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Collaboration with universities is an important innovation strategy for enterprises. However, currently very little research has focused on how such university-industry collaborative innovation activities should be managed. The paper aims to discuss this issue.

This paper introduces the university-industry collaborative innovation practices of Zhejiang NHU Company in China. By using a case study as the method, this paper aims to illustrate the mechanism of university-industry collaborative innovation and how to manage the collaborative innovation activities efficiently.

Zhejiang NHU Company established a university-industry collaborative innovation link through three innovation platforms: the technology R&D center, the ZJU-NHU joint-research center, and the national engineer center. Zhejiang NHU Company manages its collaborative relationships with universities through this innovation network.

NHU Company managed the collaborative relationship efficiently with the institutions, representing an effective degree of university-industry collaborative innovation management.

This paper investigates the current psychological state of Chinese tourism practitioners and their career resilience during the ongoing COVID-19 pandemic. It empirically examines the effects of COVID-19 on Chinese tourism practitioners' professional attitudes and their career belief in the future. The study is intended to guide enterprises and governments to design effective strategies/policies to deal with the effect of this unfavorable environment.

The sample consists of 442 tourism practitioners in 313 tourism enterprises in China. The data were collected via a targeted online survey based on a well-structured questionnaire. The data were analyzed using statistical procedures including multilevel regression analysis.

The study results show that Chinese tourism practitioners have strong career resilience in the face of current turbulent time. After testing, the model shows that career beliefs and social support have a significant positive impact on the professional attitudes of tourism practitioners, and that career resilience has a partial mediating effect on their career beliefs, social support and professional attitude.

This study enriches the existing literature on career belief, social support and career resilience. It provides a new interpretation on how career belief and social support impact career resilience and thus shape tourism practitioners' professional attitudes during pandemics.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

Unintended consequences of knowledge management (KM) can be harmful if they are calamitous. However, they can occasionally be advantageous during catastrophes. The purpose of this study is to investigate how KM can be accidentally propitious during the COVID-19 pandemic using the case of Netflix.

Explanatory factor analysis, multilevel and multiple regressions were used with a sample of 45 countries.

In the authors’ sample, the hypothesized direct relationship between culture (i.e. individualism, power distance and indulgence) and collective pandemic resilience (CPR) was found. In addition, the hypothesized moderating effect of Netflix KM on the relationship between culture and CPR was partially confirmed. The findings suggest that KM during the pandemic can generate an unintended consequence of intensifying the degree of CPR.

Small sample size, data paucity and the constructed variable of CPR might limit the generalizability of this study’s results. Nonetheless, one important research implication is that KM qua unintended consequences can have a significant moderating effect on the relationship between culture and resilience.

This paper highlights how organizations and society can cocreate the value of KM accidentally for the benefit of a larger public during calamities. Also, firms should proactively search for a wider application of their KM beyond their original intention.

This paper initiates a new discussion of positive consequences of unintended KM. Unlike individual-level studies of collective resilience in the past, to the best of the authors’ knowledge, this study generates country-level implications for the first time.

University–industry (UI) collaboration is essential for knowledge and technology exchange between higher education institutions and industries, enabling enterprises to accelerate innovation. However, few studies have investigated the collaborative innovation mechanism through which UI collaboration can enhance the accumulation of firms' intellectual capital (IC) and how this, in turn, affects their innovation-driven development.

Drawing from the knowledge management and collaborative innovation theory, this research proposes a theoretical framework of the inter-organization relationship between enterprises and universities to investigate the influence mechanism of UI collaboration, including academic engagement and commercialization, on corporate performance as well as the mediating role of IC by employing survey that covers 177 UI collaborations.

Empirical results show that human capital and relational capital fully mediate the relationship between academic engagement UI collaboration and corporate economic performance, while human capital partially mediates the relationship between commercialization UI collaboration and corporate economic performance. Additionally, structural capital and relational capital partially mediate the relationship between academic engagement and corporate innovation performance, while structural capital fully mediates the relationship between commercialization and corporate innovation performance.

This study empirically investigates how academic engagement and commercialization impact corporate performance (i.e. innovation dimension or economic dimension). It uncovers this relationship's underlying mechanism by documenting the IC's mediating impact.

This paper aims to examine how integration modes impact the acquirer knowledge diffusion capacity of overseas mergers and acquisitions (M&As) effected by emerging market firms and the role played by the global innovation network position of the acquiring firms in affecting this relationship.

Through the use of structural equation modelling and bootstrap testing, the hypotheses are tested by drawing upon a sample of 102 overseas M&As effected by listed Chinese manufacturing companies.

The results show that acquirers from emerging countries are unable to increase the knowledge diffusion capacity unless they choose the right post-merger integration mode. This paper also finds that the relationship between integration mode and knowledge diffusion is channelled through the centrality and structural holes of acquirers in the global innovation networks. When considering the combinations of different resource similarities and complementarities of the acquired firms, differences emerge in the integration model and network embedded path of acquirers in emerging countries.

Emerging market multinational enterprises should consider post-merger integration as a crucial facilitator to the crafting of global innovation network positions that promote knowledge diffusion. The choices of integration mode and brand management autonomy should be matched with the resource similarities and complementarities that exist between the acquirer and target firms.

Based on the resource orchestration theory and by focussing on network centrality and structural hole as the crucial links, this study provides a nuanced understanding of the relationship between post-merger integration and knowledge diffusion and sheds light on latecomer firms from emerging countries.

Previous studies showed that the unconscious-intuitive strategy resulted in a better choice for it is more predictive of actual interest. This benefit may be influenced by occupational engagement, for the dual process of career decisions takes it as a tool for multidevelopment and optimal adjustment. Thus, we replicated (and extended) the study of Motl et al. (2018) through two experiments to identify the role of three pre-decisional strategies and then explore the combined effects of occupational engagement and these strategies. The purpose of this paper is to address these issues.

The authors replicated (and extended) the study of Motl et al. (2018) through two experiments. First, both studies adopted generalized linear mixed-effects models for statistical analyses to distinguish random and fixed effects. Second, Study 2 used a computer-based process-tracing program called “Mouselab” to explore the effect of the pre-decisional strategy self-generated on participants' interest appraisals over time.

Study 1 found that engagement helped promote participants' interest experience when decisions as usual and the intuitive strategy did not produce optimal choices. Further, people with more prior knowledge about situations no longer achieved as many benefits from their allocated strategy (i.e. rational strategy) as those with less. Study 2 failed to find adequate advantages of the intuitive strategy. Specifically, people with less search depth (the heuristic-intuitive strategy) were more interested in their choices. Nevertheless, when the strategy was manipulated as variability of search (VS), it only found the promotion of engagement, but it neither found the interaction between engagement and strategy nor did strategy itself.

The present paper provides mixed support for adaptive career decision-making. Career counselors can use occupational engagement levels as a reference for pre-decisional strategy selection and coach clients to adopt a proper decision-making process/method to make interest forecasts.

Point-of-interest (POI) recommendation techniques play a crucial role in mitigating information overload and delivering tailored services. To address limitations in conventional POI recommendation systems, constrained by sparse user-POI interactions and incomplete consideration of temporal dynamics, POI recommendation based on the spatial-temporal graph (STG-POI) is proposed.

Spatial-temporal sequence graphs from geographical locations and user interaction history data are constructed, which are used to mine spatial-temporal sequence information. Using the data filtered by the band-pass filter, graph neural networks with distance-awareness and sequence-awareness are applied to capture high-order spatial-temporal connections within diverse graph topologies. The model leverages contrastive learning for self-supervised disentanglement of graph representations, providing self-supervised signals for sequential and geographical intent perception, thereby achieving more precise POI personalization.

Compared to the baseline model GSTN, experiments on the Foursquare and Gowalla data sets reveal that STG-POI improves testing AUC by 2.0%, 2.1%, 2.0% and decreases logloss by 1.9%, 3.3%, 0.3%, respectively. These results indicate the model’s effectiveness in capturing spatial-temporal information, surpassing mainstream POI recommendation baseline models.

This approach constructs a dual graph from user interaction data, harnessing sequential and geographical information as self-supervised signals. It yields decoupled representations of these influences, offering a comprehensive insight into user behaviors and preferences within location-based social networks, thus enhancing recommendation accuracy and interpretability. This approach addresses the challenge in graph convolutional network where only rough and smooth features are conducive to recommendation by using band-pass filters to significantly reduce computational complexity, thereby enhancing recommendation speed by filtering out noise data that does not contribute to recommendation performance. Experimental results indicate that this model surpasses current mainstream approaches in POI recommendation tasks, effectively integrating both geographical and temporal features.

The purpose of this paper is to apply the concept of equivalent initial flaw size (EIFS) to the anisotropic nickel-based single crystal (SX) material, and to predict the fatigue life on this basis. The crack propagation law of SX material at different temperatures and the weak correlation of EIFS values verification under different loading conditions are also investigated.

A three-parameter time to crack initial (TTCI) method with multiple reference crack lengths under different loading conditions is established, which include the TTCI backstepping method and EIFS fitting method. Subsequently, the optimized EIFS distribution is obtained based on the random crack propagation rate and maximum likelihood estimation of median fatigue life. Then, an effective driving force based on anisotropic and mixed crack propagation mode is proposed to describe the crack propagation rate in the small crack stage. Finally, the fatigue life of three different temperature ESE(T) standard specimens is predicted based on the EIFS values under different survival rates.

The optimized EIFS distribution based on EIFS fitting - maximum likelihood estimation (MLE) method has the highest accuracy in predicting the total fatigue life, with the range of EIFS values being about [0.0028, 0.0875] (mm), and the mean value of EIFS being 0.0506 mm. The error between the predicted fatigue life based on the crack propagation rate and EIFS distribution for survival rates ranges from 5% to 95% and the experimental life is within two times dispersion band.

This paper systematically proposes a new anisotropic material EIFS prediction method, establishing a framework for predicting the fatigue life of SX material at different temperatures using fracture mechanics to avoid inaccurate anisotropic constitutive models and fatigue damage accumulation theory.

Deglobalization and the coronavirus disease 2019 (COVID-19) pandemic have severely hindered multinational enterprise (MNE) investment. At the same time, digital technology is seriously challenging it with traditional production factor flows. Few studies have realized that the impact of digitalization is not limited to either transaction costs or the location-boundness of firm-specific advantages (FSAs), but extends to profound changes in the fundamental essence of MNEs. There is still limited understanding of this body of knowledge as a whole, including how its subtopics are interrelated. This study took the production factor change perspective to review MNE theory in the digital era. Therefore, this study aims to identify any upcoming and undeveloped themes in order to provide a platform suited to direct future research.

This paper presents a summary and a review of 151 articles published between 2007 and 2020. Such review was conducted to systematically explain the connotations and influential mechanisms of digital empowerment on MNE theory. This was achieved by using the CiteSpace citation visualization tool to build a keyword co-occurrence network.

The research findings pertain to how digitalization expands, breaks through, and even reshapes traditional MNE theory from four distinctive angles: the influential factors of internationalization, the process of internationalization, competitive advantage, and location choice. The findings are followed by the presentation of future research directions.

This paper presents an examination of MNE theory in the digital era from the perspective of production factor change. In doing so, it identifies significant theoretical innovation opportunities for future scholarly research priorities.