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This study aims to explore the role of equity investment in knowledge-driven business model innovation (BMI) in context of open modes according to the evidence from China’s primary market.

Based on the database of China’s private market and data set of news clouds, the statistic approach is applied to explore and explain whether equity investment promotes knowledge-driven BMI. Machine learning method is also used to prove and predict the performance of such open innovation.

The results of logistic regression show that explanatory variables are significant, providing evidence that knowledge management (KM) promotes BMI through equity investment. By further using back propagation neural network, the classification learning algorithm estimates the possibility of BMI, which can be regarded as a score to quantify the performance of knowledge-driven BMI

The quality of secondhand big data is not very ideal, and future empirical studies should use first-hand survey data.

This study provides new insights into the link between KM and BMI by highlighting the important roles of external investments in open modes.

From the perspective of investment, the findings of this study suggest the importance for stakeholders to share knowledge and strategies for entrepreneurs to manage innovation.

The concepts and indicators related to business models are difficult to quantify currently, while this study provides feasible and practical methods to estimate knowledge-driven BMI with secondhand data from the primary market. The mechanism of knowledge and innovation bridged by the experience from investors is introduced and analyzed.

The significant performance implications of individual-focused and group-focused transformational leadership have been documented for established firms, but the issue of whether they are complementary or substitutive still remains a puzzle, and whether their relationship differs in new firms remains unanswered. The purpose of this paper is to investigate the relationship between individual-focused and group-focused transformational leadership in different organizational structures in new firms.

The study draws on survey data of 209 questionnaires from 63 teams in 63 new firms in China.

The results suggest that individual-focused and group-focused transformational leadership are substitutive when the organizational structure is mechanistic, and are complementary when the structure is organic.

The study contributes to the debate on the relationship between individual-focused and group-focused transformational leadership by comparing organizational structure characteristics and offering a comprehensive understanding of the issue.

This paper aims to build a theory model to examine the influencing mechanism of entrepreneurial environment on new firm performance based on network view and resource‐based view, and then carry out empirical research.

Data were obtained from 112 new firms, through the use of questionnaires, in Changchun city, China.

Evidence indicates that the entrepreneurial environment has an impact on new firm performance through entrepreneurial network and resource acquisition. The results show that eight out of ten hypotheses are supported.

In a hostile, dynamic and complex environment, the new firm should enhance its entrepreneurial network to ensure resource acquisition and then promote performance.

The paper shows definitely the significance of entrepreneurial network as a bridge between external environment and resource acquisition and new firm performance. Entrepreneurial network and resource acquisition are identified as important intermediary variables, and resource combination ability as a moderating variable. This paper examines the influence of external environment on new firm performance. The research has some theoretical and managerial implications for new firms' survival and obtaining growth in highly uncertain and turbulent environments.

In the paradox of personalized services and privacy risks, what factors influence users’ decisions is considered an interesting issue worth exploring. The current study aims to empirically explore privacy behavior of social media users by developing a theoretical model based on privacy calculus theory.

Privacy risks, conceptualized as natural risks and integrated risks, were proposed to affect the intention of privacy disclosure and protection. The model was validated through a hybrid approach of structural equation modeling (SEM)-artificial neural network (ANN) to analyze the data collected from 527 effective responses.

The results from the SEM analysis indicated that social interaction and perceived enjoyment were strong determinants of perceived benefits, which in turn played a dominant role in the intention to disclose the privacy in social media. Similarly, trust and privacy invasion experience were significantly related to perceived risks that had the most considerable effect on users’ privacy protection intention. And the following ANN models revealed consistent relationships and rankings with the SEM results.

This study broadened the application perspective of privacy calculus theory to identify both linear and non-linear effects of privacy risks and privacy benefits on users’ intention to disclose or protect their privacy by using a state-of-the-art methodological approach combining SEM and ANN.

Social media provides a convenient way to popularise first aid knowledge amongst the general public. So far, little is known about the factors influencing individuals’ adoption of first aid knowledge on social media. Drawing on the information adoption model (IAM), this study investigates the joint effects of cognitive factors (e.g. perceived information usefulness (PIU)), affective factors (e.g. arousal (AR)) and social factors (e.g. descriptive norms (DN)) on first aid knowledge adoption (KA) and examines their antecedent cues from the perspective of information characteristics.

The data were collected from 375 social media users, and the structural equation model was adopted to analyse the results.

The results indicate that PIU, AR and DN all have positive direct effects on first aid KA. Additionally, the study highlights the positive synergistic effect of AR and PIU. Furthermore, the study suggests that AR is determined by message vividness (MV) and emotional tone (ET), whilst DN are determined by peer endorsement (PEE) and expert endorsement (EXE).

Our research is groundbreaking as it delves into the adoption of first aid knowledge through social media, thus pushing the boundaries of existing information adoption literature. Additionally, our study enhances the IAM by incorporating emotional and social elements and provides valuable insights for promoting the spread of first aid knowledge via social media.

Flow force is one of the crucial factors affecting the performance of conical throttle valves. The purpose of this paper is to determine the relationship between the flow force and operating parameters of the conical throttle valve.

The flow force of the throttle valve can be obtained by the difference between the axial force and static pressure on the valve spool. In this paper, the internal fluid is divided into two regions and the axial force and static pressure are obtained, respectively. In addition, a two-dimensional axisymmetric simulation model and experimental test are carried out to validate the results of the flow force.

It can be seen that the theoretical, simulation and experimental results exhibit high agreement with each other and the error between them decreases with the increase in the size of the opening. The curves of pressure distribution reveal that the pressure on the spool first decreases then increases when it reaches the minimum pressure at the orifice. Additionally, the minimum pressure decreases with the increase of opening and pressure difference. The results also indicate that the increase in the size of the opening and inlet pressure has a positive effect on the flow force. However, the increase in outlet pressure has a negative effect on the flow force.

In this paper, the flow force calculation model of conical throttle valve is established and the influence of operating parameters on the flow force of conical throttle valve is studied.

The aim of this paper, which is based on Cheng et al.’s (2012) research, is to re-examines the relationship between a firms’ voluntary disclosure of forward-looking non-financial information (FNFI) and investment efficiency.

The authors chose firms that publicly traded in the Shenzhen and Shanghai stock markets from 2005 to 2011, a total of 926 firms and 6,482 firm-year. To control the endogeneity problem between FNFI and investment efficiency, the authors lagged the FNFI variable for one year.

The authors found that FNFI alleviates a firms’ underinvestment but leads to overinvestment. These effects become gradually weaker over time. In addition, the results indicate that corporate governance helps improve the quality of FNFI, thus boosting investors’ confidence and easing financial constraints.

The limitations of this paper are mainly focused on the measure of FNFI. The authors only considered the quantity of FNFI disclosed by firms and ignored other characteristics of FNFI.

Based on Cheng et al.’s (2012) static findings, this paper considers the dynamic role of FNFI in firms’ investment decisions.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.