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This study aims to answer the question of how business models (BMs) maintain stability while coping with environmental uncertainties. This study proposes a dynamic co-evolution of knowledge management and business model transformation based on a comparative analysis of the focal firms’ BMs and their main partners in two e-commerce ecosystems in China.

The open data of listed companies regarding the introduction of emerging topics on the transformation tendency of BMs in the post-COVID-19 business world is qualitatively analysed. The theoretical foundation is based on a critical review of the literature.

Three aspects of the co-evolution between knowledge management and business model transformation are introduced. These three aspects are as follows: knowledge integration helps with multi-system business integration and decision-making collaborations; knowledge sharing helps to enhance cognitive ability and network value based on businesses; and the creation of new knowledge helps enrich the knowledge base and promote the transformation of BMs.

Solely attributing a firm’s ability to cope with environmental uncertainties to its business model weakens the importance of its knowledge management. This study argues that the co-evolution between knowledge management and business model transformation also plays a key role in a firm’s response to issues post-COVID-19.

This study calls for the development of a normative theory of co-evolution between knowledge management and business model transformation, implying uncharted territories of knowledge management based on interaction with business model designs in e-business ecosystems.

The purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also aimed to investigate the relationship between the orientation of graphite flakes and the failure behavior of the material under compressive loads as well as the effect of image size on the accuracy of stress–strain behavior predictions.

This paper presents a microstructure-based model that utilizes the finite element method (FEM) combined with representative volume elements (RVE) to simulate the hardening and failure behavior of ferrite-pearlite matrix gray cast iron under uniaxial loading conditions. The material was first analyzed using optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) to identify the different phases and their characteristics. High-resolution SEM images of the undeformed material microstructure were then converted into finite element meshes using OOF2 software. The Johnson–Cook (J–C) model, along with a damage model, was employed in Abaqus FEA software to estimate the elastic and elastoplastic behavior under assumed plane stress conditions.

The findings indicate that crack initiation and propagation in gray cast iron begin at the interface between graphite particles and the pearlitic matrix, with microcrack networks extending into the metal matrix, eventually coalescing to cause material failure. The ferritic phase within the material contributes some ductility, thereby delaying crack initiation.

This study introduces a novel approach by integrating microstructural analysis with FEM and RVE techniques to accurately model the hardening and failure behavior of gray cast iron under uniaxial loading. The incorporation of high-resolution SEM images into finite element meshes, combined with the J–C model and damage assessment in Abaqus, provides a comprehensive method for predicting material performance. This approach enhances the understanding of the microstructural influences on crack initiation and propagation, offering valuable insights for improving the design and durability of gray cast iron components.

Based on the perspective of knowledge management capability, this paper aims to reveal the internal mechanism of the digital empowerment of mobile social platforms to improve the operational performance of Chinese traditional retail enterprises. Such improvements have crucial theoretical value and practical implications for Chinese traditional retail enterprises to achieve transformation and sustainable development.

This study applied the typical analysis method, selected China’s leading mobile social platform, WeChat, as a typical case, and observed and analyzed the public data of the traditional retail industry and social platforms and interviews with relevant enterprises. On this basis, this study used the inductive and deductive methods of qualitative research to conduct an in-depth analysis of the mechanism by which WeChat’s digital empowerment improves the operational performance of Chinese traditional retail enterprises. It also discussed the critical role and path knowledge management capabilities play in this mechanism.

This research demonstrated that mobile social platforms empower Chinese traditional retail enterprises to build diversified digital channels, enhance the knowledge acquisition capability of enterprises and thus improve their performance; empower Chinese traditional retail enterprises to build digital community networks, enhance the knowledge diffusion capability of enterprises and thus improve their performance; and empower Chinese traditional retail enterprises to integrate online and offline businesses, enhance the knowledge integration capability of enterprises and thus improve their performance.

This study clarifies the internal mechanism of how the digital empowerment of mobile social platforms can improve the performance of Chinese traditional retail enterprises. This mechanism implies that knowledge management capabilities (knowledge acquisition, diffusion and integration capability) are the underlying logic for Chinese traditional retail enterprises to achieve higher performance levels. This has important practical implications for managers of Chinese traditional retail enterprises to leverage the digital infrastructure of mobile social platforms to achieve the sustainable development of enterprises.

This study provides an in-depth analysis of how the traditional retail industry uses digital social platforms to improve operational performance from the perspective of knowledge management capabilities, which can further promote the theoretical research and practical development of digitalization and knowledge management. At the same time, this study explored the research on the operational performance of Chinese traditional retail enterprises from the perspective of knowledge management capabilities and expanded the research on knowledge management in related fields. The authors have initially sorted out the impact of knowledge management capabilities on the operational performance of Chinese traditional retail enterprises in the digital era. This will help better understand the role and function of knowledge management in strategic transformation and expand the application of knowledge management theory.

Online portfolio selection sequentially allocates wealth among a set of assets and aims to maximize the investor’s cumulative return in the long run. Various existing algorithms in the finance and accounting area adopt an indirect approach to exploit one asset characteristic through the channel of assets’ expected return and thus cannot fully leverage the power of various asset characteristics found in the literature. This study aims to propose new algorithms to overcome this issue to enhance investment performance.

We propose a parameterized portfolio selection (PPS) framework, which directly incorporates multiple asset characteristics into portfolio weights. This framework can update parameters timely based on final performance without intermediate steps and produce efficient portfolios. We further append L1 regularization to constrain the number of active asset characteristics. Solving the PPS formulation numerically, we design two online portfolio selection (OLPS) algorithms via gradient descent and alternating direction method of multipliers.

Empirical results on five real market datasets show that the proposed algorithms outperform the state of the arts in cumulative returns, Sharpe ratios, winning ratios, etc. Besides, short-term characteristics are more important than long-term characteristics, and the highest return category is the most important characteristic to improve portfolio performance.

The proposed PPS algorithms are new end-to-end online learning approaches, which directly optimize portfolios by asset characteristics. Such approaches thus differ from existing studies, which first predict returns and then optimize portfolios. This paper provides a new algorithmic framework for investors’ OLPS.

This paper aims to unveil the importance of knowledge management on a firm’s strategic emergency response during the great negative shock from global public health threats. Through analyzing how representative firms in China’s new economy industries dealt with the COVID-19 pandemic before, during and after the crisis, the significant problems confronted by these firms are pointed out, and the important role knowledge management capabilities played in solving these problems is proven.

The open data of listed companies regarding the important role knowledge management played in firms’ strategic emergency response during the COVID-19 pandemic are qualitatively analyzed. Based on theoretical sampling, this paper selects representative samples of enterprises and analyzes the positive response measures they took after being hit by this public health event to gain qualitative insight into the importance of knowledge management capabilities in strategic emergency response.

Three aspects of the important role of knowledge management capabilities in a firm’s strategic emergency response during the COVID-19 pandemic are introduced: before the crisis, firms should strengthen the acquisition, sharing and integration of knowledge so that they can intensify their monitoring for uncertain risks; during the crisis, firms should boost the transmission, transformation and diffusion of knowledge to improve emergency cooperation; and after the crisis, companies should reinforce knowledge evaluation, creation and application to enhance “immunity” in similar emergencies.

This paper has important implications for bolstering strategic emergency management practice and knowledge management capability among firms. Future research must focus on the following two aspects for further investigation: the dynamic relationship between firm knowledge management capability and strategic emergency response ability; and the collaboration system between firm knowledge management and strategic emergency response behaviors.

This paper discusses the important role knowledge management capabilities play in firms’ strategic emergency responses based on insights gained from the significant changes that the COVID-19 pandemic caused to representative Chinese new economy firms. By analyzing the three stages of before, during and after the emergency, this paper proposes the exact efforts that new economy companies should make in improving knowledge management capability.

This study reveals the trigger of innovative behavior from the perspective of intrinsic and extrinsic spiritual inspiration and provides a new research idea for the formation mechanism of innovative behavior. The purpose of this study is to provide certain guidance and implications for enterprises to cultivate and enhance employees’ innovative behavior.

We conducted three studies, collected multi-source data (N = 1,175) from different countries longitudinally, as well as used hierarchical regression analysis and fuzzy-set quantitative comparative analysis to verify the theoretical model.

According to the findings, both spiritual leadership and career calling have a positive impact on employees’ innovative behavior through the mediating effect of autonomous motivation and the moderating effect of person-vocation fit.

Innovative behavior is the positive professional pursuit of employees, which is difficult to form without the motivation of spiritual factors. Spirituality is a complex concept that contains intrinsic and extrinsic spiritual factors, both of which could stimulate employees’ innovative behavior. Although many discussions have been held on this topic in recent years, little attention has been paid simultaneously to the motivating effects of the two perspectives. Drawn from self-determination theory, this study explores the mechanisms of two spiritual motivation paths (i.e. the intrinsic and extrinsic spiritual motivation paths) in the improvement of employees’ innovative behavior.

Motivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.

We estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.

Applying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.

Our analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.

The findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

Skippable video advertising offers users the option to skip directly to their desired video content after viewing a limited amount of advertising and is becoming a format increasingly preferred by advertisers. This study constructed a mediator model based on advertising attitude to examine the influence of users' perceived control and perceived intrusiveness on brand attitude and advertising avoidance intention in skippable video ads.

The study considered the structural equation model using a self-reporting measure method. The research model was tested using a sample of 302 respondents.

The results showed that perceived control positively affected ad and brand attitude and negatively affected advertising avoidance intention. Perceived intrusiveness negatively affected attitudes toward the advertising and the brand but positively affected advertising avoidance intention. Ad attitude and brand attitude had no significant influence on advertising avoidance intention. The results also confirmed that ad attitude mediates the path from perceived control and intrusiveness to brand attitude.

This study further enriches the theoretical development of skippable video ads and expands the perspective and scope of interactive advertising research.

This paper aims to propose cooperative control strategies for dual-arm robots in different human–robot collaborative tasks in assembly processes. The authors set three different regions where robot performs different collaborative ways: “teleoperate” region, “co-carry” region and “assembly” region. Human holds the “master” arm of dual-arm robot to operate the other “follower” arm by our proposed controller in “teleoperation” region. Limited by the human arm length, “follower” arm is teleoperated by human to carry the distant object. In the “co-carry” region, “master” arm and “follower” arm cooperatively carry the object to the region close to the human. In “assembly” region, “follower” arm is used for fixing the object and “master” arm coupled with human is used for assembly.

A human moving target estimated method is proposed for decreasing efforts for human to move “master” arm, radial basis functions neural networks are used to compensate for uncertainties in dynamics of both arms. Force feedback is designed in “master” arm controller for human to perceive the movement of “follower” arm. Experimental results on Baxter robot platform show the effectiveness of this proposed method.

Experimental results on Baxter robot platform show the effectiveness of our proposed methods. Different human-robot collaborative tasks in assembly processes are performed successfully under our cooperative control strategies for dual-arm robots.

In this paper, cooperative control strategies for dual-arm robots have been proposed in different human–robot collaborative tasks in assembly processes. Three different regions where robot performs different collaborative ways are set: “teleoperation” region, “co-carry” region and “assembly” region.

The stringent requirements for environmental protection have induced the extensive applications of water-lubricated journal bearings in marine propulsion. The nonlinear dynamic analysis of multiple grooved water-lubricated bearings (MGWJBs) has not been fully covered so far in the literature. This study aims to conduct the nonlinear dynamic analysis of the instability for MGWJBs.

An attenuation rate interpolation method is proposed for the determination of the critical instability speed. Based on a structured mesh movement algorithm, the transient hydrodynamic force model of MGWJBs is set up. Furthermore, the parameters’ analysis of nonlinear instability for MGWJBs is conducted. The minimum water film thickness, side leakage, friction torque and power loss of friction are fully analyzed.

With the increase of speed, the journal orbits come across the steady state equilibrium motion, sub-harmonic motion and limit circle motion successively. At the limit circle motion stage, the orbits are much larger than that of steady state equilibrium and sub-harmonic motion. The critical instability speed increases when the spiral angle decreases or the groove angle increases. The minimum water film thickness peak is at the rotor speed of 4,000 r/min for the MGWJB with Sa = 0°. As rotor speed increases, the side leakage decreases slightly while the friction torque and the power loss of friction increase gradually.

Present research provides a beneficial reference for the dynamic mechanism analysis and design of MGWJBs.