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The purpose of this paper is to investigate the factors influencing user adoption behavior in the context of conflicting health information, thereby contributing to the understanding of users' information decision-making and adoption processes within intricate information environments. Furthermore, this research endeavors to offer guidance for mitigating the adverse effects of contradictory health information on users' cognitive perceptions.

First, this study used literature coding to identify factors that influence user adoption behavior in the context of conflicting health information. Secondly, we utilized the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to unveil the key influencing factors based on centrality and causality. Lastly, utilizing the Interpretive Structural Model (ISM), we constructed the multilevel hierarchical structure model of influencing factors to delineate the relationships among factors across different levels.

The key causal factors influencing user adoption behavior in the context of conflicting health information include user basic characteristics, experience accumulation, and information type. The key result factors consist of psychological emotion, perceived trade-offs, and the source credibility. The significant elements include users' psychological emotions, perceived trade-offs, emotional support, source credibility, and informational support. Furthermore, user adoption behavior in the context of conflicting health information is a manifestation of the interplay among surface-level, mid-level, and deep-level factors.

Theoretically, this paper enriches the existing relevant theoretical framework and offers a novel perspective for further investigation into user adoption behavior concerning conflicting health information. Practically, this study extracts factors that can influence user adoption behavior in the context of conflicting health information which is an essential reference value for guiding and optimizing user adoption behavior.

This paper expands the system of factors influencing user adoption behavior in the context of conflicting health information from individual, information and social perspectives. Utilizing DEMATEL quantitative analysis and ISM multilevel hierarchical models, this research examines and illustrates the significance of the influencing factors and their interrelationships.

Presentation of the different industrial carbon linkages of India. The purpose of this paper is to understand the direct and indirect impact of these industrial linkages.

This study uses a hypothetical extraction method with its various extensions. Under this method, different carbon linkages of a block are removed from the economy, and the effects of carbon linkages are determined by the difference between the original and the post-removal values. Energy and non-energy carbon linkages are also estimated.

“Electricity, gas and water supply (EGW)” at 655.61 Mt and 648.74 Mt had the highest total and forward linkages. “manufacturing and recycling” at 231.48 Mt had the highest backward linkage. High carbon-intensive blocks of “EGW” plus “mining and quarrying” were net emitters, while others were net absorbers. “Fuel and chemicals” at 0.08 Mt had almost neutral status. Hard coal was the main source of direct and indirect emissions.

Net emitting and key net forward blocks should reduce direct emission intensities. India should use its huge geographical potential for industrial accessibility to cheaper alternative energy. This alongside with technology/process improvements catalyzed by policy tools can help in mitigation efforts. Next, key net-backward blocks such as construction through intermediate purchases significantly stimulate emissions from other blocks. Tailored mitigation policies are needed in this regard.

By developing an understanding of India’s industrial carbon links, this study can guide policymakers. In addition, the paper lays out the framework for estimating energy and non-energy-based industrial carbon links.