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Green technology innovation is the organic combination of green development and innovation driven. It is also a powerful guarantee for shaping sustainable competitive advantages of manufacturing enterprises. To explore what kind of executive incentive contracts can truly stimulate green technology innovation, this study aims to distinguish the equity incentive and reputation incentive, upon their contractual elements characteristics and green governance effects, and then put forward suggestions for green technology innovation accordingly.

This study establishes an evaluation model and uses empirical methods to test. Concretely, using data from A-share listed manufacturing companies for the period from 2007 to 2020, this study compares and analyzes the impact of equity and reputation incentive on green technology innovation and explores the relationship between internal green business behavior and external green in depth.

This study finds that reputation incentives focus on long-term and non-utilitarian orientation, which can promote green technology innovation in enterprises. While equity incentives, linked to performance indicators, have a inhibitory effect on green technology innovation. Internal and external institutional factors such as energy conservation measures, the “three wastes” management system, and environmental recognition play the regulatory role in the relationship between incentive contracts and green technology innovation.

Those findings validate and expand the efficient contracting hypothesis and the rent extraction hypothesis from the perspective of green technology innovation and provide useful implications for the design of green governance systems in manufacturing enterprises.

This study had a threefold aim: to examine the impact of a Simmelian-tie tripartite alliance on corporate green innovation; to determine the chain-mediating roles of knowledge acquisition and knowledge integration; and to identify the moderating effect of network routines on the relationship between a Simmelian tie and green innovation.

Data were collected through 487 valid survey questionnaires from Chinese small and medium-sized manufacturing enterprises (SMEs). The authors examined the data through a structural model using partial least-squares structural-equation modeling (PLS-SEM) to test the research hypotheses.

The results reveal several key factors with positive impacts on enterprise green innovation. Specifically, a Simmelian tie significantly and positively affects enterprise green innovation. The results further reveal that knowledge acquisition and integration play mediating roles, while a network routine positively moderates the relationships among a Simmelian tie, knowledge acquisition and integration, and corporate green innovation.

This study is among the earliest empirical studies to investigate the influence of Simmelian ties on corporate green innovation for manufacturing companies. This study provides a theoretical basis for managers of firms, especially those of SMEs with limited resources, to fully use Simmelian ties to achieve environmentally sustainable innovation. In addition, this study validates and extends knowledge-management theory by verifying the linking roles of knowledge acquisition and integration and facilitating role of network routines.

Traditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between their physical properties, which in turn affects the stability and reliability of the model performance.

A novel multivariable grey prediction model is constructed with different background-value coefficients of the dependent and independent variables, and a one-to-one correspondence between the variables and the background-value coefficients to improve the smoothing effect of the background-value coefficients on the sequences. Furthermore, the fractional order accumulating operator is introduced to the new model weaken the randomness of the raw sequence. The particle swarm optimization (PSO) algorithm is used to optimize the background-value coefficients and the order of the model to improve model performance.

The new model structure has good variability and compatibility, which can achieve compatibility with current mainstream grey prediction models. The performance of the new model is compared and analyzed with three typical cases, and the results show that the new model outperforms the other two similar grey prediction models.

This study has positive implications for enriching the method system of multivariable grey prediction model.

This paper aims to explore the effects of director network on open innovation. As an informal institutional arrangement, the director network is an important source for the enterprise to obtain external information, which provide resource basis for open innovation. Chief Executive Officer (CEO) as the top of management team could make short-sighted decisions for personal interests; this paper also investigates the moderating role of CEO short-sightedness between director network and open innovation.

This paper takes 4,102 Chinese listed companies from 2007 to 2020 as the research sample. By introducing network centrality and structural hole to measure director network and using data mining to extract key words related to CEO short-sightedness from annual reports, this paper constructs several multiple linear regression models to analyze the impact of director network on open innovation and the moderating role of CEO short-sightedness.

The analysis finds that director network can facilitate corporate open innovation. Enterprises can acquire more external resources in high centrality and structural hole of director network and promote ability for corporate open innovation. The relationship between director network and open innovation is negatively moderated by CEO short-sightedness. When the level of corporate governance and analyst attention is high, the negative effect of CEO short-sightedness on the innovation effect of directors’ networks is suppressed.

This is the first empirical paper to investigate the promotion effect of director network on open innovation as well as the negative moderating role of CEO short-sightedness. The findings bring new perspectives to the open innovation and enlightenments for practical activities from social relationship aspect.

This research aims to build on the pre-existing corpus of literature through the integration of the technology acceptance model (TAM) and usage habit to more accurately capture the determinants associated with social media addiction among university students. This study seeks to delineate how usage habit and TAM may be used as predictors for addiction potential, as well as provide greater insight into current trends in social media usage across this population demographic.

A cross-sectional research design was employed to investigate the determinants of social media addiction among university students in Malaysia at the onset of their tertiary education. A self-administered survey, adapted from prior studies, was administered to a sample of 217 respondents. The hypotheses on social media addiction were subsequently tested using a partial least squares structural equation modeling (PLS-SEM) approach.

Usage habit was found to be a direct and strong predictor of this type of addiction, as well as all TAM variables considered in the research. Additionally, by integrating TAM with usage habit, the study revealed a comprehensive and multi-faceted understanding of social media addiction, providing an important insight into its complexity in the Malaysian context. Although several other factors have been identified as potential contributors to social media reliance and addictive behavior, it appears that usage habit is paramount in driving these addictive tendencies among university students.

This expanded model holds significant implications for the development of interventions and policies that aim to mitigate the adverse effects of social media addiction on students' educational and psychological well-being. The study illustrates the applicability of the TAM in examining addictive behaviors within emerging contexts such as the Malaysian higher education sector, thus contributing to the extant literature on the subject.

The integrated TAM and habit model is an effective predictor of social media addiction among young adults in developing countries like Malaysia. This highlights the importance of actively monitoring and controlling users' interactions with technology and media platforms, while promoting responsible usage habits. Educators can use these findings to create tailored educational programs to educate students on how to use technology responsibly and reduce their risk of becoming addicted to social media.

This study provides a unique perspective on social media addiction among university students. The combination of TAM and usage habit has the potential to shed significant light on how variables such as perceived usefulness (PU) and perceived ease of use (PEOU) may be associated with addictive behaviors. Additionally, by considering usage habit as an explanatory factor, this research offers a novel approach to understanding how addictions form over time.