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Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

The purpose of this study is to identify the factors affecting brand competitive positioning (BCP) and its components in the home appliance industry and also to develop a scale for it, considering both the seller’s and the buyer’s side.

The factors were investigated both qualitatively and quantitatively. Data was collected from findings of previous research as well as interviews with experts in the industry. After conducting thematic analysis, the extracted factors were confirmed by experts. A total of 400 samples was used to test the BCP scale. Respondents were the customers of some selected home appliance brands.

The results of exploratory and confirmatory factor analyses indicated that seven main factors influenced BCP, including product quality, service quality, perceived price, sales and distribution, marketing communication, market orientation and reputation and background. Also, the five components of BCP are distinctiveness, desirability, credibility, value for money and top of the mind awareness.

Modelling a new scale on BCP is of considerable importance. Using mixed method, the current study presents a new scale named Brand Competitive Positioning Scale.

The purpose of this study is to suggest an approach to regain consumer trust after negative effects of greenwashing that draws corporations and consumers into a conflicted relationship.

The authors collect and interpret qualitative data from in-depth interviews to develop a theoretical approach that enables the rebuilding of trust between greenwashing corporations and their consumers using the concept of psychological resilience.

This analysis indicates that the approach is an interaction between consumers with green brand loyalty and greenwashing corporations. This type of consumer demands emotional factors, functional factors and legitimate factors in the process of psychological resilience, and after greenwashing, corporations should select appropriate recovery strategies to stimulate these protective factors.

Previous research studied green consumer trust in the marketing field but did not explore the core of trust which was regarded as a cognitive process. This paper investigates green consumer behaviour under the perspective of psychological resilience and makes an innovative attempt to understand drivers of regaining consumer trust. Previous research works put forward a series of strategies related to regaining trust, but they did not discuss the mechanisms by which these strategies work. Using the method of grounded theory, we attempt to reveal the “black box” of consumers cognition after greenwashing and propose a strategy for regaining consumer trust.

The purpose of this study is to investigate the application of non-destructive testing methods in measuring bearing oil film thickness to ensure that bearings are in a normal lubrication state. The oil film thickness is a crucial parameter reflecting the lubrication status of bearings, directly influencing the operational state of bearing transmission systems. However, it is challenging to accurately measure the oil film thickness under traditional disassembly conditions due to factors such as bearing structure and working conditions. Therefore, there is an urgent need for a nondestructive testing method to measure the oil film thickness and its status.

This paper introduces methods for optically, electrically and acoustically measuring the oil film thickness and status of bearings. It discusses the adaptability and measurement accuracy of different bearing oil film measurement methods and the impact of varying measurement conditions on accuracy. In addition, it compares the application scenarios of other techniques and the influence of the environment on detection results.

Ultrasonic measurement stands out due to its widespread adaptability, making it suitable for oil film thickness detection in various states and monitoring continuous changes in oil film thickness. Different methods can be selected depending on the measurement environment to compensate for measurement accuracy and enhance detection effectiveness.

This paper reviews the basic principles and latest applications of optical, electrical and acoustic measurement of oil film thickness and status. It analyzes applicable measurement methods for oil film under different conditions. It discusses the future trends of detection methods, providing possible solutions for bearing oil film thickness detection in complex engineering environments.

Continuous knowledge contribution behaviors (CKCB) are critical for the healthy development of online medical communities (OMCs). However, it is unclear that if and how contributors' prior actions and the responses they received from the community influence the nature of their future contributions. Drawing upon the Information Systems Continuance theory and Service Feedback theory, the purpose of the study is to examine the impact of knowledge contribution performance (KCP) on doctors' CKCB. Evaluation of social motivation, financial incentive and the moderating influence of expertise level (EL) provided further insight into the pathways that motivate various forms of CKCB.

In order to better understand the CKCB of physicians in OMCs, the authors divided it into two categories: A_CKCB (active CKCB) and P_CKCB (passive CKCB). Information Systems Continuance theory and Service Feedback theory are adapted and integrated with empirical findings from previous research on OMCs to develop a model of CKCB. This study used ordinary least squares (OLS) regression to test hypotheses in the preexisting research model based on data collected from a Chinese OMC platform.

The results show that KCP helps develop several facets of CKCB. According to the findings, doctors' CKCB improved dramatically after receiving feedback from A_CKCB and P_CKCB, but feedback from peers did not promote CKCB. This study found that financial rewards only have a significant positive effect on P_CKCB, and that the level of expertise has a negative effect on the effect. The findings also demonstrated that doctors' level of expertise moderates the relationship between fA_CKCB (a comprehensive evaluation of doctors' A_CKCB) and A_CKCB.

Future studies should look at the role of self-efficacy as a mediator and attitudes as a moderator in the link between KCP and various forms of CKCB. This will help authors figure out how important KCP is for physicians' CKCB. And future research should use more than one way to gather data to prove the above roles.

This study makes a significant contribution to understanding the association between CKCB and KCP by highlighting the significance of distinguishing between the various forms of CKCB and their underlying causes.

This research has advanced both the theory and practice of OMCs' user management by illuminating the central role of KCP in this context.

This study examines the relationship between higher education improvement and tacit knowledge importance. In this context, the scarcity of empirical and theoretical studies on acquiring tacit knowledge to enhance academic performance in higher education suggests that this research area holds significant importance for experts and policymakers. Consequently, this study aims to explore the factors that influence academic research performance at Chinese universities by acquiring tacit knowledge.

To achieve the study aims, the current approach utilizes the research technique based on the socialization, externalization, internalization and combination (SECI) model and knowledge management (KM) theory. To analyze the study objective, the authors collected data from post-graduate students at Chinese universities and analyzed it using structural equation modeling (SEM) to test the model and hypotheses.

The results indicated that social interaction, internalization and self-motivation have a positive impact on academic research performance through the acquisition of tacit knowledge. Furthermore, the findings suggest that academic researchers can acquire more knowledge through social interaction than self-motivation, thereby advancing research progress.

This study addresses the critical issues surrounding the acquisition of tacit knowledge and presents a comprehensive framework and achievements that can contribute to achieving exceptional academic performance.

This study aims to evaluate the thermal performance of phase change materials (PCMs) microcapsules (MCs) attached using SiO₂ microspheres and investigate the thermal regulation effect on the coated denim fabric.

The PCM microcapsule was prepared by in situ polymerization using a mixture of solid paraffin and butyl stearate as core material (CM) and methyl methacrylate as a monomer. The SiO₂ microparticles were attached to the outer layer of the membrane to enhance the thermal performance of MCs. The morphology, chemical structure, latent heat storage and thermal resistance of MCs were characterized. PCM MCs were coated on the denim fabric and thermo-gravimetric analysis was conducted; thermal insulation and thermal infrared imaging performance of the coated fabrics were also investigated.

The diameters of SiO₂ particles and PCMs MCs were 300-500 nm and 1 μm, respectively. SiO₂ was wrapped on single-wall PCMs MCs with the mass ratio of 1:5. With the addition of SiO₂, the phase transition temperature range of MCs increased from 34°C to 39°C, and the endothermic and exothermic latent heat decreased by 5.35 J/g and 10.07 J/g, respectively. The degradation rate of MCs was significantly slowed down at high temperature. The denim fabric coated with MCs revealed thermal regulation property. After absorbing heat, the MCs slowed down the rate of heat loss and extended the heat release time.

The phase transition temperature of the composite CM was wide, and the latent heat storage was reduced. The addition of SiO₂ particles can significantly slow down the rate of heat loss, but it further reduces the latent heat storage performance.

The method developed provided a simple and practical solution to improve the thermal regulation performance of fabrics.

The method of adjusting the phase transition temperature range of the composite CM is novel and many applications could be found in preparation of PCMs and thermal management.

Retail investors are prone to be affected by information dissemination in social media with the rapid development of Web 2.0. The purpose of this study is to recognize the factors that may impact users' retweet behavior, namely information dissemination in the online financial community, through machine learning techniques.

This paper crawled data from the Chinese online financial community (Xueqiu.com) and extracted author-related, content-related, situation-related, stock-related and stock market-related features from the dataset. The best information dissemination prediction model based on these features was determined by evaluating five classifiers with various performance metrics, and the predictability of different feature groups was tested.

Five prevalent classifiers were evaluated with various performance metrics and the random forest classifier was proven to be the best retweet prediction model in the authors’ experiments. Moreover, the predictability of author-related, content-related and market-related features was illustrated to be relatively better than that of the other two feature groups. Several particularly important features, such as the author's followers and the rise and fall of the stock index, were recognized in this paper at last.

This study contributes to in-depth research on information dissemination in the financial domain. The findings of this study have important practical implications for government regulators to supervise public opinion in the financial market.

This paper explores how platforms reconfigure versatile digital resources to achieve marketing agility in international markets.

We draw on a case study of a Chinese digital platform to explore the processes and mechanisms of reconfiguring during marketing agility development. Data from different sources are collected, including interviews, informal dialogue and archival data.

Versatile digital resources create productive applications for previously less amendable marketing and nonmarketing resources to be malleable, editable and reconfigurable in marketing agility development. This study identifies and clarifies three versatile digital resource-enabled reconfiguration activities in marketing agility building: recombining digital artifacts, repurposing human capital and cross-pollinating markets.

Since our study adopts a case study method, future research can extend our insights by using quantitative methods to test and verify our theoretical framework.

First, we provide insights into how organizations can reconfigure versatile digital resources to achieve the benefits of marketing agility in international markets. Second, while recruiting new employees during internationalization is vital, we suggest that assisted by digital artifacts, firms can repurpose the existing workforce, such as via multitasking, swift task-switching and flexible job redirecting to satisfy dynamic international business requirements with lower adjustment costs. Third, we offer two localization approaches in which firms can use digital artifacts as the enabler to remix sociocultural elements with local adaptations to develop glocal content and decentralize content production to generate inclusive local content.

We provide a process model that specifies how platforms reconfigure versatile digital resources to achieve marketing agility in international markets. Furthermore, we provide novel insights into the literature on marketing agility in international markets and localization.

The purpose of this study is to explore the evolutionary path and stable strategy for the competitive dissemination between disinformation and knowledge on social media to provide effective solutions to curb the dissemination of disinformation and promote the spread of knowledge.

Based on the social capital (SC) theory, the benefit matrix is constructed and an evolutional game model is established in this paper. Through model solving and Matrix Laboratory (MATLAB) simulation, the factors that influence disinformation-believing users (DUs) and knowledge-believing users (KUs) to choose different strategies are analyzed.

The initial dissemination willingness, the disinformation infection probability, the knowledge infection probability and the knowledge penetration probability are proved to be crucial factors influencing the game equilibrium in the competitive dissemination process of disinformation and knowledge. Moreover, some countermeasures and recommendations for the governance of disinformation are proposed.

Currently most research interest lies in the disinformation dissemination model but ignores the interaction between disinformation and knowledge in the diffusion process. This study reveals the dynamic mechanism of social media users disseminating disinformation and knowledge and is expected to promote the formation of cleaner cyberspace.