Araştırma Makalesi
A Comprehensive Text Mining Application to Understand Social Media's Impact on Consumer Perception of Green Consumption
Öz
This study proposes a comprehensive deep learning algorithm to understand how social media influences consumers' perceptions of green consumption. The COVID-19 pandemic has prompted society to pay more attention to the interactions between humans and nature. Promoting green consumption is essential to achieve sustainable development goals, necessitating the ability to understand and reshape the public's perception of sustainability. Previous research has used behavioral models and surveys to study green consumption, but they often overlook the perspective of social media. This study employs deep learning algorithms to analyze text and video content on social media to gain insights into customer behaviors and preferences. Data from Twitter and YouTube was collected to develop deep learning algorithms for text classification. In conclusion, this study uses text mining applications to analyze text and video content on social media, aiming to understand how it affects consumers' perceptions of green consumption. The findings provide significant insights into the influence of social media on consumer behaviors and preferences.
Anahtar Kelimeler
Kaynakça
- Akenji, L., Consumer scapegoatism and limits to green consumerism., Journal of Cleaner Production, 63 (2014): 13-23.
- Gilg, A., Stewart, B. ve Nicholas, F., Green consumption or sustainable lifestyles? Identifying the sustainable consumer., Futures, 37.6 (2005): 481-504.
- Dong, H. ve ark., Uncovering regional disparity of China's water footprint and inter-provincial virtual water flows., Science of the total environment, 500 (2014): 120-130.
- Sharifi, A., Co-benefits and synergies between urban climate change mitigation and adaptation measures: A literature review., Science of the total environment, 750 (2021): 141642.
- Sun, X. ve ark., The impact of awe induced by COVID-19 pandemic on green consumption behavior in China., International Journal of Environmental Research and Public Health, 18.2 (2021): 543.
- Jian, Y. ve ark., The impacts of fear and uncertainty of COVID-19 on environmental concerns, brand trust, and behavioral intentions toward green hotels., Sustainability, 12.20 (2020): 8688.
- Banbury, C., Robert S., ve Saroja S., Sustainable consumption: Introspecting across multiple lived cultures., Journal of Business Research, 65.4 (2012): 497-503.
- Costa, C. S. R., ve ark., Consumer antecedents towards green product purchase intentions., Journal of Cleaner Production, 313 (2021): 127964.
- Al Mamun, A., ve ark., Intention and behavior towards green consumption among low-income households., Journal of environmental management, 227 (2018): 73-86.
- Zaremohzzabieh, Z., ve ark., The effects of consumer attitude on green purchase intention: A meta-analytic path analysis., Journal of Business Research, 132 (2021): 732-743.
- Hui, Z. ve Khan, A. N., Beyond pro-environmental consumerism: role of social exclusion and green self-identity in green product consumption intentions., Environmental Science And Pollution Research, (2020): 1-13.
- [Wang, Y., Research on the Influence Mechanism of Green Cognition Level on Consumers' Green Consumption Behavior: An Empirical Study Based on SPSS, 2021 International Conference on Management Science and Software Engineering (ICMSSE) (2021):175-178.
- Fraj, E. ve Martinez, E., Ecological consumer behaviour: an empirical analysis., International Journal Of Consumer Studies, 31.1 (2006): 26-33.
- D’Souza, C., Taghian, M. ve Khosla, R., Examination of environmental beliefs and its impact on the influence of price, quality and demographic characteristics with respect to green purchase intention., Journal Of Targeting, Measurement And Analysis For Marketing, 15.2 (2007): 69-78.
- Biswas, A. Impact of Social Media Usage Factors on Green consumption Behavior Based on Technology Acceptance Model., Journal Of Advanced Management Science, 4.2 (2016): 92-97.
- Ahamad, N. R. ve Ariffin M., Assessment of knowledge, attitude and practice towards sustainable consumption among university students in Selangor, Malaysia., Sustainable Production And Consumption, 16 (2018): 88-98.
- Bedard, S. ve Reisdorf, C. A., Millennials' green consumption behaviour: Exploring the role of social media., Corporate Social Responsibility And Environmental Management, 25.1 (2018): 1388-1396.
- Jalali, S. S. ve Haliyana, K., Understanding Instagram Influencers Values in Green Consumption Behaviour: A Review Paper., Open International Journal of Informatics, Vol 7.Special Issue 1 (2019): 47-58.
- Sajeewanie, L. C., ve ark., Integrated Model for Green Purchasing Intention and Green Adoption: Future Research Direction., Journal Of Sociological Research, 10.2 (2019): 23-66.
- Semprebon, E., ve ark. (2019)., Green Consumption: A Network Analysis in Marketing., Marketing Intelligence & Planning, 37.1 (2019): 18-32.
- Huseynov, F. ve Özkan Yıldırım, S., Online Consumer Typologies and Their Shopping Behaviors in B2C E-Commerce Platforms., Sage Open 9.2 (2019): 1-19.
- Jain, V. K., ve ark., Social Media And Green Consumption Behavior Of Millennials., Journal Of Content, Community & Communication, 11 (2020): 221-230.
- Chi, N. T. K., Understanding the effects of eco-label, eco-brand, and social media on green consumption intention in ecotourism destinations., Journal Of Cleaner Production, 321 (22021): 1-17
- Han, H., ve ark., Exploring public attention about green consumption on Sina Weibo: Using text mining and deep learning., Sustainable Production And Consumption, 30.23 (2021): 1-27.
- Saraç, Ö., Kültür Turistlerinin Sürdürülebilir Tüketim Davranışlarının Cinsiyete Göre Farklılıkları Safranbolu Üzerinde Bir Araştırma., Journal Of Humanities And Tourism Research, 12.2 (2022): 265-283.
- Meyers-Levy, J. ve Maheswaran, D., Exploring differences in males' and females' processing strategies, Journal Of Consumer Research, 18(1) (1991): 63-70.
- Ma, Y. ve Qiao, E., Research on Accurate Prediction of Operating Energy Consumption of Green Buildings Based on Improved Machine Learning, 2021 IEEE International Conference On Industrial Application Of Artificial Intelligence (IAAI) (2021): 144-148.
- Tang, H., ve ark., Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers, IEEE Access 8 (2020): 35546-35562.
- Yazdavar, A. H., ve ark., Multimodal mental health analysis in social media., Plos ONE 15.4 (2020): 1-27.
- Balcıoğlu, Y. S., Detection of depression and anxiety synmptoms via Twitter after Covid-19 with machine learning., 2. Başkent International Conference On Multidisciplinary Studies (2022): 261-265.
- Li, Z., ve ark., Spatial preserved graph convolution networks for person re-identification., ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 16.1s (2020): 1-14.
- Tanveer, M., ve ark., Machine learning techniques for the diagnosis of Alzheimer’s disease: A review., ACM Transactions on Multimedia Computing, Communications, and Applications, (TOMM) 16.1s (2020): 1-35.
- Zhou, X., Video Expression Recognition Method Based on Spatiotemporal Recurrent Neural Network and Feature Fusion., Journal of Information Processing Systems, 17.2 (2021): 337-351.
- Kunte, A. V. ve Panicker, S., Using textual data for Personality Prediction:A Machine Learning Approach., Conference: 2019 4th International Conference on Information Systems and Computer Networks (ISCON) (2019):529-533.
Sosyal Medyanın Tüketicilerin Yeşil Tüketim Algısı Üzerindeki Etkisini Anlamak için Kapsamlı bir Metin Madenciliği Uygulaması
Öz
Bu çalışma, sosyal medyanın tüketicilerin yeşil tüketim algılarını nasıl etkilediğini anlamak için kapsamlı bir metin madenciliği uygulaması gerçekleştirmektedir. COVID-19 salgını, toplumun insan ve doğanın nasıl etkileşime girdiğine daha fazla dikkat etmesine neden olmuştur. Yeşil tüketiciliği teşvik etmek, sürdürülebilir kalkınma hedeflerine ulaşmak için gereklidir; bu da kamuoyunun sürdürülebilirlik algısını anlama ve değiştirme becerisini gerektirir. Önceki araştırmalar, yeşil tüketimi incelemek için davranışsal modeller ve anketler kullanmıştır, ancak bunlar genellikle sosyal medyanın bakış açısını göz ardı etmiştir. Bu çalışma, müşteri davranışları ve tercihleri hakkında içgörüler elde etmek için sosyal medyadaki metin ve video içeriğini analiz etmek için metin madenciliği algoritmaları kullanmaktadır. Bu çalışmada Twitter ve YouTube'dan veri toplanarak metin sınıflandırma için metin madenciliği algoritmaları uygulanmıştır. Sonuç olarak bu çalışma, sosyal medyada yer alan metin ve video içeriklerini analiz ederek tüketicilerin yeşil tüketim algılarını nasıl etkilediğini anlamak amacıyla metin madenciliği uygulamaları kullanmaktadır. Elde edilen bulgular, sosyal medyanın tüketici davranışları ve tercihleri üzerindeki etkisine ilişkin önemli içgörüler sunmaktadır.
Anahtar Kelimeler
Kaynakça
- Akenji, L., Consumer scapegoatism and limits to green consumerism., Journal of Cleaner Production, 63 (2014): 13-23.
- Gilg, A., Stewart, B. ve Nicholas, F., Green consumption or sustainable lifestyles? Identifying the sustainable consumer., Futures, 37.6 (2005): 481-504.
- Dong, H. ve ark., Uncovering regional disparity of China's water footprint and inter-provincial virtual water flows., Science of the total environment, 500 (2014): 120-130.
- Sharifi, A., Co-benefits and synergies between urban climate change mitigation and adaptation measures: A literature review., Science of the total environment, 750 (2021): 141642.
- Sun, X. ve ark., The impact of awe induced by COVID-19 pandemic on green consumption behavior in China., International Journal of Environmental Research and Public Health, 18.2 (2021): 543.
- Jian, Y. ve ark., The impacts of fear and uncertainty of COVID-19 on environmental concerns, brand trust, and behavioral intentions toward green hotels., Sustainability, 12.20 (2020): 8688.
- Banbury, C., Robert S., ve Saroja S., Sustainable consumption: Introspecting across multiple lived cultures., Journal of Business Research, 65.4 (2012): 497-503.
- Costa, C. S. R., ve ark., Consumer antecedents towards green product purchase intentions., Journal of Cleaner Production, 313 (2021): 127964.
- Al Mamun, A., ve ark., Intention and behavior towards green consumption among low-income households., Journal of environmental management, 227 (2018): 73-86.
- Zaremohzzabieh, Z., ve ark., The effects of consumer attitude on green purchase intention: A meta-analytic path analysis., Journal of Business Research, 132 (2021): 732-743.
- Hui, Z. ve Khan, A. N., Beyond pro-environmental consumerism: role of social exclusion and green self-identity in green product consumption intentions., Environmental Science And Pollution Research, (2020): 1-13.
- [Wang, Y., Research on the Influence Mechanism of Green Cognition Level on Consumers' Green Consumption Behavior: An Empirical Study Based on SPSS, 2021 International Conference on Management Science and Software Engineering (ICMSSE) (2021):175-178.
- Fraj, E. ve Martinez, E., Ecological consumer behaviour: an empirical analysis., International Journal Of Consumer Studies, 31.1 (2006): 26-33.
- D’Souza, C., Taghian, M. ve Khosla, R., Examination of environmental beliefs and its impact on the influence of price, quality and demographic characteristics with respect to green purchase intention., Journal Of Targeting, Measurement And Analysis For Marketing, 15.2 (2007): 69-78.
- Biswas, A. Impact of Social Media Usage Factors on Green consumption Behavior Based on Technology Acceptance Model., Journal Of Advanced Management Science, 4.2 (2016): 92-97.
- Ahamad, N. R. ve Ariffin M., Assessment of knowledge, attitude and practice towards sustainable consumption among university students in Selangor, Malaysia., Sustainable Production And Consumption, 16 (2018): 88-98.
- Bedard, S. ve Reisdorf, C. A., Millennials' green consumption behaviour: Exploring the role of social media., Corporate Social Responsibility And Environmental Management, 25.1 (2018): 1388-1396.
- Jalali, S. S. ve Haliyana, K., Understanding Instagram Influencers Values in Green Consumption Behaviour: A Review Paper., Open International Journal of Informatics, Vol 7.Special Issue 1 (2019): 47-58.
- Sajeewanie, L. C., ve ark., Integrated Model for Green Purchasing Intention and Green Adoption: Future Research Direction., Journal Of Sociological Research, 10.2 (2019): 23-66.
- Semprebon, E., ve ark. (2019)., Green Consumption: A Network Analysis in Marketing., Marketing Intelligence & Planning, 37.1 (2019): 18-32.
- Huseynov, F. ve Özkan Yıldırım, S., Online Consumer Typologies and Their Shopping Behaviors in B2C E-Commerce Platforms., Sage Open 9.2 (2019): 1-19.
- Jain, V. K., ve ark., Social Media And Green Consumption Behavior Of Millennials., Journal Of Content, Community & Communication, 11 (2020): 221-230.
- Chi, N. T. K., Understanding the effects of eco-label, eco-brand, and social media on green consumption intention in ecotourism destinations., Journal Of Cleaner Production, 321 (22021): 1-17
- Han, H., ve ark., Exploring public attention about green consumption on Sina Weibo: Using text mining and deep learning., Sustainable Production And Consumption, 30.23 (2021): 1-27.
- Saraç, Ö., Kültür Turistlerinin Sürdürülebilir Tüketim Davranışlarının Cinsiyete Göre Farklılıkları Safranbolu Üzerinde Bir Araştırma., Journal Of Humanities And Tourism Research, 12.2 (2022): 265-283.
- Meyers-Levy, J. ve Maheswaran, D., Exploring differences in males' and females' processing strategies, Journal Of Consumer Research, 18(1) (1991): 63-70.
- Ma, Y. ve Qiao, E., Research on Accurate Prediction of Operating Energy Consumption of Green Buildings Based on Improved Machine Learning, 2021 IEEE International Conference On Industrial Application Of Artificial Intelligence (IAAI) (2021): 144-148.
- Tang, H., ve ark., Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers, IEEE Access 8 (2020): 35546-35562.
- Yazdavar, A. H., ve ark., Multimodal mental health analysis in social media., Plos ONE 15.4 (2020): 1-27.
- Balcıoğlu, Y. S., Detection of depression and anxiety synmptoms via Twitter after Covid-19 with machine learning., 2. Başkent International Conference On Multidisciplinary Studies (2022): 261-265.
- Li, Z., ve ark., Spatial preserved graph convolution networks for person re-identification., ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 16.1s (2020): 1-14.
- Tanveer, M., ve ark., Machine learning techniques for the diagnosis of Alzheimer’s disease: A review., ACM Transactions on Multimedia Computing, Communications, and Applications, (TOMM) 16.1s (2020): 1-35.
- Zhou, X., Video Expression Recognition Method Based on Spatiotemporal Recurrent Neural Network and Feature Fusion., Journal of Information Processing Systems, 17.2 (2021): 337-351.
- Kunte, A. V. ve Panicker, S., Using textual data for Personality Prediction:A Machine Learning Approach., Conference: 2019 4th International Conference on Information Systems and Computer Networks (ISCON) (2019):529-533.
Toplam 34 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Yönetim Bilişim Sistemleri |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Erken Görünüm Tarihi | 18 Mart 2024 |
| Yayımlanma Tarihi | |
| Yayımlandığı Sayı | Yıl 2024 Cilt: 17 Sayı: 1 |
