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AI-Driven Recommendation: Impact on Consumer Engagement and Purchasing Decisions in Toledo City.

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Title: AI-Driven Recommendation: Impact on Consumer Engagement and Purchasing Decisions in Toledo City.
Authors: Macapaz, Johnreill Kleint M.1 johnreillmacapz@gmail.com, Repollo, Marly1, Forniza, Dhanna Quell1, Poloyapoy, Cassandra A.1, Mala-ay, Francis Veron B.1, Barabat, Rose Marie M.1, Lumaino, Ade Rose D.1, Broncano, Junpray A.1, Adorable, Cirilo A.1, Perater, Renna1, Botanas, Stella Marie D.1, Alqueza, Jessalyn M.1
Source: Psychology & Education: A Multidisciplinary Journal. 2026, Vol. 58 Issue 5, p678-685. 8p.
Subject Terms: Recommender systems, Consumer behavior, Stimulus & response (Psychology), Consumers, Regression analysis, Internet marketing
Geographic Terms: Toledo (Ohio)
Abstract: This study examined the impact of AI-driven personalized recommendations on consumer engagement and purchasing decisions among consumers in Toledo City. Guided by the Stimulus-Organism-Response (S-O-R) Model, the study considered AI-driven personalized recommendations as the stimulus influencing consumer engagement and purchasing behavior. A descriptive-correlational research design was used, with data collected from 150 local consumers through a researcher-developed questionnaire. Descriptive statistics, Pearson's correlation coefficient, and regression analysis were applied to analyze the data. The findings revealed that consumers generally showed positive perceptions toward AI-driven personalized recommendations, consumer engagement, and purchasing decisions. Results also indicated a significant positive relationship between AI-driven personalized recommendations, consumer engagement, and purchasing decisions. Regression analysis further showed that AI-driven personalized recommendations significantly influence how consumers interact with digital platforms and make purchasing choices. The study concludes that effective AI-driven personalization enhances consumer engagement and positively affects purchasing decisions. These findings provide valuable insights for local businesses and marketers in improving digital marketing strategies through personalized and data-driven approaches. [ABSTRACT FROM AUTHOR]
Copyright of Psychology & Education: A Multidisciplinary Journal is the property of SciMatic Inc., Adnan Menderes Technocity and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22Psychology+%26+Education%3A+A+Multidisciplinary+Journal%22">Psychology & Education: A Multidisciplinary Journal</searchLink>. 2026, Vol. 58 Issue 5, p678-685. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+behavior%22">Consumer behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Stimulus+%26+response+%28Psychology%29%22">Stimulus & response (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Consumers%22">Consumers</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+marketing%22">Internet marketing</searchLink>
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  Data: This study examined the impact of AI-driven personalized recommendations on consumer engagement and purchasing decisions among consumers in Toledo City. Guided by the Stimulus-Organism-Response (S-O-R) Model, the study considered AI-driven personalized recommendations as the stimulus influencing consumer engagement and purchasing behavior. A descriptive-correlational research design was used, with data collected from 150 local consumers through a researcher-developed questionnaire. Descriptive statistics, Pearson's correlation coefficient, and regression analysis were applied to analyze the data. The findings revealed that consumers generally showed positive perceptions toward AI-driven personalized recommendations, consumer engagement, and purchasing decisions. Results also indicated a significant positive relationship between AI-driven personalized recommendations, consumer engagement, and purchasing decisions. Regression analysis further showed that AI-driven personalized recommendations significantly influence how consumers interact with digital platforms and make purchasing choices. The study concludes that effective AI-driven personalization enhances consumer engagement and positively affects purchasing decisions. These findings provide valuable insights for local businesses and marketers in improving digital marketing strategies through personalized and data-driven approaches. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Psychology & Education: A Multidisciplinary Journal is the property of SciMatic Inc., Adnan Menderes Technocity and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=194814470
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