dolayı Algılanan Davranışsal Kontrol (ADK) değişkeni sağlıklı bir şekilde veri sağlayamamıştır. Yeterli sayıda aktif kargomat kullanıcısına ulaşılamadığı ve R² değeri 0.122 olarak elde edildiği için araştırmada davranıştan ziyade kullanıcı niyetinin açıklanmasına odaklanılmıştır. ADK’nın niyeti dolaylı olarak hem niyeti hem de davranışı etkilemesi literatüre paralel bir sonuç elde edildiğini göstermektedir. Fakat teknoloji kullanımının tabana yayılımının hızlanmasıyla birlikte kargomatlar fiziksel olarak geniş bir alana yayılmasıyla kargomat teknolojisi son adım teslimatındaki kullanım oranı artacaktır. Kargomatlar son adım teslimatı sürecinde elektronik son adım teslimatı adıyla alt başlık açabileceği öngörülmektedir.

Keywords

Planlı Davranış Teorisi, Lojistik, Tüketici Davranışı, Son Adım Teslimat, E-Ticaret

Investigating Smart Parcel Locker System s in the Framework of Planned Behavior Theory: A Study of Electronic Last Mile Delivery Processes on E-Commerce Platforms

Abstract

As digitalization touches human life at many different points, consumers’ perceptions toward innovations have been a matter of curiosity for experts. In this context, examining the factors affecting consumer acceptance of new technologies constitute the main problem of the research. The study uses planned behavior theory (PBT), which is suitable for the purpose of the research and has been frequently mentioned in the literature. The study also uses structural equation modeling due to the presence of more than one relationship in the PBT model. The study uses the program SmartPLS 3, which has also been frequently used for structural equation modeling in the literature. The results from the reliability and validity analyses of the study data were found to be within accepted values, and thus the SmartPLS 3 path analysis was conducted. The study accepted all its hypotheses except for the H4. Perceived behavioral control (PBC) variable could not provide reliable data regarding the additional questions included in the study because less than 30% of the participants used locker technology. Because a sufficient number of smart parcel locker users could not be reached and the obtained R² value was 0.122, the research focused on explaining user intent rather than behavior. The fact that PBC affects both intention and behavior indirectly reveal a result to have been obtained that is parallel to the literature. However, with the accelerated use of technology and the spread of parcel lockers to a wider area, the rate of smart parcel locker technology usage in last mile delivery will increase. By benefitting from technology, this study predicts that the last mile delivery process with parcel lockers will be called electronic last mile delivery. 

Keywords

Theory of Planned Behavior, Logistics, Consumer Behavior, Last Mile Delivery, E-Commerce, Planlı Davranış Teorisi, Lojistik, Tüketici Davranışı, Son Adım Teslimat, E-Ticaret

References

• Ajzen, I. (1991). “The Theory of Planned Behavior”, Organizational (1991).
• Ajzen, I. ve Fishbein, M. (2000). Attitudes and the attitude-behavior relation: reasonedand
• Ajzen, I., ve Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological bulletin, 82(2), 261 (1975).
• Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control (pp. 11-39). Springer, Berlin, Heidelberg.
• Azjen, I. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs.
• Ajzen, I., ve Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of experimental social psychology, 22(5), 453-474. (1986).
• Ajzen, I. ve Sheikh, S. (2013). Action versus inaction: Anticipated affect in the theory of planned behavior.
• Journal of Applied Social Psychology. 43. 10.1111/j.1559-1816.2012.00989.x.
• Automatic Processes, European Review of Social Psychology, 11(1), 1-33. doi: 10.1080/14792779943000116. (2000).
• Aurambout, J.-P., Gkoumas, K. ve Ciuffo, B. (2019). Last mile delivery by drones: an estimation of viable market potential and access to citizens across European cities. European Transport Research Review.11. 10.1186/s12544-019-0368-2.
• Ba, S., ve Pavlou, P. A. (2002). Evidence of the effect of trust building technology in electronic markets: Price premiums and buyer behavior. MIS quarterly, 243-268.
• Beldad, A., & Hegner, S. (2018). Determinants of fair trade product purchase intention of Dutch consumers according to the extended theory of planned behaviour. Journal of consumer policy, 41(3), 191-210.
• Cho, J. (2004). Likelihood to abort an online transaction: influences from cognitive evaluations, attitudes, and behavioral variables Information & Management 41 (2004) 827–838.
• Chou, Y-C., Chuang, H. ve Shao, B. (2015). The Impact of E-Retail Characteristics on Initiating Mobile Retail
• Services: A Modular Innovation Perspective. Information & Management. 53. 10.1016/j.im.2015.11.003.
• Dalvi-Esfahani, M., Alaedini, Z., Nilashi, M., Samad, S., Asadi, S., & Mohammadi, M. (2020). Students’ green
• information technology behavior: Beliefs and personality traits. Journal of cleaner production, 257, 120406.
• Eticaret.gov.tr (2022). https://www.eticaret.gov.tr/istatistikler Erişim Tarihi: 03.03.2022
• Featherman, M. S., ve Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International journal of human-computer studies, 59(4), 451-474.
• Gao, L., Wang, S., Li, J., & Li, H. (2017). Application of the extended theory of planned behavior to understand individual’s energy saving behavior in workplaces. Resources, Conservation and Recycling, 127, 107-113.
• Giampietri, E., Verneau, F., Del Giudice, T., Carfora, V. ve Finco, A. (2018). A Theory of Planned behaviour perspective for investigating the role of trust in consumer purchasing decision related to short food supply chains. Food Quality and Preference. 64. 160-166.
• Hansen, T. (2008). Consumer values, the theory of planned behaviour and online grocery shopping. International Journal of Consumer Studies. 32. 128 - 137. 10.1111/j.1470-6431.2007.00655.x.
• Hair, J. F., Hult, G. T., Ringle, C. M. ve Sarstedt, M. (2017). A Primer on partial least squares structural equation modelling (PLS-SEM). Los Angeles: Sage Publication. Second Edition (2017).
• Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
• Hair, J. F., William C. B., Barry J. B., ve Rolph E. A. (2010). “Multivariate Data Analysis”, 7th ed., Pearson Education. (2010).
• Hansen, J. M., Saridakis, G., & Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers in human behavior, 80, 197-206.
• Kalafatis, S. P., Pollard, M., East, R., & Tsogas, M. H. (1999). Green marketing and Ajzen’s theory of planned
• behaviour: a cross‐market examination. Journal of consumer marketing. 16(5), 441–460.
• Kamble, S., Gunasekaran, A., & Arha, H. (2019). Understanding the blockchain technology adoption in supply chains-Indian context. International Journal of Production Research, 57(7), 2009-2033.
• Kurtuluş, K. (2010). Araştırma yöntemleri. Türkmen Kitabevi, 2010
• Morrıs, M. G., ve Venkatesh, V. (2000). Age differences in technology adoption decisions implications for a changing work force. Personnel Psychology, 53(2), 375–403. Doi10.1111j.1744- (2000).
• Özdamar, K. (2004). Paket programlar ile istatistiksel veri analizi. Eskişehir: Kaan Kitabevi, 2004
• Patent Application Publication Amazon, (2017). United States Patent Application Publication Curlander et al. multi-level fulfillment center for unmanned aeral vehicles Pub. No.: US 2017/0175413 A1 Pub. Date: Jun. 22, 2017.
• Seghezzi, A., Siragusa, C., & Mangiaracina, R. (2022). Parcel lockers vs. home delivery: a model to compare last-mile delivery cost in urban and rural areas. International Journal of Physical Distribution & Logistics Management.
• Toraman, Y. (2022). Dijital Türk Lirasının (DTL) Kullanım Kabulü: Teknoloji Kabul Modeli (TKM) ve Planlı Davranış Teorisi (PDT) Çerçevesinde İncelenmesi. Sosyoekonomi, 30(54), 357-376.
• TÜBA, (2020). Küresel Salgın Değerlendirme Raporu Türkiye Bilimler Akademisi (TÜBA) 2020.
• TÜİK, (2020). https://data.tuik.gov.tr/Bulten/Index?p=Hanehalki-Bilisim-Teknolojileri-(BT)-Kullanim- Arastirmasi-2020-33679. Erişim Tarihi: 08.12.2021
• Vakulenkoa, Y., Shamsb P., Hellströma, D. ve Hjort, K. (2019). Service innovation in e-commerce last mile delivery: Mapping the ecustomer journey, Journal of Business Research. 461-468. https://doi. org/10.1016/j.jbusres.2019.01.016
• Vakulenkoa, Y., Hellströma, D. ve Hjort, K. (2017). What’s in the parcel locker? Exploring customer value in e-commerce last mile delivery. Journal of Business Research.
• Yang, H., Lee, H., & Zo, H. (2017). User acceptance of smart home services: an extension of the theory of planned behavior. Industrial Management & Data Systems.
• Yuen, K.F., Wang, X., Ng, L. ve Wong, Y. (2018). An investigation of customers’ intention to use self-collection services for last-mile delivery. Transport Policy. 66. 1-18. 10.1016/j.tranpol.2018.03.001.
• Wang, Y., Zhang, D., & Liu, Q., Shen, F ve Lee, L H. (2016). Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions. Transportation Research Part E: Logistics and Transportation Review. 93. 279-293. 10.1016/j.tre.2016.06.002.
• Wearesocial.com, (2022). https://wearesocial.com/uk/blog/2022/01/digital-2022-another-year-of-bumper- growth-2/ Erişim Tarihi:04.03.2022
• Wu, J-H., ve Wang S-C. (2004). “What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model”, Information & management, 42(5), 2004, s.719-729.