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The main objective of this research is to investigate if there is a long-term relationship between “terrorism” and sustainable “economic growth” in Big Ten Countries.

The data was tested via Panel ARDL Analysis. The growth rate (GR) is the dependent variable, and the “Global Terror Index (GTI)” is the independent variable as the terror indicator. The ratio of Foreign Direct Investment (FDI) to the Gross Domestic Product (GDP), and the ratio of External Balance (EB) to Gross Domestic Product (GDP) are included in the model as the control variables due to their effect on the growth rate. A Panel ARDL analysis is conducted to examine the existence of long-term co-integration between terror and the economy. The planning of the study, the formation of its theoretical and conceptual framework, and the literature research were carried out in 2 months, and the collection of data, the creation of the methodology and the analysis of the analyzes were carried out in 2 months, the interpretation of the findings and the development of policy recommendations were carried out within a period of 1 month. The entire study was completed in a total of 5 months.

Results showed that “Terror” has a negative impact on “Growth Rate” in the long term while “External Balance” and “Foreign Direct Investment” positively affect the Growth Rate. The coefficients for the short term are not statistically significant.

The sample is only limited to Big Ten including China, India, Indonesia, South Korea, Argentina, Brazil, Mexico, Turkey, Poland and South Africa. The period for annual data collection covers the years between 2002 and 2019 and due to the unavailability of data.

Considering the risks and the mutual negative effect that turns into a vicious circle between terrorism and the economy, it is necessary to eliminate the problems that cause terrorism in the mentioned countries, on the one hand, and to develop policies that will improve economic performance on the other.

Trustful law enforcement bodies have to be established and supported by all technological means to prevent terror. The conditions causing terror have to be investigated carefully and the problems causing terror or internal conflict have to be solved. International cooperation against terrorism has to be strengthened and partnerships, information, experience sharing have to be supported at the maximum levels.

It is certain that terror might have a negative influence on the performance of economies. But the limited number of studies within this vein and the small size of their sample groups mostly including single-country case studies require conducting a study by using a larger sample group of countries. Big Ten here represents at least half of the population of the world and different regions of the Globe.

Social networks (SNs) have recently evolved from a means of connecting people to becoming a tool for social engineering, radicalization, dissemination of propaganda and recruitment of terrorists. It is no secret that the majority of the Islamic State in Iraq and Syria (ISIS) members are Arabic speakers, and even the non-Arabs adopt Arabic nicknames. However, the majority of the literature researching the subject deals with non-Arabic languages. Moreover, the features involved in identifying radical Islamic content are shallow and the search or classification terms are common in daily chatter among people of the region. The authors aim at distinguishing normal conversation, influenced by the role religion plays in daily life, from terror-related content.

This article presents the authors' experience and the results of collecting, analyzing and classifying Twitter data from affiliated members of ISIS, as well as sympathizers. The authors used artificial intelligence (AI) and machine learning classification algorithms to categorize the tweets, as terror-related, generic religious, and unrelated.

The authors report the classification accuracy of the K-nearest neighbor (KNN), Bernoulli Naive Bayes (BNN) and support vector machine (SVM) [one-against-all (OAA) and all-against-all (AAA)] algorithms. The authors achieved a high classification F1 score of 83\%. The work in this paper will hopefully aid more accurate classification of radical content.

In this paper, the authors have collected and analyzed thousands of tweets advocating and promoting ISIS. The authors have identified many common markers and keywords characteristic of ISIS rhetoric. Moreover, the authors have applied text processing and AI machine learning techniques to classify the tweets into one of three categories: terror-related, non-terror political chatter and news and unrelated data-polluting tweets.

Explore how the degree of humanization affects user misconduct, and provide effective misconduct prevention measures for the wide application of artificial intelligence in the future.

Based on the “Uncanny Valley theory”, three experiments were conducted to explore the relationship between the degree of humanization of service machines and user misbehavior, and to analyze the mediating role of cognitive resistance and the moderating role of social class.

There is a U-shaped relationship between the degree of humanization of service machines and user misbehavior; Social class not only regulates the main effect of anthropomorphism on misbehavior, but also regulates the intermediary effect of anthropomorphism on cognitive resistance, thus affecting misbehavior.

The design of the service robot can be from the user’s point of view, combined with the user’s social class, match different user types, and provide the same preferences as the user’s humanoid service robot.

This study is an important reference value for enterprises and governments to provide intelligent services in public places. It can prevent the robot from being vandalized and also provide users with a comfortable human-computer interaction experience, expanding the positive effects of providing smart services by government and enterprises.

This study avoids and reduces users' misbehavior towards intelligent service robots, improves users' satisfaction in using service robots, and avoids service robots being damaged, resulting in waste of government, enterprise and social resources.

From the perspective of product factors to identify the inducing factors of improper behavior, from the perspective of social class of users to analyze the moderating effect of humanization degree and user improper behavior.

Factors that may influence risk and/or vulnerability to young people’s involvement in terrorism are currently unclear. Understanding the factors that contribute to a young person’s risk profile is a high priority for Youth Justice Services to enable the development of targeted interventions and subsequent risk reduction. The purpose of this study is to systematically identify and critically review studies relating to young peoples’ involvement in terrorism to understand potential risk and/or vulnerability factors and the implications for intervention.

Literature was evaluated using the systematic review method. Twelve papers were selected for the systematic review according to the inclusion criteria.

Twelve factors emerged that indicated relevance to terrorism by young people and were combined into five overarching themes: contextual, social, psychological factors, trauma and use of time.

This systematic review is one of the first of its kind relating to terrorism by young people. The reported findings will be valuable to practitioners seeking to understand the risk and vulnerability factors related to terrorism by young people and the implications for intervention. Directions for future research are explored.

The purpose of this study is to investigate the impact of terrorist attacks on the volatility and returns of the stock market in Tunisia.

The employed sample comprises 1250 trading day from the Tunisian stock index (Tunindex) and stock closing prices of 64 firms listed on the Tunisian stock market (TSM) from January 2011 to October 2015. The research opts for the general autoregressive conditional heteroscedasticity (GARCH) and exponential generalized conditional heteroscedasticity (EGARCH) models framework in addition to the event study method to further assess the effect of terrorism on the Tunisian equity market.

The baseline results document a substantive impact of terrorism on the returns and volatility of the TSM index. In more details, the findings of the event study method show negative significant effects on mean abnormal returns with different magnitudes over the events dates. The outcomes propose that terrorism profoundly altered the behavior of the stock market and must receive sufficient attention in order to protect the financial market in Tunisia.

Very few evidence is found on the financial effects of terrorism over transition to democracy cases. This paper determines the salient reaction of the stock market to terrorism during democratic transition. The findings of this study shall have relevant implications for stock market participants and policymakers.

The MENA region is very rich in terms of natural resources. At the same time, the MENA region has also been a victim of terrorism during the last few years. This study is an attempt to investigate whether there is any relationship between natural resources and terrorism in the MENA region.

We have focused on 15 resource-rich countries located in the MENA region for the period 2002–2019. We have applied appropriate econometric techniques and have also controlled for other dominant determinants of terrorism while studying the relationship between these two variables.

The results provide solid evidence in favor of the hypothesis that natural resources encourage terrorism. We find that natural resources have positively impacted terrorism. Besides, the natural resources, other factors such as per capita GDP, trade openness, political stability, domestic investment and government expenditures have negatively impacted terrorism. Moreover, the findings suggest that FDI and corruption are irrelevant in explaining terrorism while the findings regarding employment level and terrorism are unexpected. The obtained results are robust to alternative estimating methodologies.

The results have serious policy implications for the MENA region. The MENA region in general is suggested to devise appropriate policies regarding their huge natural resources so as to tackle the terrorism problem effectively. Similarly, paying favorable attention to trade liberalization, political stability, government expenditures, investment, rising income of the population in the presence of macroeconomic stability in the form of lower inflation would also help the MENA region to eradicate the problem of terrorism.

The available literature has largely ignored the role of natural resources in explaining the problem of terrorism. Therefore, this study has provided relatively new evidence regarding the determinants of terrorism.

This paper aims to offer the first known synthesis of peer-reviewed literature on trade-based money laundering (TBML). Given the topic is in its nascent stage yet gaining prominence across scholarship and practice, this foundation is pertinent for future TBML research.

A systematic literature review was undertaken with a formulaic search string. Both qualitative (thematic) and quantitative (meta) analysis methods were used to illustrate the findings.

The systematic literature review, using qualitative and quantitative synthesis, led to a thematic categorization of extant TBML literature into four categories: TBML risk assessment, TBML detection, the role of professionals and understanding of TBML. Due to the limited number of studies, insights that can be drawn from the extant literature on the best way to combat TBML are also limited.

As the first systematic literature review on TBML, this study identified that the existing TBML literature has focused on increasing the understanding of the phenomenon in terms of its definition and mechanisms, detection, linkage with other crimes, such as organized crime and terrorism financing, and risk assessment frameworks. The originality of these findings lies in identifying areas future researchers might explore to broaden the academic literature.

The paper aims to understand how state and non-state domestic terrorism impacts MNEs in foreign markets. Despite the burgeoning literature on terrorism within international business (IB), most research has focused on international terrorism, or terrorism generally. Consequently, there has been limited research examining how domestic or local based terrorism impacts foreign firms.

This is a conceptual paper.

Domestic terrorism is the most common form of terrorism in the world today and involves the state and non-state actors. Non-state domestic terrorism can be low intensity or high intensity. High intensity non-state-domestic terrorism typically involves regular and protracted political violence, along with inter-communal violence. This can expose MNEs to considerable operational, governance and legitimacy pressures.

The paper contributes to the gap in IB terrorism research with regards domestic or local based terrorism. Drawing on IB theory and critical terrorism research, the paper addresses the nature and impact of domestic terrorism within IB. The authors’ paper shows the operational, governance and legitimacy pressures of both state and non-state domestic terrorism for MNEs in host markets. While most IB scholars consider the threat of non-state terrorism for international firms, this study shows how domestic state terrorism benefits and constrains foreign firms.

This study aims to investigate the impact of terrorism on financial inclusion that is achieved through automated teller machine penetration and bank branch expansion.

Eight countries that are the most terrorized countries in the world were analysed using the panel fixed effect regression model and the generalized linear model.

The results provide evidence that terrorism reduces the level of financial inclusion in countries experiencing terrorism, but the presence of strong legal institutions, accountability governance institutions and political stability governance institutions mitigate the adverse effect of terrorism on financial inclusion.

A growing literature has shown that terrorism affects the economy, yet little is known about its impact on financial inclusion.

Achieving social sustainability has become a critical challenge in global supply chain networks, particularly during complex crises such as terrorism. The purpose of this study is to explore how institutional forces influence the social sustainability approaches of logistics service providers (LSPs) in high terrorism-affected regions (HTAR). This then leads to investigating how the key factors interact with Institutional Theory.

An exploratory multiple-case study research method was used to investigate six cases of different-sized logistics LSPs, each in an HTAR. The data was collected using semistructured interviews and triangulated using on-site observations and document analysis. Thematic analysis was used in iterative cycles for cross-case comparisons and pattern matching.

The findings interact with Institutional Theory and the three final-order themes. First, management processes are driven by coopetition and innovation. Second, organizational resources, structure and culture lead to an ineffective organizational design. Finally, a lack of institutionalization creates institutional uncertainty. These factors are rooted in many other first-order factors such as information sharing, communication, relationship management, capacity development, new process developments, workforce characteristics, technology, microlevel culture and control aspects.

This study answers the call for social sustainability research and enriches the literature on social sustainability, Institutional Theory and LSPs in HTARs by providing illustrations showing that institutional forces act as driving forces for social sustainability initiatives by shaping the current management processes. Conversely, the same forces impede social sustainability initiatives by shaping the current organizational designs and increasing institutional uncertainty.