Reducing fertilizer use and increasing its efficiency will improve the quality of farmland and resource conservation. These are necessary steps to achieving green development in agriculture. Nevertheless, fertilizer-reduction and efficiency-increasing technologies (FREITs) remain limited. To improve the situation, 538 farmers in Jiangsu and Hubei Provinces were surveyed with the goal of measuring the information acquisition ability (IAA) of farmers using an Item Response Theory (IRT) model. A model of improved technology selection was employed in conjunction with an IV-probit model to examine the impacts of IAA on farmers’ adoption of FREITs. The results showed that 34.76% of the surveyed farmers had adopted FREITs, with 12.45% and 26.02% having adopted Soil Testing and Formula Fertilization Technology (STFFT) and Organic Fertilizer Replacement Technology (OFRT), respectively. Second, farmers who used more information access channels had greater IAA, which significantly improved their adoption of FREITs. Third, participation in technical training and an increased degree of technical understanding increased the probability of farmers adopting FREITs. The results remained robust after accounting for endogeneity and correlation. Consequently, enhancing farmers’ IAA, organizing technical training, and improving technical publicity will promote the adoption of FREITs.

Artificial intelligence (AI) capability has demonstrated significant potential in driving knowledge recombination, fostering knowledge discovery and enhancing organisational innovation performance. However, the interplay between AI capability and corporate innovation failure remains underexplored. To address this research gap, this study investigated the impact of AI capability on corporate innovation failure while further examining the moderating role of top management team (TMT) digital knowledge and its variation under different levels of integrative leadership shown by chief executive officers (CEOs). Using panel data from 3,829 firm-year observations in China from 2017 to 2022, the empirical analysis reveals that AI capability significantly reduces the likelihood of corporate innovation failure. Moreover, TMT digital knowledge exerts a significant positive moderating effect on this relationship. Further analysis reveals that this positive moderating effect is more pronounced when the CEO exhibits a high level of integrative leadership.

Promoting the transformation of green consumption has emerged as a critical pathway to address ecological constraints. However, existing research exhibits significant discrepancies in understanding the relationship between green attributes and purchase intention and lacks a deep unpacking of the underlying individual psychological mechanisms. Employing three situational experiments, this study integrates the chain effects of green attribute centrality (GAC), environmental concern (EC), and green trust (GT) to systematically explore the formation mechanism of consumers’ green purchase decisions. Results show three key findings: (1) When consumers exhibit high EC, GAC enhances their green purchase intention (GPI) more effectively; however, when EC is low, GAC demonstrates no significant differential impact on GPI. (2) The interaction between GAC and EC influences GPI via green perceived value (GPV) (i.e., this interaction effect is mediated by GPV). (3) GT further moderates these relationships: under high GT conditions, consumers with high EC show a stronger preference for high-centrality green products; conversely, under low GT conditions, such consumers exhibit a greater preference for low-centrality green products. This study provides a novel theoretical explanation and practical pathway for bridging the “attitude-behavior gap” in green consumption. It not only deepens the understanding of the mechanisms underlying green consumption decision-making but also offers precise references for enterprises to implement differentiated green marketing strategies and for policymakers to guide public green consumption.