ABSTRACT. This research investigates the progress and challenges of artificial intelligence (AI) powered time series forecasting models by focusing on model accuracy and validation with an example of a gasoline price forecasting model. Since time series data is sequential, vastly different in nature, and often influenced by sentiment and government policy as opposed to these of large language models (LLMs) that are widely used for words and pixels, the main research question is: How can AI-powered time series forecasting improve the forecasting accuracy? Moreover, since time series data is less readily available in terms of public datasets as opposed to that of LLMs and since the sequential order of a time series data has to be strictly preserved, what are the unique challenges? Furthermore, since time series data is not necessarily independent and identically distributed in general, one of the major challenges is how to guarantee that the future performance will repeat in the same pattern as in the historical data regardless how well the forecasting model fits the training data.

ABSTRACT. Artificial Intelligence (AI) encompasses technologies enabling machines to perform tasks requiring human intelligence, such as speech recognition, data analysis, and reasoning. Since its inception by John McCarthy in 1955, AI has evolved significantly; with advancements in deep learning, notably the reintroduction of deep neural networks by Geoffrey Hinton in 2006, AI is being propelled into mainstream use. The recent emergence of large language models (LLMs), exemplified by OpenAI’s ChatGPT, has sparked unprecedented interest and demonstrated AI’s transformative potential.

Digital transformation, central to Industry 4.0, is reshaping the manufacturing industry's processes including supply chain management (SCM). While AI technologies like machine learning, robotics, and computer vision have been widely adopted in manufacturing under Industry 4.0, integrating LLM-based AI tools, like chatbots and agents, remains a relatively new phenomenon. LLM-based AI tools offer promising capabilities for optimizing manufacturing processes by enhancing decision-making, automating processes, and improving operational efficiency. Despite this potential, systematic research on integrating LLMs into manufacturing processes remains limited.

This study explores the capabilities, benefits, and challenges of incorporating LLM-based AI into different processes in the manufacturing industry, specifically focusing on the SCM process. Using a case study approach, the research examines an organization’s use of LLM-based AI to understand its impacts and challenges. Insights from the case study aim to provide a foundation for generalizing findings and broad application of LLM-based technologies across the manufacturing sector. This research contributes to understanding how LLMs can advance Industry 4.0 initiatives, offering practical and strategic guidance for manufacturers navigating digital transformation.

ABSTRACT. Capacity expansion is a critical decision for family businesses, requiring a complex evaluation of financial, strategic, and operational factors. Traditional decision-making processes often rely on managerial experience and intuition, which can lead to suboptimal outcomes in uncertain environments. The integration of artificial intelligence (AI) offers new possibilities for enhancing decision-making by providing data-driven insights, predictive analytics, and scenario modeling.

This study examines how AI-based decision support systems can improve capacity expansion decisions while considering key internal factors that influence the process. Specifically, it explores the role of socioemotional wealth (SEW), generational dynamics (GEN), and the heterogeneity of top management teams (HET) in shaping decision outcomes. By analyzing case studies and applying machine learning algorithms to historical decision data, the research aims to identify patterns and correlations that can support more informed and strategic decision-making.

The findings suggest that AI can complement traditional managerial decision-making by reducing biases, identifying hidden risks, and optimizing resource allocation. Moreover, incorporating internal factors into AI models ensures that the decision-making process aligns with the unique characteristics and long-term objectives of family businesses. This study contributes to the growing literature on AI applications in business strategy and provides practical insights for family enterprises seeking to leverage technology for sustainable growth.

Project No. 2023-2.1.2-KDP-2023-00017 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation fund, financed under the KDP2023 funding Scheme.

ABSTRACT. This paper examines the current state of research on Explainable Artificial Intelligence (XAI) and its potential to transform decision-making in product development, particularly within the context of human-AI collaboration. While AI-driven insights are increasingly prevalent in product creation, the "black box" nature of many AI models poses significant challenges to trust, understanding, and effective integration with human expertise.

Existing literature highlights the growing recognition of XAI's importance in various domains, but its application to the specific complexities of product development remains relatively nascent. Current research often focuses on technical aspects of XAI, such as developing new explanation methods. There is a gap in understanding how different XAI techniques can be effectively integrated into product development workflows to support human-AI partnerships. Furthermore, the literature lacks comprehensive frameworks for designing and evaluating XAI-enabled decision-making processes in this context. This paper addresses this gap by investigating the central research question: How can XAI help the transparent and trustworthy decision-making during the Human-AI collaboration in product development?

We have analyzed existing literature to identify key challenges and opportunities related to XAI in product development, including the types of explanations that best facilitate understanding and trust, the impact of XAI on decision quality and efficiency. By synthesizing current knowledge and highlighting areas for future research, this review aims to provide a foundation for developing effective XAI strategies that empower human product developers to leverage AI insights confidently and collaboratively.

ABSTRACT. Current research into Artificial Intelligence, B2B and customer service interaction highlights AI’s potential to increase efficiency in business operations . Yet, in many B2B customer service interactions, conventional AI models fail to meet the demands of complex, multi-step interactions which demand more personalized solutions . Furthermore, these tools are difficult to integrate into many information systems and prioritizes automation over augmentation – replacing rather than assisting humans. This study investigates AI-driven “calls-to-action” – real-time contextually-relevant directives agents can use to handle inquiries. This helps customer service (CS) agents streamline decision-making and reduce resolution time without conducting extensive research for complex inquiries. AI models are trained using data from past CS cases from a partner company, incorporating example actionable solutions created by CS agents. The experiments also evaluate various techniques for generating actionable solutions: zero shot learning, fine-tuning and Retrieval Augmented Generation. The model’s performance is evaluated based on resolution time, quality and agent’s productivity. Agents also evaluate AI-generated actionable solutions. Preliminary findings indicate that AI-augmented customer service “calls-to-action” can lead to faster resolution times, improved decision making and enhanced CS agent productivity. This study seeks to further highlight the potential of AI to enhance operational efficiency and client satisfaction.

ABSTRACT. Existing research alludes to various performance benefits that coopeting firms can achieve by leveraging entrepreneurial orientation, organizational structure, and knowledge management as key antecedents. However, innovation performance, particularly in dynamic and competitive environments, demands a stronger focus on technological orientation and strategic positioning. Given the increasing intensity of innovation activities, firms must effectively integrate technology-driven insights with strategic decision-making processes to sustain competitive advantage. Despite their critical role, technological orientation and strategic positioning remain largely overlooked in the coopetition literature. To address this gap, our study utilizes the dynamic capability and relational view theory to investigate the role of focal firms’ technological orientation and strategic positioning on the ambidextrous innovation within coopetition. Given that slack resources provides firms with the flexibility to experiment and adapt, they can significantly influence the firm’s performance. Therefore, our study further investigates the moderating effects of partner firm’s technological slack and financial slack on the principle relationship. To empirically test the model, we employ a panel dataset spanning from 2014 to 2023 consisting of firms operating in Western European countries. Our study makes important contributions to the innovation and coopetition literature by explaining the role of partner firms' slack resources in enhancing the benefits of strategic positioning and technological orientation for both exploration and exploitation innovation. Besides, our study provides managerial insights concerning the need to access and strategically collaborate with partners that possess excess financial and technological slack to support both risk taking and long term innovation initiatives.

ABSTRACT. Technologies like blockchain, artificial intelligence, and quantum computing are drastically changing the logistics and supply chain industry. Challenges in logistics and supply chain management (SCM), such as demand forecasting, inventory management, and vehicle routing, often include computational issues that grow exponentially with data volume—an area in which quantum computing shines. Compared to classical computing, quantum computing, acknowledged as a revolutionary technique, provides quicker and more effective solutions to challenging computational issues. The research conducted over the last decade on quantum computing applications in logistics and supply chain management is systematically reviewed in this study. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, the review analyzed peer-reviewed articles published between 2015 and 2025, sourced from the Scopus database. Using keywords such as “quantum computing,” “logistics,” and “supply chain management,” the search identified 158 records, with 61 articles meeting the inclusion criteria. Findings indicate a growing interest in quantum computing for SCM, particularly since 2020, reflecting advancements in quantum hardware like IBM’s quantum systems. Quantum algorithms, including quantum annealing, have been applied to combinatorial optimization problems, like warehouse location selection, distribution planning, supplier selection, and production planning. Quantum machine learning has also been used in predictive maintenance, demand forecasting, inventory control, and vehicle routing. Notwithstanding these developments, existing hardware constraints and computational difficulties still restrict useful applications. To further transform logistics and supply chain management (SCM) through cost savings and quality enhancements, future research should focus on improving quantum technology, creating scalable algorithms, and increasing real-world implementations.

ABSTRACT. The relationship between Industry 4.0 (I4.0) and sustainability performance has received considerable attention in literature. However, the lack of consistency and the high variability in empirical findings leave many questions unanswered for both research and practice. For instance, while I4.0 technologies are expected to enhance resource efficiency, their substantial energy demands raise concerns about the overall energy balance. To shed light on this issue, our paper examines empirical studies on the link between I4.0 technologies adoption and sustainability performance by means of a meta-analytic technique. A sample of 15,620 observations across 60 samples from 40 independent empirical studies - using a rigorous search protocol and sample construction - are analyzed. A subgroup analysis has been further undertaken using six measures of sustainability performance, namely, environmental sustainability, economic sustainability, social sustainability, circular economy, sustainable operational performance and overall sustainable performance. Preliminary results confirm a positive correlation between I4.0 and sustainable performance. This study contributes to the evolving understanding of the interplay between I4.0 technologies and sustainability by reconciling conflicting results in prior literature and developing a unified framework.

ABSTRACT. Due to the lack of direct access to the sea the Landlocked Developing Countries (LLDCs) are faced with challenges related to their geography depriving them of direct access to seaborne trade, compounded by cumbersome transit and border procedures, and inadequate transport infrastructure of the coastal transit countries. LLDCs therefore face high transport and trade transaction costs that remain a major stumbling block for their integrating into the global economy and their overall development. This study compares transport and trade costs of LLDCs with those of coastal neighbouring countries. The research involves gathering extensive relevant data along with an econometric modeling/analysis to estimate transport and trade costs. Furthermore, the study reflects on the impact analysis of the recent external shocks such as COVID-19. Understanding trade costs is essential for formulating policy interventions designed to reduce such costs in LLDCs.

ABSTRACT. Urbanisation presents both opportunities and challenges for last-mile delivery. As cities grow, the increasing demand for goods and services drives the expansion of online retail and e-commerce. Despite this, urban infrastructure development usually lags behind population increase, which causes major logistical problems like traffic congestion, inadequate road systems, and insufficient transit systems, especially in developing economies like Ghana. Organisations (logistics/delivery firms) should be able to react to certain changes in a quick and effective way in a business environment experiencing rapid transformation. Agile logistics seeks to provide high levels of customer satisfaction by reacting swiftly through carefully monitoring shifts in consumer demands and reorganising logistical procedures and structure in accordance with customer needs. This study explores the impact of urbanisation on last-mile delivery performance, focusing on the mediating role of agile logistics capability. It employs the dynamic capabilities theory, which posits that logistics agility allows firms to adapt to urban challenges, hence improving last-mile delivery performance. Also, this study makes use of the quantitative research design to collect data from delivery firms. Unlike previous studies that examine these variables in isolation, this research will provide a holistic understanding of how logistics agility can mitigate urbanisation-induced inefficiencies in last-mile delivery. The insights will be valuable for policymakers, urban planners, and logistics firms seeking to develop adaptive and technology-driven last-mile strategies in rapidly urbanising regions, particularly in developing economies like Ghana.

ABSTRACT. Improving transport efficiency in freight transport systems is essential to reduce operating costs as well as greenhouse gas emissions. Improvements in one part of the system can lead to negative consequences in other parts, making it difficult to predict the overall effects and thereby made the right decisions. In logistics these contradictions are recognized as trade-offs. The purpose of this study is to identify trade-offs in improving transport efficiency in the road freight system and to explain their underlying logic in order to mitigate negative impacts. The work is addressed through three questions: What are the main categories of trade-offs in road freight systems? How do these trade-offs affect transport efficiency? How can the negative effects of these trade-offs be minimized?

A multi-step methodology was used to map trade offs, combining examples from industry with mapping and categorisation of these through expert focus groups. The process followed five steps: Collection of trade-off examples; Discussion and synthesis of trade-offs in an expert focus group; Categorisation of trade-offs; Evaluation; Discussion in which the underlying logic behind the trade-offs was discussed.

Five primary categories of trade-offs were identified in freight systems: Capacity, Cost, Operations, Service, Packaging. The study highlights how trade-offs in different parts of the transport system affect transport efficiency and shows that these trade-offs are complex and multifaceted. The trade-offs can be described and analysed by the goals and measures that guide decisions, and by considering actors involved.

ABSTRACT. Forecasting in volatile industry environments presents significant challenges due to uncertainty, noise, and psychological biases affecting human judgment. This study examines the confidence and precision levels of expert and non-expert forecasts in the container shipping industry, a sector characterized by cyclical fluctuations and reduced market predictability. By conducting a forecasting tournament, we compare the predictive performance of experts, non-experts, and basic algorithmic models. Drawing on existing literature in decision-making and forecasting accuracy, we investigate the interplay of predictability, experience, and feedback availability as key determinants of human forecasting accuracy and the extent of overconfidence among forecasters and measure how environmental noise impacts predictive performance. The study leverages systematic and non-systematic data components derived from Container Trade Statistics (CTS) to evaluate freight rate and trade volume predictions. Participants are grouped according to expertise levels and assessed using digital surveys distributed at monthly intervals. The algorithmic model generates forecasts for the same periods and metrics to facilitate comparative analysis. The methodological approach involves decomposing time-series data into observed values, trend, seasonality, and residuals (noise) using Python-based statistical models. Predictability is quantified using the Signal-to-Noise Ratio (SNR) where the examined SNR is a critical factor contributing to altered levels of predictability. Key findings contribute to a broader academic discourse on bounded rationality and the limits of human forecasting capabilities in high-uncertainty industries. The results offer insights into whether observed trends in the highly volatile shipping environment are sufficiently repetitive and decipherable for human predictions or whether algorithmic models provide superior accuracy.

ABSTRACT. Emerging metrics from the business community and associated research suggest that an organization’s use of accelerators can lead to positive outcomes, including reduced innovation-to-market cycle time, improved supply chain collaboration, and reduced supplier-based risks and costs. This research employs a nested case study design to examine nine accelerator programs managed by three different accelerator management companies on behalf of one large organization. The resulting coding schema suggests that accelerator programs can help an organization adapt to the rapid advancement of industry innovation and help overcome the challenges to innovation adoption by reducing risk and leveraging other supplier relationships. Using an ecosystem perspective, we derive propositions around the success of a phenomenon termed “supplier cultivation,” which we define as a dynamic process where the resources provided by the focal organization and selected strategic suppliers serve as the basis to nurture startups to become trusted suppliers. The likelihood of supplier cultivation increases by casting a right-sized net in the planning phase, adaptability of the organization in restructuring the procurement process, adaptability of a startup in pivoting from original ideas, and ensuring appropriate mentoring and top-management support. We propose supplier cultivation as an additional method to improve innovation adoption in large organizations.

ABSTRACT. This study examines the impact of a firm's customer internationalization on its operating performance, as well as the moderating role of government support in this relationship. The findings reveal an inverted U-shaped relationship between customer internationalization and operating performance, indicating that while international expansion can enhance performance initially, excessive expansion may lead to declining efficiency. Additionally, the study highlights the positive moderating effect of government support in this nonlinear relationship, suggesting that firms can gain advantages and mitigate transaction costs. Empirically, the results emphasize the importance of strategically aligning customer internationalization with firm operations to sustain performance improvements. Furthermore, the study offers practical insights for managers on effectively navigating the customer internationalization process and utilizing government support to address potential challenges.

ABSTRACT. The integration of Artificial Intelligence (AI) in business decision-making processes presents both opportunities and challenges, particularly concerning trust. AI represents a new generation of technologies capable of interacting with the environment and simulating human intelligence. The adoption rate of AI systems for decision-making is unprecedented, often likened to the transformative impact of the internet. This shift of agency and control from humans to technology can fundamentally alters our understanding of business decisions. While AI reshapes business decision-making, the precise nature of these changes is still unfolding, necessitating more studies. In the interorganizational context, such as supply chain decisions, trust has been a core element of business relationships, enabling them and reducing opportunism. Trust also plays a pivotal role in the adoption and success of disruptive technologies. For instance, blockchain or IoT technology requires a network of organizations that trust each other. However, in environments with high existing interorganizational trust, entities often fail to see the relevance of these technologies. As new technologies are introduced, new layers of trust in the technology replace existing interpersonal and interorganizational trust. Similarly, the trust users develop in AI technology will determine the role of trust in organizations moving forward. This study aims to understand the role of trust and its paradigm shift with AI in interorganizational decision-making. To do so, we conduct an interview study with organizational decision-makers across various industries and contexts, systematically combining this with existing knowledge of trust in organizational contexts to generate new insights.

ABSTRACT. Efficient coordination of production and distribution is essential in modern supply chains. The Two-Echelon Production Routing Problem (2E-PRP) addresses this challenge by optimizing the flow of goods across interconnected production facilities and distribution centers. Our research presents a novel variant of the 2E-PRP inspired by a real-world manufacturing industry that handles customized products. It adheres to a two-echelon vendor-managed inventory policy, integrating decisions related to production, inventory, and transportation. In the first echelon, a production facility supplies customers and warehouses through direct shipments. In the second echelon, warehouses function as distribution hubs, serving additional customers via vehicle routing. Effective production planning, inventory management, and routing are crucial for timely delivery, customer satisfaction, and cost-effectiveness. To address this challenge, we propose a mixed-integer linear programming (MILP) model along with a simulated annealing algorithm that incorporates path relinking. We compare our meta-heuristic against MILP solutions obtained from a commercial solver, focusing on small problem instances. For medium and large instances, we extend a two-phase iterative heuristic based on the classic production routing problem introduced by Absi et al. (2015). Through numerical experiments using benchmark data, we validate our approach, demonstrating that simulated annealing with path relinking produces high-quality solutions. We also investigate two scenarios: (1) multiple warehouses serving shared customers and (2) each warehouse catering to a distinct set of customers. This comparison offers valuable insights for decision-makers looking to optimize production routing strategies.

ABSTRACT. In today's uncertain world, social, economic, and environmental sustainability challenges make resilience a priority for industries, necessitating better decision-making supported by appropriate indicators (Dizdaroglu et al., 2017).

While economic and environmental factors are well-studied (Luthin et al., 2024), social sustainability remains underexplored yet (Jabbarzadeh et al., 2018). Industrial activities create challenges such as employment disparities, gender gaps, and skills shortages, which impact supply chain adaptability during crises (Atanda et al., 2019). Measuring these issues is vital for risk management and policy development (Silva et al., 2023). This research develops a framework for monitoring social sustainability in industry. A literature review identified indicators across four areas:

1.Gender Gap shows employment rates, wages, job security, and senior role access disparities.  2.Employment by Skill analyses mismatches between education and jobs, connecting economic activity with qualifications.  3.Digital Skill Gap highlights the need for skilled IT workers in a tech-driven economy.  4.Safety at Work tracks incidents to evaluate job risks and global workplace safety standards. 

Data from 27 EU countries were obtained from Eurostat and normalized (OECD/EC-JRC, 2008). Aggregating indicators related to workforce social dimensions (Badri Ahmadi et al., 2017; Kumar and Garg, 2017; Narimissa et al., 2020) enables the monitoring of social sustainability over time and across countries, highlighting disparities that necessitate policy intervention. Radar charts enhance comparative analysis and reveal insights, for instance, by highlighting performance gaps in social resilience among different countries.

This framework tracks sustainability trends and guides data-driven policy resilience.

ABSTRACT. Social and environmental sustainability in manufacturing companies has become a meaningful concern. Accordingly, public governments all over the world are implementing industrial policies to encourage or impose responsible corporate behavior. In this context, several research studies have focused on policy-related barriers to industrial sustainability that could undermine their effectiveness. However, open questions remain regarding the specific obstacles and key success factors associated with different typologies of policy instruments and their comparative assessment. We fill this research gap, by exploring the issue of policy design in the case study of the Autonomous Province of Bozen-Bolzano in Italy, where the public government has shown a strong orientation towards sustainability. Data is collected through interviews with representatives of both the public administration and companies. The interviewees are asked for their opinions on policy-related factors that might hinder, slow down or facilitate the implementation of sustainability practices by companies and sustainability measures by the public government. The preliminary results show that while certain obstacles are common to all types of instruments (e.g., data measurement and evaluation), others are particularly relevant to a subset (e.g., economic constraints). Concurrently, some critical success factors are observed. These usually depend on the restrictiveness of the instrument (e.g., simplification of administrative procedures is suggested for command-and-control and market-based instruments). The findings aim to provide a theoretical contribution to the design of industrial sustainability policies and recommendations for effective policymaking.

ABSTRACT. Background: The proliferation of online illicit drug markets has facilitated anonymous transactions and expanded access to a global consumer base. This shift leverages international postal and courier systems, whose detection capacity is limited due to the exponential growth in parcel volumes. Yet, scholarly inquiry into the nexus between illicit drug trafficking and postal logistics remains limited. This study addresses this gap by developing a model designed to estimate the volume of undetected illicit drug flows moving cross-border via postal parcels per year. The ultimate goal is to help enhance detection capacity tailored to this specific logistic context. Methods: An estimation model was constructed to assess the annual illicit drug flows crossing the United Kingdom, a prominent hub for online-originated drug smuggling. Employing Aziani’s approach {Aziani, 2018 #96}, in conjunction with a comprehensive review of empirical literature about online drug purchases, we estimated the proportion of illicit substances transported through postal services. Results: The findings indicate that the estimated net flow of illicit drugs entering the UK via parcel post substantially exceeds officially reported seizure rates. This discrepancy suggests a lack of adequate monitoring and/or detection capacity that weighs on society and on the integrity of legitimate supply chains. Conclusion: This research introduces an operational model for quantifying the prevalence of drug trafficking within specific cross-border logistics sectors. Such estimations provide critical insights for enabling adaptive, evidence-based interventions and targeted policy responses to address evolving threats due to the increasing digitalization of illicit trade.

ABSTRACT. The transition to net-zero emissions in transport requires a coordinated decision-making approach that accounts for the interconnections between different transport modes, energy infrastructure, and stakeholder priorities. East Midlands Airport (EMA), the UK’s busiest pure cargo airport and a major passenger hub, provides a unique case study due to its multimodal transport ecosystem, which integrates freight, passenger mobility, and energy infrastructure. Existing decarbonization efforts often rely on isolated technological solutions, overlooking the system-wide trade-offs, synergies, and infrastructure interdependencies that influence long-term feasibility. Addressing these challenges requires structured analytical tools to enhance the understanding of stakeholder incentives, policy interventions, and cross-sector interactions. This study applies fuzzy cognitive mapping (FCM), stakeholder interviews, and focus groups to examine the key enablers and barriers of the net-zero transition within the EMA ecosystem. Findings highlight government policies and regulations, infrastructure capacity, technology adoption, and stakeholder engagement as critical elements shaping decision-making processes. The study underscores how these factors interact to either accelerate or constrain decarbonization efforts. Understanding these systemic relationships provides a structured framework for evaluating complex, multi-stakeholder challenges and informing strategic decision-making. By adopting a systems-thinking approach, this research emphasizes the need for integrated and inclusive decision frameworks that support infrastructure investment, policy coordination, and stakeholder alignment. The insights contribute to evidence-based decision-making in multimodal transport hubs, offering scalable strategies for accelerating net-zero transitions in similar ecosystems.

ABSTRACT. The transition to green shipping has predominantly focused on technological innovations to support the adoption of alternative fuels. However, there is limited research on the future competition for these scarce alternative fuels between the shipping sector and other industries. Additionally, the dynamics between shipping companies and fuel providers in adopting renewable fuels remain underexplored. In this study, we address these gaps through the lens of factor market rivalry (FMR), analyzing how competition for scarce resources shapes strategic decisions in the maritime sector. By investigating collaborative strategies between shipping companies and fuel providers, we explore pathways to facilitate decarbonization and overcome challenges related to fuel scarcity. Drawing on data collected from key stakeholders, we evaluate how shipping companies can reduce emissions, secure alternative fuels, and gain competitive advantages in a rapidly decarbonizing industry. This study applies the FMR framework to maritime green hydrogen adoption, revealing how resource scarcity influences competition and collaboration in decarbonization. It offers policy recommendations to promote investment, equitable access, and regulatory measures. By addressing strategic industry responses, the research guides stakeholders in ensuring a sustainable, low-carbon shipping future.

ABSTRACT. Electrification of heavy-duty freight is essential for reducing emissions. As logistics service providers have begun to electrify their fleets and move towards large-scale roll-out, there is an increasing need to understand how electrification scale affects cost and operations before any investment decisions. Previous studies have shown that external factors such as electricity prices and battery technology have a significant non-linear impact on total costs as well as operational performance and limits. Moreover, transition towards electrification may require a deployment path with different fleet mixtures of electric and internal combustion engine trucks, with which the influential factors and the corresponding costs, operational performance can significantly differ. To address this, our study introduces a simulation-based analytical framework to examine how changing electrification rates impact fleet-level cost effectiveness and operations for a given network under varying conditions. Our study integrates advanced Electric Vehicle Routing Problem (EVRP) optimization with comprehensive cost modeling to simulate operational dynamics such as routing, charging, and scheduling with different levels of electric vehicle deployment and extraneous uncertain factors. Our framework generates fleet-level performance, and correspondingly an electrification scaling curve that captures non-linear behaviors and potential economies of scale in phased transitions. The study offers a data-driven decision-support tool for decision-makers evaluating incremental investments in electrification, thus enabling logistics service providers to move beyond smaller scale pilot projects into larger scale and more cost-efficient fleet electrification.

ABSTRACT. Green hydrogen and its derivatives play a crucial role in the transition to decarbonized energy systems, with ports emerging as key enablers of hydrogen production and distribution. Leveraging their existing infrastructure and strategic position at the intersection of shipping and energy sectors, ports have the potential to function as hydrogen hubs. However, the specific conditions and mechanisms through which they contribute to the clean energy transition remain understudied. In this study, we investigate ports’ roles within the hydrogen value chain through thematic analysis and stakeholder interviews. We develop a conceptual framework outlining the functions ports can assume in the hydrogen value chain and identify key stakeholders and criteria relevant to hydrogen hub site selection. Building on these insights, we propose a multi-actor, multi-criteria decision-making model that captures diverse stakeholder perspectives. Our findings reveal that ports can serve four distinct roles in the hydrogen value chain: industrial hubs, value-added logistics centers, transport nodes, and bunkering stations. Each of these roles presents unique strategic and operational challenges, which in turn influence the criteria for site selection and the stakeholders involved in the decision-making process. Our research contributes to port development and hydrogen hub planning by identifying critical factors for establishing green hydrogen hubs. We provide strategic guidance for planning and policy development, highlighting the need to balance public and commercial interests through joint decision-making between public and private actors.

ABSTRACT. The world has been moving towards renewable sources of electricity. However, while the share of renewable energy in the installed capacity has increased globally, the same has not translated into actual power generation primarily due to their intermittency, variability and non-dispatchability. In this work, we assess the suitability of the Time of Use (TOU) pricing in increasing the share of renewable energy in the actual electricity consumption. While existing literature on TOU pricing aims to flatten the demand curve to reduce the peak demand, our work uses TOU pricing to align demand with the availability of intermittent renewable energy supply, thus increasing its utilization. For this, we identify factors under which TOU pricing is better than the fixed tariff pricing from a consumer savings viewpoint. Our analysis reveals that high price elasticity, high variability of demand, high inflexibility of non-renewable energy sources, and increase in renewable energy availability independent of demand increase the attractiveness of TOU pricing. In many parts of the world, increased share of intermittent renewable energy in the total grid capacity has witnessed lower financial viability due to reduction of power plant surplus and higher underutilization. To mitigate this, we propose a guaranteed wholesale price contract that the distributor could offer to the suppliers. Our analysis shows that TOU pricing in conjunction with such contracts can improve both renewable energy capacity utilization and financial viability of the power plants.

ABSTRACT. This study contributes to develop digital transformation capability in supply chain toward sustainability structure by using data-driven approach, since prior studies have focused on establishing metrics for digital transformation capabilities, while inadequate emphasis have been given on developing these capabilities for sustainability within the dynamic preferences. In addition, the attributes inherent in digital transformation capabilities can only be represented through qualitative information; thus, triangular fuzzy numbers are needed to be employed to express this uncertain information. In addressing these deficiencies, this study integrates a dynamic capabilities view with mimetic capabilities and utilizes a data-driven approach that incorporates content and bibliometric analyses, the entropy weighted method, the fuzzy Delphi method, exploratory factor analysis, reliability test, and fuzzy evaluation-decision-making trial and evaluation laboratory to determine the valid digital transformation capability in supply chain attributes toward sustainability. The findings indicate that digital mimetic and collaborative innovation capability, together with digital network capability from the perspective of mimetic and coordinating capabilities, constitute key capabilities that must be emphasized to improve sustainability. In practices, public-private partnership, technology benchmarking and transfer, network interconnectivity, ethical technology implementation and supply chain eco-design must be prioritized.

ABSTRACT. Through interviews with individuals who are experts in digital twin supply chain fields in Sweden and their subsequent discoveries, this research paper shed light on the use of digital twin technology for supply chain management and decision analysis. The study investigates the factors that drive and limit the implementation of digital twin systems in logistics operations and supply chain management, as well as the challenges encountered along the way. The key factors behind the increasing use of digital twins include improving operations efficiency and integrating real-time data for sustainability purposes, as highlighted in a research study outcome; however, major barriers arise due to the high costs of implementation and complexities in technology, along with concerns over data security issues raised by the same research report findings highlighting practical obstacles, such as linking digital twins with existing systems. this study highlights the significant potential of digital twin technology while also addressing the challenges that must be overcome for broad adoption in the supply chain sector. This study, importantly, expands our understanding of how digital twin technology is used in industries by examining expert perspectives. In general, the findings are useful for those from academia and industry trying to understand the challenges of implementing technological innovations in supply chains.

ABSTRACT. Industrial metaverse (IM) represents a transformative integration of digital twins, the Internet of Things (IoT), artificial intelligence (AI), with physical manufacturing operations to create immersive, interconnected industrial ecosystems. In an era of intensifying global competition, IM offers manufacturers a strategic window by enabling real-time process simulation, predictive maintenance, and immersive remote collaboration, thereby enhancing productivity, operational resilience, and sustainability. However, while the potential of IM is widely recognized, its operationalization poses complex challenges across technological, organizational, and strategic dimensions. This paper examines practical pathways for transitioning IM from a futuristic vision to an actionable reality within contemporary industrial ecosystems. Drawing on case studies from two pioneering industrial companies and a university-based smart production lab, the research synthesizes insights from in situ demonstrators, a bi-monthly expert panel comprising industry, technology providers, and academic representatives, and sandbox experiments. Three critical pathways to IM implementation are identified: (1) technological integration and infrastructure readiness, emphasizing the adoption of real-time simulation and decentralized data architectures; (2) organizational transformation, involving new governance structures, workforce reskilling, and agile operational models to support metaverse-enabled workflows; and (3) strategic value realization through demonstrable business outcomes such as improved return on investment, enhanced productivity, and optimized resource usage. By integrating empirical experiences with theoretical frameworks, the study offers a structured roadmap for IM adoption, advancing discourse in management engineering and providing actionable insights for modern manufacturing environments. The findings underscore the importance of coordinated technology deployment and adaptive organizational strategies in harnessing potential of industrial metaverse.

ABSTRACT. With the development of blockchain technology, firms have got the opportunity to provide consumers with detailed and trustful information that enhances their trust in the products’ quality and, consequently, increases their willingness to buy (Choi, 2019; Zhang et al., 2022). However, using blockchain comes at a cost. From the manufacturer’s perspective, there is the cost of operating the technology, which is composed of a fixed cost and a per product unit marginal cost. As for consumers, using this technology often gives rise to privacy concerns, as blockchain platforms usually require participants to give personal data by registering their digital identity prior to logging in the platform and using its different functionalities (Pun et al., 2021; Zhang et al., 2022). In this study, we consider the case of a manufacturer selling two vertically differentiated products. Assuming a multiplicative positive impact of using blockchain on the consumer’s valuation of the product, we build a stylized model based on consumer’s utility and self-selection to study the impact of using blockchain on the conditions of co-existence of the two products on the market, the products’ optimal prices, their respective market shares, the manufacturer’s profit and his differentiation strategy.

Preliminary results show that the two products will continue to coexist while blockchain is being used for the higher quality one only if the loss of consumer utility due to privacy concerns is within a certain interval. The results also demonstrate a cannibalization effect of blockchain under specific conditions.

ABSTRACT. The growth of e-commerce is motivating the development of innovative solutions for efficient and sustainable supply chains in the B2C parcel delivery market. Particularly, the focus is on the last-mile logistics as the crucial phase. In this context, self-collection is a recent but consolidated delivery strategy, allowing customers to autonomously collect parcels from dedicated facilities, such as pick-up points. Reduced reliance on home delivery allows for the consolidation of parcels at these points, likely decreasing the number of operators need to visit; therefore, it is expected to lower their daily workload and generate cost savings. Accordingly, (i) the reorganization of delivery operations based on proposed pick-up point location scenarios and (ii) the quantification of the associated economic impacts emerge as two relevant issues. This paper addresses these challenges by proposing a novel districting approach to group urban areas into delivery districts, each assigned to a specific operator. The approach ensures that the workload of each district remains within predefined limits, ensuring timely deliveries. A key feature of the approach is the incorporation of points’ visit probabilities, thereby accounting for the uncertainty associated with customers' preferred delivery options. The approach is applied to the case of Poste Italiane in the city of Bologna, using detailed customer data and historical delivery volumes. The results yield robust planning solutions, demonstrating the operational, economic and environmental benefits of integrating self-collection into last-mile delivery while providing actionable insights for enhanced decision-making.

ABSTRACT. Vessel platooning is an emerging strategy in maritime transport that promises enhanced security and operational efficiency by coordinating vessels into convoys. Despite growing conceptual interest, its formal optimization modeling remains underexplored, especially in terms of synchronized departure timing, platoon formation, and risk-sharing mechanics. This study establishes a pioneering Mixed-Integer Linear Programming (MILP) framework to model the operational feasibility of vessel platooning. The model explicitly incorporates key constraints such as vessel-specific departure times, speed synchronization, platoon size limits, and security risk cost-sharing, enabling coordinated routing without allowing solo travel for follower vessels. By optimizing leader assignment and convoy scheduling, the model demonstrates cost reductions of 13–17% compared to uncoordinated travel, driven by shared security investments and minimized risk exposure. This work lays the first formal MILP foundation for vessel platooning logistics, bridging a major gap in the literature by addressing both time-sensitive coordination and scalable fleet planning under risk-aware constraints.

ABSTRACT. Over the past 25years, despite a general trend towards growth, international trade had significant fluctuations, reflecting the impact of a variety of factors both specific and external to the maritime industry, which affect both global supply and demand. The goal of this study is to delve into the factors that generate such volatility and to examine how logistics and trade, being variables dependent on the general context in which economic growth takes place, exhibit oscillations correlated with global crises and economic shocks. Throughout the surveyed period, multiple disruptive events have altered the progress of the economy and supply chains. Such events are categorized as financial and health crises, armed and unarmed conflicts, and extreme natural and technological events, that have affected the functioning of the logistics. During the past few years, the frequency and intensity of these events have increased, creating a more uncertain and complex environment for the containerised trade. This study seeks to provide elements of analysis for a deeper understanding of the interaction between crises and logistics, to anticipate future disruptions, and to prepare preventive strategies. To this end, it is crucial to identify and make sense of cyclical behaviours to mitigate the adverse effects of upcoming global shocks. The research applies econometric tools to analyse the cyclical, seasonal, and logistical impacts of recent disruptive shocks. The goal is to isolate the effect of events, controlling for other explanatory factors, and to estimate elasticities that reflect the magnitude of the shock-induced changes on the variables of interest.

ABSTRACT. This research investigates the adoption of Artificial Intelligence (AI), blockchain, and drones in the Pharmaceutical Supply Chain (PSC), focusing on the driving role of Goods Suppliers (GSs), Service Providers (SPs), Technology Providers (TPs), and customers. A mixed-methods approach was employed, including a survey of 74 professionals and 12 semi-structured interviews. The results show higher adoption of AI (36% of respondents) compared to drones (18%) and blockchain (12%). Key AI applications include managing shipment deviations, selecting isothermal packaging, and reverse logistics. Blockchain is mainly used for counterfeiting prevention, supply chain redistribution, and data security, while drones are utilized for delivering to hard-to-reach areas and small parcel deliveries. TPs play a central role in driving digital technology (DT) adoption, particularly by creating synergies with providers of complementary products and services. AI adoption is also customer-driven, as pharmaceutical companies demand proof of packaging solutions, prompting packaging providers to use AI. SPs act as intermediaries between packaging providers and pharmaceutical companies. Additionally, multi-stakeholder organizations are crucial in fostering DT adoption by aligning priorities between regulatory decision-makers and PSC companies and facilitating the transition of DT from a technological concept to a practical application. While AI’s full potential—particularly in generative AI and decision-making—remains underexplored, blockchain faces challenges due to decentralized governance, and drone adoption is expected to grow with evolving regulations. Overall, the findings contribute to understanding the current landscape of DT adoption in the PSC and provide insights into overcoming barriers to accelerate the integration and standardization of AI, blockchain, and drones.

ABSTRACT. This paper scrutinizes the prevailing methods of agile project management (APM) in the industry. A research model is developed through theoretical justifications and tested through survey data from project management professionals using structural equation modeling. This study contributes to moving APM to a theory-oriented approach from practice-oriented approach while establishing the measurements and validating the research model. It helps establish the maturity of APM as an effective organizational initiative to pursue organizational excellence and transformation. Building upon the existing and well-validated theories, this study allows us to advance APM by identifying critical constructs, validating measurements, and testing theoretical frameworks. In addition, a holistic investigation of APM with theoretical foundations facilitates the diffusion of its principles among industry practitioners who are already equipped with the knowledge set and its knowledge dissemination for apprentices.

ABSTRACT. An important objective of most organizations is to improve quality performance, and typically many metrics are used to evaluate and track progress toward quality goals. Yet, overemphasis on certain types of metrics and goals can lead to suboptimal, even undesirable, results. To explore the tensions between goals, metrics and performance, this research reviews the philosophy and literature of quality management and organizational performance, as well as illustrative case scenarios. From this, relevant concepts and frameworks are identified and countermeasures are discussed.

ABSTRACT. Organizations often fail to adhere to good quality practices because of internal/external (environmental) pressures to address short-term internal targets/goals that might be felt necessary in a competitive environment. In such circumstances, quality practices could present organizational and individual conflicts. It is conceivable that under such conflicting situations some quality practices may be de-emphasized by workers to fulfill alternative priorities that might be presented by higher authorities. Especially, when workers perceive a threat to their employment. This study engages diverse theory and empirical data to examine the impact of worker supportive organizational culture in helping workers resist and cope with dysfunctional demands by superiors. The data suggests that quality practices are not de-emphasized by workers when there are pressures to do so (in the name of business exigencies) when these practices are followed as a part of a quality oriented socio-technical system consisting of open communication by top management, worker autonomy, worker supportive organizational culture and quality enriching reward mechanisms. In the presence of open communication, worker supportive organizational culture appear to be the most useful condition compared to the others. The study expands the quality literature by clarifying how superior induced job security/safety concerns could impact worker quality practices and performance outcomes in the absence (and presence) of worker supportive organizational conditions.

ABSTRACT. This paper presents a systematic approach for developing new services and service processes by integrating the voice of the customer (VOC) using SERVQUAL and Quality Function Deployment. By quantifying the VOC and prioritizing customer demands, companies can identify significant factors influencing service quality and operationalize these prioritized factors into service improvement strategies. With a focus on the restaurant industry, the study uses pre- and post-service surveys to classify service winners and service qualifiers. This approach provides service providers the agility to adapt to changing customer demands, shorter service life cycles, and increasing competitive pressures, including rapid technological advancements and the drive for sustainable practices.

The need for effective new product development (NPD) and new service development (NSD) strategies is gaining greater attention as they are necessary for addressing ever-changing customer demands, shorter product and service life cycles, and growing competitive pressures driven by digital innovation and the integration of sustainable practices. NPD, rooted in the manufacturing sector, centers on designing and successfully launching new tangible products (Schilling & Hill, 1998; Ulrich & Eppinger, 2016). Whereas, NSD focuses on creating and enhancing services, with a primary goal of improving customer experiences (Fitzsimmons & Fitzsimmons, 2006). NSD has become increasingly vital for service organizations to differentiate themselves and deliver value through innovative service solutions, despite the inherent challenges in quantifying customer experiences and expectations

ABSTRACT. Firms are increasingly adopting cloud computing, not the least in the form of 'software as a service' (SaaS). While SaaS can reduce firms' information technology (IT) expenses, it also implies more limited software customization options. Another approach to utilizing external IT expertise is through outsourcing IT operations (e.g., hardware and software updates). While this strategy allows firms to focus on their core competencies, it involves risks related to a lack of control and not developing internal IT expertise. The consequences of such strategies for manufacturing firms are, however, not clear. To add to this knowledge, this study develops a model in which it is hypothesized that the relationship between digital strategy and performance (innovation, market, and financial) is sequentially mediated through (1) in-house IT operations (IITO) and digital dynamic capabilities (DDC) and (2) in-house IT architecture (IITA) and DDC. The proposed model is investigated through a questionnaire survey of Danish manufacturing firms (n=271) carried out in 2024-2025. Although the first sequential mediation hypothesis is not supported, in line with expectations, results show that IITO has a significant and positive effect on performance when mediated by DCC. Furthermore, the results support the hypothesis that the relationship between digital strategy and performance is sequentially mediated through IITA and DCC. However, in contrast to expectations, the relationships between digital strategy and IITA and between ITTA and DCC are negative. The surprising results are further analyzed and discussed to provide explanations.

ABSTRACT. Pandemics, such as the COVID-19 pandemic, or extreme weather events have clearly highlighted the importance of supply chain resilience and steered research in new directions. Wieland and Durach (2021) presented two different perspectives on resilience: a preventive perspective that focuses on avoiding disruptions and a reactive perspective that emphasizes the ability to recover from disruptions. Paul and Chowdhury (2021) and Dev et al. (2021) emphasize that the ability of logistics systems to adapt and recover quickly must become a strategic necessity for organizations worldwide, given the accumulation of disruptions and their increasingly far-reaching consequences. It became elementary for companies that established and proven collaboration strategies with suppliers such as Vendor Managed Inventory (VMI) or consignment warehousing were revised and supplemented by additional resilience measures in order to maintain the possibility of escalation or taking proactive countermeasures.

Based on the results of a finished project, this paper aims to present its key qualitative findings by examining and assigning various resilience measures to collaboration strategies to strengthen the ability of a company to proactively build supply chain resilience. In addition, decision criteria for the combination of collaboration strategies and resilience measures shall be presented and a decision model how to select the best suited resilience measure depending on the anticipated supply chain risks. The findings were derived from a mixed method approach comprising systematic literature reviews on how to set up resilient collaboration strategies with suppliers and the analysis of company specific criteria when which collaboration strategy is preferably used.

ABSTRACT. The literature on entrepreneurial firms is extensive but lacks current, comprehensive analysis of primary data on the numerous factors that make the difference between aspiring to and attaining entrepreneurial success (financial self-sufficiency). This study analyzes primary data from a statistically significant sample of firms formed and surviving over seven years. It identifies key discriminators between aspiration and success using Partial Least Squares Structural Equation Modeling (PLS-SEM) with higher-order constructs. The study examines the roles of socio-demographic factors, human capital, and internal and external firm factors, using a two-stage approach for model validation. The findings highlight the complex interplay of these elements, providing valuable insights for policymakers and entrepreneurial support organizations. Additionally, the study suggests areas for future research, including contextual variations, longitudinal analysis, and additional moderators.

ABSTRACT. Effective decision-making in healthcare requires reliable and efficient diagnostic tools, especially for conditions like heart failure, which remains a major global health challenge. This study presents an AI-driven decision support model using SWIN U-Net, a hybrid deep learning architecture that combines convolutional neural networks (CNNs) with transformer models to enhance left ventricle segmentation and ejection fraction (EF) estimation—two critical indicators for diagnosing and managing heart failure.

Using the EchoNet-Dynamic dataset, which includes diverse patient demographics and imaging conditions, this model ensures broad applicability and clinical relevance. Unlike traditional AI models that analyze sequential temporal data, SWIN U-Net processes echocardiographic videos as independent still frames, significantly improving computational efficiency while maintaining high segmentation accuracy (97.55%). This approach outperforms conventional CNN-based architectures and achieves near-human performance levels in cardiac imaging analysis.

From a decision-making perspective, this research addresses key challenges in scalability, efficiency, and resource optimization in medical imaging. The computationally efficient preprocessing pipeline allows for seamless integration into clinical workflows, reducing decision latency while maintaining diagnostic accuracy. Furthermore, by bridging AI techniques from large language models (LLMs) with medical imaging, this study highlights new directions for AI-driven decision-making in healthcare.

This research lays a foundation for future advancements in AI-powered cardiac care, particularly in resource-constrained environments, where optimizing diagnostic speed, accuracy, and accessibility is crucial. The findings emphasize how hybrid AI architectures can drive better clinical decisions, ultimately improving patient outcomes and healthcare delivery.

ABSTRACT. The sustainability transition can be perceived as a risk to be managed or as an opportunity for innovation and growth, often presenting a daunting challenge that many still struggle to meet. In practice, the radical change demanded across the organization for a true sustainable transition often poses substantial difficulties for the incumbent firm, which can lead to slow or even abandoned innovation and transition strategies. This situation can be likened to the general challenges addressed in innovation management research regarding radical or breakthrough innovation, with reasons for slow or abandoned initiatives cited not only as path dependency, competence traps and organizational inertia but also one of attention and differences with prevailing logics. It is this cognitive path we explore, answering calls for further research into the interrelationship of structure, action, and embedded agency, and the factors affecting decision making, in situations of sustainability transitions and sustainability-related innovation. The meta-theory of institutional logics provides a powerful lens for analysing the relationship between structure and actions, and it allows for multiple levels of analysis and a systems approach with micro-meso-macro layers, integrating agency and structure. Using a qualitative case study approach, we analysed the introduction of innovation and sustainability logics to the existing inter-institutional field of two long-established technology-driven industry incumbents. This research project sheds light on the role of logics in sense-making and decision-making and how different categorical elements of their microfoundations are used or not in the reconciliation of competing logics in situated decision-making.

ABSTRACT. Supplier sustainability risk (SSR) refers to the detrimental effects on buying firms when news of their supplier’s sustainability misconduct becomes public. Prior studies have documented how SSR harms buying firms; yet, research on its underlying drivers remains scarce. Based on transaction cost theory, this study fills this gap by examining the structural root cause of SSR. Specifically, we study how supply base (horizontal and vertical) complexity is associated with SSR occurrence and how organizational learning and administrative proximity attenuate the adverse effects. Our sample includes 416 firm-year observations from 163 SSR events during 2015-2018, together with over 18000 buyer-supplier relationships data from Bloomberg SPLC. By leveraging the data, we conduct panel Poisson regression analysis using the two-stage control function approach. As a result, we find that horizontal complexity increases SSR while vertical complexity does not. Contrary to our expectations, organizational learning amplifies horizontal complexity-SSR. However, it attenuates the occurrence of SSR caused by vertical complexity. Our results also show that the relationship between horizontal complexity and SSR occurrence is mitigated by administrative proximity, but this is not the case for vertical complexity. The findings contribute to the literature by uncovering the negative effects of supply base complexity on the occurrence of SSR. Also, the study reveals how organizational learning and administrative proximity reduce those negative effects, aiding sourcing managers in SSR mitigation.

ABSTRACT. The online second-hand market is rapidly growing, with global resale apparel expected to reach $350 billion by 2028. This market benefits consumers with lower prices, helps sellers resell their used items and earn money, and reduces negative environmental impact. However, consumer uncertainty about product quality remains a challenge. This stems from the inability to physically inspect items, the risk of inaccurate descriptions from individual sellers, and the common absence of return policies. To address this, platforms offer optional authentication services. Sellers or consumers can opt for verification, ensuring product conditions match descriptions. The key questions that arise are: Why and when does a seller purchase the authentication service? When do consumers purchase the product and this service? What is the optimal authentication fee to maximize the platform's profit? This study finds that a high-quality seller signals their type by purchasing authentication at an intermediate fee. When the fee is low, both seller types use it, but a high-quality seller sets a lower price. If the fee is too high, neither uses it, making them indistinguishable to consumers. Consumers are more likely to opt for authentication when uncertainty about product quality or authentication outcomes is high. Our study suggests authentication services reduce uncertainty about product quality, making platforms more appealing by adjusting fees and accuracy. A high-quality seller can signal his quality through authentication, increasing sales and profits. Additionally, platform managers can leverage this by competitively pricing authentication, enhancing accuracy, and using it to attract quality sellers, boosting transactions and profitability.

ABSTRACT. Procurement capabilities are crucial to enable the procurement organization’s ability to adapt and sustain performance in the face of changing external conditions. Recent research has shown that external trends affect the capabilities required at the procurement organization (Bals et al., 2019; Delke et al., 2023). Furthermore, industry-specific trends can influence the procurement capabilities needed in an industry and for specific purchased products. For example, the increasing use for electronic components made the automotive industry more reliant on suppliers of electronic and semiconductor component (SEC) suppliers. In the future, automakers’ procurement capabilities must match an evolving SEC supply network. However, uncertainties regarding technological development, legal regulations, and the value proposition of suppliers in the SEC supply network pose a challenge as to what capabilities procurement will need to support the industry’s further automation, connectivity and electrification. Therefore, this study aims at exploring how external forces are expected to influence automakers’ procurement of SEC and what procurement capabilities the automakers will need to address the challenges and opportunities posed by the external environment. We adopt the frame of the next 15 years (2025-2040), in which external trends are expected to further influence the procurement of these components. We adopt a Delphi study with experts from different tiers of the SEC supply network, including automakers, Tier 1 supplier and semiconductors supplier. The expected results of the study are a list of external forces that are likely to impact the supply network and the corresponding procurement capabilities needed to address these external forces.

ABSTRACT. The study examines the potential of Latin American countries to reposition themselves within global supply chains under the concepts of near-shoring and friend-shoring. As geopolitical, economic, and technological developments (re)establish hegemonic powers and ties, companies are driven to rethink sourcing strategies to secure competitiveness and resilience in volatile environments, leading to a reorganization of global supply chains. Such dynamics challenge countries to (re-)position themselves within supply chains, presenting opportunities and risks for Latin America. Drawing on global value chain theory and economic geography frameworks, the analysis explores how proximity to key markets, favourable trade agreements, and resource endowments may enhance Latin America’s comparative advantage. However, structural challenges such as inadequate infrastructure, political instability, lack of technology, and skills mismatches pose significant barriers to fully capitalize the near-shoring trends. Furthermore, the study also addresses how friend-shoring, driven by ideological and security alliances, may offer selective benefits, favouring countries with stronger institutional ties to the U.S., China, G7 or BRICS+. Through a critical review of recent policy initiatives, investment flows, and case studies, the paper argues that while Latin America holds substantial potential to upgrade its role in global supply chains, success will depend on comprehensive policy reforms, targeted investments in human capital and institutions, a refocus on regional integration, and geopolitical (dis)alignment. Ultimately, the paper highlights the uneven nature of these opportunities, suggesting that the benefits of global supply chain reconfiguration will likely be concentrated in countries capable of aligning political and economic roadmaps with the resulting international trade demands.

ABSTRACT. This study investigates the impact of environmental, social, and governance (ESG) achievements on corporate profitability, using a two-stage least squares (2SLS) regression model to address potential endogeneity concerns. By analyzing firm-level data from 2002 to 2022, we examine how ESG performance influences financial outcomes, focusing on the interplay between sustainability initiatives and profitability metrics. Our findings show a significant relationship between ESG achievements and profitability with a significant variation among different industries. This suggests that firms integrating robust ESG strategies not only enhance their market reputation but also generate financial gains.

ABSTRACT. The effectiveness of healthcare quality initiatives is vital for enhancing patient outcomes and achieving organizational excellence. As such, investigating targeted quality initiatives is of interest to healthcare professionals and scholars. This research examines an industry framework of healthcare quality competencies to explore relevant measurements arising from the dynamic nature of today’s healthcare quality landscape. We integrate healthcare quality competencies and results to develop a research model and validate its effectiveness using survey data obtained from healthcare workers. Measurement reliability and validity of constructs were checked before analyzing the research model. Findings were discussed to provide insights to practitioners and researchers in the healthcare field.

ABSTRACT. Several reforms of primary health care aim at transforming primary care into community care to achieve greater effectiveness of health services and reduce the burden of secondary care. There is however a high degree of uncertainty regarding the most appropriate organizational model of community health care. This study presents a structured literature review concerning the organization and management of Community Health Centres (CHC). A search was conducted on Scopus, covering the period from 1989 to 2024, and using the search string “Community AND Health AND Centre AND (Organization OR Management)”. This search resulted in 2,430 articles, which reduced to a sample of 47 highly-relevant articles. Forward and backward snowballing was conducted on these articles, uncovering 11 new studies. These 68 articles were read in full, leading to exclude some articles with a final sample of 55 articles. The most widely studied national contexts are the United Kingdom, Italy, and The Netherlands. Thematic analysis suggests a focus of literature on the following key issues: the integration and collaboration of CHC with other actors of the healthcare ecosystem, the importance of the local institutional context in shaping the implementation and impact of CHC, the need to address workforce and policy challenges, to integrate digital healthcare solutions, and to enhance patient-centred care models within CHC.

ABSTRACT. Widespread adoption of online consultation channels (e.g., videos, chats, etc.) and online healthcare communities (OHCs) has led doctors to increasingly engage with patients on these digital channels for consultation and information sharing alongside traditional in-person consultations. The extent of engagement varies among doctors, with some engaging only on online consultation channels, while others primarily engaging on OHCs, and some diversifying their activities across both channels. While existing research has investigated the impact of engagement on online consultation channels and OHCs, individually, on doctors' economic returns, the effects of diversifying doctors' efforts on both channels simultaneously have not been examined. Active engagement on distinct online channels warrants significant efforts and multitasking from the doctor's end. It may lead to cognitive overload, impacting the doctor's productivity and, eventually, their economic returns. Using game-theoretic models, we demonstrate that concurrent diversification across both channels leads to lower economic returns than exclusive engagement on online consultation channels or OHC. We explain the findings using cognitive load and multitasking theory. Additionally, our study examines how the doctors' geographic locations and patient's privacy concerns influence these decisions. We find that doctors achieve greater economic returns by engaging more in OHCs when dealing with privacy-sensitive diseases. Also, diversifying their engagement on online consultation channels yields higher economic returns for doctors practicing in economically disadvantaged regions. This research contributes to the healthcare operations literature, showing the most effective level of engagement diversification based on a doctor's specialization, location of practice, and ability to manage multitasking and technological resources.

ABSTRACT. Sustainability in military aviation relies on accurate failure prediction and proactive maintenance, essential for safety, operational readiness, cost efficiency, and environmental responsibility. Predictive maintenance, powered by AI and data analytics, enables real-time monitoring of aircraft systems to anticipate failures, reduce downtime, optimize resource allocation, and extend component lifespan. To the best of our knowledge, the prediction of time to failure of aircraft components using sustainability costs has not been explored in the literature, with related studies only predicting aircraft downtime and mission readiness. To address this gap, we propose a novel data-driven predictive framework using machine and deep learn ing to predict time to failure for military aircraft components. Using real-world data from an anonymous Indian aerospace and defense company, we benchmark our framework against the “predict, then optimize” approach, demonstrating around 60% improvement in the objective function. Practically, our approach enables defense organizations to schedule maintenance more effectively, reducing material consumption, and preventing catastrophic failures. However, our study is currently validated with only one dataset, and further research with multiple datasets is necessary to test robustness. To our knowledge, this framework is novel and has not previously been applied to predict time to failure for military aircraft components.

ABSTRACT. In-vehicle coupon distribution is a novel marketing strategy that enables real-time engagement with consumers. As businesses seek more effective ways to personalize promotions, predictive analytics and machine learning have emerged as powerful tools to optimize coupon distribution and redemption. This study examines key factors influencing in-vehicle coupon effectiveness by applying advanced predictive modeling techniques to secondary data. We identify critical variables, including consumer behavior patterns, contextual triggers, and engagement dynamics, that shape the success of in-vehicle promotions.

By integrating insights from marketing, information systems, and predictive analytics literature, our research provides a structured approach to optimizing coupon distribution. Machine learning models allow for dynamic targeting, improving promotional effectiveness and enhancing consumer response rates. The findings offer practical implications for marketers looking to refine their engagement strategies using data-driven decision-making. Additionally, we highlight how marketing decision support systems can leverage real-time insights to improve campaign performance.

Beyond theoretical contributions, this study outlines future research opportunities in automated promotional strategies, focusing on enhancing personalization through dynamic consumer interaction. As predictive analytics and machine learning continue to transform marketing, businesses must adopt innovative methods for refining consumer targeting and maximizing sales impact. This research contributes to the growing field of marketing analytics, positioning in-vehicle coupon distribution as a scalable, data-driven sales strategy that integrates predictive modeling, machine learning, and marketing optimization to drive business outcomes.

ABSTRACT. Declining indoor air quality (IAQ) in the United States, particularly exacerbated by energy-efficient buildings with low air exchange rates, poses significant human health risks, including respiratory issues, headaches, and fatigue. Understanding the factors driving the growth of harmful indoor air contaminants is crucial for effective mitigation. This study leverages the comprehensive dataset from the US Environmental Protection Agency’s Building Assessment Survey and Evaluation (US EPA BASE) Study to investigate the complex relationships between indoor air contaminants, reported occupant health symptoms, and contributing environmental factors.

Utilizing data from 100 office buildings across 25 states, we aim to answer two key questions: Which specific fungal, bacterial, and VOC contaminants within the BASE dataset are associated with increased frequency of respiratory impacts (chest tightness), headaches, and fatigue? How do measurable indoor environmental parameters and outdoor factors (including climatic regime, geography, weather patterns, building characteristics like age, use, and site urbanization) contribute to the growth and concentration of these identified health-impacting contaminants?

By integrating contaminant concentration data, self-reported health outcomes, and detailed building/environmental information, this analysis seeks to pinpoint significant indoor and outdoor drivers for contaminant proliferation. The findings are expected to identify key factors amenable to monitoring or intervention, informing targeted strategies to improve IAQ and reduce associated health burdens. This research aligns with initiatives like ARPA-H's call for a novel healthy building index by providing data-driven insights into the environmental determinants of indoor air contaminants linked to adverse health outcomes, ultimately aiming to promote healthier indoor environments.

ABSTRACT. This paper details innovative pedagogical approaches adopted within our Information Systems and Computer Science (IS/CS) department, leveraging the transformative potential of Artificial Intelligence (AI), specifically large language models (LLMs) and prompt engineering. We present a paradigm shift from traditional static learning materials to dynamic, AI-powered educational resources. Our approach centers on the creation of "dynamic textbooks," where LLMs, guided by carefully crafted prompts, generate customized learning content tailored to individual student needs and learning styles. This allows for real-time adaptation of material complexity, explanation style, and assessment methods, fostering a more engaging and effective learning experience. Furthermore, we demonstrate the application of AI in gamifying ethical governance training. By designing interactive scenarios and ethical dilemmas through prompt engineering, we create immersive learning environments where students actively grapple with complex ethical challenges in AI development and deployment. The conversational and adaptive nature of LLMs allows for personalized feedback and Socratic questioning, promoting deeper ethical reasoning and critical thinking. These AI-driven innovations have demonstrably enhanced our agility in lesson planning and assignment creation. The ability to rapidly generate and modify content allows us to respond to emerging trends in the field and student feedback with unprecedented speed. This newfound dynamism fosters a more relevant and responsive educational experience, equipping students with the skills and ethical grounding necessary to navigate the rapidly evolving landscape of AI. Preliminary results indicate increased student engagement, improved comprehension of complex concepts, and a heightened awareness of ethical considerations in AI.

ABSTRACT. Technological evolution nowadays can be observed or defined in different terms, sectors and markets because users, companies or institutions are highly influenced by the changes that are implying. This study intends to examine how some of these technological evolution, analyzed through e-government, innovation management and strategic planning, affect performance through digital transformation in education, enhancing and expanding digital and electronic services and making it easier for beneficiaries to access services in Jordan's educational system. To analyze it, we have considered a sample of 384 staff members from the educational sector at Jordan's public universities, all of them attested to the steady advancements in a number of areas of the educational sector's digital transformation. Structural equation modelling was developed to examine the hypotheses. According to the study's hypotheses, the model successfully described the phenomenon under investigation with regard to digital transformation performance in e-government, innovation management and strategic planning. Nonetheless, a positive correlation was discovered between performance and these technological variables. This study emphasizes how switching from traditional to sophisticated electronic governments has real and immediate advantages, particularly for the educational sector. This change is both essential and very beneficial since it improves the educational system's overall performance by strengthening its capacity to satisfy technical, professional, and human needs. The research makes a substantial contribution by examining the ways in which these factors affect performance of the educational sector in public universities, improving employee performance and task completion speed with high quality and efficiency while saving time, money and effort.

ABSTRACT. A global problem in grocery retail is expired perishable products generating food waste. The solutions to reduce food waste is currently constrained by the technology used in physical grocery retail. The purpose of this paper is to explore the development paths in implementing available technologies and their potential outcomes toward a comprehensive solution to address food waste challenge. Our critical evaluation of current literatures finds that an integrated solution does not exist yet. However, its elements, in form of technology, are either available or partially actualized. The evolution of these elements is proposed along three maturity levels – item identification, item pricing, and item inventory management. In the current situation (level 0), item inventory management and identification rely on legacy solutions constraining improvement and not using recently available technologies. At level 1 (Granular item identity), the use of 2D barcode allows for a more granular identification of product (best before date, item age, etc.) at item level. Building on this improvement enables grocery retailers to efficiently implement a more responsive and adaptive pricing by reducing the manual task in attaching discount stickers (level 2). Lastly, we identify a missing element in literature and practice, ad hoc inventory management (level 3), which currently is feasible in terms of technology readiness, representing the last step for an integrated solution. The proposed maturity model helps grocery retailers to navigate required development efforts to harness the beneficial outcomes, thus gradually achieving the ultimate goal of zero food waste.

ABSTRACT. In the dynamic and complex business environment, firms need to maintain competitive advantage through innovation. Open innovation, which involves collaborating with external partners to acquire external knowledge and resources, has become a primary mode of firm innovation. Existing literature primarily focuses on the impact of supply chain relationships on innovation investment and performance, while neglect the willingness of open innovation. Based on the Resource Dependence Theory (RDT) and Knowledge-Based View (KBV), this study explores the differential effects of supplier concentration and customer concentration on firms’ willingness of open innovation, and also the moderating roles of business uniqueness and supply network betweenness centrality.This study utilizes supply chain and patent data from Chinese listed manufacturing firms from 2013 to 2023 to test the proposed relationships. Heckman’s two-stage model and fixed-effects panel regression models are employed to examine the hypotheses.The empirical results indicate that: (1) supplier concentration has a significantly negative relationship with the open innovation of focal firms, while customer concentration has a significantly positive impact; (2) business uniqueness positively moderates the relationship between customer concentration and open innovation, but its moderating effect on supplier concentration is not significant; (3) network centrality exerts a differential moderating effect on the impact of supply chain concentration: when focal firms occupy a higher centrality, the inhibitory effect of supplier concentration is significantly enhanced, while the promoting effect of customer concentration is noticeably weakened.

ABSTRACT. The high elevation of mountain roads increases the energy intensity of logistics, as vehicles use more power on steep slopes, leading to higher emissions. To address this, many are turning to sustainable solutions like electric vehicles (EVs), supported by government policies and CO2 regulations that promote its adoption to combat climate change. EVs could revolutionize various industries, acting as a general-purpose technology (GPT). However, challenges like high costs, ethical concerns about battery production, and inadequate charging infrastructure hinder their widespread adoption. Current research often focuses on isolated aspects of EV technology, neglecting the interconnected nature of advancements. In this regard, this study aims to explore key innovation pathways and future potentials influencing the competitive landscape of EVs as GPTs. Our methodology involved collecting and analyzing 23,290 patent documents from the Derwent Innovation Index using descriptive statistics and Latent Dirichlet Allocation (LDA) topic modeling. The analysis shows a notable increase in patent applications for EV technologies starting in 2010, driven by sustainable mountain transportation. A decline post-2018 is linked to COVID-19 impacts, market consolidation, and a focus on existing technologies, suggesting industry maturation. Eight key technological areas were identified: Electric vacuum pump, Electric drive system, EV body structure, EV charging methods, Intelligent control systems, Electric power control, Battery technology, and Rotor shaft design. This study highlights the transformative role of electric vehicles in advancing sustainable mountain logistics and contributes to the understanding of EVs as a general-purpose technology.

ABSTRACT. Maintenance is an essential support function in manufacturing, responsible for ensuring the health of equipment and machinery. Driven by data-richness in maintenance operations, contemporary maintenance scholarship focuses predominantly on novel digital technologies towards the vision of high-reliability systems with near-perfect uptime. Yet, this digital ideal is far from reality, constrained in industry by the slow renewal of maintenance strategies and practices. This gap – a discrepancy between technological aspirations and real-world progress – suggests that achieving modernized, high-performance maintenance needs drastic increases in labor productivity. Maintenance is a hard-to-automate, hands-on occupation where human actions are needed for many tasks. This makes humans, not technology, the weakest link for growth.

To address this gap, we use a capabilities theory lens and a multiple case study to strategize human resources in maintenance operations in two polar yet interconnected environments: automotive manufacturing (conventional, stable) and battery manufacturing (nascent, dynamic). We first collaborated with maintenance practitioners to develop a theoretical framework comprising operational capabilities (‘execute’, 'plan’, ‘control’, ‘improve’) and dynamic capabilities (‘build’, ‘integrate’, ‘reconfigure’). We then collected and analyzed 85 documents (workplace ads and role descriptions) from four Swedish automakers and battery manufacturers, extracting essential Knowledge, Skills, Abilities, and Other characteristics (KSAOs) among maintenance workers and linking them to higher-order human capabilities. We also made comparisons across environments (automotive, battery) and occupational roles (technicians, supervisors, engineers, managers). The theoretical framework and empirical findings offer a comprehensive foundation for strategizing human resources in maintenance and revealing the KSAOs that emerge into operational and dynamic capabilities.

ABSTRACT. With the increasing pressure on minimizing environmental impact, the importance of adopting sustainable practices has never been greater. Remanufacturing is becoming a well-established sustainable practice that focuses on recapturing value from end-of-use products and reducing environmental footprint. The remanufacturing, especially the planning and control of the operation, is complex due to the inherent high uncertainty of product returns in terms of quantity, quality, and timing. While research on remanufacturing is growing, there is limited empirical data on how remanufacturers plan and control their operations in practice, and what challenges occur in the production planning and control (PP&C) of remanufacturing operations.

This paper explores the execution of PP&C at three OEM and four non-OEM remanufacturing plants through study visits and semi-structured interviews. Findings reveal operational challenges. While remanufacturers take different approaches to forecasting and managing product returns, the uncertainty and limited information available on returned products determines how production schedules, material requirements, and human workforce are planned.

The paper provides empirical evidence on current operational challenges for PP&C in remanufacturing and highlights areas for future research.

ABSTRACT. The weighted tardiness problem has been researched extensively by researchers. It is about sequencing a set of independent jobs with priority weights on a single machine with the objective of minimizing weighted tardiness. This is considered a difficult problem to solve and accordingly designated as NP-hard. Hence, we propose a greedy heuristic to determine approximate and quick solutions.

The one machine total weighted tardiness problem is stated as follows. There is a set of n jobs ready at time zero to be scheduled on a single machine that is continuously available. Each of the n jobs (1, ..., n) is to be processed without interruption on a single machine which can handle only one job at a time. Associated with a job i, there is a processing time pi, a due date di, and a priority weight wi. The completion time of job i depends on the schedule. The objective is to find a sequence which minimizes the total weighted tardiness.

We propose a greedy insertion-based heuristic for this problem. Our heuristic is based on some theoretical properties that we develop and apply. Due to the simplicity of our proposed algorithm, it is easy to obtain quick solutions. Furthermore, smaller problem sets can be solved even manually by using our heuristic. We compare our heuristic with some well-known heuristics that are available in literature. Initial results indicate that our proposed heuristic performs very well when compared to these rules.

ABSTRACT. Online Collecting Platforms (OCPs) collect used products from consumers, disassemble them, and sell them as raw materials/components to remanufacturers (or recycling companies). OCPs exhibit two characteristics that distinguish them from traditional collecting platforms. (I) The estimated price quoted to consumers before the receipt of their used products, as the difference between the estimated price and the final (actual) price impacts consumer’s utility. (II) The network externality resulting from connecting the group of consumers to the group of remanufacturers, implying that the number of users on one side of the platform affects that on the other side. We analytically investigate whether OCPs should quote the estimated price to consumers and whether they should charge or subsidise remanufacturers joining the platform. Our main findings indicate that the impact of the estimated price on the actual price quoted to consumers and the membership fee applied to remanufacturers depends on the magnitude of the remanufacturer’s network externality, and that OCPs benefit from the estimated price strategy at the first development stages of the platform and/or when the estimated price is low. If the estimated price is not quoted, OCPs should subsidise the remanufacturers only when both the magnitude of consumers’ network externality and that of remanufacturers’ network externality are large; otherwise, OCPs should charge the remanufacturers. Under the estimated price strategy, OCPs should subsidise the remanufacturers when the magnitude of the remanufacturer’s network externality and the estimated price are either both large or both small; otherwise, OCPs should charge the remanufacturers.

ABSTRACT. The non-contractible feature of product freshness results in the challenges of freshness management in fresh produce supply chains. The emergence of advancing technologies such as the Internet of Things (IoT) and smart sensors offers the marketplace great opportunities to track valuable data of fresh produce as it moves through the supply chain. Such data enables fresh produce supply chains to achieve end-to-end transparency. This can subsequently help grocery retailers and their supply chain partners improve the visibility of quality and freshness of their fresh produce. Knowing fresh produce’s quality and freshness can also help managers make better decisions in their operations. Motivated by the emerging practices of advancing technologies in fresh produce supply chains around the world, this research is developed to investigate the influences of technical innovation on supply chain management of fresh fruits and vegetables. Specifically, our research aims to address the following research questions. How to define more transparent information in freshness management of fresh produce supply chains? How does the value of information affect supply chain members’ operations such as decisions relating to their profits and food waste management? What about the entire supply chain? To the best of our knowledge, our research is among the first studies to explore the influences of technical innovation on freshness management in fresh produce supply chains. Findings in our research address the complex nature of non-contractable quality in fresh produce supply chains and fills the research gaps in the literature.

ABSTRACT. The linear economy system rapidly depletes natural resources, increases waste, and raises carbon emissions, posing threats to the environment. The Sustainable Circular Supply Chain is closely interrelated to the three fundamental pillars of environmental, economic, and social sustainability. Moreover, Industry 5.0's cutting-edge technologies and human-machine collaboration boost the resilience of Sustainable Circular Supply Chains. This paper investigates the enabling effect of Industry 5.0 on Sustainable Circular Supply Chain becoming more mature and resilient at the same time. Two research questions are raised in this regard. First question is “What are the most important factors that increase resilience in Sustainable Circular Supply Chain management and how should the effects of these factors on the three pillars of sustainability be assessed?”; and second, “According to which criteria do the technological opportunities provided by Industry 5.0 have the highest impact in reaching the maturity level in Sustainable Circular Supply Chain?” AHP (Analytic Hierarchy Process)- TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) technique is employed for the hierarchical assessment of enablers influencing Sustainable Circular Supply Chain maturity. By identifying the elements that are most impacted by environmental, economic, and social sustainability criteria, the anticipated outcomes will assist firms develop long-term resilience. The implications of the research include offering decision-makers in Sustainable Circular Supply Chain management a holistic framework for creating more resilient, long-lasting, and flexible solutions.

ABSTRACT. How can organizations tailor their activities to maximize the benefits behind the adoption of artificial intelligence (AI)? The purpose of this paper is to examine the challenges and drivers for the adoption of AI with a view to draw implications for organizational growth and understanding its return on investment. Furthermore, while in the information technology literature contributions about adoption, success and failure abounds, there is still much to be learned from the outcomes of increased AI usage in organizations. First, the range of applications is very wide. Second, from operational benefits to augmented decision-making, AI brings possibilities well beyond the benefits that come from only automating complex adaptative processes. Third, AI brings a set of novel risks yet to be fully understood, even when using commercially available solutions rather than just research-oriented ones. We use a wide scoping structured literature review and expert user interviews to extract drivers and barriers to AI adoption. We find that certain sets of technology-oriented and organizational strategies enable organizations to phase in responsible and profitable AI usage. We also derive implications for managers.

ABSTRACT. Omnichannel grocery retailers use store-based fulfillment of online orders for a variety of reasons (e.g., faster deliveries, labor availability). Introducing store-based fulfillment in brick-and-mortar stores influences the inventory management of a store, and the food waste it generates, in two opposing ways. Fulfilling one more demand stream from the same inventory can be expected to create a pooling effect, which reduces the waste-to-sales ratio. However, the last-expired-first-out picking policies used by store employees to serve online customers may generate higher levels of waste in the store. Using granular data from a grocery retailer, we study the impact that introducing online fulfillment to existing stores has on food waste. We rely on a staggered difference-in-difference approach to account for the way in which online fulfillment was adopted throughout the chain. We find the waste-to-sales ratio to increase, on average, with the introduction of online fulfillment at brick-and-mortar stores. However, substantial heterogeneity in this increase exists across product categories and stores. We exploit this heterogeneity to understand more about the dynamics of online order fulfillment and to caution retailers about several unforeseen performance impacts of online operations.

ABSTRACT. Production Sharing Agreements (PSAs) are among other mechanisms used to define the revenue split between governments and international oil companies (IOCs), influencing investment decisions and production quantities. Our previous analysis of Uganda’s PSA framework assumed a predetermined fixed production schedule, neglecting the flexibility IOCs have in response to uncertain oil prices. This study extends prior work by incorporating an optimal production plan using a real options framework, where firms dynamically adjust production in response to market conditions.

We model oil price uncertainty and examine how the PSA structure—royalties, cost recovery limits, and profit-sharing mechanisms—affects the firm’s optimal production path. Using optimization, we determine the value-maximizing production plan over a 25-year horizon.

The results of the study aim to provide valuable insights for policymakers designing regimes that balance government revenues with investment incentives. The study also aims to highlight the importance of incorporating managerial flexibility when evaluating PSA outcomes, as real options valuation reveals strategic responses that traditional models overlook.

ABSTRACT. In unsecure times, authorities responsible for societal infrastructure and services need to further explore and develop alternatives of their current supply chains. Swedish authorities have been tasked to analyse and increase security of supply for critical products and components. For many types of products and parts it may be an option to start, or ramp up, local emergency production in time of crisis. When it comes to electronics products they often have long and lobal supply chains why it may be difficult to source and produce locally, thus remanufacturing or refurbishing may be a viable emergency production option. A managerial question to research here is when remanufacturing is a viable emergency production option and what obstacles exist to prepare for such emergency production. In this study of two cases of electronics small-scale remanufacturing in Sweden is presented. Case company alfa is a lego remanufacturer who remanufactures mainly spare parts on contractual basis for several industries. Case company beta is an original equipment supplier of power electronics and mainly produce new modular products, but as a service to regular customers they also take back and remanufacture old modules. The two cases conceptualize two major options to consider in securing local electronics supply when global supply chains have failed. The case companies’ different situations are discussed together with previous findings in literature.

ABSTRACT. Background: Humanitarian food procurement demands rapid, cost-effective, and context-sensitive sourcing in crisis prone settings. A diverse body of local and regional procurement (LRP) experiments highlights potential gains in timeliness, cost, nutrition, and socio economic impact, but practitioners lack an integrated, stage based framework to guide modality choices under varying emergency conditions. Methods: We purposively reviewed 15 seminal reports and studies spanning country-level endline evaluations (Kenya, Rwanda, Eastern Africa, Cambodia, Senegal, Guatemala, Nicaragua), global modeling and impact studies (computational and time-series approaches), empirical timeliness and cost comparisons using matched project data, strategic policy documents, and operational updates on volumes and modalities. Recurring decision criteria were extracted via thematic coding and synthesized into nine sequential stages. Results: The nine stage decision framework comprises: (1) Context & Principles; (2) Market Assessment; (3) Operational Feasibility; (4) Nutrition & Cultural Fit; (5) Socio Economic & Systemic Impact; (6) Risk & Resilience; (7) Sustainability & Environment; (8) Policy & Stakeholder Alignment; and (9) Multi Criteria Scoring & Decision. For each stage we define key criteria—such as cost competitiveness, supplier capacity, logistics feasibility, dietary requirements, and social outcomes—identify LRP and international procurement advantages, and illustrate application with examples from the reviewed reports. Conclusions: This structured, evidence based framework enables humanitarian practitioners and policymakers to select fully local, hybrid, or international procurement modalities tailored to crisis phases and operational contexts. By following these stages, agencies can optimize response speed and cost, strengthen smallholder engagement, uphold nutrition and equity objectives, and enhance resilience under emergency conditions.

ABSTRACT. Supply chain resilience has become a crucial strategic objective owing to increasing turbulence in the operational environments. However, as a developing field shaped by successive disruptions, it faces two key challenges: first, a lack of consensus on its conceptualization and second, a limited empirical validation of resilience-building strategies. This study leverages the COVID-19 pandemic as a large-scale test case, offering a unique opportunity to examine supply chain resilience in action and address these challenges. We review empirical studies that focused on pandemic-induced disruptions, with an aim of synthesizing diverse approaches to the conceptualization, measurement, and integration of supply chain resilience. Our findings indicate that supply chain resilience, at its core, focuses on maintaining performance levels within acceptable limits with minimal additional investment, in the wake of disruptions. Moreover, developing resilience has evolved into a circular process, where supply chains are either continuously preparing for, or adapting to, disruptions. From a measurement perspective, supply chain resilience remains subjective during preparation phases but becomes more objective and measurable once disruptions materialize. Additionally, we identify three key capabilities that proved essential for resilience during the pandemic: Flexibility, achieved through redundancy and diversification; Visibility, enabled by investments in information collection, processing, sharing, and workforce competency; and Collaboration, strengthened vertically along the supply chain, horizontally through coopetition, and informally through personal connections. This study contributes to both theory and practice by advancing the understanding of supply chain resilience and proposing an empirically validated framework to enhance resilience against future disruptions.