Dr Hajar Fatorachian

Questionnaire, consent form, and transcript on supply chain resilience and risk management strategies. Data collected on 17 August 2022.

Data sets related to how consumer perceptions of sustainable packaging influence brand loyalty and purchasing decisions.

Navigating the new norm: The impact of AI, digitalization, and remote work on regional economies, with a focus on West Yorkshire

Presentation exploring the impact of digitalization on the development of sustainable cold chains, with a focus on waste efficiency. Objectives: - To gain an overview and understanding of the factors related to Industry 4.0 and its enabling technologies, such as the Internet of Things (IoT), Cyber-Physical Systems (CPSs), Cloud Computing Technologies (CCT), Big Data Analytics (BDA), and others in the context of cold chains - Identify the primary waste sources in cold chains and investigate the potential applications of innovative (smart) digital solutions for cold chain waste management. - To propose an operational framework to demonstrate the interplay between industry digital solutions and cold chain operations and its potential impact on waste management

Understanding human behaviour in supply chain disruption management (SCDM) requires moving beyond purely rational models. While traditional decision‑making frameworks focus on empirical factors, they often overlook the role of behavioural economics and organizational culture in shaping responses to crises. This study examines how supply chain managers navigated risks and cultural shifts during the COVID‑19 pandemic, offering insights into the interplay between personal risk values, cultural cohesion, and SCDM risk levels. Using a retrospective approach, the study gathered data from 21 supply chain managers in the fast‑moving consumer goods (FMCG) and food supply chains. Questionnaires captured their attitudes towards risk, decision‑making patterns, and organizational cultural shifts before, during, and after the pandemic. Descriptive statistical analyses revealed that SCDM risk levels peaked at the height of the crisis, while cultural cohesion and personal risk values declined. Interestingly, the relationship between cultural cohesion and personal risk value intensified during the pandemic and continued to strengthen post‑pandemic. A similar trend was observed between personal risk value and SCDM risk levels, which became more pronounced over time. However, the link between cultural cohesion and SCDM risk level was strongest during the crisis but faded in pre‑ and post‑pandemic periods. These findings contribute to the growing field of behavioural operations by demonstrating the significance of psychological and cultural factors in crisis decision‑making. They underscore the need for supply chain strategies that integrate behavioural insights, recognizing that human responses to disruption are shaped by more than just rational calculations. By acknowledging the evolving dynamics of risk perception and cultural adaptation, organizations can develop more resilient and human‑centric approaches to supply chain management in times of crisis.

This study explores the transformative role of Industry 4.0-enabled digitalisation in enhancing waste efficiency and sustainability within cold supply chains. In response to the urgent need to mitigate CO2 emissions and waste amid a ‘climate emergency’, the research investigates how advanced digital technologies, including Artificial Intelligence (AI), Internet of Things (IoT) and predictive analytics, can optimise waste management practices. Using qualitative methods, including focus groups and semi-structured interviews with industry experts, data were analysed through NVivo 14 software to identify key opportunities and challenges in digital integration. Findings reveal that digital solutions significantly reduce waste through real-time monitoring, predictive maintenance and enhanced transparency. The study contributes to Sustainable Development Goals (SDGs) by addressing food security, environmental sustainability and energy efficiency. These insights provide practical guidance for policymakers and businesses seeking to align cold chain operations with global sustainability targets and Net Zero objectives, demonstrating the strategic value of Industry 4.0 technologies in tackling pressing ecological and operational challenges.

The integration of advanced predictive models is pivotal for optimizing demand forecasting and inventory management in cold chain logistics. This study evaluates the application of machine learning techniques—ARIMA (Auto-Regressive Integrated Moving Average) and Multiple Linear Regression (MLR)—to forecast demand trends and analyze key drivers in a mid-sized cold chain operation. Trained on a multi-year sales dataset, the ARIMA model excelled in capturing seasonal patterns, while the MLR model effectively incorporated multivariable factors such as temperature, product type, and promotional activity. Both models demonstrated strong predictive accuracy, with low Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), offering reliable and computationally efficient solutions for mid-sized operations. The findings underscore the novelty of combining ARIMA’s time-series capabilities with MLR’s multivariable analysis to address complex demand drivers. By aligning with Resource-Based View (RBV) and Supply Chain Resilience Theory, this research advances the understanding of AI-driven predictive models as strategic tools for enhancing operational efficiency, reducing waste, and promoting sustainability in cold chain logistics. This work sets the stage for future innovations in AI-driven supply chain optimization.

The global textile industry is undergoing a significant transformation fueled by digital advancements, shifts in consumer behavior, and evolving market dynamics. This study investigates how digital platforms, such as e-commerce and customer relationship management (CRM) systems, can enhance operational efficiency, customer engagement, and market positioning for textile companies. Through the integration of primary data from focus groups and surveys, as well as secondary data from industry reports, this research identifies key strategies for digital adoption and market competitiveness. Using Company A as a case study, this paper explores how digital tools can address current industry challenges and foster growth in a competitive landscape. The findings reveal that implementing CRM systems significantly improves customer retention and personalized services, while adopting e-commerce platforms reduces operational inefficiencies and expands market reach. These results contribute to understanding the role of digital transformation in traditional industries, particularly with respect to customer engagement, branding, and operational optimization.

Background The consultancy industry, valued at £18.6 billion, faces evolving challenges and opportunities in the post-COVID era. Consultancy and bid writing firms play a critical role in helping businesses secure contracts and funding, particularly through the preparation of Pre-Qualification Questionnaires (PQQs) and Invitations to Tender (ITTs). Despite the importance of bid writing, there is limited literature on how it can be leveraged for lead generation and business growth. Methods A mixed-methods approach was employed in this study, combining qualitative and quantitative research. Secondary research involved the review of existing literature and data from government databases, internal company records, and published reports. Primary research included surveys and semi-structured interviews with professionals from the Facilities Management and Logistics sectors. A case study of a UK-based bid-writing consultancy was also used to support the analysis. Results The findings highlighted significant knowledge gaps in bid writing among businesses, with only 12.5% of survey respondents expressing confidence in their bid-writing abilities. Despite this, 83.1% showed interest in receiving bid-writing training. The study also revealed that expertise, relationships, and past work are prioritized by businesses when selecting consultancy firms. Additionally, the integration of Artificial Intelligence (AI) in business operations was identified as a key factor for enhancing efficiency and maintaining competitiveness. Conclusions There is a clear opportunity for consultancy and bid-writing firms to address the knowledge gaps in bid writing by offering targeted training programs. Effective networking and a robust social media presence are essential for generating new business leads and maintaining client relationships. Furthermore, integrating AI technologies into business operations can provide consultancy firms with a competitive edge. This study provides actionable insights for consultancy and bid-writing firms to enhance their lead generation strategies and improve their market positioning.

The rapid growth of on-demand transportation services, driven by technological advancements and evolving urban mobility patterns, has significantly transformed urban transportation. However, this shift has highlighted a critical need to understand the interaction between on-demand services and the broader urban transportation ecosystem. This study addresses the challenge posed by the expansion of on-demand transportation, which competes with traditional public transit and raises questions about sustainability and energy efficiency. This research aims to bridge this gap through an explorative literature review. The methodology involves a systematic review of existing literature on on-demand transportation systems, focusing on themes such as operational efficiency, energy transition, and policy implications. By synthesizing and analyzing this body of literature, the research seeks to uncover insights into the current state of on-demand transportation, identify challenges and opportunities, and suggest directions for future research. Additionally, this study aims to develop operational and theoretical frameworks to support policy formulation and implementation in urban transportation planning. By integrating findings from existing case studies with insights from the literature, these frameworks will guide policymakers and urban planners in promoting sustainable, energy-efficient on-demand transportation systems. Ultimately, the research aims to contribute to evidence-based policies and practices that support the sustainable development of urban transportation networks in response to changing mobility trends. The study highlights key insights into the impact of on-demand transportation services on urban mobility, addressing challenges such as operational efficiency, energy transition, and policy implications. It also proposes operational and theoretical frameworks to guide sustainable policy formulation and implementation in urban transportation planning.

In recent decades, Supply Chains (SCs) have been exposed to disruptive changes (such as geopolitical threats (i.e., Brexit, US-China Tensions), economic recessions, and natural disasters) and have become increasingly vulnerable to these incidents, due to the increased collaboration and an intensified focus on supply chain efficiencies [1, 2, 3]. Pandemics and other systemic threats such as natural disasters, wars and political upheavals disrupt supply chains and global supply networks. COVID-19 is not only the most recent example of this, but it has also affected supply chains and economies across the entire globe, and has been among the most disruptive, in recent years, with upheaval occurring across social, economic, and political dimensions, in a very rapid fashion [1, 2, 4, 5, 6, 7].

In recent decades, the growing awareness that supply chains are increasingly vulnerable to unexpected disruptions has led to the development of the field of Supply Chain Disruption Management (SCDM). While significant progress has been made, particularly during the COVID-19 pandemic, there is still a notable gap in understanding the human-centred rationale behind SCDM decisions beyond traditional supply chain factors like cost and asset availability. Current literature effectively addresses the empirical reasons for specific SCDM strategies but falls short in exploring the cognitive, social, and cultural factors influencing these choices, such as cognitive biases, group dynamics, and organizational culture. This work aims to assess the existing knowledge in SCDM, highlight the lack of research linking behavioural economic theories and organizational culture to SCDM, and identify where these connections exist and their significance, thereby proposing future research directions. Our study suggests that advancing SCDM requires investigating how behavioural economics and organizational culture influence decision-making and outcomes, with a focus on leadership styles, risk management, Industry 4.0 technologies, and inter-organizational collaboration, especially during crises.

In today's rapidly evolving global business landscape, the convergence of Industry 5.0 and the globalization process of supply chain management has emerged as a pivotal driver of competitiveness and sustainability for organizations worldwide. This paper delves into the critical importance of Industry 5.0 in reshaping the dynamics of supply chain management on a global scale. Industry 5.0 represents the latest phase in the ongoing industrial revolution, characterized by the seamless integration of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Big Data Analytics, and Cyber-Physical Systems. This transformation transcends traditional boundaries by fostering unprecedented levels of connectivity, intelligence, and automation across industrial processes. Globalization, on the other hand, has redefined supply chain management by expanding market reach, reducing costs, and enhancing access to diverse resources and markets. The interplay between globalization and Industry 5.0 introduces a new paradigm in supply chain management that demands exploration.

Data sets related to the following research: Strategic Lead Generation and Competitive Positioning for Bid Writing Consultancy Firms.

Purpose- In recent years, there has been a notable surge in the utilization of emerging technologies, notably the Internet of Things (IoT), within the realm of business operations. However, empirical evidence has underscored a disconcerting trend whereby a substantial majority, surpassing 70%, of IoT adoption initiatives falter when confronted with the rigors of real-world implementation. Given the profound implications of IoT in augmenting product quality, this study endeavors to scrutinize the extant body of knowledge concerning IoT integration within the domain of agricultural logistics operations. Furthermore, it aims to discern the pivotal determinants that exert influence over the successful assimilation of IoT within business operations, with particular emphasis on logistics. Design/Methodology/Approach- The research utilizes a thorough systematic review methodology coupled with a meta-synthesis approach. In order to identify and clarify the key factors that influence IoT implementation in logistics operations, the study is grounded in the Resource-Based View theory. It employs rigorous grounded theory coding procedures, supported by the analytical capabilities of MAXQDA software. Findings- The culmination of the meta-synthesis endeavor culminates in the conceptual representation of IoT adoption within the agricultural logistics domain. This representation is underpinned by the identification of three overarching macro categories/constructs, namely: (1) IoT Technology Adoption, encompassing facets such as IoT implementation requisites, ancillary technologies essential for IoT integration, impediments encountered in IoT implementation, and the multifaceted factors that influence IoT adoption; (2) IoT-Driven Logistics Management, encompassing IoT-based warehousing practices, governance-related considerations, and the environmental parameters entailed in IoT-enabled logistics; and (3) the Prospective Gains Encompassing IoT Deployment, incorporating the financial, economic, operational, and sociocultural ramifications ensuing from IoT integration. The findings underscore the imperative of comprehensively addressing these factors for the successful assimilation of IoT within agricultural logistics processes. Originality- The originality of this research study lies in its pioneering effort to proffer a conceptual framework that furnishes a comprehensive panorama of the determinants that underpin IoT adoption, thereby ensuring its efficacious implementation within the ambit of agricultural logistics operations. Practical Implications- The developed framework, by bestowing upon stakeholders an incisive comprehension of the multifaceted factors that steer IoT adoption, holds the potential to streamline the IoT integration process. Moreover, it affords an avenue for harnessing the full spectrum of IoT-derived benefits within the intricate milieu of agricultural logistics operations.

This study investigates how Small and Medium-sized Enterprises (SMEs) in the fast-food sector can enhance customer engagement and brand loyalty through effective social media strategies. Using a customer survey focused on Company A as a case study, the research examines consumer behaviour, preferences, and responses to the brand’s social media content. Drawing on theories from organizational learning, strategic management, organizational behaviour, and change management, the study emphasizes the role of Firm-Generated Content (FGC) in influencing consumer behaviour and brand perception, focusing on visually compelling and interactive content. The findings indicate that while paid advertisements are effective for short-term visibility, fast-food SMEs face challenges in sustaining organic engagement, which is crucial for building lasting customer relationships. Instagram

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This chapter explores the evolving discourse on sustainable luxury within the framework of the United Nations' sustainable development goals (SDGs) and the integration of Industry 5.0 technologies. It investigates how the luxury sector can balance opulence with environmental and social responsibility, leveraging digital transformation to enhance sustainability. The case study of Kering's transition from traditional luxury to a sustainable approach through digital tools exemplifies the industry's shift. This study outlines the enablers, challenges, and future opportunities for luxury brands to integrate sustainability and digital innovation in an era where consumer expectations are evolving towards responsible consumption.

This study explores waste generation patterns in cold chain logistics, emphasizing the interrelationships between product categories, promotional activities, and inventory inefficiencies. Using real-world data from Company A, a comprehensive methodological approach, including time-series analysis and Ordinary Least Squares (OLS) regression, was employed to identify critical drivers of waste. The findings demonstrate that promotional activities significantly increase waste levels, notably through overproduction and misaligned demand forecasting. Furthermore, clear seasonal patterns emerged, pinpointing specific periods of peak waste linked to promotions and festive demand spikes. The analysis also highlighted warehouse inefficiencies as key contributors to waste, suggesting targeted logistical optimizations as essential. The study's novelty lies in its application of the Technology-Organization-Environment (TOE) framework to structure insights into AI-driven waste reduction strategies specifically tailored for cold chain operations. Unlike existing research, this study integrates AI-powered predictive analytics, sustainable packaging solutions, and waste categorization models, offering an empirically validated, actionable framework for supply chain managers. These results contribute significantly to existing literature by moving beyond generic operational improvements, directly addressing how technological, organizational, and regulatory factors collectively influence waste mitigation. The practical implications highlight the necessity for organizations to adopt a holistic, technology-enabled, and sustainability-oriented approach, ensuring long-term economic and environmental benefits.

Consumer trust is vital in the personal care and cosmetics industry as artificial intelligence (AI) and machine learning (ML) reshape digital interactions. With the sector undergoing rapid digital transformation, understanding how AI influences trust is critical. This study explores the factors affecting consumer trust in AI-driven beauty solutions in the UK and Ireland, focusing on transparency, ethical AI governance, and personalized digital experiences. A systematic literature review was conducted across Web of Science, Scopus, PubMed, IEEE Xplore, and Google Scholar, covering studies published between 2010 and 2023. The research was guided by the Critical Realism framework, enabling the examination of both observable factors (e.g. technological functionality, data privacy) and underlying influences (e.g., social, cultural, and organizational trust dynamics). Screening followed predefined criteria based on the PRISMA framework, ensuring a transparent and structured approach to the inclusion and exclusion of studies. The results indicate that consumer trust is strongly influenced by transparency, efficiency, and the ethical handling of AI-driven technologies. Personalized digital experiences contribute to greater trust and engagement, yet privacy concerns remain a significant barrier to AI adoption. The study highlights the importance of ethical AI frameworks and regulatory measures in fostering trust and ensuring the sustainable integration of AI technologies in the cosmetics and personal care sector. For industry practitioners, this study provides strategies to enhance consumer trust in AI-driven personalization, including greater transparency in data usage, strengthened privacy protections, and ethical AI governance.

The urgency of addressing the climate emergency has underscored the need for immediate action to mitigate its catastrophic effects, with cold chains identified as significant contributors to greenhouse gas emissions and environmental degradation. This paper explores the potential of digitalization to enhance sustainability within cold chains by leveraging technologies such as IoT, AI, and blockchain. Through a comprehensive investigation, the study aims to identify opportunities for optimizing processes, reducing waste, and minimizing environmental impact throughout the cold chain network. The research addresses urgent sustainability challenges, investigates the influence of digitalization on waste management, and provides practical insights aligned with global sustainability goals. Drawing on theoretical frameworks and empirical evidence, the study develops a framework integrating Industry 4.0 principles with Sustainable Corporate Theory to address waste efficiency in cold supply chains. The framework offers practical strategies for operationalizing waste efficiency and promoting sustainability within cold chain management. Implications of the findings include enhanced efficiency, improved product quality, sustainability compliance, and cost savings. However, the study acknowledges limitations and highlights avenues for future research, including technological challenges, behavioural factors, environmental impact assessment, and long-term sustainability considerations. Overall, this paper contributes to advancing our understanding of digital transformation in cold chain logistics and underscores the importance of addressing sustainability challenges in supply chain management.

Objective - Artificial Intelligence (AI) has become a pivotal technology in transforming logistics performance. This paper aims to comprehensively understand how AI-enabled solutions improve efficiency, accuracy, and responsiveness in logistics operations. The study focuses on synthesizing current research to explore AI applications across various logistics domains, such as predictive analytics, autonomous vehicles, and smart warehousing. Methodology/Technique – A systematic review approach was used to gather and analyze existing literature on AI applications in logistics. The review covered studies published in recent years, highlighting the advancements and impact of AI on logistics processes. The methodology included selecting relevant sources, categorizing AI applications, and assessing their effects on different logistics functions. Finding – The findings reveal that AI adoption substantially improves logistics operations, including enhanced operational performance, cost reduction, and increased customer satisfaction. Specific AI applications, such as predictive analytics for demand forecasting, autonomous vehicles for transportation, and smart warehousing for inventory management, were identified as key contributors to these improvements. However, challenges such as data privacy concerns and integration complexities were also noted. Novelty – This study's novelty lies in its comprehensive analysis of AI applications across various logistics domains, offering a holistic view of AI's role in optimizing logistics performance. This paper highlights the benefits of AI adoption and addresses the associated challenges, providing insights into future research directions and practical implications for leveraging AI in logistics. Type of Paper: Review JEL Classification: C61, C62, D83. Keywords: Artificial Intelligence; Logistics; Performance Improvement; Predictive Analytics; Autonomous Vehicles; Smart Warehousing Reference to this paper should be referred to as follows: Fatorachian, H. (2024). Leveraging Artificial Intelligence for Optimizing Logistics Performance: A Comprehensive Review, GATR-Global J. Bus. Soc. Sci. Review, 12(3), 146–160. https://doi.org/10.35609/gjbssr.2024.12.3(5) _____________________________________

The COVID-19 pandemic has been one of the most severe disruptions to normal life, impacting how businesses operate. The academic literature in the areas of supply chain and operations management has been trying to explain how this has affected decision-making in businesses. However, the existing literature has predominantly overlooked organisational culture and behavioural economic theories. This paper contends that considering the decisions made in supply chain disruption management involve groups and the individuals within them, the relevance of behavioural economic concepts becomes paramount. As such, the objective of this paper is to conduct an integrative literature review, utilising the purposive sampling method to explore the dearth of academic work connecting behavioural economic theories and organisational culture to supply chain disruption management. Additionally, the paper aims to offer guidelines for future research in this domain. Enhancing our comprehension of these domains concerning supply chain disruption management would empower firms to better anticipate their parties’ decisions, refine their decision-making models, and cultivate stronger relationships with suppliers and customers.

This chapter seeks to explore the intricate relationship between sustainable supply chain management and Industry 5.0, emphasizing the broader context of sustainable development. By examining the challenges and opportunities arising from technological advancements in artificial intelligence, automation, big data, and the internet of things, the chapter aims to shed light on how supply chain practices can align with economic, social, and environmental sustainability values amid the intensification of socio-environmental issues and the increasing prevalence of Industry 5.0.

The alternative financing space has undergone significant evolution, driven by factors such as the financial crisis, technological advancements, and the recent global pandemic. This evolution has positioned alternative financing as a pivotal channel for entrepreneurs seeking funding. In this chapter, we delve into the intersection of digital entrepreneurship and Industry 5.0 within the context of alternative financing methods. Our focus is on examining emerging trends, understanding the role of financial technology (Fin Tech), and exploring the symbiotic relationship between finance and entrepreneurship. By exploring the intersection of digital entrepreneurship and Industry 5.0 in the realm of alternative financing, this chapter illuminates the transformative dynamics shaping the financial landscape for entrepreneurs. The implications of these insights extend to both practitioners and scholars, providing a nuanced understanding of the evolving trends and the critical role played by FinTech in facilitating alternative financing methods. Entrepreneurs can gain valuable insights into leveraging digital advancements for funding, while policymakers and industry stakeholders can use this information to inform strategies that foster a conducive environment for sustainable and innovative financial solutions.

The global textile industry is undergoing rapid transformation driven by digital technologies, shifting consumer preferences, and evolving market dynamics. This study explores how digital platforms—particularly e-commerce, CRM systems, and digital marketing tools—enhance operational efficiency, customer engagement, and market positioning for textile wholesalers. Grounded in the Technology Acceptance Model (TAM), Diffusion of Innovations (DOI) Theory, and the Resource-Based View (RBV), the research identifies key drivers and barriers to digital adoption. A mixed-methods approach, incorporating focus groups, surveys, and industry reports, supports a comprehensive analysis. A case study of Company A demonstrates how digital tools can address sector-specific challenges, increase competitiveness, and support sustainable growth. Findings show CRM systems improve customer retention through personalised service, e-commerce reduces inefficiencies and broadens market reach, and digital marketing boosts brand visibility. By integrating behavioural, organisational, and strategic perspectives, this study offers actionable insights for textile wholesalers seeking to thrive in a digitally evolving economy.

This study examines how digital interventions associated with Industry 5.0 can enhance waste efficiency across food supply chains. Using an exploratory and systematic literature review, the research identifies key forms of waste - including temperature abuse, packaging waste, overproduction, product damage, obsolescence and human error - and evaluates how advanced digital technologies can help prevent or mitigate these inefficiencies. Drawing on Sustainable Corporate Theory, the study develops a theoretical framework that integrates technologies such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain to support real-time monitoring, predictive maintenance and improved transparency throughout the food chain. The findings demonstrate that Industry 5.0-enabled digitalisation has the potential to significantly improve waste efficiency and overall supply chain performance by fostering greater responsiveness, traceability and collaboration among supply chain actors. The study contributes to academic and industry discussions by offering a strategic and system-level framework for leveraging Industry 5.0 technologies to support sustainable and efficient waste management within contemporary food chains.

Amid rising global concerns around social inequality and environmental degradation, this study examines the impact of social entrepreneurship orientation (SEO) and environmental orientation (EO) on sustainability outcomes among firms in Ecuador—an emerging economy with a progressive regulatory framework. Drawing on data from 474 Ecuadorian firms across multiple sectors, this study investigates the influence of distinct dimensions of SEO-social innovativeness, risk-taking, proactiveness, and socialness on social performance, as well as the impact of internal and external dimensions of EO on green innovation performance (GIP). Based on partial least squares structural equation modelling, the findings reveal that among SEO dimensions, socialness, defined as the centrality of social mission, exerts the strongest positive influence on social performance. External EO, reflecting responsiveness to stakeholders and regulatory pressures, significantly predicts GIP, whereas internal EO demonstrates no significant effect. Furthermore, regulatory forces positively influence both social performance and GIP and function as critical institutional drivers in shaping sustainable business practices. Moreover, social performance significantly enhances green innovation outcomes, reinforcing the interconnectedness of social and environmental priorities in corporate sustainability. By integrating strategic orientations and institutional pressures, this study advances the understanding of how sustainable entrepreneurship unfolds in emerging markets and provides practical implications for firms and policymakers seeking to align profit, purpose, and environmental responsibility.

The FinTech industry has revolutionized traditional financial services by leveraging technology to offer innovative solutions for consumers and businesses alike. However, startups entering the Business-to-Consumer (B2C) market face significant challenges, including regulatory compliance, securing product-market fit, and building customer trust in a competitive landscape. This paper explores these challenges through a mixed-methods approach, combining surveys and interviews to gather insights from FinTech professionals, consumers, and a case study of a UK-based startup, ‘Company A’. The findings highlight the critical importance of regulatory adherence, particularly in securing Financial Conduct Authority (FCA) approval and addressing consumer concerns around data security and digital payment reliability. The study also examines strategies for product-market fit and market positioning in the growing FinTech sector, offering practical recommendations for startups to overcome barriers to entry. Key recommendations include the adoption of value-added services (VAS), transitioning from B2C to B2B models for scalability, and leveraging regional FinTech hubs for support. The study contributes to the understanding of FinTech startup challenges and offers a strategic roadmap for achieving long-term success in the B2C financial services market.

This study explores the integration of artificial intelligence (AI) and blockchain technologies within secure digital frameworks to enhance emissions tracking and data integrity in cold chain logistics. Using Company A's extended supply chain as a case study, the research applies the Scope 3 Greenhouse Gas (GHG) Protocol to quantify indirect emissions across transportation, production, and storage activities. Findings indicate that downstream transportation is the largest emissions contributor, followed by inefficiencies in production and storage. The methodology combines quantitative data from Company A's operational records—including transportation logs, refrigeration efficiency, and supplier emissions—with external datasets such as the Carbon Cloud database. AI-driven predictive analytics, alongside a linear regression model, identify key emissions drivers such as transportation distance and refrigeration energy consumption. Additionally, blockchain enhances data integrity through cryptographic hash functions that secure real-time emissions records. Optimization algorithms further reduce emissions by refining delivery routes and improving refrigeration efficiency. Grounded in the Technology-Organization-Environment (TOE) Framework, Institutional Theory, and Dynamic Capabilities Theory, the findings underscore the strategic value of integrating AI and blockchain for real-time emissions monitoring, operational optimization, and regulatory compliance. This research provides actionable insights into scalable emissions management frameworks, offering a transformative approach to reducing environmental impact and aligning cold chain logistics operations with sustainability goals.

This study explores the adoption of smart technologies in sustainable logistics and mobility systems, focusing on the operational and environmental impacts, barriers to adoption, and key facilitators. The research is grounded in two prominent theoretical frameworks: the Technology Acceptance Model (TAM) and institutional theory. These frameworks provide a comprehensive lens to examine both internal organisational factors (e.g. perceived usefulness and ease of use) and external institutional pressures (e.g. regulatory mandates and industry norms) that influence the adoption of technologies such as IoT, Big Data Analytics, AI/Machine Learning, blockchain, and Electric Vehicles (EVs). Using a survey-based methodology, data were collected from organisations within the logistics and mobility sectors. The findings reveal that IoT and Big Data Analytics are the most widely adopted technologies, driven by their perceived operational benefits and ease of use. However, adoption of AI/Machine Learning, blockchain, and EVs remains constrained by high costs and infrastructural limitations, particularly for small and medium-sized enterprises (SMEs). Regulatory incentives, stakeholder collaboration, and public-private partnerships were identified as key facilitators of adoption, highlighting the importance of addressing both financial and technical barriers.

© 2020 Informa UK Limited, trading as Taylor & Francis Group. Considering the crucial role Information Technology (IT) plays in achieving performance improvements in business processes, this paper aims to explore the potential impact of the fourth industrial revolution–Industry 4.0 and its associated technological advances on Supply Chain (SC) performance. This study is exploratory research, conducted based on inductive reasoning, which aims to bring new insights into the topic, and to provide forward-thinking for future research. Hence, through conducting a systematic literature review, the paper attempts to explore the impact of Industry 4.0 on SC performance and to conceptualise and develop findings into an operational framework underpinned by Systems Theory. Based on this research, the application of Industry 4.0-enabling-technologies is expected to bring about significant performance improvements in SCM by enabling a holistic approach towards supply chain management resulting from extensive supply chain integration as well as information sharing and transparency throughout the supply chain. Moreover, these technologies allow for huge performance improvements within individual supply chain processes such as procurement, production, inventory management and retailing through enabling process integration, digitisation and automation, and bringing about novel analytical capabilities.

The increasing complexity of urban logistics in smart cities requires innovative solutions that leverage real-time data, predictive analytics, and adaptive learning to enhance efficiency. This study presents a predictive analytics framework integrating digital twin technology, IoT-enabled logistics data, and cybernetic feedback loops to improve last-mile delivery accuracy, congestion management, and sustainability in smart cities. Grounded in Systems Theory and Cybernetic Theory, the framework models urban logistics as an interconnected network, where real-time IoT data enable dynamic routing, demand forecasting, and self-regulating logistics operations. By incorporating machine learning-driven predictive analytics, the study demonstrates how AI-powered logistics optimization can enhance urban freight mobility. The cybernetic feedback mechanism further improves adaptive decision-making and operational resilience, allowing logistics networks to respond dynamically to changing urban conditions. The findings provide valuable insights for logistics managers, smart city policymakers, and urban planners, highlighting how AI-driven logistics strategies can reduce congestion, enhance sustainability, and optimize delivery performance. The study also contributes to logistics and smart city research by integrating digital twins with adaptive analytics, addressing gaps in dynamic, feedback-driven logistics models.

This study investigates how Industry 4.0 technologies can optimize transportation efficiency and contribute to global sustainability goals by reducing CO2 emissions. In response to the pressing climate emergency, the research examines the role of the Internet of Things (IoT), Artificial Intelligence (AI), and predictive analytics in enhancing operational performance and aligning transportation systems with Sustainable Development Goals (SDGs), particularly Goal 13 (climate action) and Goal 9 (industry, innovation, and infrastructure). Using a qualitative research approach, semi-structured interviews and focus groups were conducted with industry experts, and the data were analyzed using thematic analysis and qualitative network mapping in NVivo software. The findings reveal that IoT enhances real-time monitoring, AI enables dynamic route optimization, and predictive analytics supports proactive maintenance, collectively achieving an average emission reductions of 30%. However, adoption is hindered by infrastructure gaps, high implementation costs, skill shortages, and fragmented regulatory frameworks. This study integrates the Technology–Organization–Environment (TOE) framework and Sustainable Corporate Theory to provide a structured analysis of digital transformation in transportation. The findings offer strategic insights for policymakers and industry stakeholders, highlighting the need for stronger regulatory support, targeted incentives, and digital infrastructure investments.

This study explores how digital technologies and data analytics can transform urban waste management in smart cities by addressing systemic inefficiencies. Integrating perspectives from the Resource-Based View, Socio-Technical Systems Theory, Circular Economy Theory, and Institutional Theory, the research examines sustainability, operational efficiency, and resilience in extended supply chains. A case study of Company A and its demand-side supply chain with Retailer B highlights key drivers of waste, including overstocking, inventory mismanagement, and inefficiencies in transportation and promotional activities. Using a mixed-methods approach, the study combines quantitative analysis of operational data with advanced statistical techniques and machine learning models. Key data sources include inventory records, sales forecasts, promotional activities, waste logs, and IoT sensor data collected over a two-year period. Machine learning techniques were employed to uncover complex, non-linear relationships between waste drivers and waste generation. A waste-type-specific emissions framework was used to assess environmental impacts, while IoT-enabled optimization algorithms helped improve logistics efficiency and reduce waste collection costs. Our findings indicate that the adoption of IoT and AI technologies significantly reduced waste by enhancing inventory control, optimizing transportation, and improving supply chain coordination. These digital innovations also align with circular economy principles by minimizing resource consumption and emissions, contributing to broader sustainability and resilience goals in urban environments. The study underscores the importance of integrating digital solutions into waste management strategies to foster more sustainable and efficient urban supply chains. While the research is particularly relevant to the food production and retail sectors, it also provides valuable insights for policymakers, urban planners, and supply chain stakeholders. By bridging theoretical frameworks with practical applications, this study demonstrates the potential of digital technologies to drive sustainability and resilience in smart cities.

Transportation and logistics systems are becoming increasingly complex and critical to modern infrastructure. This paper proposes a novel AI-enhanced fault-tolerant control framework to address the dual challenges of physical malfunctions and cyber threats. By leveraging advanced machine learning algorithms and real-time data analytics, the proposed methodology aims to enhance the reliability, safety, and security of transportation and logistics systems. This research explores the foundations and practical implementations of AI-driven anomaly detection, predictive maintenance, and autonomous response systems. The findings demonstrate significant improvements in system resilience and robustness, making a substantial contribution to the field of intelligent transportation management.

The surge in popularity of on-demand transportation services, fueled by advancements in technology and changing urban mobility patterns, has significantly reshaped urban transportation dynamics. This transformation presents challenges to traditional public transportation, raising questions about sustainability and energy efficiency. This research addresses these challenges through an explorative literature review, focusing on operational efficiency, energy transition, and policy implications. By synthesizing and analyzing existing literature, the study uncovers insights into on-demand transportation, identifies challenges and opportunities, and proposes avenues for further research. The study also develops operational and theoretical frameworks to support policy formulation and implementation in urban transportation planning, offering guidance for policymakers and urban planners. Ultimately, this research aims to contribute to developing evidence-based policies and practices that foster sustainable urban transportation networks.

Urbanization has led to significant traffic congestion, presenting challenges for traditional traffic management systems that rely on static and rule-based approaches. These systems struggle to adapt to real-time changes in traffic patterns, resulting in inefficiencies and delays. Intelligent Transportation Systems (ITS), leveraging advanced technologies such as sensors, communication networks, and data analytics, offer promising solutions. This study aims to develop and validate a conceptual framework integrating deep learning, reinforcement learning, and transfer learning into ITS for dynamic and adaptive traffic management. An explorative literature review identifies key constructs, including real-time data collection, data preprocessing, adaptive signal control, and predictive analytics. The framework is validated through case studies from Singapore, Los Angeles, and Rio de Janeiro, demonstrating practical implementation and impact. The findings highlight the potential of learning-based ITS solutions to enhance traffic flow, reduce congestion, and improve urban transportation networks, contributing to the broader vision of smart cities.

This study explores how logistics optimisation can accelerate the transition from linear to circular supply chains, supporting global sustainability goals. Grounded in closed-loop supply chain (CLSC) theory, it investigates how advanced logistics models—such as route planning algorithms, fuel consumption prediction, and emission reduction techniques—can enhance resource efficiency, reduce waste, and close material loops. The research combines five years of historical data on vehicle performance, routes, and fuel use with real-time information on traffic and weather, integrated through public APIs. Using regression analysis for fuel prediction, multi-criteria optimisation for balancing cost and emissions, and dynamic routing algorithms responsive to live conditions, the study identifies strategies for improving both efficiency and sustainability. Results reveal measurable reductions in fuel consumption, emissions, and logistics costs, demonstrating the value of data-driven optimisation in implementing circular economy (CE) practices. By addressing trade-offs between cost, performance, and environmental impact, this research provides actionable insights for organisations seeking to shift from traditional linear models toward more resilient and resource-efficient circular frameworks. Overall, it highlights the critical role of predictive analytics and optimisation tools in operationalising CLSC principles and advancing sustainable logistics management.

The increasing global demand for sustainable production practices has propelled green manufacturing to the forefront of industrial innovation. Central to this movement is the integration of smart sensors for real-time data collection, an approach that promises significant advancements in waste reduction and energy efficiency. This chapter explores how smart sensor technology can transform traditional manufacturing processes by enabling continuous monitoring, data-driven decision-making, and predictive maintenance. Through a comprehensive review of real-time monitoring applications, the chapter discusses various sensor types, their roles in capturing critical data on emissions, resource consumption, and waste outputs, and their integration into green manufacturing systems. Case studies from diverse industries illustrate the substantial impact of smart sensors in optimising energy use, reducing material waste, and supporting circular economy models. Further, the chapter examines the technical challenges and data privacy considerations associated with real-time monitoring and proposes solutions to enhance adoption. By leveraging intelligent data analytics and Internet of Things (IoT) connectivity, manufacturers can achieve a more responsive, eco-friendly production environment. This chapter concludes with a discussion on the future implications of smart sensor technology in green manufacturing, highlighting the role of regulatory standards and industry partnerships in driving innovation for a sustainable future.

The digital transformation of cold supply chains is gaining importance due to its potential to improve efficiency, reduce waste, and support global sustainability goals. This study examines the key barriers to adopting Industry 4.0 technologies in cold chain management. These issues are especially pronounced for small and medium-sized enterprises (SMEs) and organizations operating in resource-constrained regions. A qualitative research design was employed, using semi-structured interviews and focus groups with stakeholders involved in cold chain operations, technology development, and policymaking. Thematic and qualitative network analysis identified several interconnected obstacles, notably high upfront investment costs, insufficient technological infrastructure, fragmented regulatory environments, and resistance to organizational change. Financial constraints were found to be closely linked with technological limitations, collectively restricting the ability of firms to invest in digital upgrades. The study emphasizes the necessity of a holistic and coordinated response to these challenges. Recommended strategies include financial incentives and support schemes, targeted investments in digital infrastructure, regulatory harmonization, and workforce training initiatives. Overall, the research provides a structured roadmap for phased digital transformation in cold supply chains, offering practical guidance for stakeholders and policymakers. Addressing these interconnected barriers can enhance sustainability, operational resilience, and competitiveness amid increasing global supply chain pressures.