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Illicit trade is pervasive in many nations and may be influenced by the level of national IQ. The current interdisciplinary paper aims to study the association between national intelligence and illicit trade across nations.

The illicit trade index scores for 84 countries, developed by the Economics Intelligence Unit, are used to measure the dependent variable. The independent variable is national intelligence, while economic development, unemployment and Hofstede’s cultural dimensions are the control variables. Two-level hierarchical linear models (HLMs) are used to empirically test the above-mentioned association.

The empirical results suggest that the higher the degree of national intelligence, the lower is the degree of illicit trade across nations. In addition, economic development, unemployment and national culture play an important role in explaining cross-country differences in illicit trade.

Regulatory authorities should find the results of this cross-national research useful in evaluating the likelihood of illicit trade from a cognitive perspective, and in implementing reforms to curb this type of economic crimes.

This interdisciplinary study makes novel contributions to the literature on economic and financial crimes. First, for the first time to the best of the authors’ knowledge, an association between national intelligence and illicit trade is examined. A second original contribution of this study compared to earlier research is related to the use of two-level HLMs. Third, the investigation of the association between intelligence and illicit trade takes a new control variable into consideration, i.e. unemployment, a variable which is found to have a significant effect on illicit trade and that has not been used directly in relationship with illicit trade so far.

This chapter describes the business goals, purpose, and strategy of public diplomatic services. It reinforces diplomatic organizations’ fundamental bureaucratic administrative culture (Tier 1). The bureaucratic culture of diplomacy is deconstructed, and each of the five layers is described in detail. The authors also explain why focusing on the artifacts and behavior layers are the dominant and essential starting points for analysis in diplomatic cultures. The public service culture (Tier 2) overlays and mediates the bureaucratic culture.

Additionally, the authors describe the influence that political appointees as leaders may play in shaping public service cultures. Next, the authors explain how diplomatic cultures reflect the core values of a state’s culture. Next, the chapter outlines the landscape of external influencing cultures (Tier 3) in diplomacy. Finally, the knowledge, learning, and collaboration (KLC) culture of diplomacy is considered, with opportunities for future growth.

This paper aims to employ the use of focus groups composed of enforcement officers to explore and identify the financial methods used by terrorism-related organisations in Malaysia.

The study used an open-ended question and focus group methods to gather information from 20 Malaysian enforcement officers with extensive experience in dealing with terrorism-related activities, as they strive to prevent and counter terrorism incidents. In addition, experienced practitioners and field experts also contributed to the study.

The study reveals various innovative financial methods used by terrorist-linked organisations to evade detection by local enforcement agencies. These findings are consistent with previous research, which highlights the intelligence of these organisations in avoiding detection by financial regulators.

The findings are based on the perspectives of enforcement officers involved in preventing and countering terrorism activities. Further research could be conducted to gather insights from other government agencies, such as the judiciary or local agencies.

The study offers practical suggestions for organisations and institutions on effectively monitoring and taking appropriate actions in financial activities related to terrorism.

This study provides unique insights into the financial methods of terrorism-related organisations in an emerging country in Southeast Asia. Its findings can be applied throughout the region, given the country’s global connectivity. Furthermore, the study is distinctive in that it provides information from enforcement officers within terrorism-related government organisations, an area where resources are limited. The study also considers the impact of the pandemic on the development of these financial innovations by terrorist organisations.

The multidimensional complexity of urban settlements is increasing and the problem of spaces and territories brought to the scale of smart cities is a critical global issue. This study aims to analyse the scientific production in the Web of Science (WoS) on the relationship between smart cities and the eight urban dimensions defined by the World Economic Forum (WEF) in the period 1990 to 2021, in order to establish which countries lead the knowledge related to the search for sustainable living conditions for people and how this knowledge contributes to improving stakeholders' decision-making.

The methodological steps followed in the study were: (1) Identification and selection of keywords. (2) Design and application of an algorithm to identify these selected keywords in titles, abstracts and keywords using WoS terms to contrast them. (3) Data processing was performed from Journal Citation Report (JCR) journals during the year 2022.

This study identified the authors, institutions and countries that publish the most globally on the topic of Smart Cities. The acceleration in the integration of new technologies and their impact on population conglomerates and their relationship with urban dimensions were also analysed. The evidence found indicates that the USA and China are leading in this field.

This bibliometric study was designed to analyse a knowledge space not addressed in the scientific literature referred to the relationship between the concept of smart cities and the urban dimensions established by the WEF, the identification of new technologies that are converging to promote developments of new ways of managing urban dimensions and propose new knowledge spaces.

This study aims to develop a methodology for predicting the resilience of individual firms to economic crisis, using historical government data to optimize one of the most important and costly interventions that governments undertake, the huge economic stimulus programs that governments implement for mitigating the consequences of economic crises, by making them more focused on the less resilient and more vulnerable firms to the crisis, which have the highest need for government assistance and support.

The authors are leveraging existing firm-level data for economic crisis periods from government agencies having competencies/responsibilities in the domain of economy, such as Ministries of Finance and Statistical Authorities, to construct prediction models of the resilience of individual firms to the economic crisis based on firms’ characteristics (such as human resources, technology, strategies, processes and structure), using artificial intelligence (AI) techniques from the area of machine learning (ML).

The methodology has been applied using data from the Greek Ministry of Finance and Statistical Authority about 363 firms for the Greek economic crisis period 2009–2014 and has provided a satisfactory prediction of a measure of the resilience of individual firms to an economic crisis.

The authors’ study opens up new research directions concerning the exploitation of AI/ML in government for a critical government activity/intervention of high importance that mobilizes/spends huge financial resources. The main limitation is that the abovementioned first application of the proposed methodology has been based on a rather small data set from a single national context (Greece), so it is necessary to proceed to further application of this methodology using larger data sets and different national contexts.

The proposed methodology enables government agencies responsible for the implementation of such economic stimulus programs to proceed to radical transformations of them by predicting the resilience to economic crisis of the firms applying for government assistance and then directing/focusing the scarce available financial resources to/on the ones predicted to be more vulnerable, increasing substantially the effectiveness of these programs and the economic/social value they generate.

To the best of the authors’ knowledge, this study is the first application of AI/ML in government that leverages existing data for economic crisis periods to optimize and increase the effectiveness of the largest and most important and costly economic intervention that governments repeatedly have to make: the economic stimulus programs for mitigating the consequences of economic crises.

This study aims to investigate whether the increasing robot adoption will affect employment rate and wages to contribute to the economic cycle and sustainable development in the world.

The authors introduce a two-way fixed effect model and ordinary least-squares (OLS) model to evaluate the influence based on relevant data of the eighteen countries with the largest robot stocks and robot densities in the world from 2006 to 2019 to test the influences and do the robustness test and endogeneity test by using empirical models.

The authors’ research findings suggest that increasing robot adoption can cause strong negative impacts on employment for both males and females in these economies. Second, the effect of robots on reducing job opportunities has penetrated different industries. It means that this negative impact of robots is comprehensive for the industry. Third, robot adoption can have a strong positive influence on wages and increase workers' incomes.

The limitations of the study are that the influence of industrial intelligence technologies on the circular economy is diversities in different countries. Thus, this study should consider the development levels of different economies to do additional confirmatory studies.

This study makes out the correlations between industrial robots and the employment market from the circular economy perspective. The result proves the existence of this influence relationship, and the authors propose some suggestions to promote sustainable economic development.

This paper addresses the activity of industrial intelligence technologies in the labor market. The employment market is an important part of the circular economy, and it will benefit social development if the government provides appropriate guidance for social investment and industrial layout.

This study is one of the few studies which considered the impact of industrial robots on employment and wages from the perspective of different industries, and this is very important for the circular economy in the world. The results of this paper provide an instructive reference for government policymakers and other countries to stabilize the labor market and optimize human resources for sustainable economic development.

The purpose of this paper is to identify the historical trends and status of the national development of artificial intelligence (AI) from a nationwide perspective and to enable governments at different administrative levels to promote AI development through policymaking.

This paper analyzed 248 Chinese AI policies (36 issued by the state agencies and 212 by the regional agencies). Policy bibliometrics, policy instruments and network analysis were used to reveal the AI policy patterns. Three aspects were analyzed: the spatiotemporal distribution of issued policies, the policy foci and instruments of policy contents and the cooperation and citation among policy-issuing agencies.

Results indicate that Chinese AI development is still in the initial phase. During the policymaking processes, the state and regional policy foci have strong consistency; however, the coordination among state and regional agencies is supposed to be strengthened. According to the issuing time of AI policies, Chinese AI development is in accordance with the global situation and has witnessed unprecedented growth in the last five years. And the coastal provinces have issued more targeted policies than the middle and western provinces. Governments at the state and regional levels have emphasized familiar policy foci and played the role of policymakers, along with regional governments that also functioned as policy executors as well. According to the three-dimension instruments coding, the authors found an uneven structure of policy instruments at both levels. Furthermore, weak cooperation appears at the state level, while little cooperation is found among regional agencies. Regional governments cite state policies, thus leading to the formation of top-down diffusion, lacking bottom-up diffusion.

The paper contributes to the literature by characterizing policy patterns from both external attributes and semantic contents, thus revealing features of policy distribution, contents and agencies. What is more, this research analyzes Chinese AI policies from a nationwide perspective, which contributes to clarifying the overall status and multi-level relationships of policies. The findings also benefit the coordinated development of governments during further policymaking processes.

Manufacturing in Mauritius is mostly export-oriented. Any supply chain (SC) failure or resilience deficit may result in cancellation of orders and loss of customers, market share and revenue and reduce capability to compete globally. Addressing this challenge is complex, although digital technologies and artificial intelligence (AI) models can improve resilience by assisting decision-making and mitigate risks, thus infusing greater predictability across the SC.

Supply chains are facing increasing disruptions and uncertainties owing to extreme weather events, the war in Ukraine, market volatility and the ongoing COVID-19 pandemic, among other factors. Manufacturing industries and their supply chains essentially create thousands of jobs that enable economic growth and sustain export capability. In addition, they need to maintain or increase both productivity and efficiency and recover quickly from unforeseen or unexpected challenges – that is they need to be resilient. Transformation initiatives, whether in production or supply chain management (SCM), are never easy. Process changes not supported by data or hurried human decisions can sometimes have unintended consequences, mainly adverse. However, in times of greater uncertainty (war and pandemic), setbacks can have greater consequences on the business. Manufacturers are already apprehensive and report slowing exports as recession concerns have caused consumers and businesses to pull back on spending. There is therefore a need to reduce uncertainty and augment resilience by unlocking and synthesising insights that emanate from the power of data analytics, AI and machine learning to improve the resilience efficiency balance.

This chapter will discuss the opportunities arising from the adoption and implementation of digital technologies and AI in SCM, leading to better value creation, less greenhouse gas emissions and resilience. The hurdles that enterprises are facing to integrate AI in their logistics and SCs will also be highlighted. This work comments on initiatives that uphold the objectives of SDG 8 – decent work and economic growth, SDG 9 – industry, innovation & infrastructure and SDG 13 – climate action.