Previous studies reveal that the failure rate of innovation in enterprises is higher than expected. The reason for innovation failures is due to not only the customers’ resistance towards innovation, subsequent changes and ignored factors that promote customer acceptance, but also the other disregarded factors that motivates the resistance towards innovation adaptation. The literature proved the customers’ innovation resistance (CIR) consists of two categories. Passive innovation resistance (PIR) described as initial resistance behavior before evaluating the new product; and active innovation resistance (AIR) introduced as the resistant behavior after evaluating innovative products. However, few studies have investigated the simultaneous influence of both PIR and AIR, especially using the empirical methods. The main purposes of this study are (1) to construct a model that reflects on both PIR and AIR as two categories of CIR; (2) to provide empirical evidences to highlight the influences and correlation of PIR and AIR. The method used in this paper is partial least squares path modeling (PLS-PM) method to test our model and the hypotheses. The results from this research mainly indicate that (1) PIR is negatively influenced AIR; (2) PIR (including both cognitive and situational PIR) has negative effect on customers’ innovation adoption intention (IAI) while (3) the correlation between AIR and IAI is opposite; (4) the degree of customers’ IAI with high-perceived stimulation group and low-perceived stimulation group will reverse according to the changes of PIR and AIR. This paper is helpful for enterprises to improve their innovation success rate by analyzing users’ resistance to innovation.

There is a lack of comprehensive evaluation on the impact of ESG rating differences on the green transformation of energy enterprises in the transition era. This study leverages data from companies listed on the Shanghai Stock Exchange in China, applying double machine-learning algorithms to precisely estimate the causal relationship between variations in ESG ratings and the green transition efficiency of energy companies. The research shows that the difference in ESG ratings of third-party rating agencies significantly promotes the efficiency of green transformation of energy enterprises. This paper also studies the influencing factors of this effect: First, ESG rating differences significantly promote the improvement of green transition efficiency of energy enterprises; Second, the positive effect is more pronounced in energy companies with more balanced board structures. Finally, energy companies with high capital market attention can also contribute to this positive impact. Through the mechanism test, this paper finds that enterprise green innovation is an important mechanism for ESG rating divergence to positively promote the efficiency of energy enterprises’ green transformation. Furthermore, this paper analyzes the impact of ESG rating on enterprises from the perspective of market cognition and short-term behavior, which provides a new perspective for analyzing the practice of enterprises pursuing long-term transformation. The study also calls for a more sober reflection on the trend toward ESG in society.

As environmental issues grow increasingly complex and multifaceted, the synergistic effects of environmental policies and their implementation methods have become central to the environmental policy system. This paper analyzes panel data from all A-share-listed companies in China between 2013 and 2022 and aims at comprehensively evaluate the role and impact of command-and-control, market-incentive, and public-participation environmental policies along with their combinations on corporate green transformation by using a double machine learning method. The results indicate that (1) all three types of environmental policies positively influence corporate green transformation, with market-based policies having the most pronounced effect; (2) the synergistic effects of policy combinations further enhance corporate green transformation, especially the combination of market-incentive and public-participation environmental policies; (3) heterogeneity analysis highlights differences in the effects of these environmental policies and their combinations on corporate attributes and regional factors; and (4) mechanism analysis indicates that green innovation and financial constraints indirectly drive the high-quality green transformation of enterprises.