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Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

This study aims to identify and explore different risk typologies associated with consumer acceptance of purchase recommendations from voice assistants (VAs).

Drawing on components of perceived risk, consumer trust theory, and consumption value theory, a research model was proposed and tested using structural equation modeling (SEM) with data from 482 voice shoppers.

The results reveal that, unlike risks associated with physical harm, privacy breaches, and security threats, a variety of other concerns—including financial, psychological, social, performance-related risks, time loss, and the overall perceived risks—significantly influence consumers' willingness to accept VAs purchase recommendations. The effect is mediated by trust in VA purchase recommendations and their perceived value. Different types of risk affect various consumption values, with functional value being the most influential. The model explains 58.6% of the variance in purchase recommendation acceptance and significantly elucidates the variance in all consumption values.

This study contributes crucial knowledge to understanding consumer decision-making processes as they increasingly leverage AI-powered voice-based dialogue platforms for online purchasing. It emphasizes recognizing diverse risk typologies associated with VA purchase recommendations and their impact on consumer purchase behavior. The findings offer insights for marketing managers seeking to navigate the challenges posed by consumers' perceived risks while leveraging VAs as an integral component of modern shopping environments.

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 aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

A stylized fact in finance literature is the belief in positive relationship between ex ante return and risk. Hence, a rational investor, by utility preference axiom can only consider committing fund in asset which promises commensurate higher return for higher risk. Questions have been asked as to whether this holds true across securities, sectors and markets. Empirical evidence appears less convincing, especially in developing markets. Accordingly, the author investigates the nature of reward for taking risk in the Nigerian Capital Market within the context of individual assets and markets.

The author employed ex post design to collect weekly stock prices of firms listed on the Premium Board of Nigerian Stock Exchange for period 2014–2022 to attempt to answer research questions. Data were analyzed using a unique M Vec TGarch-in-Mean model considered to be robust in handling many assets, and hence portfolio management.

The study found that idea of risk-expected return trade-off is perhaps more general than as depicted by traditional finance literature. The regression revealed that conditional variance and covariance risks reveal minimal or no differences in sign and sizes of coefficients. However, standard errors were also found to be large suggesting somewhat inconclusive evidence of existence of defined incentive structure for taking additional risk in the market.

In terms of choice of methodology and outcomes, this research adds substantial value to body of knowledge. The adapted multivariate model used in this paper is a rare approach especially for management of portfolios in developing markets. Remarkably, the research found empirical evidence that positive risk-expected return trade-off, as known in mainstream literature, is not supported especially using a typical developing country data.

The purpose of this paper is to examine the relationship between accounting conservatism and pledging of shares by controlling shareholders of a firm to obtain a loan. The pledging of shares by the controlling shareholders of a firm results in alterations to the payoff and risk structure for these shareholders. Since accounting numbers have valuation implications, pledging of shares by a controlling shareholder has an impact on accounting policy choices made by the firm. The purpose of this paper is to examine the impact of controlling shareholder share pledging to obtain a loan on a specific accounting policy choice, namely, conservatism.

The paper uses a large data set from India comprising 14,786 firm years consisting of 1,570 firms belonging to 58 industries for a period of 11 years (2009–2019). The authors use ordinary least square regression with robust standard errors. The authors conduct robustness checks and the results are consistent across alternative statistical methodologies and alternative measures of the primary dependent and independent variables.

The primary results show that pledging of shares by the controlling shareholders results in higher conditional conservatism and lower unconditional conservatism. Further analysis reveals that the relationship is stronger when the controlling shareholder holds a majority ownership in the firm. Additionally, the results show that for business group affiliated firms, which are unique to developing countries, both the conditional and the unconditional conservatism are incrementally lower when the controlling shareholder pledges the shares. For family firms with a family member as CEO, the conditional conservatism is incrementally higher and the unconditional conservatism is incrementally lower. Finally, the authors show that the results hold when the pledge intensity variable is measured with a one-year lag and finally, the authors show that conditional conservatism is incrementally higher in the year of the increase in the pledge and the year after, but there is no such incremental impact on unconditional conservatism.

The research is limited to the listed firms in India. Since majority of the listed firms are controlled by families and the family firms around the world are heterogeneous the findings of the research may not be applicable to other countries.

The study has implications for policy-making and monitoring of the pledging by the controlling shareholders. It also helps the investors in making investment decisions with respect to family firms in India.

The study is unique as it focuses on the relationship between pledging of shares by the controlling shareholders and its impact on accounting conservatism. To the best of the authors’ knowledge, this is the first research integrating these two aspects.

Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

Knowledge is the source of competitive advantage, but when shared at all levels. Unfortunately, there is a universal unruly present in the form of knowledge hiding at employees’ level, but the causes and remedies are still vague as past studies have rarely investigated the causes of daily knowledge hiding behavior. Against this backdrop, this study aims to entail a daily diary method investigation of the role of daily abusive supervision in daily employees’ knowledge hiding through the mediation of dehumanization and moderation of psychological capital.

The data for this study is collected using a daily diary method approach, which estimates the daily workplace events and their continuous influence on employees’ feelings (i.e. dehumanization) and actions (knowledge hiding). The daily responses of 279 respondents were considered useful for analysis purposes.

The findings of the study revealed that the daily events of abusive supervision have both direct and indirect (through dehumanization) influence on employees’ daily knowledge hiding behavior. Moreover, psychosocial capital has a significant conditional influence in the relationships of negative workplace treatments (abusive supervision and dehumanization) and their outcomes (i.e. knowledge hiding).

The study provides some theoretical and practical insights by providing the explanatory and coping mechanism between continuous abusive supervision and daily knowledge hiding behavior.

There is a dearth of literature that has focused on daily episodes of abusive supervision, dehumanization and knowledge hiding behavior. Furthermore, the moderating role of psychological capital has also been rarely investigated.

This study investigates the influence of consumption values on green Fast-Moving Consumer Goods (FMCG) purchase behaviour in the context of green household cleaning products in the Western Province of Sri Lanka.

We used the survey strategy and 326 effective responses as the sample of this study.

Our findings reveal that specific consumption values, specifically functional, conditional and epistemic values, significantly impact green FMCG purchase behavior towards green household cleaning products. However, social and emotional values did not substantially influence this behavior.

The results of our study suggest practical implications for green FMCG marketers aiming to boost consumer adoption of green household cleaning products in Sri Lanka. To achieve this, marketers should focus on enhancing consumer value perceptions and strategically emphasize the consumption values consumers prioritize. Green FMCG marketers have a competitive advantage in the Sri Lankan market by doing so.

This research addresses a notable gap in the literature concerning green FMCG purchase behavior related to green household cleaning products within international and local contexts. Furthermore, this study distinguishes itself by adopting the Theory of Consumption Values as its foundational theory, offering fresh insights compared to previous research employing alternate theories, such as the Theory of Planned Behavior and the Theory of Reasoned Action, to examine similar phenomena.

Integrating anxiety and uncertainty management (AUM) theory and theory of organizing, this study aims to contribute to the knowledge management literature by examining the interdependent and bidirectional nature of knowledge transfer between expatriates and host country nationals (HCNs). Specifically, the authors investigate how receivers’ cognitive response to senders’ behaviors during their interactions becomes an important conduit between senders’ behaviors and the successful transfer of knowledge.

The authors used the actor partner interdependence model to analyze data from 107 expatriate-HCN dyads. The authors collected the responses of these expatriate-HCN dyads in Shanghai, Taipei, Hong Kong, Vietnam, South Korea, Malaysia, Thailand, Indonesia and India.

Receivers’ interaction anxiety and uncertainty, as a response to senders’ relationship building behaviors, mediate the relationship between senders’ relationship building behaviors and successful knowledge transfer. When senders are expatriates, senders’ communication patience and relationship building behaviors interact to reduce the direct and indirect effects of both receivers’ interaction anxiety and uncertainty. However, when senders are HCNs, the moderation and moderated mediation models are not supported.

The study contributes to the knowledge management literature by investigating knowledge transfer between expatriates and HCNs using an interpersonal cross-cultural communication lens. The authors make refinements to AUM theory by going beyond the sender role to highlighting the interdependence between senders and receivers in the management of anxiety and uncertainty which, in turn, influences the effectiveness of cross-cultural communication. The study is also unique in that the authors underscore an important yet understudied construct, communication patience, in the successful transfer of knowledge.