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This article examines the contribution of artificial intelligence to augmenting Intelligent Transportation Systems (ITS) to enhance traffic flow, safety, and sustainability.

The research investigates using AI technologies in ITS, including machine learning, computer vision, and deep learning. It analyzes case studies on ITS projects in Poznan, Mysore, Austin, New York City, and Beijing to identify essential components, advantages, and obstacles.

Using AI in Intelligent Transportation Systems has considerable opportunities for enhancing traffic efficiency, minimizing accidents, and fostering sustainable urban growth. Nonetheless, issues like data quality, real-time processing, security, public acceptability, and privacy concerns need resolution.

This article thoroughly examines AI-driven ITS, emphasizing successful applications and pinpointing significant difficulties. It underscores the need for a sustainable economic strategy for extensive adoption and enduring success.

This study aims to develop an integrative framework for strategic intelligence (SI) tailored to guide companies navigating systemic disruptions within global supply chains, identifying key determinants for its effective deployment. Current literature on management systems addresses SI components individually, hindering a precise definition and implementation strategy. This systematic review aims to fill these gaps by establishing a conceptual model of SI capability, emphasizing the interdependence of its dimensions.

Following the Joanna Briggs Institute (JBI) mixed-method analysis approach and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, this systematic review synthesizes empirical studies, conceptual papers, mathematical models and literature reviews on SI capability dimensions. It adopts a flexible approach to explore SI within supply chain resilience during systemic crises.

The study enhances and broadens the field of dynamic capabilities (DCs) by advancing knowledge on SI as a dynamic capability inducing resilience within supply chains facing systemic risks. Additionally, it synthesizes and offers perspective on a rapidly expanding body of literature from the past three years, identifying emerging trends and gaps.

This research focused on three capacities: Supply Chain Visibility (SCV), Environmental Dynamism (ED) and Timely Seizing and Detection-Making (TSDM). While other dynamic capabilities may enhance SC resilience (SCR), this study emphasized the analytical and decision-making dimensions critical for improving SCR.

This systematic literature review introduces a novel conceptual framework, providing a foundation for empirical investigations. By offering an integrated theoretical perspective, the study proposes actionable research propositions and insights into SI’s strategic role in crisis management within supply chains.

Since conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.

Two well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.

While the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.

The findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.

To the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

This study aims to investigate the market reaction in the cyclical consumer sector to the US–Houthi conflict. Furthermore, the authors explore the impact of this conflict on market reactions by market and region.

Using an event study methodology, this paper analyze a sample of 1,973 companies. This paper used multiple event windows, including a 15-day period before the invasion announcement as the preinvasion event and a 15-day period after the invasion announcement as the postinvasion event.

The authors find that pre the event of war, the market tended to show a positive reaction, but toward the event day until post event, the market in the consumer cyclical sector actually reacted significantly negatively to the conflict, especially in developed and developing markets. The Asia and Pacific market is the market that feels the most negative impact from the US–Houthi conflict compared to other markets. Furthermore, in terms of industry types in the consumer staples sector, Food and Tobacco and Personal and Household Products and Services felt the negative impact, although the majority of all industries reacted significantly negatively.

This study focuses on the US–Houthi conflict, an event that has not been extensively studied in the context of market reactions. Unlike previous research, this study specifically examines the impact of the conflict on the consumer cyclical sector, emphasizing the significance of trade route disruptions, particularly the Suez Canal, on global markets. By providing insights into how such geopolitical events affect different regions and industries, this study offers valuable guidance for policymakers and managers in mitigating the adverse effects of geopolitical risks on market stability.

This study aims to examine market reactions to the Israel–Hamas conflict in neighboring countries, particularly focusing on the Middle East North Africa (MENA) region.

The study adopts an event study methodology, employing average abnormal return (AAR) and cumulative abnormal return as measures to assess market reactions. The sample for this study comprises 1,314 companies, with October 9, 2023, identified as the event day for analysis.

The results of our study indicate that countries in close proximity to Israel and Palestine encountered detrimental effects on their capital markets, as evidenced by negative responses observed across various sectors. Our analysis also reveals that countries in the midst of conflict, particularly Israel, experienced a decrease in their stock markets across various sectors, with the exception of materials and real estate. In addition, our investigation reveals disparities in market responses according to different categories of company size.

This research is the first to study market reactions to Israel–Hamas in the MENA region at the company level.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.