Existing methods for detecting algorithmic pricing rely on high-frequency data with sub-daily timestamps, yet academic researchers and competition authorities typically have access only to daily price snapshots. This paper develops a heuristic that identifies likely algorithmic pricers in such low-resolution data by combining two standardised metrics: the Average Rate of Price Changes (ARPC), capturing the frequency of price adjustments, and a Market Presence Ratio (MPR), capturing sustained retailer engagement. Requiring both metrics to exceed a threshold reduces false positives from promotional sellers and transient market participants. We apply the heuristic to a dataset of 10,365 unique retailer-category observations from the PriceSpy price comparison platform, covering seven national markets and 16 product categories over an 18-month period (July 2021–December 2022). Our results show that algorithmic pricing is prevalent across markets, with algorithmic pricers present in all 16 categories in the United Kingdom and in all seven countries for six categories. Prevalence is notably higher in consumer electronics than in household appliances. As a lower-bound estimate, approximately 4.5% of retailers are classified as algorithmic pricers at our baseline threshold. The heuristic provides a practical screening tool for competition authorities investigating algorithmic pricing adoption using commercially available data.
Increasing industrial complexity has made the interactions among production, quality, reliability, and logistics processes progressively more difficult to understand through static process representations or conventional screen-based simulation tools alone. In industrial systems, operational decisions often generate cascading effects across interconnected subsystems, requiring engineers and operators to develop a system-level understanding of process dynamics and interdependencies. This paper proposes an immersive simulation approach to support the exploration and understanding of industrial process dynamics through interactive and immersive environments. Rather than focusing on virtual reality as a visualization technology alone, the proposed approach combines immersive interaction with discrete-event simulation models that reproduce the operational behavior of industrial production and logistics systems. Users can directly manipulate operational variables and observe how changes propagate across throughput, bottleneck formation, quality dynamics, machine availability, inventory behavior, and resource utilization. The approach is structured around the identification of key operational dynamics and their translation into immersive simulation scenarios representing critical aspects of industrial production and logistics systems. Seven simulation scenarios were developed and deployed in a controlled course-based environment involving 32 participants, using industrial-grade simulation tools and VR technologies. Although implemented in an educational context, the scenarios are grounded in industrial operational dynamics and are discussed in terms of their transferability to industrial training and capability-development settings. Participant feedback indicated high perceived improvements in conceptual clarity (average rating of 4.81 out of 5), understanding of process interdependencies and the connection between theoretical models and operational system behavior (4.53 out of 5). Results also highlighted the role of immersive interaction in supporting active exploration of process dynamics and operational trade-offs. The paper contributes to the literature on operational capability development by demonstrating how immersive simulation environments can support the understanding of complex industrial process dynamics and provide interactive representations of operational knowledge that are difficult to communicate through conventional analytical or training approaches.
In digital transformation contexts, organizational change remains challenging especially when employee resistance is underestimated and change management efforts are fragmented. This study presents a systematic review on organizational change with a focus on resistance dynamics, change management frameworks, and the role of emerging technologies in technology-enabled transformation. Following PRISMA 2020 guidelines, 83 peer-reviewed articles were screened and analyzed using thematic and bibliometric approaches. This review integrates resistance to change, change management models, and digital transformation within a unified socio-technical perspective. The findings show that resistance is shaped by interrelated individual, organizational, and cultural factors. On the other hand, successful transformation is consistently associated with transparent communication, inclusive leadership, psychological safety, and cultural alignment. Moreover, the study shows that technologies such as gamification, AI-powered chatbots, and virtual and augmented reality can strengthen engagement, learning, and participation when embedded within broader change management processes. The bibliometric analysis identifies major intellectual trends, influential contributors, and emerging directions in the field. Overall, the study offers an integrated theoretical and practical foundation for understanding how human and technological factors jointly shape organizational change in digital transformation.
The development of artificial intelligence has profoundly reshaped the ways in which personal data are generated, processed, and retained, placing intelligent systems at the heart of debates on privacy and fundamental rights. This article examines, from a European Union legal perspective, the application of the General Data Protection Regulation (GDPR) to AI and assesses whether the principles and rights enshrined in European law—particularly the rights to erasure, to be forgotten, and to rectification—can be effectively exercised once information has been absorbed by machine learning models. The study examines the main legal and technical challenges arising from the nature of AI, which does not store data in a static form but transforms it into knowledge, thereby complicating its localisation, alteration, or deletion. It also analyses the relationship between the GDPR and the Artificial Intelligence Act (AIA), emphasising their complementary roles and the need to ensure coherence between the two regulatory frameworks. From a legal and ethical standpoint, the paper considers phenomena inherent to AI systems—such as hallucinations, algorithmic bias, and neurodata—to illustrate how they challenge essential principles such as accuracy, minimisation, and purpose limitation, and how they test the rights of individuals in contexts where information cannot truly be “forgotten”. Finally, it proposes alternative mechanisms, mitigation strategies, and emerging solutions aimed at preserving individuals’ effective control over their data in the algorithmic age, thereby reinforcing privacy protection and public trust in the responsible use of new technologies.
Open innovation has become a key strategic lever for small and medium-sized enterprises (SMEs) operating in dynamic and uncertain economic environments. However, few studies empirically examine how open innovation influences SME internationalization, particularly in developing countries. This study addresses this gap by analyzing the internal and external determinants, where internal determinants originate within the organization and external determinants stem from its surrounding environment, together shaping organizational outcomes. Using a mixed-methods approach, the research combines qualitative interviews with managers of innovative firms in Tunisia and quantitative analysis through linear regression to evaluate the proposed theoretical model. The findings identify critical factors affecting internationalization and highlight the strategic role of business angels and venture capital investors in developing international networks and stimulating open innovation. These results make a significant contribution to the literature on innovation and SME internationalization by elucidating the mechanisms through which external investor support enhances organizational agility and international competitiveness. The practical implications are relevant for policymakers and entrepreneurial ecosystem actors seeking to foster an environment conducive to the global growth of SMEs.
Unlike large companies that often have the means and resources for e-fulfillment process with automation, Small and Medium-sized Enterprises (SMEs) encounter challenges in fulfilling the massive orders as they rely on manual operation. While many existing researchers have explored e-fulfillment process enhancement solely, a lack of research regarding process synchronization. E-fulfillment processes are interconnected, suboptimal inventory management will lead to longer order-picking time, as warehouse workers might waste time looking for misplaced items. This has led to a growing shift toward a holistic approach that aims to synchronize e-fulfillment processes for better efficiency. This study introduces an Intelligent E-Fulfillment Synchronization Model (IEFSM). The novelty is Inbound-Inbound synchronization in e-fulfillment processes, through coordinated sequential optimization with closed-loop feedback. It employs Artificial Intelligence (AI) and Augmented Reality (AR) technology to improve the processes of product information preparation, inventory management, and order picking as a holistic framework. The proposed generate attractive product descriptions with the aid of AI. Statistical methods and rule-based calculations are adopted for pricing recommendations and inventory optimization. AR provides real-time support in order picking. A pilot case study demonstrates 88% operational time savings, 9% sales growth, 88% fewer unresolved shortages, 100% picking accuracy and 70% order picking time reduction.
In the competitive digital service sector, leveraging user-generated data for strategic operational improvements is a critical engineering and management challenge. This study presents a business intelligence framework that integrates Artificial Intelligence (AI) with established management theory to operationalize technology acceptance drivers from unstructured text. We develop a systematic methodology that employs ChatGPT for theory-guided keyword generation to identify and measure the core constructs of the Technology Acceptance Model (TAM)—perceived ease of use, perceived usefulness, and Behavioral intention to use—within a massive dataset of 1,694,581 user reviews from leading US food delivery apps. Through a robust data processing pipeline incorporating sentiment analysis (VADER, AFINN) and Ordinary Least Squares (OLS) regression, we validate the framework’s efficacy, demonstrating that the AI-measured constructs explain 85.4% of the variance in users’ intention to use (R 2 = 0.854, p < 0.001). The results indicate that user perceptions of ease of use (β = 0.29, p < 0.001) and usefulness (β = 0.51, p < 0.001) are significant predictors of adoption intention. This research provides a tangible, data-driven framework for managers and engineers to systematically diagnose user experience, prioritize feature development, and formulate product strategies. The proposed methodology offers a replicable, theory-AI integrated analytics pipeline for transforming unstructured textual data into actionable engineering and business intelligence, offering a pathway to connect large-scale data analytics with strategic management decision-making.
Despite the critical role of cement manufacturing industries in infrastructure development, its contribution to a significant share of global carbon emissions underscores the urgency of integrating sustainable practices. Digital transformation offers potential to enhance transparency, resource efficiency, and circular economy transitions, yet its implementation faces significant challenges. This study examines the barriers impeding the adoption of digital transformation in the cement manufacturing industry of emerging economies, with particular attention to Bangladesh. Through a systematic literature review and expert validation, seventeen key barriers were identified and assessed using the Interval-Valued Pythagorean Fuzzy DEMATEL (IVPF-DEMATEL) method. The analysis highlights “limited flexibility in reverse logistics” and “lack of environmental commitment among top management” as the most prominent barriers, while “reluctance to invest in recycling” and “inefficient management practice” emerge as critical causal factors. The findings provide a structured understanding of the interdependencies among these barriers, enabling policymakers, practitioners, and industry leaders to prioritize root causes over symptomatic effects. This research contributes to the limited body of knowledge on digital transformation in the cement sector of emerging economies and offers practical pathways for advancing sustainability through the adoption of digital transformation.
This study proposes an optimization model to strategically locate blood hubs and route blood product flows. The model considers real-world factors, including depreciation costs at hubs, blood wastage, product decomposition at lab centers, and handling of multiple blood products. It employs both single and multiple allocation strategies to mitigate shortages and meet all demand. The proposed multi-objective model aims to: (i) minimize the total cost, such as transportation costs, depreciation costs, and hub installation costs, and (ii) minimize the maximum time that products stay in the network. Computational results show that NSGA-II produces diverse Pareto-efficient solutions and achieves small optimality gaps for small-sized instances when compared with the ε-constraint method, while remaining effective for larger instances where exact methods become computationally intractable. The ε-constraint method is used to derive Pareto optimal solutions, and NSGA-II is applied to solve the proposed model. Sensitivity analysis indicates that blood demand and depreciation costs are the most influential parameters, and that optimal solutions occur at moderate hub capacities. Managerial implications reveal that the optimal number and location of hubs are determined not solely by geography but also by demand distribution, donor availability, and product compatibility.
The digital transformation of maintenance operations, driven by the fourth industrial revolution and characterized by the widespread adoption of emerging technologies, is essential for enhancing operational efficiency, optimizing asset management, and increasing equipment reliability. Despite the significant benefits, maintenance organizations face numerous challenges in implementing these technologies and ensuring successful transformations, including technological integration complexities, cultural resistance, skill gaps, and aligning digital strategies with broader business objectives, among others. This work addresses the critical gap in structured methodologies for evaluating and guiding digital maturity in Maintenance Digital Transformation (MDT) by proposing a comprehensive maturity assessment model. The model is grounded in nine key maturity dimensions spanning organizational culture, technology & data management, leadership & management aspects, organizational development & change, digital strategy, knowledge & skills, and internal integration. Building on emerging research in this field, data collection and advanced statistical techniques, and feedback from experts in the field, this work adopts a structured and rigorous methodology to (1) identify and test the maturity dimensions and subdimensions driving the success of this transformation, (2) adopt well-established guidelines to develop a structured and comprehensive maturity assessment model allowing organizations to assess their current MDT maturity level, and (3) perform an initial empirical validation of the proposed maturity grid. The resulting maturity grid provides organizations with detailed guidelines on their current digital maturity level and actionable recommendations for advancing to higher stages of maturity. The model offers diagnostic insights and a strategic roadmap to guide maintenance organizations through the multifaceted digital transformation process.
The transition towards Industry 5.0 requires patterns that offer solutions for the design of organisational structures capable of integrating human–machine cooperation into decision-making processes, thereby improving organisational productivity and enabling the transformation of business models. This article presents a reusable and scalable enterprise architecture pattern that integratesco-intelligence elements, digital twins and an intelligent enterprise interaction manager. The research was carried out in four stages: (1) analysis of the pattern concept; (2) pattern design; (3) implementation on the Enterprise Architect platform using the ArchiMate language; and (4) application in two scenarios: a drone swarm factory and a public disaster response agency. The application of the pattern reduced the model size by approximately 75%, compared to modelling without the synthesis proposed by the co-intelligence artefact. These results show a significant decrease in visual and cognitive complexity, as well as confirming the reusability and adaptability of the pattern.
The construction industry suffers from high accident rates and inadequate safety management. Internet of Things (IoT) integration holds promise for improving safety. However, research has focused on adoption barriers without exploring key success factors, and many rely on a single weighting technique, which can yield method-sensitive priorities and limited actionable guidance for implementation planning and resource allocation. To address these gaps, this study proposes a robust decision-support framework for IoT integration that identifies and ranks barriers and success factors and derives consensus priorities by integrating multiple Fuzzy Analytic Hierarchy Process (FAHP) variants with a game-theoretic, metaheuristic optimization model, thus mitigating method sensitivity issues. A literature review and a survey of 22 experts from China and Hong Kong support the study using a novel two-module approach. In the first module, weight computation utilizes an improved FAHP and its extensions to evaluate the significance of IoT-related factors. The second module aggregates these weights with metaheuristic algorithms integrated into a hybrid game theory model that minimizes discrepancies among methods and yields consolidated priorities and normalized coefficients. Comparative analysis shows that the particle swarm optimization-based model achieves the most accurate weight distributions with deviations of 7.35E-02 for technological barriers, 6.81E-03 for economic barriers, and 6.72E-03 for technical and operational success factors. Other models perform best for operational, construction management, and organizational culture categories. Results indicate that incompatibility among technologies is the most critical technological barrier, while the impact on productivity due to wearable devices constitutes the most prominent economic barrier. Furthermore, regarding the success factors, enhancing customer satisfaction is the leading customer and market-driven driver, facilitating knowledge sharing among organizations is the most influential organizational and cultural enabler. These insights offer actionable guidance for industry stakeholders and demonstrate the potential of IoT technologies to enhance construction safety and overall project performance.
This study explores the entrepreneurial intentions of university students from different educational, economic, and social backgrounds by comparing four European Union (EU) countries (Italy, Austria, Sweden, Greece) to an EU-candidate country (Bosnia and Herzegovina). Data were collected through surveys on a convenience sample of 301 students. The hierarchical regression and formal statistical hypothesis testing assess and compare the role of individual factors and contextual activating factors. In doing so, the paper adopts and adapts the EPIC tool, making it suitable for cross-country comparison. The results indicate a lack of significance of the risk-taking dimension, and a striking similarity in the influence of resources as a contextual activating factor, despite the differences of the investigated countries. In addition, the results indicate the individual mindset dimensions that significantly contribute to the entrepreneurial intentions of EU students (innovation-oriented, persistence, and peculiarity), and the different predictors for students from Bosnia and Herzegovina (innovation-oriented and action-oriented). The paper contributes to the stream of research on entrepreneurial intentions in higher education by assessing the individual and contextual factors within a fine-grained cross-cultural comparison. Insights for institutions and policymakers to enhance support and resources for aspiring entrepreneurs can ultimately be derived.
As Artificial Intelligence (AI) systems and data-driven tools become integral to governmental decision-making, the ability to interpret and reason with visual information emerges as a critical competence for operating effectively in AI-mediated analytical environments. However, empirical evidence on the level of data visualization literacy within public administrations remains limited. To address this gap, the study provides a large-scale, diagnostic, and descriptive analysis of Data Visualization Literacy (DVL) performance in a real public organizational setting, using a standardized assessment instrument. A cross-sectional survey of 1,219 public employees was conducted using a bilingual Spanish–Valencian adaptation of the Mini-VLAT (12 items; 25 seconds per item), evaluating participants’ capacity to interpret, analyze, and reason with graphical representations of data. Mean performance reached 57.8% correct, with 27.1% omissions and 15.1% errors. Tasks involving proportional or relational reasoning—particularly stacked charts—produced the lowest accuracy and the highest nonresponse. Performance patterns were consistent: accuracy declined with age, improved with higher educational attainment, and varied across departments. Omissions under time pressure, rather than misinterpretation, were the predominant source of error. The findings underscore the importance of treating DVL as part of the institutional infrastructure, through periodic diagnostics, shared graphic-interoperability standards, targeted domain training under time constraints, and longitudinal monitoring to preserve epistemic control while harnessing AI’s speed and scale.
Due to increasing global political and economic uncertainties and awakening public awareness caused by drastic climate change in recent years, stakeholders are increasingly demanding sustainability and robustness in supply chains. During the pandemic, supply chains experienced multiple pressures, particularly for products in high demand such as food and medical supplies. In this context, researchers have devoted attention to sustainable supply chain management (SSCM) and supply chain risk management (SCRM). That made these two research fields have many intersections, but currently there is no literature discussing the similarities and differences between the two, and there is no definitive definition of the nexus between the two. This study makes a novel contribution by executing a structured literature review of 65 articles related to SSCM and SCRM published between 2007 and 2025, conducting descriptive and thematic analyses to explore the interface between them, developing a theoretical framework, identifying key lessons for practitioners and illuminating future research directions.
Artificial intelligence (AI) is reshaping neuromarketing by enabling the real-time analysis of neurometric, biometric, and psychometric data to optimize consumer engagement. This study investigates how AI-enhanced neuromarketing influences social media marketing strategies, using a structural equation modeling (PLS-SEM) approach to assess relationships between neuromarketing knowledge, application, activities, and social media communication. Data were collected through a survey of 416 Romanian university students and professors with practical exposure to neuromarketing tools in educational environments. The results confirm that neuromarketing knowledge significantly improves practical application (β = 0.726, p < 0.001), which in turn enhances both marketing activities (β = 0.555, p < 0.001) and social media communication effectiveness (β = 0.633, p < 0.001). AI was found to amplify these effects through predictive analytics, real-time consumer data processing, and automated content optimization. Ethical considerations—such as privacy risks and algorithmic bias—are acknowledged, and the academic sample limits generalizability to commercial contexts. Future research should explore cross-industry applications, diverse cultural settings, and longitudinal impacts to strengthen external validity.
In today’s airline industry, the shift toward dynamic pricing—where prices evolve continuously rather than being fixed in fare classes—has become more than a trend; it represents a pivotal transformation in revenue management (RM). Yet, many existing systems still rely on legacy multi-fare-class reservation frameworks and conventional business logic, leaving them ill-equipped to handle modern demands. These systems often struggle with integrating competitive pricing data, capturing nuanced passenger behavior across web and mobile platforms, and reacting swiftly to real-time market shifts. More critically, they lack the capacity for processing the vast, granular datasets essential for artificial intelligence (AI)-powered revenue strategies. To bridge this gap, we developed a rule-based dynamic pricing algorithm that not only aligns with classic bid-price principles but also leverages both real-time data and the practical expertise of revenue managers. This algorithm is sales-target-driven and designed for responsiveness in live environments. Building on this foundation, we introduced a multi-layered software architecture tailored for airlines. Additionally, we offer practical recommendations for database structuring—both logical and physical—and propose streamlined business processes to enhance responsiveness under high computational loads and complex system integrations. Notably, a major Chinese airline has implemented our system prototype, marking a significant advancement in its RM capabilities.
This research is the first to introduce a mathematical model for hybrid workforce scheduling with a focus on enhancing in-person interaction. By integrating organizational requirements and individual preferences, it provides a framework that can be tailored to different institutional needs, offering a valuable tool to managers aiming to design hybrid work schedules. A mixed-integer linear programming model is developed to optimize weekly hybrid work schedules by maximizing in-office interactions and minimizing conflicts with employees’ remote work preferences. Numerical experiments, based on randomly generated instances, explore trade-offs between interaction and satisfaction under varying parameter values, including minimum office attendance, fixed office days, and penalties for violating employee preferences. The findings provide practical insights for managers: (1) Increasing structured office attendance substantially enhances interaction but sharply decreases employee satisfaction, particularly when fixed office days remove individual choice. (2) Skewed remote work preferences further complicate scheduling, potentially increasing dissatisfaction and reducing flexibility. (3) However, measures such as moderate enforcement of in-office presence achieves a balance, maintaining relatively high interaction with limited dissatisfaction. The proposed model can serve as a practical decision-support tool, adaptable to diverse organizational contexts and policies, offering a customized approach to equitable hybrid workforce planning.
With the urgent call for sustainability and the rapid advancement of information technology, especially machine learning and artificial intelligence, the food industry faces unparalleled opportunities and challenges in optimizing resources and enhancing performance. By that call, we foresee a massive change, especially to the way research has been traditionally conducted, it is hypothesized for a surge of research and increasing adoption of innovative tools such as deep learning (DL) and optimization techniques by the research community and the field to address critical challenges in food systems. This study investigates the intersection of commercial, sustainability, and technology domains through advanced bibliometric analysis enhanced by deep document clustering, aims to uncover the multifaceted nature of these interconnected fields, addressing how emerging technologies and sustainability practices shape commercial strategies in the field, providing a panoramic perspective for reader about the dynamic development of the field. Utilizing a multi-tiered literature search strategy and employing deep document clustering techniques, the analysis reveals distinct thematic clusters—ranging from macroeconomic demand challenges to technical advancements in AI-driven agricultural practices and sustainable supply chain management—that underscore the deep connections among commercial, sustainability, and technological domains. The results not only validate the hypothesis of increasing convergence among these fields but also highlight the critical role of advanced methodologies in uncovering latent patterns and facilitating strategic and operational improvements. Moreover, the findings demonstrate that deep document clustering is a powerful tool for bibliometric analysis, offering nuanced insights that can be further refined in future studies. Overall, this research contributes to a holistic understanding of the field, emphasizing the need for continued interdisciplinary collaboration and innovation to drive sustainable development in a global business environment.
This research presents a comprehensive and pioneering systematic review of the emerging literature examining the impacts of COVID-19 on supply chain and operations management. We selected over 200 research publications from Web of Science, EBSCO, and Google Scholar databases, encompassing a broad range of peer-reviewed, high-quality journals in business and management. We classify these research articles into two principal categories: their industry focus and supply chain management principles. Notably, we find that companies within healthcare, food, online retailing, transportation, logistics, and tourism industries have been most significantly impacted by COVID-19. Sustainability, inventory management, risk management, and strategic management emerged as the most critical and frequently studied principles and practices affected by the pandemic. Finally, we identify research gaps in the current literature and suggest potential future research topics for more detailed exploration of COVID-19’s impact on supply chain and operations management. We believe our systematic review and analysis not only provide a comprehensive understanding of the most serious disruption to modern global supply chains but also suggest mitigation strategies and resilience planning for supply chain management.
