İklim Değişikliği ile Savaş İçin Tüketici Karar Verme Modelinin Yenilenmiş Versiyonu
Bu çalışma aşırı tüketim ve iklim değişikliği temelinde tüketici karar verme modelinin revize edilmiş yeni bir halini sunmaktadır. Bu revize edilmiş tüketici karar verme modeli ile birlikte aşırı tüketimden kaynaklanan olumsuz iklim değişiklikleri minimize edilebilir. Bu teorik araştırmanın temelini, geleneksel beş aşamalı tüketici karar verme modeli ile yapay zekâ, aşırı tüketim ve iklim değişikliği literatürü oluşturmaktadır. Çalışma toplam tüketimin yaklaşık toplamda satın alınan mal ve hizmetlere eşit olduğu varsayımını baz almaktadır. Bu araştırma, iklim değişikliğinin olumsuz etkileri ile savaşmak için gereksiz satın almaları önleyecek yapay zekâ uygulamalarını tüketici karar verme modeline uyarlayarak tüketici karar verme modelinin revize edilmiş yeni bir halini ortaya koymaktadır. Makro düzeyde yapay zekâ uygulamaları, tüketici karar verme süreci ve iklim değişikliğine dair bilinen bir çalışma bulunmamaktadır. Hâlihazırdaki bu çalışma, literatürdeki bu boşluğu doldurarak gelecek çalışmalar için genel bir yön belirlemeyi hedeflemektedir. Öte yandan, araştırmanın ana kısıtlaması ampirik ispatların eksikliğidir. Bu yüzden sunulan modelin test edilmesi için ampirik çalışmalara gerekmektedir.
Anahtar Kelimeler:
yapay zekâ, iklim değişikliği, tüketici davranışları, tüketici karar verme süreci, yapay zekâ, iklim değişikliği, tüketici davranışları, tüketici karar verme süreci
An Updated Consumer Decision-making Model to Tackle Climate Change
Tapping into excessive consumption and climate change, this study introduces an updated consumer decision-making model to optimize purchases. By doing so, negative outcomes of excessive consumption on the climate change could be minimized. This theoretical research is informed by the traditional five stage decision-making model and related literature including artificial intelligence, excessive consumption, and climate change. In order to tackle harmful impact of the climate change, the research proposes an updated consumer decision-making model adopting Artificial Intelligence applications to prevent unnecessary purchases. There is not any known study observing the relationship between AI applications, consumer decision-making process, and climate change at macro level. By filling this gap in the literature, the current study aims to create an overall direction for future research. On the other hand, the main limitation of the research is the lack of empirical evidence. Hence further empirical studies are needed to test proposed model for validation.
Keywords:
artificial intelligence, climate change, customer behaviour, consumer decision making process,
___
- Bae, J. K., and Kim, J. (2010). Integration of heterogeneous models to predict consumer behavior. Expert Systems with Applications, 37(3), 1821-1826.
- Bajželj, B., Richards, K.S., Allwood, J.M., Smith, P., Dennis, J.S., Curmi, E. and Gilligan, C.A., 2014. Importance of food-demand management for climate mitigation. Nature Climate Change, 4(10), pp.924-929.
- Belch G. And Belch M. (2009) Advertising and Promotion: An Integrated Marketing Communications Perspective, 8th ed. Homewood, IL: Irwin.
- Chiang, L. L. L., and Yang, C. S. (2018). Does country-of-origin brand personality generate retail customer lifetime value? A Big Data analytics approach. Technological Forecasting and Social Change, 130, 177-187.
- Cohen, S.A., Higham, J.E. and Cavaliere, C.T., 2011. Binge flying: Behavioural addiction and climate change. Annals of Tourism Research, 38(3), pp.1070-1089.
- Davenport, T., Guha, A., Grewal, D., and Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.
- Du, S., and Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961-974.
- Ehrlich, P.R. and Goulder, L.H., 2007. Is current consumption excessive? A general framework and some indications for the United States. Conservation Biology, 21(5), pp.1145-1154.
- Erevelles, S., Fukawa, N., and Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of business research, 69(2), 897-904.
- Etzioni, A., and Etzioni, O. (2017). Incorporating ethics into artificial intelligence. The Journal of Ethics, 21(4), 403-418.
- Huang, M. H., and Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172.
- Huntingford, C., Jeffers, E.S., Bonsall, M.B., Christensen, H.M., Lees, T. and Yang, H., 2019. Machine learning and artificial intelligence to aid climate change research and preparedness. Environmental Research Letters, 14(12), p.124007.
- IDC, (2019).
- Kietzmann, J., Paschen, J., and Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263-267.
- Kumar, V., Rajan, B., Venkatesan, R., and Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155.
- Lamb, C. W., Hair, J. F., and McDaniel, C. (2011). Essentials of marketing. Cengage Learning.
- Ładyżyński, P., Żbikowski, K., and Gawrysiak, P. (2019). Direct marketing campaigns in retail banking with the use of deep learning and random forests. Expert Systems with Applications, 134, 28-35.
- Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S.S., Hosseinzadeh-Bandbafha, H. and Chau, K.W., 2018. Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production. Science of the Total Environment, 631, pp.1279-1294.
- Nair, H. S., Misra, S., Hornbuckle IV, W. J., Mishra, R., and Acharya, A. (2017). Big data and marketing analytics in gaming: Combining empirical models and field experimentation. Marketing Science, 36(5), 699-725.
- Nishant, R., Kennedy, M. and Corbett, J., 2020. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, p.102104.
- Schwab, K. (2017). The fourth industrial revolution. Currency.
- Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. Journal of Retailing, 94(4), vi–xi
- Shirdastian, H., Laroche, M., and Richard, M. O. (2019). Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter. International Journal of Information Management, 48, 291-307
- Solomon M., Bamossy G., Askegaard S., Hogg M.K. (2006) Consumer Behaviour. A European perspective, 3rd ed. Prentice Hall Financial Times.
- Syam, N., and Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135–146.
- Negnevitsky, M. (2005). Artificial intelligence: a guide to intelligent systems. Pearson education.
- Pomerol, J. C. (1997). Artificial intelligence and human decision making. European Journal of Operational Research, 99(1), 3-25.
- Reid, R.D. and Bojanic, D.C. (2009) “Hospitality Marketing Management” John Wiley & Sons
- Yayın Aralığı: Yılda 4 Sayı
- Başlangıç: 2020
- Yayıncı: Erhan Aydın