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The authors aim to measure the effects of ambidextrous innovation and its mix strategy on organizational obsolescence with the moderating roles of environmental turbulence.

Hierarchical regression analysis was used to test the hypotheses on the basis of the survey data of 198 firm employees in China.

Both exploratory and exploitative innovations significantly restrain organizational obsolescence. The complementary strategy of ambidextrous innovation is negatively correlated to organizational obsolescence, but the impact of balanced strategy is not significant. Environmental turbulence negatively moderates the relationship between exploratory innovation and organizational obsolescence, whereas it cannot moderate the effect of exploitative innovation.

The empirical and conceptual findings have important implications for organizations countering obsolescence through the construction of a mix strategy of ambidextrous innovation. This study enriches and extends obsolescence theory by introducing the joint mechanism of ambidextrous innovation and environmental turbulence.

Knowledge management (KM) capability plays an important role in the promotion of firm performance in the knowledge economy era. However, empirical evidence on how KM capability affects firm performance is still limited. The study therefore aims to explore the impacts of internal and external KM capabilities on firm performance via the parallel mediation of efficiency-centered and novelty-centered business model innovations (BMIs).

The authors empirically analyzed a survey data of 295 Chinese innovative enterprises by applying partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA).

According to the results of PLS-SEM, the relationship between internal KM capability and firm performance is not significant, instead it is fully mediated by efficiency-centered and novelty-centered BMIs. External KM capability can directly and positively affect firm performance, while the relationship is also partially mediated by BMIs. Furthermore, the authors recognized the antecedent conditions for high-level and low-level firm performance by fsQCA analysis, which substantiate the above findings.

It not only enriches the literature that links KM and innovation management but also contributes to the new theoretical perspective on firm sustainable growth. Methodologically, it combines symmetric and asymmetric analyses together. Additionally, it provides some insights for managers to understand how KM capability drives firm performance through BMI.

This study aims to explore the impacts of the knowledge structure of CEO on corporate innovation strategy in the background of China’s national policy of innovation-driven development.

Regression analysis is used to test the direct effects and the moderating roles of financial and power incentives. It screens 685 listed companies from Chinese stock market, with the time window from 2016 to 2018.

CEO’s knowledge breadth has a significant impact on innovation strategy, but the moderating effects of power and financial incentives are not significant. CEO’s knowledge depth is negatively correlated to corporate innovation strategy; moreover, power incentive significantly strengthens the relationship, whereas financial incentive significantly weakens it.

Firms are suggested to optimize CEO knowledge structure and organizational incentive system for better implementing innovation-driven development strategy.

It is beneficial to the exploration of the micro-mechanism that enables corporate innovation strategy. Scholars may gain additional insights into the strategic management of corporate innovation from the perspective of CEO’s knowledge structure.

Trade and environment are essential issues closely related to the development of the national economy and the improvement of people’s livelihood in the new era. The Report to the 19th National Congress of the Communist Party of China (CPC) listed the construction of a strong trading power as an important part of building a modern economic system and pollution prevention and treatment as one of the three key battles to win the decisive victory of building a moderately prosperous society in all respects. However, the relationship between trade and environmental pollution is still very controversial in the existing literature, and there is a paucity of literature on the relationship between trade and environmental pollution based on micro data.

This paper merged China’s Firm-Level Pollution Database with China’s Industrial Enterprise Database and China’s industry tariff rates. Additionally, by virtue of the quasi-natural experiment of China’s accession to the World Trade Organization (WTO), a difference in difference (DID) model was constructed to alleviate the endogeneity issue.

According to the results, the trade barrier decrease (trade liberalization) significantly reduces the intensity of SO₂ emissions, a major pollutant of enterprises, as the intensity of SO₂ emissions decreased 2.16% for each unit decrease of the trade barrier. The analysis of the mechanisms shows that the SO₂ emission intensity of enterprises is mainly due to the decrease of enterprises’ pollution emission rather than the decrease of output, and the decrease of enterprises’ pollution emission is mainly caused by the enterprises’ cleaner production process rather than the end treatment of pollution emission. The decrease of coal use intensity is an important mechanism of the decrease of SO₂ emission intensity caused by the decrease of trade barriers. Among the technical effects of the change of the trade barrier affecting enterprises’ pollution emission, biased technical change rather than neutral technical change dominates.

The findings of this paper imply that expanding openness can enhance China’s social welfare not only through the economic growth mechanisms identified in the classical literature, but also through environmental improvements. This provides useful policy insights for promoting the construction of a strong trading power and winning the battle against pollution in the new era.

The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc.

Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index’s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree.

The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved.

The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model.

In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.

In order to discover a new adsorbent that can be used to purify dye wastewater in the textile and apparel industry, a novel type of graphene oxide/gluten composite material using an improved acid bath coagulation method was synthesized, which can remove methylene blue in an aqueous environment.

After experimentally compounding different ratios of graphene oxide and gluten, the graphene oxide/gluten composite material with 20% graphene oxide content and superlative adsorption effect was chosen. The synthesized material was characterized by different techniques such as FT-IR and SEM, indicating the microstructure of the material and the success of the composite. Various factors were considered, namely, the influence of temperature, dosage, pH and contact time. The isotherms, kinetics and thermodynamic parameters were successively discussed.

The qmax value of 214.29 mg/g of the material was higher compared to the general sorbent, thus, the graphene oxide/gluten composite material was a suitable sorbent for methylene blue removal. Overall, the graphene oxide/gluten composite material can be considered as an effectual and prospective adsorbent to remove methylene blue in the textile and apparel industrial effluent.

Graphene oxide is a potentially excellent sorbent. However, the high dispersibility of GO is detrimental to adsorption, it disperses rapidly in an aqueous solution making separation and recovery difficult. The high load capacity and recyclability of gluten as a colloid make it a suitable carrier for fixing GO. Studies on the combination of GO and GT into composite adsorption material and for the removal of dyes from dyeing wastewater have not been reported. The composite material research on GO and GT can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.