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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.

The study aimed at identifying job insecurity during COVID-19 as a determinant of green entrepreneurial intentions under the mediating role of entrepreneurial passion. Further, moderated mediation model of environmental consciousness and environmental knowledge on the relationship between job insecurity during COVID-19 and green entrepreneurial intentions through entrepreneurial passion as a mediator is also measured.

The study employed Statistical Package for the Social Sciences (SPSS) and analysis of a moment structure (AMOS) to calculate the measurement and structure model. Data was collected from the twin cities of Pakistan (Islamabad and Rawalpindi). A total of 389 usable responses were included in the analyses.

Considering entrepreneurship as a process, the result shows a significant relationship between the study variables. Also, it is realized that entrepreneurial passion serves as an important mediator between job insecurity during COVID-19 and green entrepreneurial intention. Both entrepreneurial knowledge and environmental consciousness have an indirect conditional effect on the relationship between job insecurity during COVID-19 and green entrepreneurial intention with entrepreneurial passion as a mediator. The study tested a novel relationship between job insecurity during COVID-19 and entrepreneurial passion under the lens of contingency theory and entrepreneurial event theory.

The extreme necessity to choose an alternative to full-time jobs is entrepreneurial endeavors that can be explored by entrepreneurs. Policymakers can capitalize on this scenario by proposing entrepreneurial opportunities which are green as well. The term Green represents an environmental friendly business, which one can start in the context of COVID-19 when everyone is oriented to cleanliness, personal hygiene and environment friendly.

The study tested a novel relationship between job insecurity during COVID-19 and entrepreneurial passion under the lens of contingency theory and entrepreneurial event theory. It contributes to the present stock of knowledge and understanding of the subject by contextualizing the concept of green entrepreneurial intention.

Upon graduating from university, many engineers will work in new product development and/or technology adoption for continuous improvement and production optimization. These jobs require employees to be cognizant of ethical practices and implications for design. However, little engineering coursework, outside the traditional ABET (Accreditation Board for Engineering and Technology) required Engineering Ethics course, accounts for the role of ethics within this process. Because of this, engineering students have few learning opportunities to practice and reflect on ethical decision-making.

This paper highlights one approach to integrating ethics into an engineering course (outside of engineering ethics). Specifically, the study is implemented within a five-week module with a focus on big data ethics, as part of a Supply Chain Management Technology course (required for Industrial Engineering Technology majors), using metacognition as the core assessment.

Four main themes were identified through the qualitative data analysis of the metacognitive reflections: (1) overreliance on content knowledge, (2) time management skills, (3) career connections and (4) knowledge extensions.

Three notable points emerged which contribute to the literature. First, this study showcased one example of how an ethics module can be integrated into an engineering course (other than Engineering Ethics). Second, this study demonstrated how metacognitive reflections can be used to reinforce student self-awareness of the learning process and connections to big data ethics in the workplace. Finally, this study exhibited how metacognitive reflection assignments can be deployed as a teaching and learning assessment tool, providing an opportunity for the instructor to make immediate changes as needed.

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.

This study aims to dissect firm knowledge into two main types: internal firm knowledge (knowledge workers) and external firm knowledge (relational knowledge with other firms). This study aims to investigate how each type affects the productivity of the firms. This study also examines how this effect differs among Egyptian firms in the agriculture, manufacturing and service sectors.

The authors use firm-level data in Egypt on the sectoral level. The properties of instrumental variables regression using two-stage least-squares estimation are adopted to overcome endogeneity and omitted variable bias in the empirical estimations.

The study’s findings reveal that the effects of internal and external knowledge on the firm productivity are sector-specific; knowledge-workers and relational knowledge are two times more effective for agriculture than manufacturing and service firms; external knowledge plays a vital role in increasing productivity relative to internal knowledge for the manufacturing sector; finally, internal and external knowledge has the same effect on the service firms.

This research adds to the body knowledge-based theory of the firm by examining the effects of internal and external knowledge on the firms’ productivity. In particular, the paper differentiates this effect across three sectors: agriculture, manufacturing and services. This paper also suggests a novel empirical methodology to address endogeneity and omitted variable bias in this literature of firm knowledge and productivity.

Development theory in college describes and explains how students develop. This chapter explores ways to balance and consolidate differentiation and integration in this theory. First, it traces the origins, history and current development of the theory, which evolves from an integrative understanding to a differentiated one. Subsequently, it identifies the tensions between integration and differentiation in this evolution. This chapter consider two directions towards the theoretical consolidation of differentiation and integration: (1) returning to how integrative understandings were achieved and exploring research directions that further advance integrative understandings; (2) recognizing the parallel evolution of North American student development theory in theorising about learning from a critical realism perspective, and, by overlaying this theory upon such a perspective, reconstructing it towards consolidation. This chapter concludes by discussing two implications for further higher education research that draws on student development theory.

The purpose of this paper is to investigate how supplier concentration influences a buyer firm's R&D intensity. This study proposes a mediation and moderation model to test this relationship in the Chinese household appliance industry. Specifically, this study tests the mediation effect of operational slack on the relationship between supplier concentration and R&D intensity and the moderation effect of financial constraints on this relationship.

Drawing upon real options theory and resource dependence theory, the proposed relationships are tested with the Chinese household appliance market using financial data from listed companies over a ten-year span from 2012 to 2021. Fixed effects (within-group) panel regression models are used to test the hypotheses. In addition, the authors use the bias-corrected bootstrap method to test the mediation effect.

The authors find that supplier concentration negatively affects a buyer firm's R&D intensity and that internal operational slack mediates this relationship. Interestingly, financial constraints from the external financing organization weaken the negative relationship between the buyer firm's supplier concentration and R&D intensity.

Based on the argument of real options theory and resource dependence theory, this study provides novel insights into the issue of how concentration on several major suppliers may reduce buyer firms' R&D intensity. First, this study introduces operational slack as a form of internal uncertainty that mediates the supplier concentration–R&D intensity relationship. Second, this study suggests that the effect of supplier concentration on R&D intensity is contingent upon firms' financial constraints from external financial organizations, disclosing a synergetic interactive effect of supplier concentration and financial constraints on firms' R&D activities. Third, this study is conducted in the unique institutional context of China, providing meaningful insights into the relationship between supplier concentration and R&D intensity.