In the contemporary era, strategic leadership style plays significant role in entrepreneurial performance. The key purpose of this study is to examine the influence of hybrid (self, shared and opinion) leadership in women entrepreneurial performance towards sustainable growth. This mixed method study investigated data in two ways. First, the study analyzed the data and measured the hypotheses employing the partial least squares structured equation model (PLS-SEM) in SmartPLS software 4 packages. Second, fsQCA explores multiple causal relationships between the constructs. The fsQCA results claim that the multiple causal relationships among the shared, self and opinion leadership have strong significant impact on women entrepreneurial performance. In particular, it is addressed that different entrepreneurial performances are positively associated with the extent of self, shared and opinion leadership. Theoretically, this study contributes to the understanding of women leadership behavior in entrepreneurial performance with a mixed statistical analysis. The study has valuable insights for the women entrepreneurs and concerned

Social media influencer marketing has emerged as an essential marketing strategy in the online interactive environment. This study investigates the impact of influencer-consumer fit (ICF) on behavioral intentions; intention to co-create brand value (ICC) and purchase intention (PI), with the serial mediation of influencer authenticity (IA) and attitude toward brand (ATB). A self-administered questionnaire was distributed to followers of social media influencers in Pakistan. The data were collected from 421 female followers of social media influencers through survey and partial least squares—structural equation modeling was used for data analysis. The findings reveal that ICF impacts IA, while the latter impacts ATB. ATB in turn impacts behavioral intentions. The direct effects suggest that ICF impacts consumers’ PI but not the ICC. However, with the serial mediation of IA and ATB, the relationship becomes significant. The findings of this study may assist managers in building brand strategies to achieve excellence in a highly dynamic and competitive market by leveraging the power of influencer marketing.

Since the United States (U.S.) is falling behind the net-zero carbon emissions target by 2050, understanding effective long-term policies to reduce carbon intensity/emissions is essential for the country. To address key literature gaps, this study aims to provide novel evidence of the comparative assessment of the long-term effectiveness of artificial intelligence (AI) patterns, environmental policy stringency (EPS), renewable energy consumption (REC), natural resource rents (NRR), and trade openness (TO) to facilitate the outcome of ecological sustainability (ECOI), which can either reduce or increase carbon emission intensity. This study is based on ecological modernization theory (EMT) and assumes these variables. The analysis employs the autoregressive distributed lag (ARDL) and the vector error correction model (VECM) based Granger causality test, which uses U.S. national-level data from 1990–2022 (33 years). The long-run ARDL findings suggest that AI and REC significantly enhance ECOI, lowering carbon emissions. Moreover, the NRR and EPS are insignificantly related to the result, but these two factors can also reduce emissions. Surprisingly, TO significantly reduces ECOI, increasing emissions in the long run, driven by carbon-intensive product imports and global trade-integrated industrial activities. These results are essential for federal policymakers to emphasize AI innovations and renewable transitions and control trade openness with eco-friendly policies on imports, enabling the country to reduce significant levels of carbon intensity/emissions in the long run and remain ahead of the net-zero-carbon target.