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Firms are driven to ride on the digital wave in today’s open innovation ecosystem. This study aims to explore the effect of digital transformation (DT) on knowledge-intensive business services (KIBS) firms’ innovation ambidexterity, namely, radical versus incremental innovation, respectively. Meanwhile, the authors evaluated the moderating role of the complexity of R&D collaboration portfolio (i.e. organizational diversity and geographic diversity) in the above relationships.

Using a panel data set of 171 Chinese listed firms in the information and communications technology services industry from 2010 to 2018, the proposed hypotheses were empirically attested.

It is found that DT has a positive relationship with radical innovation and an inverted U-shaped relationship with incremental innovation. In terms of the R&D collaboration portfolio, organizational diversity positively moderates the relationships between DT and innovation ambidexterity, respectively. The geographic diversity weakens the inverted U-shaped effect of DT on incremental innovation; however, its moderating role in the link between DT and radical innovation is not empirically verified.

Extant scholars mainly addressed the interplay between KIBS firms and their manufacturing clients, while this study reveals the different consequences of DT on KIBS firms’ innovation ambidexterity to highlight the role of KIBS firms is an independent and essential innovator in a knowledge-driven economy. Notably, the findings contribute to knowledge management (KM) and R&D literature by confirming the diversity of the R&D collaboration portfolio is a critical KM strategy for KIBS firms to develop and promote external knowledge resources.

The purpose of this study is to examine whether and how independent director-CEO friendliness has an impact on the enterprise's sustainable growth capability and further explore how corporate social responsibility (CSR) and executive compensation affect the relationship in the Chinese context.

Using a sample of Chinese-listed companies from 2010 to 2020, the study adopts fixed effects models to empirically analyze the effect of independent director-CEO friendliness on the enterprise's sustainable growth capability and the roles of CSR and executive compensation.

This study finds that independent director-CEO friendliness is significantly positively correlated with the sustainable growth capability of an enterprise, and this effect is enhanced with the improvement of the degree of CSR fulfillment. What is more, the positive relationship between independent director-CEO friendliness and the enterprise's sustainable growth capability becomes stronger with higher executive compensation.

Given that the existing research on sustainable growth capability mainly focused on the macroeconomic field, this study is of great theoretical significance in exploring the relationship between independent director-CEO friendliness and the enterprise's sustainable growth capability from the micro-level, contributing to the research on the enterprise's sustainable growth capability. In addition, this study considers the boundary conditions of CSR and executive compensation from internal and external perspectives, respectively, as it is innovative to elucidate organizational development from the perspective of internal and external balance.

Given that environmental issues have become increasingly critical in business operations, from the lens of guanxi, this study explores the impact of relational capital on green innovation in a knowledge-driven context of new energy enterprises. Additionally, the moderating effect of corporate environmental responsibility (CER) on the above relationship is analyzed.

This study takes 162 Chinese new energy enterprises from 2010 to 2020 as the research sample. For empirical analysis, factor analysis is adopted to comprehensively measure relational capital, while green innovation is embodied in two dimensions, namely radical green innovation (RGI) and incremental green innovation (IGI).

Relational capital significantly promotes RGI and IGI. Moreover, it is found that implementing CER strengthens the positive relationship between relational capital and RGI but weakens the positive relationship between relational capital and IGI.

It is evident that existing literature on green innovation mainly focused on a single perspective rather than from different dimensions. In addition, few scholars have drawn from stakeholder theory to elucidate the interaction of relational capital with corporate responsibility practices. In this regard, this study examines the link between relational capital and green innovation while examining the moderating effect of CER, which provides valuable insights for future research on relational governance and innovation management. Furthermore, this study innovatively centers on new energy enterprises in China, which are pioneers and facilitators of green development, as the research subject. Considering relevant studies are still nascent in this domain, our empirical results are of extensive practical guidance for managers and practitioners to promote environmental sustainability.

The purpose of this paper is to study the effect of nano hydroxyapatite (HA) and graphene oxide (GO) particles on thermal and mechanical performances of 3D printed poly(ε-caprolactone) (PCL) filaments used in bone tissue engineering (BTE).

Raw materials were prepared by melt blending, followed by 3D printing via 3D Discovery (regenHU Ltd., CH) with all fabricating parameters kept constant. Filaments, including pure PCL, PCL/HA and PCL/GO, were tested under the same conditions. Several techniques were used to mechanically, thermally and microstructurally evaluate properties of these filaments, including differential scanning calorimetry, tensile test, nano indentation and scanning electron microscope.

Results show that both HA and GO nano particles are capable of improving mechanical performance of PCL. Enhanced mechanical properties of PCL/HA result from reinforcing effect of HA, while a different mechanism is observed in PCL/GO, where degree of crystallinity plays an important role. In addition, GO is more efficient at enhancing mechanical performance of PCL compared with HA.

For the first time, a systematic study about effects of nano HA and GO particles on bioactive scaffolds produced by additive manufacturing for BTE applications is conducted in this work. Mechanical and thermal behaviors of each sample, pure PCL, PCL/HA and PCL/GO, are reported, correlated and compared with literature.

Numerous finite elements are proposed based on analytical solutions. However, it is difficult to find the solutions for complicated governing equations. This paper aims to present a novel formulation in the framework of assumed stress quasi-conforming method for the static and free vibration analysis of anisotropic and symmetric laminated plates.

Firstly, an initial stress approximation ruled by 17 parameters, which satisfies the equilibrium equations is derived to improve the performance of the constructed element. Then the stress matrix is treated as the weighted function to weaken the strain-displacement equations. Finally, the Timoshenko’s laminated composite beam functions are adopted as boundary string-net functions for strain integration.

Several numerical examples are presented to show the performance of the new element, and the results obtained are compared with other available ones. Numerical results have proved that the new element is free from shear locking and possesses high accuracy for the analysis of anisotropic and symmetric laminated plates.

This paper proposes a new QC element for the static and free vibration analysis of anisotropic and symmetric laminated plates. In contrast with the complicated analytical solutions of the equilibrium equations, an initial stress approximation ruled by 17 parameters is adopted here. The Timoshenkos laminated composite beam functions are introduced as boundary string-net functions for strain integration. Numerical results demonstrate the new element is free from shear locking and possesses high accuracy for the analysis of anisotropic and symmetric laminated plates.

Facing the global public health emergency (GPHE), the conflict of cultural differences and the imbalance of vital resources such as knowledge among different organizations are becoming more severe, which affects the enthusiasm and sustainability of firms' innovation heavily. It is an urgent problem to be solved for firms how to make use of internal knowledge and external power to help firms' sustainable innovation (FSI). Thus, the purpose of this study is to deeply analyze how firms' internal knowledge diversity (KD) and external ego-network structures [ego-network density (ED) and honest brokers (HB)] affect FSI, as well as how the ego-network structures (ED and HB) moderate the relationship between KD and FSI based on the perspective of the ego network.

Based on the data of the alliance innovation networks of China's new energy industries in 2009–2019, this study uses the social network analysis method and negative binomial regression model to explore the effect of KD and ego-network structures (ED and HB) on FSI, as well as the moderating effects of ego-network structures (ED and HB) on the relationship between KD and FSI based on the perspective of ego network.

This study finds that KD, ED and HB can boost FSI. Moreover, ED plays a negative moderating role in the relationship between KD and FSI. However, the negative moderating effect of HB on the relationship between KD and FSI is not significant.

This study presents fresh empirical evidence and new insights for firms on how to make full use of firms' internal KD and external ego-network structures to facilitate FSI.

First, this study not only enriches the research on the consequences of KD but also expands our understanding of the knowledge-based view to some extent. Second, this study not only enriches the motivation research of the FSI based on the perspective of ego-network in the context of the GPHE but also expands the application scope of social network theory and sustainable innovation' theory in part. Third, this paper is a new attempt to apply social network theory and knowledge-based view at the same time.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.