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This study develops a model of emergent strategy formation at a large telecommunications firm. It integrates prominent traditions in strategy process research—strategy as patterned action, as iterated resource allocation and as practice—to show how emergent strategy originates as a project through autonomous strategic behavior, then subsequently becomes realized as a consequence of mobilizing wider support to provide impetus, manipulating strategic context to legitimate the project by constructing it as consonant with the prevailing concept of strategy, and altering structural context to embed it within organizational units, routines, and objectives. The study theorizes the role of \"practices of strategy articulation\" in emergent strategy formation, and explains why some autonomous strategic behavior becomes \"ephemeral\" and disappears rather than enduring to become emergent strategy.

Autonomous navigation systems often struggle in dynamic, complex environments due to challenges in safety, intent prediction, and strategic planning. Traditional methods are limited by rigid architectures and inadequate safety mechanisms, reducing adaptability to unpredictable scenarios. We propose SafeMod, a novel framework enhancing safety in autonomous driving by improving decision-making and scenario management. SafeMod features a bidirectional planning structure with two components: forward planning and backward planning. Forward planning predicts surrounding agents’ behavior using text-based environment descriptions and reasoning via large language models, generating action predictions. These are embedded into a transformer-based planner that integrates text and image data to produce feasible driving trajectories. Backward planning refines these trajectories using policy and value functions learned through Actor–Critic-based reinforcement learning, selecting optimal actions based on probability distributions. Experiments on CARLA and nuScenes benchmarks demonstrate that SafeMod outperforms recent planning systems in both real-world and simulation testing, significantly improving safety and decision-making. This underscores SafeMod’s potential to effectively integrate safety considerations and decision-making in autonomous driving.

As urban centers evolve into smart cities, sustainable mobility emerges as a cornerstone for ensuring environmental integrity and enhancing quality of life. Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to significantly improve efficiency and safety, and reduce environmental impacts. This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm tailored for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. By incorporating a local reward system that values efficiency, safety, and passenger comfort, and a parameter-sharing scheme that encourages inter-agent collaboration, our MA2C algorithm presents a comprehensive approach to urban traffic management. The MA2C algorithm leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing both environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces.

Integrating shared autonomous vehicles (SAVs) in urban transportation systems holds transformative potential but is accompanied by notable challenges. This study, conducted in Saudi Arabia (KSA), aims to address these challenges by identifying and prioritizing the key barriers and policies that are necessary if we are to successfully adopt SAVs. A comprehensive analysis was performed through a literature review and expert consultations, revealing 24 critical barriers and 10 policies for solving them. The research employed a three-phase methodology to evaluate and rank the policies proposed to overcome these barriers. Initially, the study assessed the specific barriers and policies related to SAVs. Subsequently, the Fuzzy Analytic Hierarchy Process (FAHP) was employed to evaluate the relative importance of these barriers. Finally, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS) was applied to rank the policies; the process identified government-backed investment, urban planning integration, and funding for research and development in sensor and hardware technologies as the most effective policies. The study underscores the importance of targeted policies in addressing technical and infrastructural challenges. Emphasizing system reliability, cybersecurity, and effective integration of SAVs into urban planning, the findings advocate for robust government support and continued technological innovation. These insights offer a roadmap for policymakers and industry leaders in the KSA to foster a more sustainable and resilient urban transportation future.

This paper investigates the impact of ride-hailing services, particularly the integration of autonomous vehicles (AVs), on urban transportation systems. The paper discusses the challenges faced by ride-hailing platforms in managing a fleet of both AVs and conventional vehicles (CVs) within the spatial network of a city. It examines the approaches and methods used to manage demand allocation for AVs and CVs, considering the strategic behavior of human drivers and considerations for possible regulations. Using mean-field game theory, this paper proposes efficient strategies for managing fleet operations along with those of traffic optimization and service efficiency. The analysis highlights the complexities of integrating AVs into existing transportation systems and advocates for the development of robust theoretical traffic models for regulatory decisions and improved urban mobility.

Vehicle interactions with different driving behaviors in mixed traffic conditions, in which autonomous vehicles (AVs) and manual vehicles (MVs) coexist, would result in unstable traffic flow leading to a potential crash risk. A proactive traffic management strategy is required to enhance both safety and mobility by preventing hazardous events in connected environments. The purpose of this study is to develop a Proactive Lane‐changE Assistant Strategy for Automated iNnovative Transportation (PLEASANT) to enhance traffic safety. PLEASANT is a strategy for providing lane change assistance information to vehicles approaching risky situations such as crashes, broken vehicles, and upcoming hazardous obstacles. In addition, this study proposed a comprehensive simulation framework that incorporates driving simulation and traffic simulation to evaluate the performance of PLEASANT when dealing with mixed traffic. To characterize vehicle interactions between AVs and MVs, this study analyzes driving behavior in mixed car‐following situations based on multiagent driving simulation (MADS), which is able to synchronize the space and time domains on the road by connecting two driving simulators. The characteristics of vehicle interactions between AVs and MVs were incorporated into microscopic traffic simulations. The effectiveness of PLEASANT was evaluated based on the crash potential index from the perspective of safety. The results showed that PLEASANT was capable of enhancing traffic safety by approximately 21%. PLEASANT is expected to be useful as a novel management strategy for enhancing traffic safety in mixed‐traffic environments.

PurposeBased on self-determination theory (SDT), this paper seeks to identify autonomous motivations driving nostalgia-related cultural tourism among visitors to satisfy their craving to revisit Luxor and re-root their identities. This paper looked at the nexus of destination image and past experience, as well as nostalgic visitors' revisit intention and actual behavior toward Luxor.Design/methodology/approachPartial least squares-structural equation modeling (PLS-SEM) was employed to quantitatively analyze 354 responses from Luxor's visitors, and 17 interview transcripts were narrated using MAXQDA software.FindingsAutonomous motivations influenced revisit intentions significantly, and revisit intentions acted as a strong mediator between actual visit behavior and autonomous motivations.Practical implicationsDestination marketers need to create nostalgic emotional bonds with people and destinations by planning cultural events that leave visitors with unforgettable memories of these particular moments. They also need to focus more on promotional strategies, develop messages with words that have emotional meaning and highlight crucial characteristics that tourists can quickly identify when visiting a destination.Originality/valueCultural tourism has emerged as a dominant niche sector worldwide; nevertheless, nostalgia-related cultural tourism has received less attention. As a result, the primary purpose of this paper is to provide a methodological framework for nostalgia tourism promotion in Luxor, Egypt. Luxor was chosen and has been an excellent subject for this paper, which can further evoke a sense of nostalgia. Hence, this paper prioritizes cultural site preservation and promotion.

Autonomous vehicles (AVs) are emerging with enormous potentials to solve many challenging road traffic problems. The AV emergence leads to a paradigm shift in the road traffic system, making the penetration of autonomous vehicles fast and its coexistence with human-driven cars inevitable. The migration from the traditional driving to the intelligent driving system with AV’s gradual deployment needs supporting technology to address mixed traffic systems problems, mixed driving behaviour in a car-following model, variation in-vehicle type control means, the impact of a proportion of AV in traffic mixed traffic, and many more. The migration to fully AV will solve many traffic problems: desire to reclaim travel and commuting time, driving comfort, and accident reduction. Motivated by the above facts, this paper presents an extensive review of road intersection mixed traffic management techniques with a classification matrix of different traffic management strategies and technologies that could effectively describe a mix of human and autonomous vehicles. It explores the existing traffic control strategies and analyses their compatibility in a mixed traffic environment. Then review their drawback and build on it for the proposed robust mix of traffic management schemes. Though many traffic control strategies have been in existence, the analysis presented in this paper gives new insights to the readers on the applications of the cell reservation strategy in a mixed traffic environment. Though many traffic control strategies have been in existence, the Gipp’s car-following model has shown to be very effective for optimal traffic flow performance.

The development of the Connected and Autonomous Vehicle (CAV) and Hybrid Electric Vehicle (HEV) provides a new effective means for the optimization of eco-driving strategies. However, the existing research has not effectively considered the cooperative speed optimization and power allocation problem of the Connected and Autonomous Plug-in Hybrid Electric Vehicle (CAPHEV) platoon. To this end, a hierarchical eco-driving strategy is proposed, which aims to enhance driving efficiency and fuel economy while ensuring the safety and comfort of the platoon. Firstly, an improved car-following model is proposed, which considers the motion states of multiple preceding vehicles. On this basis, a platoon cooperative car-following decision-making method based on model predictive control is designed. Secondly, a distributed energy management strategy is constructed, and a bionic optimization algorithm based on the behavior of nutcrackers is introduced to solve nonlinear problems, so as to solve the energy distribution and management problems of powertrain systems. Finally, the tests are conducted under the driving cycle of the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). The results show that the proposed strategy can ensure the driving safety of the CAPHEV platoon in different scenes, and has excellent tracking accuracy and driving comfort. Compared with the rule-based strategy, the equivalent energy consumption of UDDS and HWFET is reduced by 20.7% and 5.5% in the battery’s healthy charging range, respectively.

Purpose Professor Vijay Govindarajan’s “Three Box Solution” framework provides a useful way of looking at a transformative business innovation initiative started at General Motors almost three decades ago and now being further developed by its current CEO Mary Barra. Design/methodology/approach Drawing on 18 years of experience at GM the author offers insights into how the company used the “Three Box” aproach: 10;•9;Box 1: Strengthen the core. 10;•9;Box 2: Let go of the practices that drive the core business but hinder the new one. 10;•9;Box 3: Invented a new business model. 10; Findings GM management found creative ways to enable the current business to thrive while exploring the potential market for a visionary business model. Practical implications %2010%3BThe%20paper%20provides%20new%20insight%20into%20how%20General%20Motors%20has%20changed%20and%20how%20it%20is%20continuing%20to%20adapt%20%20emerging%20future%20markets.. Originality/value Based on actual experience of participating in strategy development this paper should help decision makers address their current actions and future strategies simultaneously.