Digital Marketing for Industrial Products in Rural Markets: Evidence from Bihar and Uttar Pradesh, India

Abstract

This study aims to investigate the dynamics of digital technology adoption in rural India by examining how digital marketing shapes the purchase intentions for industrial products such as submersible pumps, agricultural machinery, urea fertilisers, and wire or solar cables in the rural markets of Bihar and Uttar Pradesh. These regions collectively represent over a quarter of the nation’s rural population and act as key pillars of India’s agricultural sector. Despite improvements in digital connectivity, persistent barriers related to trust, the relevance of digital content, and transparency in after-sales services continue to limit the uptake of high-involvement industrial goods. Employing a cross-sectional, descriptive, and analytical research design, this study draws on quantitative data from 120 rural respondents, equally divided between Bihar and Uttar Pradesh, and is supported by qualitative insights from interviews with retailers and installers. Comprehensive statistical analyses, including reliability tests, factor analysis, ANOVA, independent t-tests, and multiple linear regression, were conducted to evaluate the effects of five digital marketing constructs: Digital Awareness, Access Affordability, Digital Trust, Content Usefulness, and Service Visibility, on rural purchase intention. The findings indicate that all five constructs exert a significant positive impact on purchase intentions, with Digital Trust and Service Visibility playing the most pivotal roles; submersible pumps and solar cables emerged as the most responsive product categories, and Uttar Pradesh displayed stronger digital engagement, underpinned by better infrastructure and digital literacy efforts.
The research utilises Exploratory Factor Analysis (EFA) for the validation of the measurement model (KMO = 0.861; Bartlett’s p < 0.001), while at the same time, it employs Cronbach’s Alpha to verify the reliability of the measurements (α ranging from 0.76 to 0.84). However, the differences in purchase intention among product categories are proven by the results of ANOVA (F = 4.12, p = 0.008), in which the high-involvement items, such as submersible pumps and solar cables, are indicated to be more digital marketing-sensitive than the low-involvement goods like urea. The independent-samples t-tests (t = 2.06, p = 0.041) reveal that Uttar Pradesh has a greater average of purchase intention (M = 3.74) in contrast to Bihar (M = 3.56), indicating that the better infrastructure and the policy-driven digital engagement in UP are contributors to the higher adoption. The multiple regression analysis (R² = 0.482, F = 21.22, p < 0.001) indicates that DT (β = 0.291, p < 0.001), SV (β = 0.258, p = 0.001), and CU (β = 0.221, p = 0.004) are the most powerful purchase intention predictors, whereas AA is just barely significant (p = 0.053).
The research aims to expand the Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TBP) by including service visibility and trust as factors in the digital marketing mix. Manufacturers, policy makers and marketers are given practical insights as to how to create adoption by using the hybrid go-to-market (GTM) strategies which link digital discovery with local service assurance. Thus, this research brings both theoretical understanding and practical application of digital marketing in the case of emerging rural economies.