Published July 30, 2025 | Version Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
Journal article Open

AI in Action: Understanding Consumer Response in Cross-Channel Marketing

Creators

  • 1. Associate Professor, Department of Commerce, Government Arts College (Affiliated to Bharathidasan University), Tiruchy (Tamil Nadu), India.

Contributors

Contact person:

Researcher:

  • 1. Associate Professor, Department of Commerce, Government Arts College (Affiliated to Bharathidasan University), Tiruchy (Tamil Nadu), India.
  • 2. Assistant Professor, CARE College of Arts and Science (Affiliated to Bharathidasan University), Tiruchy (Tamil Nadu), India.

Description

Abstract: This study aims to investigate the impact of artificial intelligence (AI) on consumer responses in the realm of crosschannel marketing. As companies adopt AI technologies to provide tailored experiences on digital platforms such as email, websites, and social media, it is crucial to understand consumer perceptions and behavioural responses. This study employs a quantitative approach, using a structured questionnaire to assess seven independent variables: personalisation, transparency, cross-channel consistency, trust in AI, ease of use, AI-generated suggestions, and privacy concerns, alongside one dependent variable, consumer response. Each item was assessed utilising a five-point Likert scale. The data collection involved an online survey disseminated through social media channels, which facilitated a broad representation of demographics without geographic limitations. A total of 184 valid responses were collected from mid-January to the end of February 2025. The data analysis took place in March, utilising JMP statistical software and applying multiple linear regression to assess the impact of AI-related constructs on consumer responses. The results indicated that comprehending the functionality of AI, the simplicity of engaging with AI-driven platforms, and the perception of personalisation play crucial roles in influencing consumers' readiness to respond to AI-generated suggestions. Nevertheless, conventional marketing elements, such as message consistency and the acceptance of AI tools, demonstrated limited predictive capability. Interestingly, personalisation showed a negative correlation, indicating a potential consumer backlash against overly aggressive targeting. The findings underscore the increasing demand for transparency, usability, and the ethical application of AI in marketing. This investigation offers actionable insights for marketers and contributes to the growing conversation about consumer-AI engagement in online settings.

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Additional details

Identifiers

DOI
10.35940/ijmh.I1820.11110725
EISSN
2394-0913

Dates

Accepted
2025-07-15
Manuscript Received on 17 April 2025 | First Revised Manuscript Received on 22 April 2025 | Second Revised Manuscript Received on 17 June 2025 | Manuscript Accepted on 15 July 2025 | Manuscript published on 30 July 2025.

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