ANALIZA SENTIMENTA ZASNOVANA NA VEŠTAČKOJ INTELIGENCIJI KAO POKRETAČ TRANSFORMACIJE DIGITALNOG MARKETINGA IZ PERSPEKTIVE PERCEPCIJE POTROŠAČA, ONLAJN SENTIMENTA I KUPOVNOG PONAŠANJA

Autori

DOI:

https://doi.org/10.59864/Oditor52602SK

Ključne reči:

Analiza raspoloženja zasnovana na veštačkoj inteligenciji, onlajn raspoloženje, poverenje potrošača, kupovno ponašanje, digitalni marketing

Apstrakt

Razumevanje emocija i stavova potrošača izraženih u digitalnom okruženju jedan je od ključnih izazova savremenog digitalnog marketinga. Iako se analiza raspoloženja zasnovana na veštačkoj inteligenciji sve više koristi za tumačenje sadržaja koji generišu korisnici, ograničen broj istraživanja istovremeno razmatra njegovu percepciju zajedno sa raspoloženjem na mreži i poverenjem potrošača u objašnjavanju ponašanja pri kupovini. Cilj ovog rada je ispitivanje veze između percepcije analize raspoloženja zasnovane na veštačkoj inteligenciji, raspoloženja na mreži, poverenja potrošača i ponašanja pri kupovini u digitalnom marketingu. Empirijsko istraživanje je sprovedeno korišćenjem strukturiranog upitnika na uzorku od 138 ispitanika. Kronbahov alfa koeficijent je korišćen za procenu pouzdanosti instrumenta, dok su hipoteze testirane korišćenjem Pirsonovog hi-kvadrat testa, Pirsonovog koeficijenta korelacije i Spirmanovog koeficijenta korelacije. Rezultati potvrđuju statistički značajne pozitivne veze između posmatranih konstrukta i ukazuju na to da su percepcija analize raspoloženja zasnovane na veštačkoj inteligenciji, pozitivno raspoloženje na mreži i poverenje potrošača povezani sa povoljnijim ponašanjem pri kupovini u digitalnom okruženju. Rezultati istraživanja doprinose boljem razumevanju uloge emocionalnih i psiholoških faktora u digitalnom marketingu i predstavljaju osnovu za buduća istraživanja koja će uključivati objektivne modele analize raspoloženja i naprednije metodološke pristupe

##plugins.themes.default.displayStats.downloads##

##plugins.themes.default.displayStats.noStats##

Reference

Ballı, A. (2025). The impact of consumer trust on purchase, satisfaction and loyalty in online shopping. Journal of Computer Research and Development, 25(7), 514–526. https://doi.org/10.5281/zenodo.16018267

Barger, V. A., Peltier, J. W., & Schultz, D. E. (2016). Social media and consumer engagement: A review and research agenda. Journal of Research in Interactive Marketing, 10(4), 268–287. https://doi.org/10.1108/JRIM-06-2016-0065

Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018, September). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute. Retrieved from: https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy/

Büttner, O. B., & Göritz, A. S. (2008). Perceived trustworthiness of online shops. Journal of Consumer Behaviour, 7(1), 35–50. https://doi.org/10.1002/cb.235

Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine, 9(2), 48–57. https://doi.org/10.1109/MCI.2014.2307227

Cambria, E., Poria, S., Hazarika, D., & Kwok, K. (2020). Sentiment analysis: What is the end game? In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 4063–4064).

Chen, X., Qin, Z., Zhang, Y., & Xu, T. (2016). Learning to rank features for recommendation over multiple categories. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 305–314). https://doi.org/10.1145/2911451.2911549

Cheung, C. M. K., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), 461–470. https://doi.org/10.1016/j.dss.2012.06.008

Chu, X., Liu, Y., Chen, X., Ding, Z., & Tao, S. (2020). What motivates a consumer to engage in microblogs? The roles of brand post characteristics and brand prestige. Electronic Commerce Research, 1–33. https://doi.org/10.1007/s10660-020-09435-3

Cvijikj, I. P., & Michahelles, F. (2013). Online engagement factors on Facebook brand pages. Social Network Analysis and Mining, 3(4), 843–861. https://doi.org/10.1007/s13278-013-0098-8

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0

de Vries, L., Gensler, S., & Leeflang, P. S. H. (2012). Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing. Journal of Interactive Marketing, 26(2), 83–91. https://doi.org/10.1016/j.intmar.2012.01.003

Digital Marketing Institute. (2025, December 9). 10 eye opening AI marketing stats. Retrieved from: https://digitalmarketinginstitute.com/blog/10-eye-opening-ai-marketing-stats-in-2025/

Diza, F., Moniharapon, S., & Ogi, I. W. J. (2016). The influence of service quality, product quality and trust on consumer satisfaction (Study at PT. FIFGROUP Manado Branch). Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi, 4(1), 109–119. https://doi.org/10.35794/emba.4.1.2016.11568

Forsythe, S. M., & Shi, B. (2003). Consumer patronage and risk perceptions in internet shopping. Journal of Business Research, 56(11), 867–875. https://doi.org/10.1016/S0148-2963(01)00273-9

Fortes, N., Rita, P., & Pagani, M. (2017). The effects of privacy concerns, perceived risk and trust on online purchasing behaviour. International Journal of Internet Marketing and Advertising, 11(4), 255–273. https://doi.org/10.1504/IJIMA.2017.10007887

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007

Giatsoglou, M., Vozalis, M. G., Diamantaras, K. I., Vakali, A., Sarigiannidis, G., & Chatzisavvas, K. C. (2017). Sentiment analysis leveraging emotions and word embeddings. Expert Systems with Applications, 69, 214–224. https://doi.org/10.1016/j.eswa.2016.10.043

Goldberg, K. (2024, August 5). Best AI for data analysis: Our top picks. Akkio. Retrieved from: https://www.akkio.com/post/best-ai-for-data-analysis-our-top-picks/

Gooljar, V., Issa, T., Hardin-Ramanan, S., & Abu-Salih, B. (2024). Sentiment-based predictive models for online purchases in the era of marketing 5.0: A systematic review. Journal of Big Data, 11(1), 107. https://doi.org/10.1186/s40537-024-00947-0

Guttmann, A. (2026, January 16). Artificial intelligence (AI) use in marketing - statistics & facts. Retrieved from: https://www.statista.com/topics/5017/ai-use-in-marketing/?srsltid=AfmBOorwrRSycWWB1fDz0WuiLTrNZcG1pnCmhnm6S2DSMmOThFPA-m7F#topicOverview

Hung, M., Lauren, E., Hon, E. S., Birmingham, W. C., Xu, J., Su, S., Hon, S. D., Park, J., Dang, P., & Lipsky, M. S. (2020). Social network analysis of COVID-19 sentiments: Application of artificial intelligence. Journal of Medical Internet Research, 22(8), e22590. https://doi.org/10.2196/22590

Hutter, K., Hautz, J., Dennhardt, S., & Füller, J. (2013). The impact of user interactions in social media on brand awareness and purchase intention: The case of MINI on Facebook. Journal of Product & Brand Management, 22(5/6), 342–351. https://doi.org/10.1108/JPBM-05-2013-0299

Hutto, C. J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550

Kumar, V. (2020). Transformative marketing: The AI-driven future. Journal of Business Research, 116, 205–210. https://doi.org/10.1016/j.jbusres.2019.10.007

Laely, N. (2016). Analysis of the effect of trust and price on customer loyalty mediated by satisfaction at PT. Telkomsel in Kediri City. JMM17, 3(02). https://doi.org/10.30996/jmm17.v3i02.802

Lăzăroiu, G., Neguriță, O., Grecu, I., & Grecu, G. (2020). Consumers’ decision-making process on social commerce platforms: Online trust, perceived risk, and purchase intentions. Frontiers in Psychology, 11, 890. https://doi.org/10.3389/fpsyg.2020.00890

Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers.

Mabokela, K. R., Celik, T., & Raborife, M. (2023). Multilingual sentiment analysis for under-resourced languages: A systematic review of the landscape. IEEE Access, 11, 15996–16020. https://doi.org/10.1109/ACCESS.2022.3224136

Manasa, K. N., & Padma, M. C. (2019). A study on sentiment analysis on social media data. In Emerging research in electronics, computer science and technology (Lecture Notes in Electrical Engineering, Vol. 545, pp. 661–667). Springer. https://doi.org/10.1007/978-981-13-5802-9_58

Mari, A. (2019). The rise of machine learning in marketing: Goal, process, and benefit of AI-driven marketing. Swiss Cognitive. Retrieved from: https://swisscognitive.ch/2019/05/09/the-rise-of-machine-learning-in-marketing-goal-process-and-benefit-of-ai-driven-marketing/

Merrilees, B. (2016). Interactive brand experience pathways to customer-brand engagement brand value co-creation. Journal of Product & Brand Management, 25(5), 402–408. https://doi.org/10.1108/JPBM-04-2016-1136

Oh, C., Roumani, Y., Nwankpa, J. K., & Hu, H. F. (2017). Beyond likes and tweets: Consumer engagement behavior and movie box office in social media. Information & Management, 54(1), 25–37. https://doi.org/10.1016/j.im.2016.03.003

Patel, A. K., Agarwal, V., Lohar, H. K., & Jha, S. (2023). A study on AI: Customer feedback and personalized marketing comparison between India and Nigeria. Journal of International Conference Proceedings, 6(4), 110–122. https://doi.org/10.32535/jicp.v6i4.2610

Poria, S., Cambria, E., Gelbukh, A., Bisio, F., & Hussain, A. (2015). Sentiment data flow analysis by means of dynamic linguistic patterns. IEEE Computational Intelligence Magazine, 10(4), 26–36. https://doi.org/10.1109/MCI.2015.2471215

Rakhmanita, A., Hurriyati, R., Disman, D., & Hendrayati, H. (2023). Future research direction on branded apps: a bibliometric analysis. Journal of Engineering Science and Technology, 18(3), 33–40.

Rissanen, H., & Luoma-Aho, V. (2016). (Un)willing to engage? First look at the engagement types of millennials. Journal of Communication Management, 21(4), 500–515. https://doi.org/10.1108/JCOM-05-2016-0035

Roggeveen, A. L., Tsiros, M., & Grewal, D. (2012). Understanding the co-creation effect: When does collaborating with customers provide a lift to service recovery? Journal of the Academy of Marketing Science, 40(6), 771–790. https://doi.org/10.1007/s11747-011-0274-1

Ryan, D. (2016). Understanding digital marketing: Marketing strategies for engaging the digital generation. Kogan Page Publishers.

Sánchez-Núñez, P., Cobo, M. J., De las Heras-Pedrosa, C., Peláez, J. I., & Herrera-Viedma, E. (2020). Opinion mining, sentiment analysis and emotion understanding in advertising: A bibliometric analysis. IEEE Access, 8, 134563–134576. https://doi.org/10.1109/ACCESS.2020.3009482

Sande, N., Adeniyi, I., & Akinkunmi, A. (2024). Social media sentiment analysis: A comprehensive analysis. https://doi.org/10.13140/RG.2.2.31094.37441

Schiffman, L. G., Kanuk, L. L., & Hansen, H. (2012). Consumer behaviour. Prentice Hall.

Shayaa, S., Jaafar, N. I., Bahri, S., Sulaiman, A., Wai, P. S., Chung, Y. W., Piprani, A. Z., & Al-Garadi, M. A. (2018). Sentiment analysis of big data: Methods, applications, and open challenges. IEEE Access, 6, 37807–37827. https://doi.org/10.1109/ACCESS.2018.2851311

Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media—Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217–248. https://doi.org/10.2753/MIS0742-1222290408

Thaw, Y. Y., Mahmood, A. K., & Dominic, P. D. D. (2009). A study on the factors that influence the consumers' trust on e-commerce adoption. In Proceedings of the International Conference for Internet Technology and Secured Transactions (ICITST 2009).

Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55, 5731–5780. https://doi.org/10.1007/s10462-022-10144-1

##submission.downloads##

Objavljeno

2026-08-22

Broj časopisa

Rubrika

Articles

##plugins.generic.recommendBySimilarity.heading##

##common.pagination##

##plugins.generic.recommendBySimilarity.advancedSearchIntro##