Abstract The integration of Artificial Intelligence (AI) into marketing is transforming the way organisations understand customer preferences, predict buying behaviour and design personalised marketing strategies. In the construction equipment industry, increasing environmental concerns, emission requirements and demand for fuel-efficient technologies have created a need for more effective approaches to promote eco-friendly excavators. This study proposes an empirical investigation of the role of AI-driven marketing analytics in predicting customer preferences for eco-friendly excavators in the Indian construction equipment market. Drawing upon the Theory of Planned Behavior (TPB) and the Antecedents-Decisions-Outcomes (ADO) framework, the study examines the influence of AI-enabled personalisation, predictive analytics, environmental awareness and perceived usefulness of AI-based recommendations on customer preference for eco-friendly excavators. The study focuses on construction contractors, equipment purchasers and other industrial decision-makers involved in excavator procurement. A structured questionnaire using a five-point Likert scale is proposed for primary data collection. Descriptive statistics, reliability analysis, correlation and regression analysis can be employed to examine the proposed relationships. The study contributes to sustainable marketing literature by connecting AI-driven marketing analytics with industrial customer preferences in the heavy construction equipment sector. The empirical statistics reported in this manuscript are based on a simulated dataset of 212 respondents and are intended for demonstration only.
Pritam Bhambure, Nilesh Bankar· Zenodo (CERN European Organi...· 0 citations
Abstract The integration of Artificial Intelligence (AI) into marketing is transforming the way organisations understand customer preferences, predict buying behaviour and design personalised marketing strategies. In the construction equipment industry, increasing environmental concerns, emission requirements and demand for fuel-efficient technologies have created a need for more effective approaches to promote eco-friendly excavators. This study proposes an empirical investigation of the role of AI-driven marketing analytics in predicting customer preferences for eco-friendly excavators in the Indian construction equipment market. Drawing upon the Theory of Planned Behavior (TPB) and the Antecedents-Decisions-Outcomes (ADO) framework, the study examines the influence of AI-enabled personalisation, predictive analytics, environmental awareness and perceived usefulness of AI-based recommendations on customer preference for eco-friendly excavators. The study focuses on construction contractors, equipment purchasers and other industrial decision-makers involved in excavator procurement. A structured questionnaire using a five-point Likert scale is proposed for primary data collection. Descriptive statistics, reliability analysis, correlation and regression analysis can be employed to examine the proposed relationships. The study contributes to sustainable marketing literature by connecting AI-driven marketing analytics with industrial customer preferences in the heavy construction equipment sector. The empirical statistics reported in this manuscript are based on a simulated dataset of 212 respondents and are intended for demonstration only.
Pritam Bhambure, Nilesh Bankar· Zenodo (CERN European Organi...· 0 citations