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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.

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 paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.

The paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing.

The paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R²) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model.

Machine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R²) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation.

This paper seeks to address emerging practices of social enterprises (SEs) in China by exploring the institutional context, organisational features and legislative framework of this new phenomenon.

The analysis is based on data drawn from secondary sources (laws and regulations, forum transcripts and news reports) and primary evidence (in‐depth study of six SE cases).

The various kinds of SEs are highly diversified in terms of social mission, organisational nature, legal form, and operational pattern; the institutional context is underdeveloped, providing growing but still limited financial, intellectual, technical, and human resources; although it allows increasing space for diversified development dynamics of SEs, the legislative system regulating SEs is still flawed in several vital ways.

This paper relies heavily on qualitative research methods to make a preliminary assessment of the development of China's SEs. Neither primary nor secondary data sources collected for this paper can be used to draw any general conclusion of statistical significance.

The paper sheds light on the overall landscape of the recent development of SEs in China, providing a descriptive and normative foundation for cross‐country comparative studies and quantitative, explanatory analysis.

The purpose of this study was to determine the determinants of financial statement fraud: the perspective of pentagon fraud theory.

This study used quantitative methods with an explanatory research design by applying secondary data on Islamic banking companies listed on the Indonesia Stock Exchange (IDX).

External pressure affects financial statement fraud, ineffective monitoring affects financial statement fraud, external auditor quality affects financial statement fraud, change in auditor affects financial statement fraud, frequent number of CEO’s picture affects financial statement fraud, external pressure affects firm size, ineffective monitoring affects firm size, external auditor quality affects firm size, change in auditor affects firm size, frequent number of CEO’s picture affects firm size, firm size affects financial statement fraud, firm size mediates the relationship between external pressure on financial statement fraud, firm size mediates the relationship between ineffective monitoring on financial statement fraud, firm size mediates the relationship between external auditor quality and financial statement fraud, firm size mediates the relationship between change in auditor and financial statement fraud, firm size mediates the relationship between frequent number of CEO’s picture and financial statement fraud.

The limitations of this research were found during the research process and can be used as input for further research and related parties in conducting the research to obtain better research results. The limitations of this study are as follows: this study only focused on Islamic banking, so it cannot be generalized to other sectors. Besides, this study only tested five independent variables, one dependent variable and one mediating variable.

For external auditors, financial statement fraud by management might be caused by many factors and is a social as well as an economic problem that must be addressed immediately. Therefore, in carrying out the duties and roles as an external auditor, they must have an attitude of independence (not taking sides) in the mental attitude that must be maintained by the auditor related to the assignment. Auditors must have sufficient technical expertise and training as auditors. In carrying out the audit, the auditor should use their professional skills in responding carefully and thoroughly. Moreover, in carrying out audit work, the auditor must have a plan, must know adequate internal control and obtain sufficiently competent audit evidence.

To the best of the authors’ knowledge, very few studies in Indonesia have applied the Beneish model. There is only one study that implemented the Beneish model, and the study examined only a few companies listed on the IDX. The findings of the present study have important implications not only for banks but also for users of financial statement accounts in Indonesia, especially for investors, auditors, regulators, taxation and other state authorities.