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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 aim of this chapter is to investigate the immigrant women entrepreneurship phenomenon in Italy by focusing on the Polish experience. Stemming from the most current data on immigration in Italy and on immigrant women entrepreneurs and women-led firms, by analysing the regions in which these firms are grounded and the economic sectors in which they are mostly involved, this chapter presents the research programme the two authors of this book have been involved in over the last year and the first preliminary results. Specifically, the two authors engaged to compare the experiences, difficulties and opportunities of immigrant women entrepreneurs of different ethnicities/communities located in Italy. The final goal is to analyse if and how the socio-cultural background of these women counts, if and how it affects their way of doing business and what is the influence of the socio-cultural Italian context. The first group of immigrant women entrepreneurs that the authors decided to analyse are the Polish. Accordingly, this chapter presents data on Polish women entrepreneurs based in Italy and the results of an interview with an expert on this community.

This paper aims to introduce a growth comprehensive pattern to explain the phenomenon of individual foreign investment, first at the global level and then at the regional level. The patterns are developed based on a number of main theories with grounded theory (GT) as the foundation, distributed on the two pull and push forces of international business theory and migration theory; simultaneously, it is classified on the three levels (attribute–consequence–value [ACV]) of means-end theory.

An embedded method is applied to generate two complementary datasets from two approaches: in-depth interviews and secondary data analysis.

In this structure, the investor plays a central role as the decision-maker based on the entrepreneur's motives for internationalization (economics-driven and psychology-driven factors) and the householders' motives for emigration (aspiration and access capabilities). The external forces considered are a push from the home country (structures) and pull from the host country (immigrant investment programs [IIPs]), in which the factor of (dis)trust/misconception as a moderator has an additional impact on this mobility. Demographic factors such as gender, region, generation/age, level of education, religion and occupation generally describe the characteristics of each specific target group.

This paper is to develop a conceptual framework.

The results of this study, in addition to fulfilling its own objectives, will also serve as the foundation for further research in several scientific fields such as economics, sociology and politics.

The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-12-2022-0786

Based on Lasswell’s communication model, this study investigates how four categories of factors (i.e. the source, content, medium and receiver) conjointly affect the relational, financial and social outcomes (i.e. what effect) of CSA communication.

With a survey (N = 366), this study found configurations of core CSA communication factors leading to three different CSA communication outcomes.

While this study found multiways to yield three different CSA outcomes, combinational logic indicated the combined effects from source, content, medium and receiver. With content and medium, individuals’ connectedness (receiver) to a CSA issue is a core factor leading to a high level of purchase intention and issue advocacy. This study also found that message strategies (i.e. informativeness, factual tone, no promotional tone) are core factors leading to a high level of trust and issue advocacy.

With the theoretical guidance, this research contributes to strategic communication practice for various entities involved in advocacy communication by enabling an improved understanding of advocacy communication factors and triggering different communication outcomes.

As CSA communication involves multiple strategies, conventional research agenda focusing on correlational and path analysis approaches provide limited understanding of communication practice. To fill this void, this study adopts a configurational approach to understand current CSA communication practices holistically.

This paper introduces a conceptual lens for leading social change in slums and informal settlements. In line with this aim, the purpose of this case study is to describe the public problem-solving approach of a social change organization situated in an informal settlement through the lens of adaptive leadership, complexity theory and social change leadership (SCL).

This paper follows an engaged reflection tradition. First, the author-practitioners describe an informal settlement case hereafter called ISC in southeast Iran where many people have historically remained undocumented and uneducated. Using complex adaptive systems theory, adaptive leadership and SCL as the conceptual lens, the paper analyzes ISC as a complex adaptive context in which the community and the government are in tension in solving problems, particularly illiteracy. The instrumental case study draws from participant observation and document analysis to describe and examine the endeavors of a community office operating within ISC. Through this reflective analysis, the authors illustrate how a social change organization can effectively tackle public issues like illiteracy within informal settlements.

This paper applies complexity leadership theory to a social context. The study illustrates how social change organizations can support the transformation of informal spaces into adaptive spaces to enact social change.

This paper reflects on engagement activity near the insecure borders of Iran, Afghanistan and Pakistan. By extending an organizational-level theory to the public sphere, this paper contributes theoretically to the complexity theory literature. Moreover, it provides a practical insight for community development and slum upgrading projects.

In 2017, it was a challenge to assess the future of global trade. It was an open question whether the US financial crisis and the recession that it triggered would mark a turning point for the liberal post–World War II world order. If one looked toward Europe, China, Latin America, and Japan, there was a flurry of activity. New trade agreements were being completed and pursued. In Washington, DC, on the other hand, President Donald Trump seemed set on ripping apart and/or renegotiating any trade deal the United States was ever part of.

This case explores Trump's opinions and emerging policy stance on trade, bilateralism, and the global economy, among others. It also gives an overview of the World Trade Organization (WTO) and the General Agreement on Tariffs and Trade (GATT) and asks whether the Trump presidency would constitute a major challenge to the WTO and what it stood for in 2017.

Inflation and federal monetary efforts to control it with interest rate hikes have very real and overwhelmingly negative consequences on US local governments following the onset of COVID-19. This study explores the post-pandemic inflationary environment of US local governments; examines the impacts of inflation and high interest rates on local government revenue, operating costs, capital costs, and debt service; reviews local government inflation management strategies, including the use of intergovernmental revenue; and assesses ongoing threats to local government financial health and financial resilience.

This study uses trend and literature analysis to comment on current issues local governments face.

The study finds that the growth of property values and resulting stability of property tax revenue has been important to local government revenues; that local governments bear very real burdens as operating and capital costs increase; and that the combination of high inflation and interest rates affects local government debt issuance by negatively affecting credit quality and interest costs, leading to municipal market contraction. Local governments have benefitted tremendously from intergovernmental revenue, but would be ill-advised to rely on it.

Vulnerabilities owing from revenue mismatch with the economy; inadequate affordable housing, inequality, and social issues; a changing workforce and tight labor market; climate change; and federal fiscal contraction—all of which are exacerbated by high inflation and interest rates—require local governments to act strategically, boldly and collaboratively to achieve fiscal health and financial resilience, and to realize positive returns of investments in people and capital.

This work is unique in addressing the post-pandemic impact of inflation and interest rates on local governments.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.