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The purpose of this study is to calculate the normal force of a two degree of freedom direct drive induction motor considering coupling effects based on an analytical model. Compared with the traditional single degree of freedom motor, normal force characteristics of two-degree-of-freedom direct drive induction motor (2DOFDDIM) is affected by coupling effect when the machine is in a helical motion. To theoretically explain the influence mechanism of coupling effect, this paper conducts a quantitative analysis of the influence of coupling effect on normal force based on the established analytical model of normal force considering coupling effect.

Firstly, the normal forces generated by 2DOFDDIM in linear motion, rotary motion and helical motion are investigated and compared to prove the effect of the coupling effect on the normal force. During this study, several coupling factors are established to modify the calculation equations of the normal force. Then, based on the multilayer theoretical method and Maxwell stress method, a novel normal force calculation model of 2DOFDDIM is established taking the coupling effect into account, which can easily calculate the normal force of 2DOFDDIM under different motions conditions. Finally, the calculation results are verified by the results of 3D finite element model, which proves the correctness of the established calculating model.

The coupling effect produced by the helical motion of 2DOFDDIM affects the normal force.

In this paper, the analytical model of the normal force of 2DOFDDIM considering the coupling effect is established, which provides a fast calculation for the design of the motor.

Gallic acid is a substance that is widely found in nature. Initially, it was only used as a corrosion inhibitor to retard the rate of corrosion of metals. In recent years, with intensive research by scholars, the modification of coatings containing gallic acid has become a hot topic in the field of metal protection. This study aims to summarize the various preparation methods of gallic acid and its research progress in corrosion inhibitors and coatings, as well as related studies using quantum chemical methods to assess the predicted corrosion inhibition effects and to systematically describe the prospects and current status of gallic acid applications in the field of metal corrosion inhibition and protection.

First, the various methods of preparation of gallic acid in industry are understood. Second, the corrosion inhibition principles and research progress of gallic acid as a metal corrosion inhibitor are presented. Then, the corrosion inhibition principles and research progress of gallic acid involved in the synthesis and modification of various rust conversion coatings, nano-coatings and organic resin coatings are described. After that, studies related to the evaluation and prediction of gallic acid corrosion inhibition on metals by quantum chemical methods are presented. Finally, new research ideas on gallic acid in the field of corrosion inhibition and protection of metals are summarized.

Gallic acid can be used as a corrosion inhibitor or coating in metal protection.

There is a lack of research on the synergistic improvement of gallic acid and other substances.

The specific application of gallic acid in the field of metal protection was summarized, and the future research focus was put forward.

To the best of the authors’ knowledge, this paper systematically expounds on the research progress of gallic acid in the field of metal protection for the first time and provides new ideas and directions for future research.

The purpose of this study is to explore growth models based on “industry-based capabilities”, “resources-based capabilities” and “institution-based capabilities” in the context of the determinants of micro, small and medium enterprises’ (MSMEs) actors’ intention to grow.

This study involved 188 owners/managers of MSMEs. The analysis was conducted using partial least squares structural equation modelling. Moreover, the importance-performance map analysis package is used to complement the study findings.

This study uses the framework of the resource-based view (entrepreneurial knowledge), industrial-based view (industrial linkage) and institutional-based view (government support and access to finance) as proxies of the tripod-based view framework. The findings show that entrepreneurial knowledge (skills, competencies and functional), government support, access to finance and industrial linkage (vertical and horizontal) significantly encourage MSMEs’ owner/manager growth intention.

Firstly, this study suggests that MSMEs actors focus on developing entrepreneurial knowledge to boost the skills, competencies and functionalities needed to improve their business capabilities, directly affecting their growth intention. Secondly, this study indicates that the growth intention of MSME players, besides increasing internal capacity, must also be supported by the external environment, such as financial institutions, government and industrial linkage.

This study offers a tripod-based view as a framework for MSMEs’ actors’ intention to grow, where the constructs in the model used so far have not been explored comprehensively in the context of MSMEs. So, the built model brings more relevant factors to explain this topic from various perspectives.

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 purpose of this paper is to investigate the role of cross-listing in overcoming liability of origin (LOO) facing emerging economy corporations (EECs).

This paper takes Chinese firms' cross-listing in Hong Kong and the firms' establishment of international joint ventures (IJVs) with foreign partners as the research setting. This is an empirical study using Heckman's self-selection model as the primary econometric technique and two-stage least square (2SLS) regressions as the supplementary estimation procedure.

Cross-listing in developed economies can serve as a signal for EECs to overcome the LOO. In addition, the regional institutional voids of emerging economies (EEs) and state ownership are prominent boundary conditions shaping this effect.

Only Chinese firms and the firms' cross-listing in Hong Kong are considered for the empirical context as a result of data availability.

This paper provides a practical solution for EECs whose internationalisation tends to be hindered by the LOO.

This study is of high importance in that it centres on a distinctive and challenging problem faced with EECs—the LOO. Besides, it ascribes this liability to a matter of information asymmetries and explores how cross-listing can serve as a signal to cope with this challenge.

This study aims to identify the key drivers of consumer engagement in e-commerce among young consumers at bottom-of-pyramid (BoP) markets and their impact on continued usage intention.

A cross-sectional research design was used to understand low-income customers’ engagement in e-commerce, specifically online shopping. The data for this study were collected from BoP customers in the Indian market. A conceptual model was proposed, and hypotheses were developed using the stimulus–organism–response (S-O-R) framework. For analysis, structural equation modeling was performed using AMOS 20.0 software to test the structural model.

The results of the study highlight that perceived importance, technology and infrastructure and social influence are key drivers of e-commerce at BoP customers. Key drivers have shown a significant positive impact on customer engagement which leads to continue usage intention of e-commerce. Furthermore, customer engagement has shown a strong relationship with continue usage intention of e-commerce.

This study indicates that young consumers’ engagement is important for e-commerce service providers to gain a market share. BoP markets offer immense opportunities to create, develop and sustain e-commerce firms for a long time, especially in India. Managers should recognize the potential of BoP markets, which can generate a huge demand for products and services on e-commerce platforms.

This study contributes both theoretically and empirically. Theoretically, this adds to the existing knowledge of customer engagement, especially in e-commerce and BoP market segment. Empirically, it tested the conceptual research model of low-income customer engagement in the e-commerce marketplace using the S-O-R framework. The study recommended practical implications for e-retailers/e-commerce service providers engaging BoP customers in a digitally connected and intensively competitive era.

Previous scholars have assumed that multinational enterprises (MNEs) can reduce the liability of foreignness and increase profitability by investing in corporate social responsibility (CSR). However, empirical validation of this assumption has rarely been attempted. This study aims to provide empirical evidence that the adoption of multi-stakeholder initiatives, which are globally recognized as signals of CSR, helps MNEs increase profits from internationalization.

Fixed effect models, which address model misspecification problems, and instrumental variable estimation, which controls for the endogeneity in firms’ choice of internationalization, offer empirical evidence supporting the moderating effects of global multi-stakeholder initiatives on the relationship between internationalization and firm performance.

This study examines the moderating role of multi-stakeholder initiatives in the relationship between internationalization and firm performance, drawing on signaling and stakeholder theories. The results suggest that the signaling effect of multi-stakeholder initiatives can help MNEs overcome the liability of foreignness and, therefore, profit from overseas markets.

Although the internationalization–firm performance relationship has been a subject of debate in the field of international business, the role of firms’ stakeholder engagement in this relationship has been largely overlooked in previous studies. In this study, the authors explore the impact of multi-stakeholder initiatives on the internationalization–firm performance relationship. Our primary contention is that multi-stakeholder initiatives have moderating effects on this relationship by reducing the liability of foreignness experienced by MNEs in host countries. Furthermore, the findings suggest that active engagement in multi-stakeholder initiatives significantly contributes to the financial success of MNEs as they internationalize.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the dual-arm robot is directly in contact with external environment, controlling the mutual force between robot and external environment is of great importance. Besides, a high compliance of the robot should be guaranteed.

An impedance control based on Particle Swarm Optimization (PSO) algorithm is designed to track the mutual force and achieve compliance control of the robot end.

The experimental results show that the impedance control coefficients can be automatically tuned converged by PSO algorithm.

The system can reach a steady state within 0.03 s with overshoot convergence, and the force fluctuation range at the steady state decreases to about ±0.08 N even under the force mutation condition.

This study examines the information transmission (return and volatility spillovers) among energy commodities (crude oil, natural gas, Brent oil, heating oil, gasoil, gasoline) and Asian stock markets which are net importers of energy (China, India, Indonesia, Malaysia, Korea, Pakistan, Philippines, Taiwan, Thailand).

The information transmission is investigated by employing the spillover index of Diebold and Yilmaz, using daily data for the period January 2000 to May 2021.

A Strong connectedness is documented between the two classes of asset, especially during crisis periods. Our findings reveal that most of the energy markets, except gasoil and natural gas, are net transmitters of information, whereas all the stock markets, excluding Indonesia and Korea, are net recipients.

The findings are helpful for portfolio managers and institutional investors allocating funds to various asset classes in times of crisis.

All data is original.