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Although the impact of environmental, social and governance (ESG) on firms' innovation has attracted attention, the existing research findings diverge. The authors believe that failure to consider both innovation input and output is an important reason for the divergence of conclusions in the extant literature when discussing the impact of ESG and firm innovation. Thus, based on signaling theory, this study aims to reconcile these divergent findings by examining the impact of ESG performance on firms' innovation efficiency.

To seek empirical evidence to support the authors’ theoretical view, the authors conduct an empirical test based on the Tobit model using 8 years of data from Chinese listed companies.

Although ESG performance effectively improves firms' innovation efficiency, the institutional-level signaling environment (including state-owned firms and regional market development) weakens the positive effect of ESG performance on firms' innovation efficiency. Further tests suggest that financing constraints partially mediate the relationship between ESG performance and firms' innovation efficiency.

By systematically revealing whether, how and under what circumstances ESG performance improves firms' innovation advantages, this study bridges the gap in the existing literature and highlights important implications to suggest how firms can better capture the value associated with ESG.

The rapid development of digital technologies drives digital entrepreneurs to pivot, a behavior that allows entrepreneurs to adjust original opportunities and explore new opportunities. This study aims to investigate the effect of the structural characteristics of digital entrepreneurial teams, the functional heterogeneity, on pivoting from the perspective of digital agility. Moreover, this study also examines the moderating effect of knowledge sharing.

Two-phase survey data were sourced from Chinese digital entrepreneurial teams through the entrepreneurial networks of MBA programs of a Chinese business school and entrepreneurial support organizations in China. The sample of 272 teams with 708 entrepreneurs was collected to test the hypotheses.

The functional heterogeneity of digital entrepreneurial teams, including industry background heterogeneity and occupational experience heterogeneity, positively impacts pivoting by providing heterogeneous knowledge and resources. Moreover, this relationship is mediated by the digital agility of the digital team, and knowledge sharing moderates the relationship between functional heterogeneity and digital agility.

While existing studies have mainly focused on the external factors, this study empirically investigates the team-level internal factors of digital pivoting in digital entrepreneurial teams, enriching the research perspective of pivoting. Moreover, the current study bridges the literature on digital agility with pivoting, broadening the theoretical mechanism of pivoting and expanding the theoretical boundaries of digital agility.

Previous studies may have overstated the benefits of entrepreneurial resilience while neglecting its potential adverse effects, especially in the context of team entrepreneurship. This study focuses on the structural characteristics of resilience, entrepreneurial team resilience diversity, to delve into the potential dark side effects of entrepreneurial resilience. Drawn upon the similarity-attraction theory, this study investigates the detrimental impact of entrepreneurial team resilience diversity on entrepreneurial performance from the team-member exchange (TMX) perspective, which describes the reciprocal relationships within a team and the moderating effect of environmental hostility.

The study collected data by conducting a two-wave survey. With the assistance of officials from entrepreneurship service agencies, entrepreneurship parks and entrepreneurship training institutions in southeast China, this study collected survey data from 361 participants of 91 entrepreneurial teams. The current research conducted empirical tests with SPSS and PROCESS macro to test the hypotheses.

Empirical results from a two-wave survey of 91 entrepreneurial teams in China reveal the detrimental effects of entrepreneurial resilience. In the context of team entrepreneurship, the diversity of a team’s entrepreneurial resilience negatively impacts entrepreneurial performance by impairing the quality of TMX. Moreover, the indirect effect of TMX is strengthened in more hostile environments.

Existing studies have mainly focused on the positive side of resilience. However, based on the similarity-attraction theory, this study explores the potential adverse effects of the structural characteristics of entrepreneurial team resilience from the perspective of team-member exchange (TMX). This study enriches the literature on resilience by demonstrating how resilience functions in new venture teams and challenges the assumption that resilience is universally beneficial to entrepreneurs. Introducing team members with high resilience in a hostile environment may not always benefit the team, deepening the understanding of entrepreneurial resilience when confronting adversities.

This study investigates the economic outcomes of the strategic deviation (SD), the fundamental and crucial question in institutional theory and strategic management. Previous studies have yielded contradictory findings. This study reconciles conflicting results by distinguishing the effects of the SD on financial and market performance, examining the mechanism of financing constraints and the boundary condition of institutional investor heterogeneity.

This research collected data from Chinese A-shares listed manufacturing firms from 2009 to 2021 from the CSMAR and Wind databases. This study conducted empirical tests using OLS models with Stata 15.

Empirical results demonstrate that the SD has different impacts on different dimensions of performance. The SD negatively impacts financial performance while positively impacts market performance. Financing constraints mediate the main effects. Moreover, transactional institutional investors positively moderate the negative effect of the SD on financial performance, whereas stable institutional investors negatively moderate the positive effect of the SD on market performance.

By systematically revealing how the SD has different effects on financial and market performance, this study reconciles the debate on the SD between institutional theorists and strategy scholars. This research makes contributions to the research stream by providing reasonable explanations for conflicting conclusions. Furthermore, by introducing the overlooked perspective of financing constraints, this research identifies crucial mediating mechanisms and highlights the double-edged effect of financing constraints, enriching our understanding of financing constraints. Finally, this study investigates the moderating effects of institutional investor heterogeneity, thereby making valuable contributions to the comprehension of boundary conditions.

In the new era of highly developed Internet information, the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinion by relevant government departments.

Aiming at the complex and nonlinear characteristics of the network public opinion, considering the accuracy and stability of the applicable model, a network public opinion prediction model based on the bald eagle algorithm optimized radial basis function neural network (BES-RBF) is proposed. Empirical research is conducted with Baidu indexes such as “COVID-19”, “Winter Olympic Games”, “The 100th Anniversary of the Founding of the Party” and “Aerospace” as samples of network public opinion.

The experimental results show that the model proposed in this paper can better describe the development trend of different network public opinion information, has good stability in predictive performance and can provide a good decision-making reference for government public opinion control departments.

A method for optimizing the central value, weight, width and other parameters of the radial basis function neural network with the bald eagle algorithm is given, and it is applied to network public opinion trend prediction. The example verifies that the prediction algorithm has higher accuracy and better stability.

Drawing on integrated insights from signaling theory and the cognitive theory of multimedia learning, this study investigates the effect of video personalization on crowdfunding performance. “Video personalization” is defined as information presented in a video in a way that is designed to promote the feeling of being and interacting with others. This study also aimed to examine the moderating effects among various signals of video personalization.

This study constructs a theoretical model of how video personalization affects crowdfunding performance through an integrated theory lens. This study measures several signals of video personalization, namely, first-person wording (FPW), second-person wording (SPW), asking questions and talking to the camera. The direct and moderating effects of video personalization on crowdfunding performance are examined by using 2,858 crowdfunding projects on Kickstarter.

This study revealed that using SPW, asking questions and talking directly to the camera positively impact crowdfunding performance, while talking to the camera attenuates the positive effect of using SPW and asking questions with respect to funding amounts.

This study contributes to the literature on resource mobilization in crowdfunding by examining how video personalization impacts resource mobilization in a crowdfunding setting. The findings extend signaling theory by broadening its boundaries. This advance is accomplished by integrating insights from cognitive science into signaling theory. This study also contributes to cognitive theory in multimedia learning by identifying novel ways to personalize videos and by broadening that work to a novel empirical context, entrepreneurship.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.