AI-Driven Sustainable Video Marketing Strategies: Harnessing Deep Learning Algorithms to Sustainable Advertising Campaigns with Special Reference to the Education Industry

Abstract

Purpose: This research explores the interplay between deep learning algorithms, trust in sustainable advertising, and prior knowledge of deep learning algorithms in shaping perceptions of sustainable advertising in the context of education. The study aims to uncover the impact of these factors on sustainable advertising and examine the moderating role of prior knowledge of deep learning algorithms. Design/methodology/approach: The study employs a quantitative research design, utilizing a structured survey instrument for data collection. Simple random sampling techniques were used to select participants from a population of 194,366 1st year students. Data analysis includes multiple regression, mediation analysis, and moderation analysis using Hayes PROCESS Model 58. Findings: The results reveal significant positive effects of deep learning algorithms (independent variable) and trust in sustainable advertising (mediator variable) on sustainable advertising (dependent variable). Prior knowledge of deep learning algorithms (moderator variable) also has a positive influence on sustainable advertising. Trust on sustainable advertising mediates the relationship between deep learning algorithms and sustainable advertising. However, above mediator relationship is negatively moderated by prior knowledge of deep learning algorithms. This suggests that prior knowledge can weaken the positive impact of trust. Originality: This research contributes to the understanding of how AI-driven marketing strategies, trust, and knowledge influence sustainable advertising perceptions. It offers valuable insights into the moderating role of prior knowledge in this context. Implications: The findings have implications for educational institutions and marketing practitioners. They suggest that deep learning algorithms and trust in sustainable advertising can positively impact sustainable advertising perceptions. However, practitioners should be cautious in situations where individuals have high prior knowledge, as trust can reduce impact. Educational institutions can use these insights to optimize their marketing campaigns and foster sustainable advertising in the education sector. Limitations of the study include the reliance on self-reported data and the potential for response bias, which may affect the generalizability of the findings. For future research, investigating the role of other potential moderators and mediators in the relationship between deep learning algorithms and sustainable advertising could provide a more comprehensive understanding of this phenomenon. Keywords: Deep Learning Algorithms, Education Industry, Prior Knowledge of Deep Learning Algorithms, Sustainable Advertising, Trust in Sustainable Advertising