利用行为推理理论方法在数字营销中利用生成人工智能的潜力

Sujata Joshi , Sonali Bhattacharya , Pankaj Pathak , N.A. Natraj , Juhi Saini , Soumya Goswami
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引用次数: 0

摘要

生成式人工智能(GAI)是一个新兴的领域,它对市场营销的影响是无可争辩的。在学术文献中,很少有证据表明影响GAI在数字营销(DM)中使用的因素。本研究通过探索与在DM中使用GAI相关的关键驱动因素和障碍来解决这一差距。利用行为推理理论(BRT),该研究验证了先前的发现,并引入了一个概念模型,概述了在DM中采用GAI以增强客户体验的态度的因素。通过进行专家访谈,采用定性归纳方法来调查在DM中使用GAI的“理由”和“反对理由”及其对客户体验的影响。生成的转录本手动编码,并使用BRT作为理论框架进行演绎主题分析。研究结果表明,在数字营销中采用GAI的四个重要主题是:创新、创意沟通和内容创作、速度、效率和节省时间、增强定制和个性化;预测分析和模拟。报告还指出,还确定了与主要障碍有关的五个重要主题:道德与知识产权侵权;安全与深度造假;采用新技术的学习生态系统;数据质量;减少人力需求。该研究进一步强调了GAI如何影响DM中的客户体验。该研究对该领域的贡献是:(a)提出了在DM中应用GAI以改善客户体验的概念框架,(b)检查了在DM中采用GAI的驱动因素和挑战,以及(c)提出了指导未来研究的研究议程。这些见解为研究人员、营销从业者和学者在GAI和数字营销的动态交叉点上导航提供了价值
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Harnessing the potential of generative AI in digital marketing using the Behavioral Reasoning Theory approach

Harnessing the potential of generative AI in digital marketing using the Behavioral Reasoning Theory approach
Generative AI (GAI) is an upcoming field and its impact on marketing is indisputable. Very little evidence in academic literature is present regarding the factors affecting the usage of GAI in Digital Marketing (DM). This study addresses this gap by exploring the key drivers and barriers associated with using GAI in DM. Leveraging Behavioral Reasoning Theory (BRT), the research validates prior findings and introduces a conceptual model outlining factors that shape attitudes toward adopting GAI in DM to enhance customer experiences.
A qualitative inductive approach was undertaken by conducting expert interviews to investigate the “reasons for” and ‘reasons against’ using GAI in DM and its impact on customer experience. The transcripts generated were manually coded and a deductive thematic analysis was done using the BRT as the theoretical framework.
The findings indicate four significant themes for adopting GAI in digital marketing viz: innovation, creative communication and content creation, speed, efficiency and timesaving, enhanced customization and personalization; predictive analytics and simulation. It also indicates five significant themes related to the key barriers were also identified viz: ethics and infringement of Intellectual Property; security and deepfake; learning ecosystem for the adoption of new technology; quality of data; reduced manpower requirement. The study further highlights how GAI influences customer experience in DM.
This study contributes to the field by (a) proposing a conceptual framework for applying GAI in DM to improve customer experiences, (b) examining the drivers and challenges of GAI adoption in DM, and (c) presenting a research agenda to guide future studies. These insights offer value to researchers, marketing practitioners, and academics navigating the dynamic intersection of GAI and Digital Marketing
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