Work

Low-carbon information quality dimensions and random forest algorithm evaluation model in digital marketing

Year: 2024

Type: article

Abstract: The growing urgency for low-carbon lifestyles necessitates developing effective strategies to promote sustainable consumer choices. This study investigates key dimensions of information quality that shape consumer behavior within digital marketing to achieve this goal. Employing a mixed-methods approach that integrates grounded theory and machine learning, this study identifies three core dimensions of low-carbon information quality: matching quality, presentation quality, and interpretability quality. These dimensions underscore the importance of aligning information with consumer needs, ensuring clear and accurate presentation, and fostering transparency for trustworthiness. A Random Forest algorithm-based evaluation model is constructed to assess low-carbon information quality, demonstrating its effectiveness in identifying high-quality, sustainable content. This research provides a practical tool for digital marketers to enhance their strategies, raise consumer awareness of sustainable options, and ultimately contribute to the growth of the low-carbon consumption market.

Source: Scientific Reports

DOI: 10.1038/s41598-024-72910-1

Authors: Weiji Gao, Zhihua Ding, Junyu Lu, Yulong Wan

Institutions: China University of Mining and Technology, Jiangsu Vocational College of Electronics and Information, Shanghai National Accounting Institute

Language: English

Topic: Environmental Sustainability in Business

Subfield: Marketing

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Responsible consumption and production, Climate action

Open Access status: gold

Estimated APC paid: $2,590