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This study aims to address the issue of perfunctory sharing that arises in knowledge governance due to a lack of willingness to share knowledge between individuals within the same organization. This knowledge-sharing process does not occur simultaneously for both parties but follows a sequential progression. Additionally, this governance model fully considers the willingness of both parties to share and effectively addresses the two knowledge characteristics that influence their willingness to do so.

This study follows inductive logic and primarily adopts an interpretive case study approach to conduct a longitudinal exploratory case study. An incubator enterprise with active knowledge-sharing activities and significant knowledge governance effects is selected as the research subject. The governance system is explained through the lens of prospect theory at the mechanism level.

In the study of the knowledge-sharing process, the authors observed a new challenge: perfunctory behavior, whereby individuals engage in knowledge-sharing activities that lack substantial effects as a way to avoid genuine sharing. From this, a new knowledge-sharing model was extracted, the cold start and hot feedback model, which follows a sequential (rather than simultaneous) progression. Using the deterministic effect of prospect theory and the principle of reference dependence, the governance mechanism of corporate knowledge sharing was analyzed from the perspective of knowledge-sharing willingness.

Based on prospect theory, this study primarily explains how the governance mechanism influences the willingness to share knowledge from the perspective of four principles. In the future, threat rigidity theory and commitment escalation theory can be combined to further analyze the willingness to share knowledge from the perspectives of pressure and cost. Empirical research methods can also be used to test and enrich the research results of this paper.

After considering the willingness to share knowledge, a new knowledge-sharing model and corresponding knowledge-sharing governance model are proposed, and prospect theory is extended to the knowledge-based theory research field.

This study aims to enhance the understanding of fiber-reinforced polymer (FRP) applications in partially confined concrete, with a specific focus on improving economic value and load-bearing capacity. The research addresses the need for a more comprehensive analysis of non-uniform vertical strain responses and precise stress–strain models for FRP partially confined concrete.

DIC and strain gauges were employed to gather data during axial compression tests on FRP partially confined concrete specimens. Finite element analysis using ABAQUS was utilized to model partial confinement concrete with various constraint area ratios, ranging from 0 to 1. Experimental findings and simulation results were compared to refine and validate the stress–strain model.

The experimental results revealed that specimens exhibited strain responses characterized by either hardening or softening in both vertical and horizontal directions. The finite element analysis accurately reflected the relationship between surface constraint forces and axial strains in the x, y and z axes under different constraint area ratios. A proposed stress–strain model demonstrated high predictive accuracy for FRP partially confined concrete columns.

The stress–strain curves of partially confined concrete, based on Teng's foundation model for fully confined stress–strain behavior, exhibit a high level of predictive accuracy. These findings enhance the understanding of the mechanical behavior of partially confined concrete specimens, which is crucial for designing and assessing FRP confined concrete structures.

This research introduces innovative insights into the superior convenience and efficiency of partial wrapping strategies in the rehabilitation of beam-column joints, surpassing traditional full confinement methods. The study contributes methodological innovation by refining stress–strain models specifically for partially confined concrete, addressing the limitations of existing models. The combination of experimental and simulated assessments using DIC and FEM technologies provides robust empirical evidence, advancing the understanding and optimization of FRP-concrete structure performance. This work holds significance for the broader field of concrete structure reinforcement.

To examine the nature of Chinese business negotiating style in Sino‐Western business negotiations in business‐to‐business markets involving large industrial projects from a social cultural point of view.

A conceptual approach developed from personal interviews.

This study reveals that the Chinese negotiator does not possess an absolute negotiating style but rather embraces a mixture of different roles together: “Maoist bureaucrat in learning”, “Confucian gentleman”, and “Sun Tzu‐like strategist”. The Chinese negotiating strategy is essentially a combination of cooperation and competition (termed as the “coop‐comp” negotiation strategy in this study). Trust is the ultimate indicator of Chinese negotiating propensities and role choices.

The focus of this study is on Chinese negotiating style shown in large B2B negotiations with Chinese SOEs.

Differing from most other studies on Chinese negotiating style which tend to depict the Chinese negotiator as either sincere or deceptive, this study points out that there exists an intrinsic paradox in Chinese negotiating style which reflects the Yin Yang thinking. The Chinese negotiator has a cultural capacity to negotiate both sincerely and deceptively and he/she changes coping strategies according to situation and context, all depending on the level of trust between negotiating partners.

The time series forecasting is an essential methodology which can be used for analysing time series data in order to extract meaningful statistics based on the information obtained from past and present. These modelling approaches are particularly complicated when the available resources are limited as well as anomalous. The purpose of this paper is to propose a new hybrid forecasting approach based on unbiased GM(1,1) and artificial neural network (UBGM_BPNN) to forecast time series patterns to predict future behaviours. The empirical investigation was conducted by using daily share prices in Colombo Stock Exchange, Sri Lanka.

The methodology of this study is running under three main phases as follows. In the first phase, traditional grey operational mechanisms, namely, GM(1,1), unbiased GM(1,1) and nonlinear grey Bernoulli model, are used. In the second phase, the new proposed hybrid approach, namely, UBGM_BPNN was implemented successfully for forecasting short-term predictions under high volatility. In the last stage, to pick out the most suitable model for forecasting with a limited number of observations, three model-accuracy standards were employed. They are mean absolute deviation, mean absolute percentage error and root-mean-square error.

The empirical results disclosed that the UNBG_BPNN model gives the minimum error accuracies in both training and testing stages. Furthermore, results indicated that UNBG_BPNN affords the best simulation result than other selected models.

The authors strongly believe that this study will provide significant contributions to domestic and international policy makers as well as government to open up a new direction to develop investments in the future.

The new proposed UBGM_BPNN hybrid forecasting methodology is better to handle incomplete, noisy, and uncertain data in both model building and ex post testing stages.

Megaproject knowledge innovation (MKI) is perceived as a critical strategy for engineering value co-creation and industrial chain upgrading. Ascertaining the impact mechanism of MKI is a crucial initial step towards improving management practices. Within the framework of complex systems in megaprojects, factors exhibit intricate interdependencies. However, the current domain of knowledge has either overlooked or oversimplified this relationship and therefore cannot propose pragmatic and efficacious strategies for enhancing MKI. To close this gap, this study develops a Bayesian network (BN) model aiming to investigate the interdependencies among MKI-related factors and their impact on MKI.

First, this study implements literature review, expert interview and field investigation to identify the influencing factor nodes for the network model development. Second, a Bayesian network was constructed by integrating the expert knowledge with Dempster-Shafer theory. Next, a MKI measurement model was established using 253 training samples. Finally, the factor significance and optimal MKI improvement strategies are identified from the sensitivity analysis and probabilistic reasoning within the BNs.

The results indicate that (1) the BN model exhibits significant reliability and holds promotion and application value in formulating MKI management strategies; (2) knowledge sharing, shared vision and leadership are the key influencing factors of MKI; and (3) simultaneously improving institutional pressure, leadership and knowledge sharing is the most optimal strategy to enhance MKI.

This study innovatively introduced the BN method into the domain of MKI management, providing an appropriate approach for modelling complex relationships among factors and investigate nonlinear influences. The developed model raises megaproject stakeholders’ awareness about factors influencing MKI and presents quantified strategies that increase the likelihood of maximising MKI levels. Its ease of generalisability positions it as a promising decision support tool, facilitating the implementation of sustainable MKI practices.

This paper aims to identify key factors influencing the development of advanced manufacturing clusters and propose governance pathways for their digital innovation ecosystems.

A quantitative analysis of the Tai-Xin Integrated Economic Zone in China is conducted using data collected through a questionnaire survey. An evaluation index for the development level of advanced manufacturing clusters is constructed, and a structural equation model is used to identify key influencing factors and governance pathways.

This paper reveals that factors such as industrial foundation, technological innovation capability, social institution environment and government policies have a significant positive impact on the development of digital innovation ecosystem in advanced manufacturing clusters. It constructs a governance model for the digital innovation ecosystem and proposes three major pathways: integration of heterogeneous innovation resources, enhancement of digital capabilities, and fostering digital collaborative governance. The crucial role of digital technology in improving data processing efficiency, optimizing resource allocation and promoting collaboration among entities is emphasized. These pathways can optimize resource allocation, boosting the competitiveness and innovation capacity of clusters.

By incorporating advanced manufacturing clusters into the digital innovation ecosystem framework, this paper enriches theoretical research on both fronts. It offers specific governance pathways and policy recommendations, providing valuable references and guidance for promoting the digital transformation and ecosystem construction of manufacturing clusters.

The purpose of this paper is to target on designing a short-term load prediction framework that can accurately predict the cooling load of office buildings.

A feature selection scheme and stacking ensemble model to fulfill cooling load prediction task was proposed. Firstly, the abnormal data were identified by the data density estimation algorithm. Secondly, the crucial input features were clarified from three aspects (i.e. historical load information, time information and meteorological information). Thirdly, the stacking ensemble model combined long short-term memory network and light gradient boosting machine was utilized to predict the cooling load. Finally, the proposed framework performances by predicting cooling load of office buildings were verified with indicators.

The identified input features can improve the prediction performance. The prediction accuracy of the proposed model is preferable to the existing ones. The stacking ensemble model is robust to weather forecasting errors.

The stacking ensemble model was used to fulfill cooling load prediction task which can overcome the shortcomings of deep learning models. The input features of the model, which are less focused on in most studies, are taken as an important step in this paper.

This paper aims to use artificial neural network (ANN) methods to predict stock price crashes in the Chinese equity market.

Three ANN models are developed and compared with the logistic regression model.

Results from this study conclude that the ANN approaches outperform the traditional logistic regression model, with fewer hidden layers in the ANN model having superior performance compared to the ANNs with multiple hidden layers. Results from the ANN approach also reveal that foreign institutional ownership, financial leverage, weekly average return and market-to-book ratio are the important variables when predicting stock price crashes, consistent with results from the traditional logistic model.

First, the ANN framework has been used in this study to forecast the stock price crashes and compared to the traditional logistic model in the world’s largest emerging market China. Second, the receiver operating characteristics curves and the area under the ROC curve have been used to evaluate the forecasting performance between the ANNs and the traditional approaches, in addition to some traditional performance evaluation methods.

The purpose of this paper is to evaluate the photochromic performance of photochromic compounds in polymer matrices.

The poly(methyl methacrylate) PMMA and epoxy resin doped with photochromic spirooxazine (SO) are prepared and the effects of ultraviolet (UV) irradiation are studied using spectrophotometer. The reversible reaction is effected using white light. Photochemical fatigue resistance of these films is also studied.

Irradiation of colourless 7′,8′‐dichloro‐1,3,3‐trimethylspiro[indoline‐2,3′‐[3H]benzo[b][1,4]oxazine] (SO) doped in PMMA and epoxy resin with UV light (366 nm) results in the formation of an intense purple‐red coloured zwitterionic photomerocyanine (PMC). The reverse reaction is photochemically induced by irradiation with white light. Photocolouration and photobleaching reactions follow a first‐order rate equation. It is found that photocoloration rate constant of (SO) in both matrices is almost the same, which is unexpected. On the other hand, the rate of photobleaching reaction of (PMC) in PMMA is twice slower than that in the epoxy resin. It seems that the presence of the two chlorine atoms at positions 7′ and 8′ of the benzooxazine moiety destabilise the PMC in epoxy resin film and results in speeding up the fading process compared to that in PMMA. SO doped in epoxy resin shows much better fatigue resistance than that doped in PMMA.

The PMMA and epoxy resin polymers doped photochromic spirobenzooxazine described in this paper were prepared and studied. The principle of study established can be applied to any type of polymer or to any type of photochromic compounds.

The photochromic materials developed can be used for different applications, such as coatings and holography.

The method developed may be used to enhance the performance of photochromic materials.

The purpose of this paper is to evaluate the photochromic performance of photochromic compounds in polymer matrices.

The poly(methyl methacrylate) (PMMA) and epoxy resin doped with photochromic spirobenzopyran were prepared and the effects of ultraviolet (UV) irradiation were studied using spectrophotometer. The reversible reaction was effected using white light. Photochemical fatigue resistance of these films was also studied.

Irradiation of colourless 1′,3′,3′‐trimethyl‐6‐nitrospiro[2H‐1‐benzopyran‐2,2′‐indoline] spiropyran (SP) doped in PMMA and epoxy resin with UV light (366 nm) results in the formation of an intense purple‐red coloured zwitterionic photomerocyanine (PMC). The reverse reaction was photochemically induced by irradiation with white light. Photocolouration of SP doped in PMMA follows a first‐order rate equation (k=0.0011 s⁻¹), while that doped in epoxy resin deviates from linearity. It was found that photobleaching follows a first‐order equation in both matrices. The photobleaching rate constant of PMC in both matrices is the same and equals 0.0043 s⁻¹. Spirobenzopyran doped in PMMA shows better fatigue resistance than that doped in epoxy resin.

The PMMA and epoxy resin polymers doped with photochromic spirobenzopyran described in the present paper were prepared and studied. The principle of study established can be applied to any type of polymer or to any type of photochromic compounds.

The photochromic materials developed can be used for different applications, such as coatings and holography.

The method developed may be used to enhance the performance of photochromic materials.