ABSTRACT. The literature consistently shows that good multi-echelon solutions offer large reductions in total inventory costs with the same or better service levels for the end customers. Generally, this is achieved by reducing the stock at upstream warehouses and moving some of this stock to the downstream retailers, serving the end customers in a smart way. However, increasing the stock levels at all retailers in large distribution systems may be difficult because of space restrictions and/or the unwillingness of some retailers to accept the need to carry more stock. Moreover, making drastic changes in stock levels at all locations in large systems at once is not without risks and often a major issue when considering implementation of multi-echelon solutions. One way to deal with these challenges in One-Warehouse-Multiple- Retailer (OWMR) systems is to allow the upstream warehouse to differentiate its service across multiple distribution channels serving different groups of retailers. We present a new approach for modeling and optimizing inventory control decisions in such multi-echelon omnichannel distribution systems, where the central warehouse serves N retailers grouped into M+1 distribution channels with different service requirements. A numerical study based on real data from Volvo Group shows that the new heuristic performs well, offering efficient service differentiation across channels and significant reductions in total inventory, without compromising the service to the end customers.

ABSTRACT. Additive manufacturing, commonly known as 3D printing, is becoming increasingly popular, leading to the emergence of 3D printing platforms. Setting pricing strategies for 3D printing platforms is challenging due to the simultaneous multiple offerings of diverse products and services on the same 3D printing platform. Mainly, 3D printing platforms derive the optimal pricing for 3D printed products by using two types of pricing strategies: partial pricing and full pricing. The core objective of the platform and the designer is to maximize their profits by using these pricing schemes. In this study, we explore the optimal pricing of these two stakeholders while the 3D printing platform imposes first-degree price discrimination. In the basic model, we derive the optimal selling price for standard and customized 3D printed products by assuming that the platform can capture all consumers’ surplus by setting the price of each unit exactly equal to the consumer’s marginal willingness to pay. Further, we estimate the profit for each stakeholder considering different costs incurred by the platform. In this research, we consider dynamic design fees and investigate first-degree price discrimination and its impacts on the optimal selling prices of differentiated substitute 3D printed products in the oligopoly game of imperfect competition setting.

ABSTRACT. The purpose of this study is to design an experiment-based methodology for promoting sustainable behaviour. Using the self-determination theory (SDT) perspective, we hypothesize that the satisfaction of SDT’s basic psychological needs, autonomy, relatedness, and competence, causally influences individuals’ decisions to engage in sustainable behaviours. We use an emerging workplace phenomenon called coworking space as a context for this study. There are three dependent variables representing sustainable behaviour in coworking context and three independent variables representing the SDT’s basic psychological needs. We propose a methodology to design a choice experiment to understand the effect of changing the three independent variables on the three dependent variables. In the choice sets, we offer two compensatory alternatives each reflecting a state with different levels of SDT’s basic psychological needs satisfaction. The states are constructed based on a random draw of SDT items. The response variable of interest is the choice of alternatives in which the individual is more likely to engage in a sustainable behaviour. Our methodology advocates the use of a sanity check for assessing the logical consistency of the compensatory alternatives created from the random draw. The outcome of choice experiment will shed light on which of the basic psychological need that is the most influential for a certain behaviour and its relative importance with respect to that behaviour.

ABSTRACT. The circular economy (CE) is rapidly gaining global and local significance. Several activities associated with the circular economy rely on reverse logistics (RL). For example, RL facilitates efficient return and reuse of products and materials. Consequently, the environmental impact of production and distribution is reduced, while product and material life are extended. Transportation is fundamental to RL and the circular economy. Given transportation’s critical role, its study in the circular economy and RL is, at best, limited. Industry 4.0 (I4.0) technologies also play an important and transformative role for circular economy practices. This study evaluates the nexus of RL, transportation, and I4.0 technology. The methodology includes empirical insights from 67 experts in the fields of CE, digitalization, and logistics to arrive at a series of research insights. Descriptive and inferencing statistics were employed to evaluate responses. The relative importance of I4.0 technologies in various RL transportation contexts is determined. End-of-life product collection is identified as the most significant potential application area. Experts agree that I4.0 technologies can aid in the collection with big data analytics, the most important I4.0 technology for transportation management within a CE. Additional findings are presented and evaluated with directions for future research identified.

ABSTRACT. This research investigates the application of deep learning techniques to improve prediction accuracy of project cost estimate at completion in response to the limitations of traditional methods due to growing complexity of modern projects. An accurate forecast is key for effective project management, reducing the risk of budget overruns. The proposed multilayer perceptron (MLP)-based deep learning model is compared against established benchmark in the form of a proven high-performing machine learning model (XGBoost) and traditional Earned Value Management (EVM). This study uses Dynamic Scheduling Library which contains the dataset of real-life projects. Mean Absolute Percentage Error serves as a key metric to evaluate forecasting accuracy across different project stages as well as Normalized Root Mean Square Error, Standard Deviation, and Mean Lags. The central hypothesis is that well-trained and tuned deep learning architectures can produce more accurate predictions of project cost estimate at completion than conventional machine learning approaches and EVM, especially during the early project phases when uncertainty is highest. Experimental results showed that while XGBoost performed well overall, MLP model consistently delivered better forecasts in early and mid stages of a project. However, in the later stages, EVM performance came out on top. The research aims to contribute to the expanding body of knowledge on artificial intelligence applications in project management. The potential success of the proposed MLP models could offer improved predictive power, enabling project managers to make better-informed decisions.

ABSTRACT. Engineer-to-Order (ETO) environments pose unique challenges for Supply Chain Planning due to high customization requirements, complexity, and data scarcity. While machine learning (ML) offers potential improvements, its implementation in ETO settings remains limited. This paper examines the relationship between performance measurement and ML adoption in ETO supply chain planning through literature review and nine industrial case studies. Our findings reveal that ML adoption in ETO settings does not primarily follow traditional improvement frameworks driven by existing metrics. Instead, organizations implement ML based on perceived challenges, planning process contradictions, and exploratory interest. We identify a bidirectional relationship: performance measurements can drive ML implementation, but ML implementation often necessitates new or refined metrics to evaluate its impact. This relationship is important for building trust in ML applications, as appropriate measurements provide mechanisms for developing both cognitive and emotional trust. The study contributes a refined conceptual model integrating performance measurement, technology adoption, and trust theory perspectives. For practitioners, it provides guidance on initiating ML implementation when performance measurement maturity is low, emphasizing the importance of establishing appropriate metrics even when adoption is challenge-driven rather than measurement-driven. For scholars, this study extends understanding of technology adoption in supply chain planning and highlights the need for ETO-specific approaches to performance measurement when implementing ML in environments characterized by customization, variability, and limited historical data.

ABSTRACT. Purpose There are different technological tools that could be used in Sales and Operations Planning but their implementation and adoption remains low. The reason is that it is not clear what are the requirements to implement these tools. These tools also affect how the S&OP process is carried out. The purpose of this research is to investigate requirements and effect of machine learning (ML) and artificial intelligence (AI) in S&OP.

Methodology This research is based on three cases where ML and AI based tools were used in the S&OP process. AI and ML usage has been studied in detail through interviews using an actor-network theory lens to understand the human and non-human actors’ involvement.

Findings The preliminary findings show that current process has to be mature in order to implement AI solutions. These solutions also require a reliable data source in order to generate accurate insights. Therefore, a digitalization strategy precedes the use of ML and AI. Additionally, changes in business policies also affect the adoption of AI in the organization.

Originality This paper explores the real-world application of ML and AI to offer insights about their implications in the S&OP process. The results also provide guidance for successful adoption of advanced technology in S&OP process.

Limitations The studied cases cover usage of AI and ML in specific areas of the S&OP process and not the entire process. More cases are required in other contexts and application to draw general conclusions.

ABSTRACT. Generative AI (GenAI) is transforming industries. Supply chain management is one of them. This study investigates the impact of GenAI on supply chain planning roles, specifically examining skill requirements, responsibilities, and role autonomy. We employ a multiple-case study design, analyzing job descriptions from companies demonstrably adopting GenAI in their planning functions (adopters) and comparable companies without evidence of such adoption (non-adopters). A longitudinal component within the adopter group tracks changes over an eight-month period. Our analysis is grounded in Role Theory, which provides a framework for understanding how job roles are defined and evolve in response to technological advancements. We address two research questions: First, how do skill requirements, as reflected in job descriptions, differ between adopters and non-adopters? Second, how have these requirements changed over time within adopter companies? We use qualitative content analysis, guided by a structured coding scheme derived from Role Theory. This allows for a systematic comparison of job descriptions across groups and over time. We anticipate that adopter companies will exhibit a greater emphasis on GenAI-related skills and a shift in responsibilities towards human-AI collaboration. This research contributes to the understanding of technology-driven role transformation. It provides empirical evidence of how GenAI is reshaping supply chain planning roles. The findings offer practical implications for workforce development and job design in the context of increasing AI integration.

ABSTRACT. The rapid evolution of Supply Chain Management (SCM), particularly in response to disruptions such as the COVID-19 pandemic, has heightened the need for an adaptive, industry-aligned educational framework. Despite SCM’s growing relevance in business strategy, academic curricula remain varied in structure and focus, with limited research evaluating their alignment with contemporary industry demands. This study investigates the current state of undergraduate SCM programs, examining how they prepare students for emerging challenges such as digital transformation, sustainability, and supply chain resilience. Employing a qualitative research approach, we analyze SCM curricula from 27 AACSB-accredited institutions in the U.S. and 10 internationally recognized universities. We complement this analysis with a systematic literature review and semi-structured interviews with both academic program directors and industry practitioners responsible for hiring SCM graduates. This study contributes to the SCM education literature by providing a comprehensive assessment of program design and identifying critical areas for curriculum enhancement. Practically, it informs strategic recommendations for redesigning SCM curricula to better align with industry needs. By bridging the theory-practice gap, this research advances efforts to equip future supply chain professionals with the skills necessary to navigate the complexities of modern supply chain ecosystems.

ABSTRACT. Agriculture supply chains, characterized by complexity and uncertainties, present challenges that hinder the progress of food and beverage firms in their efforts to achieve a green transformation. To address these challenges, this study employed a hybrid approach comprising content, bibliographic, and cluster analyses, the entropy weight method (EWM), the fuzzy Delphi method (FDM), and the fuzzy synthetic evaluation–decision-making trial and evaluation laboratory (FSE-DEMATEL) to develop a model for practices. This study contributes to the literature by reinforcing its theoretical foundation, connecting the green transformation with agriculture supply chains, and offering a visual and data-driven analysis to guide improvements toward achieving the green transformation. The results show that green growth and environmental compliance and regulations are the main causal aspects influencing the achievement of green transformation. In practice, digital agriculture development, agroforestry initiatives, greenhouse gas emissions reduction, sustainable consumption, and sustainable intensification are key priorities in driving the green transformation in agriculture supply chain management.

ABSTRACT. Supply chain (SC) mapping is the first step in addressing many contemporary SC challenges. It provides foundational knowledge about the configuration of a SC, providing a basis for in-depth SC analysis, enabling SC compliance to be checked, and informing decision-making for industry and policy makers. However, SC mapping is challenging. The dynamic nature and global complexity of modern SCs make it extremely challenging and cost prohibitive to collect primary data from the numerous SC actors across different geographies to develop accurate SC maps. This paper shows how secondary data sources (SDSs), used appropriately, can be valuable sources for mapping and analysing supply chains at different levels of detail. Here, we focus on two mapping levels, (1) global value chains (GVC) and (2) industry-level supply networks, to demonstrate how SDSs can be combined to map and analyse the multi-tier structures and networks used in the production of complex products. We use the automotive tyre as a case study. We focus on selected ingredients and chemicals used in tyre manufacturing that have significant environmental, sustainability, product safety and health concerns. The research presents new approaches for mapping multi-tier global supply chains for complex products that expand across multiple industries and sectors. The primary contributions of the study are a new framework that identifies nine categories of relevant SDSs for SC configuration research and guidance on how selected SDSs can be used in combination for SC mapping exercises. The challenges and limitations of using SDSs for SC mapping are discussed.

ABSTRACT. Value creation and appropriation within supply chains have been central in business and supply chain management (SCM) research since Brandenburger and Stuart’s (1996) work on value-based business strategy. While their framework underscores value capture among supply chain actors, contemporary challenges—globalization, resilience, sustainability, and digital transformation—necessitate a broader perspective (Chicksand & Rehme, 2018; Ketchen & Hult, 2007).

This study extends Brandenburger and Stuart’s (1996) approach by integrating insights from SCM, organizational theory, and purchasing and supply management (PSM), thereby proposing a more comprehensive Supply Chain Value Creation Framework. The framework incorporates value appropriation asymmetries, power dynamics, and the evolving role of purchasing across upstream, internal, and downstream activities (Chicksand & Rehme, 2018; Ramsay, 2005).

We adopt a multi-level perspective to expand Brandenburger and Stuart’s conceptualization, emphasizing: Total Value Creation – Shifting from dyadic to network-wide value realization (Chicksand & Rehme, 2018). Power and Value Appropriation – Investigating how asymmetrical power structures influence value distribution (Brandenburger & Stuart, 2007; Cox, 2007). Purchasing as a Value Driver – Positioning PSM as a key enabler of value co-creation, rather than mere cost reduction (Jääskeläinen & Heikkilä, 2019).

Drawing on case studies and theoretical analysis, we argue that while Brandenburger and Stuart’s (1996) model is foundational, it underestimates the complexities of contemporary supply networks. Our framework integrates a broader conceptualization of value—encompassing both customer and supplier value (Ketchen & Hult, 2007; Ramsay, 2005)—and advances SCM and PSM literature by providing a dynamic approach for firms seeking to strengthen their strategic positioning in global supply chains.

ABSTRACT. Extant literature alludes to the role of institutional, regulatory, and stakeholder pressures, among others, as key antecedents in determining environmentally-driven outcomes. Yet, questions remain concerning how differently firms’ strategic orientation could impact environmental performance specifically within the context of coopetition. Subsequently, this study utilizes the relational view theory and dynamic capability perspectives to provide insights into the important roles that coopetition experience and operational slack play in operationalizing the relationship between strategic orientation (relative exploration orientation and environmental orientation) and environmental performance. To test the theoretical model of the study, a panel data set spreading over a period of seven years is obtained from US-based firms spanning across industries. The study tests the mediation effect of coopetition experience on the relationship between strategic orientation (relative exploration orientation and environmental orientation) and environmental performance. Additionally, moderating effect of operational slack is tested on the principal relationship. Findings of the study not only make nuanced contributions to coopetition, strategic management, and sustainability literature around the concerning variables of interest, but also informs practitioners that rather than being reactive to external pressures, strategic orientation can be leveraged to dictate the strategic direction of environmental performance in the presence of coopetition experience and operational slack.

ABSTRACT. The successful reintegration of women into the labor market after maternity leave is a critical factor in fostering an inclusive and effective workforce. This paper explores the reasons why mothers return to work, as well as the organizational factors—such as corporate attitudes, workplace culture, policies, and training programs—that influence the ease and success of their reintegration. The research seeks to answer the following question: What organizational factors facilitate or hinder the successful reintegration of mothers into the workforce after maternity leave? To address this question, a systematic literature review was conducted based on the PRISMA methodology, analyzing existing research on corporate policies, workplace flexibility, and reintegration support programs worldwide. The international literature highlights that organizations with well-structured return-to-work programs, mentoring opportunities, and leadership support tend to have higher retention rates and greater employee satisfaction among returning mothers. Additionally, workplace flexibility, skill enhancement programs, and psychological support initiatives are identified as key determinants in successful reintegration. The expected results of this study include the identification of best practices that enable organizations to create a more supportive and adaptable work environment for returning mothers. The findings will contribute to both academic discourse and practical applications by providing companies with actionable insights on how to develop policies that foster employee retention, gender diversity, and career progression. Ultimately, this research aims to demonstrate that proactive reintegration strategies not only benefit individual employees but also enhance overall organizational performance and innovation.

ABSTRACT. We model a supply chain in which the buyer audits the supplier’s compliance with the social and environmental code of conduct. We investigate the effect of the buyer’s upfront commitment to price and quantity on the supplier’s compliance with the sustainability code of conduct.

ABSTRACT. Reshoring—the return of manufacturing or sourcing activities from offshore locations to high-cost countries—has gained considerable attention from both policymakers and academics since the global financial crisis. It is widely anticipated that reshoring aligns with broader mega-trends, particularly the growing emphasis on environmental and social sustainability. While existing literature suggests a potential synergy between reshoring and sustainability adoption, empirical validation remains limited.

This study investigates the environmental and social sustainability initiatives undertaken by U.S. companies that have reshored operations. It examines how sustainability adoption relates to key reshoring drivers such as cost efficiency, innovation, and quality improvement. Furthermore, the study differentiates between companies that integrated sustainability practices before reshoring, after reshoring, or not at all.

Drawing on a dataset of over 500 reshoring cases in the U.S. between 2009 and 2024—compiled from multiple secondary sources—the analysis reveals that the relationship between reshoring and sustainability varies significantly with company size. Notably, small and medium-sized enterprises (SMEs) tend to adopt sustainability measures primarily in response to cost pressures. Large companies that do not adopt environmental sustainability measures are more likely to have reshored to legitimize their products as "Made in the US".

ABSTRACT. Construction projects cause disturbances in the form of road closures, resulting in longer journey times, delays, queues, and reduced accessibility. Mega construction project can impact a city for up to 10 years. Presently there is a planning gap related to the construction production period, where traffic impact is managed on an ad-hoc basis. This leads to a lack of inclusion of transport demands in procurement and delayed decisions on mitigating actions and mobility management. The purpose of this paper is present a decision-making basis for strategic traffic management planning in mega projects. Data has been collected as part of an action research study based on a single collaborative longitudinal case study of the mega railway project Ostlänken, Sweden. Data has been gathered through participatory observations between 2019 and 2025. Several studies on sustainable transport development highlight the importance of focusing research on planning activities, decision-making, and organizational structures to understand the current situation and identify relevant initiatives for change. This study contributes by defining the concept limitations of different actors-including local municipalities, public transport companies, and developers-in planning for the shared utilization of public spaces such as roads, public recreation areas, and parking spaces. The paper makes a theoretical contribution to mega project management by incorporating a systems perspective into transport planning and demonstrating how this can be translated into procurement requirements for developers and public transport providers.

ABSTRACT. While scholarly research has generated a great deal of constructive knowledge about various forms of complexity, project management scholars have paid little attention to decision-making complexity. In this paper, we address this research gap by concentrating on how to examine decision-making complexity. Building on Lessard’s (2014) House of Project Complexity (HoPC), we develop a decision-making model that assesses decision-making complexity and evaluates the impact of decisions made under complexity on project performance. Lessard focuses on the conceptual distinction between the ‘inherent project features’ and ‘architectural features’ and their relationship with project outcomes and emergent properties—the ‘ilities.’ In contrast, this model complements Lessard’s work explaining how decision-making complexity affects performance. The results show that the cases with less decision-making complexity on project institutional features, such as regulatory approvals, perform better compared to cases with high decision-making complexity in technical and architectural features. We propose to include decision-making complexity as a “window” opening to institutional, technical, and architectural complexity features in the HoPC to further our understanding of decision-making complexity and its influence on project performance.

ABSTRACT. The incoming influx of retired and scrapped electric vehicle batteries (EVBs) is anticipated to create economic opportunities and reduce reliance on virgin materials. While recent debates have focused on whether to prioritise the emerging repurposing strategy or to accelerate recycling capabilities, a major challenge remains: securing a stable supply of waste battery feedstocks. As a substantial share of waste EVBs in China is currently traded through informal channels, or grey markets, understanding their behaviour is pivotal for redirecting waste flows. Drawing on empirical evidence, we used supply network configuration theory and system dynamics to examine China’s circular supply system and how illicit trade activities determine the effectiveness of it. The findings reveal that expanding recycling capacities without addressing supply constraints may risk underutilised assets and weaken the bargaining power of recyclers, especially in the context of volatile virgin metal prices. Policy simulations suggest that the battery passport scheme has the potential to curb illicit trade, but only when paired with stronger legal enforcement and economic incentives that make formal processing routes more competitive. However, illicit traders have capabilities critical to the functioning of circular logistics. Rather than displacing the informal sector, integrating their expertise may offer valuable insights toward building more resilient circular supply chains.

ABSTRACT. Emergency Department (ED) overcrowding is a widespread issue all over the world, driven by multiple interrelated factors, including inefficiencies in patient flow, resource constraints, and increasing demand for emergency services. Among the contributing factors are population aging and cost-containment policies, which can affect service availability and resource allocation. Possible mitigation strategies involve real-time monitoring, process analytics, and proactive management to enhance system efficiency. The growing availability of process- and patient-related data stored in Healthcare Information Systems (HIS) has facilitated the refinement and broader application of various ED crowding measures for real-time assessment. However, identifying the most representative metric remains a challenge. Although advanced metrics have been proposed in the literature, they are rarely tested against real process data, while subjective staff assessments remain the most commonly used approach. This study aims to objectively compare and assess different ED overcrowding indicators using real operational data from different Italian hospitals. Specifically, it investigates the relationship between overcrowding and its key consequences - waiting time and patient elopements - to determine the extent to which these indicators reliably capture real-time crowding conditions. The findings offer valuable insights for hospital managers on the accuracy, effectiveness, and limitations of existing overcrowding metrics, informing data-driven decision-making in ED management.

ABSTRACT. This study aimed to develop students' skills in the responsible use of generative artificial intelligence (AI), focusing on its strengths and limitations. Around 100 students in a Human Resource Management class were provided details on a fictional grocery store, and a department (e.g., produce, deli) manager's activities, relations with employees, and interactions with customers. They were assigned the role of store manager and tasked with writing performance appraisals. The first appraisal was written without using AI, then another using only AI, and finally one combining both approaches. This setting was chosen since most students can understand the operations of a grocery store, with performance appraisals being an especially human-focused task.

Initially, over 90% of students indicated that they had used AI, but most described their experience as minimal or "just playing around" with AI. Before using AI for the assignment, 39% said they wouldn't use it for performance appraisals, 42% would, and 19% were uncertain. After completing AI-assisted appraisals, 95% of students indicated they would use AI as managers when writing performance appraisals. They noted AI's benefits in providing structure, consistency, and improved writing quality. However, they found purely AI-generated appraisals lacking in personal touch and humanity. Students recognized AI's limitations in capturing interpersonal relationships and appropriate tone, describing AI-generated appraisals as formulaic and impersonal. In end-of-semester evaluations, many students highlighted this assignment series as particularly valuable, helping them better understand AI's strengths and limitations in a practical context.

ABSTRACT. The work environment and the risk of future work-related injuries are largely determined by the workplace design. However, these design decisions are often made by engineers with limited knowledge of ergonomics. To address this issue, a course in Ergonomics and Workplace Design was introduced for master's students in production engineering, focusing on hands-on experience in designing workplaces. The course follows the CDIO principles (Conceive, Design, Implement, and Operate). Students apply ergonomic and workplace design theories through a project assignment in which they redesign an assembly workplace. They begin by analyzing the problems and conceiving the potential for improvement based on a video of the current work process. The task requires scaling up production from a single workstation to an assembly line that meets capacity demands while improving ergonomics. Students implement their design solutions in the lab, using basic materials to build prototype fixtures and equipment. They must then demonstrate that their proposed design functions effectively by operating the assembly line themselves. Ergonomic analyses are conducted to verify that the risk of work-related injuries has decreased. Since its introduction in 2011, the course has been continuously improved and was awarded the university’s pedagogical prize based on student nominations.

ABSTRACT. This study explores the innovative integration of artificial intelligence and virtual reality technologies to create immersive educational experiences in higher education. We demonstrate how institutions can enhance student learning outcomes and streamline institutional communication by implementing AI-powered virtual agents within VR environments. The research focuses on the application of the Engage AI platform, which enables interactive scenarios ranging from role-played sales training to Design Thinking instruction. Students enter virtual meeting spaces where they can engage with AI agents in natural conversations, receiving immediate feedback and personalized guidance. The platform also serves as an institutional resource, allowing prospective students to learn about university programs and services through immersive VR interactions. Our findings indicate that this technology combination offers unique advantages in three key areas: (1) providing consistent, scalable practice environments for skill development, (2) enabling personalized learning experiences through adaptive AI responses, and (3) creating accessible institutional support services available 24/7. The presentation will include practical examples of implementation strategies, student feedback, and lessons learned from deploying AI-VR agents across various educational contexts. This work contributes to our understanding of how emerging technologies can be effectively integrated into higher education to enhance both learning outcomes and institutional effectiveness.

ABSTRACT. The interplay between Industry 4.0 (I4.0) technologies and sustainability performance has been gaining increasing attention by scholars and practitioners. Among the potentialities of I4.0 is the possibility to automate production and assembly lines to reduce downtimes and improve ergonomics and working conditions. However, generalizing these benefits can be challenging, as they may depend on the specific context and types of technologies implemented. Since these aspects appear to be insufficiently explored in the current literature, this study aims at exploring if and how the impact of 4.0 technologies on sustainability performance depends on the depth and breath of I4.0 adoption. The topic is explored through semi-structured interviews with six Italian manufacturing companies that use I4.0 technologies in their production and/or internal logistics processes with different levels of breath and depth. The preliminary results show that the sustainability impacts depend indeed not only on the focus area of 4.0 implementation, but also on the depth and breadth of adoption. More in detail, while all companies focusing on production automation highlight better ergonomics but lower employment rates, those automating logistics activities observe higher employee safety but also higher energy consumption. Additionally, as the level of I4.0 depth and breadth increases, not only the observed benefits, but also the possible drawbacks seem to increase. This research can provide relevant contributions to both theory and practice. It responds to the call of previous studies for more empirical investigations into the I4.0-sustainability relationship and offers best practices to manufacturing companies for sustainability improvement through I4.0 adoption.

ABSTRACT. In recent years, the pressure to adopt sustainable operational models has reshaped business practices. Supply chains, traditionally optimized for cost efficiency, now face challenges like reducing carbon emissions and embracing circular economy strategies (Feng & Jiang, 2022). Sustainable supply chain management has become central, with eco-friendly sourcing and energy-efficient logistics playing key roles (Jean, 2024). In this context, Artificial Intelligence (AI) has emerged as a powerful tool for driving sustainability (Podrecca et al., 2024). However, much of the research has focused on macro-level outcomes, with less attention on micro-level mechanisms enabling AI to achieve sustainability goals. This study addresses this gap by exploring how AI’s microfoundations drive sustainable performance. A mixed-method approach was employed, combining secondary data from Factiva and semi-structured interviews. A total of 70 cases and 13 interviews were analyzed. Data analysis, structured around the CIMO framework (Context-Intervention-Mechanism-Outcome), combined inductive and deductive approaches. Data was categorized by AI application contexts such as resource optimization, supply chain synchronization, and reverse logistics. AI interventions (e.g., machine learning, deep learning) were linked to mechanisms like data processing and emissions forecasting. The findings revealed AI’s role in optimizing supply chain processes and driving sustainability through circular economy practices, energy efficiency, and material savings. AI also improved regulatory compliance and risk management, enabling more accurate decision-making. This study contributes to both theory and practice by applying the microfoundations perspective to AI-driven sustainability, offering insights for managers to optimize AI adoption strategies and drive change.

ABSTRACT. Humanitarian logistics corresponds to unforeseen catastrophic events that disrupt societal functions, resulting in losses that surpass a community's capacity to manage with available resources. It encompasses addressing various needs, including disaster relief and support, in the context of natural, man-made, or complex emergencies in developing regions. It is crucial in disaster relief operations, encompassing activities such as site selection, inventory management, and optimization of emergency management centers to guarantee that assistance reaches impacted areas. The main objective of the paper is to pinpoint the barriers to creating a central, accessible city logistics system network that can deliver rapid and efficient assistance during a disaster. The study focuses on overcoming these barriers and highlights factors that should be taken into account while choosing the center's location to reduce the disaster's impact. Since no other research has explicitly addressed these barriers, this study is unique in that it focuses on the particular difficulties associated with choosing locations for emergency management centers in Turkey. In this context, by identifying and evaluating the barriers to choosing the best sites for emergency management centers in Turkey, this research contributes to the body of knowledge and provides a novel paradigm for overcoming these obstacles and enhancing disaster response capabilities. According to the anticipated outcomes, a thorough framework for choosing the best sites for emergency management centers will be developed, improving disaster response capabilities and reducing operating interruptions.

ABSTRACT. Purpose Traditionally valued for reducing purchasing costs, dual sourcing is now a crucial practice to achieve structural flexibility contributing to resilience in times of an increasingly disruptive and uncertain environment. However, limited research has been conducted on achieving structural flexibility by utilizing and developing different practices of dual sourcing. Hence, the purpose of this study is to understand how to achieve structural flexibility from different dual sourcing types and what kind of structural flexibility dimensions are generated through this.

Design/Methodology A multiple case study was used to investigate two cases of different dual sourcing types. This study investigated the utilization and development of different dual sourcing practices and their achievement regarding structural flexibility. Data was collected through semi-structured interviews offering insights into different practices of dual sourcing.

Findings The findings reveal that rather the integration and utilization of dual sourcing types achieve certain structural flexibility dimensions than the types themselves. Based on that different structural flexibility dimensions on different levels are achieved.

Practical Implications Implications include guidance on how to exploit different dual sourcing strategies to achieve certain structural flexibility dimensions.

Value Given the limited number of papers investigating structural flexibility on its achievement and practices, this paper is academically contributing to the literature on understanding how dual sourcing can achieve structural flexibility.

ABSTRACT. As the increase of environmental performance constitutes a concern for businesses, it is becoming necessary to predict how supply chain engagement increases environmental performance. The research gap grounds on previous literature where the positive dependency between supplier engagement and environmental performance have recognized but effects of both on various indicators of sustainability need further explanation (Belhadi et al., 2024; Butt et al., 2024; Sharma et al., 2024; Lintukangas et al., 2022). Based on the previous, the article assesses supply chain information sharing as an antecedent for both customer and supplier engagement which mediating effects to supply chain environmental performance has explained. The research question is framed as follows: How do information sharing and supplier or customer engagement impact environmental performance?

Based on quantitative survey data, we created a model with moderation and mediation and analyzed it by using PLS-SEM. The survey sample included 158 respondents from diverse industries, predominantly from medium-sized firms (51–250 employees), representing 58.2% of the sample. The questionnaire was distributed to 481 firms, yielding a response rate of 33%. Empirical findings highlight the role of supplier and customer engagement in mediating the relationship between supply chain information sharing and environmental performance. Information sharing significantly boosts both supplier and customer engagement, with supplier engagement notably enhancing environmental performance. Internal connectivity strengthens the link between information sharing and supplier engagement. Results emphasize the importance of strategic supplier engagement for achieving environmental sustainability in supply chains.

ABSTRACT. Mountain regions present unique challenges for supply chain management due to geographical, topographical, and spatial factors. These challenges make companies in these regions particularly susceptible to supply chain disruptions. This study aims to understand how companies' location influences supply chain structure, which is critical for resilience and the ability to adapt to and recover from disruptions. In this regard, the study answers the research question: How do companies in mountain regions structure their supply chains compared to those in non-mountain areas? To answer the research question, we use secondary data from the FactSet platform to analyze firm profiles and supply networks, with mountain firms identified via GPS coordinates and the GMBA V2.0 mountain map, and non-mountain firms through nearest-neighbour propensity score matching. Ordinary least squares regression is used to assess the impact of location on supply chain structure. The analyses are still in progress, but we expected that companies in mountain regions will exhibit narrower and less fragmented networks with fewer supply chain partners. This tendency is attributed to the high specialization of many mountain firms, which often serve niche markets and require suppliers with specific capabilities. Sectoral differences may also influence variations in supply chain structures. This research contributes to theory by being one of the first studies to explore how location decisions affect supply chain design, particularly the decision to locate firms in mountain regions. Practically, the results will offer best practices for companies in mountain areas and provide valuable information for policymakers to support regional resilience.

ABSTRACT. The Italian National Healthcare System ensures comprehensive health coverage based on universality, equity, and legality. Historically, services were hospital-centric, with poor integration between hospital services and primary care, leading to overcrowding of the Emergency Department (ED) and negatively affecting efficiency, patient outcomes, and costs (Lega & Mengoni, 2008). To address these issues, the focus was on enhancing primary care capacity through reforms (Mapelli et al., 2007) and establishing Community Health Centres (CHCs) where General Practitioners (GPs), specialists, nurses, and administrative staff collaborate. Between 2006 and 2015, 52 CHCs opened in the Tuscany region. This study assesses the impact of CHCs on reducing inappropriate ED visits, categorised as Minor Urgent or Non-Urgent, which contribute to overcrowding and strain resources (Berchet, 2015). It is focused on the districts of Arezzo, Siena, and Grosseto, managed by the Azienda USL Toscana Sud-Est. Previous research showed mixed results regarding the effect of improved primary care on ED use (Hong et al., 2020). This analysis uses the synthetic control method (SC). This statistical technique constructs a synthetic version of the treated unit (areas with CHCs) by combining control areas that did not receive the intervention. The SC method allows adjustment for pre-treatment characteristics, especially with one treated unit and many control units (Abadie & Gardeazabal, 2003; Abadie et al., 2010). The method creates a counterfactual unit by weighting control units to match pretreatment trends (Arkhangelsky et al., 2021). A key advantage of SC is its intuitive graphical presentation of treatment effects (Mithas et al., 2022).

ABSTRACT. We sought to improve efficiency of a pre-operative clinic by using Lean principles to enhance surgical patient clearance and reduce day-of-surgery cancellations. The Lean principles used were situational analysis, resource identification, lead time calculation, systematic guidelines creation, redundancy reduction, and communication improvement. We assessed patient readiness and changes in peri-operative efficiency in a resource-constrained environment. The current cancellation rate is less than one percent. Our documentation of the application of Lean principles to improve an existing process can provide a basis towards the development of a Lean framework to improve the pre-operative patient clearance process at other facilities.

ABSTRACT. We investigate how a network of healthcare facilities should manage non-pandemic and pandemic demand to minimize patient wait for care. At the center of our analysis is the question of whether a particular facility must operate in a specialized (i.e., caring for a single patient type) or a generalized (i.e., caring for both patient types) mode. To address this question, we develop a two-facility optimization model that captures the impact of mix-variability on patient waiting time. We provide full analytical characterization of the optimal demand allocation when facility capacities are equal and a numerical algorithm for finding the optimal solution when they are not. Our results show that a partially mixed configuration involving one specialized and one generalized facility is optimal for a wide range of pandemic and non-pandemic demand rates. Furthermore, we demonstrate that implementing the optimal partially mixed configurations could have reduced waiting times by up to 6.6% and 1.9% during England’s first and second waves of the Covid-19 pandemic, respectively, compared to the fully mixed configuration adopted by many English trusts.

ABSTRACT. Balancing the often-competing objectives of productivity and safety presents an enduring challenge in Operations Management, particularly within warehouse operations. Organizations employ various strategies to promote both safety and productivity; however, the combined impact of prioritizing speed, and safety in warehouse settings remains inadequately understood. This study employs two controlled laboratory experiments using a Virtual Reality (VR) simulator to explore how audio reminders influence warehouse vehicle drivers’ performance. It also explores the effects of timing and content specificity of safety and productivity audio reminders. The results reveal that audio reminders with generic content at generic times can significantly enhance productivity by 12%, while concurrently leading to a 9% reduction in safety. However, no effect of safety reminders was found. The second study reaffirms the absence of an effect of safety reminders when extending the investigation to audio reminders that emphasize specific content and occur at specific times. The findings suggest that although reminders can boost productivity, consistently using safety reminders—whether general or specific—might not significantly enhance warehouse vehicle drivers’ safety performance. This implies that fostering workplace safety requires long-term strategies aimed at altering employee behaviors, something which short and quick messages alone cannot achieve.

ABSTRACT. Empathy at the workplace requires a constant infusion from management to ensure satisfaction among internal and external customers. However, management struggles to measure the organization’s empathy barometer, especially in small and medium enterprises (SME) due to the small population size. The current work is a pilot study toward understanding employee empathy while triaging patients for scheduling and other relevant services. Twenty-two (22) employees of a sleep diagnostic management organization in the Midwest, USA, responded to The Toronto Empathy Questionnaire (TEQ). The sleep diagnostic management organization processes approximately 10,000 patient referrals and ~ USD 2.5 million in revenue. The results revealed that the TEQ alone could not assess empathy. Yet the pilot research project motivated the leadership to continue to conduct surveys to determine long-term empathy and launch a customer feedback survey. The management sought empathy training, citing favorable behavioral changes in employees and reduced customer concerns. The current work highlights the importance of developing research and mechanisms for SMEs with small populations to assess empathy and improve customer service.

ABSTRACT. As hybrid work environments reshape organizational dynamics, the role of trust-based management in collaborative teams within manufacturing processes requires further investigation. This systematic literature review examines how trust-based management shapes team performance in hybrid manufacturing settings, identifying key themes, challenges, and research gaps.

Following PRISMA guidelines, this study systematically reviews peer-reviewed literature from major academic databases, identifying key themes in trust-based management, collaboration, and process efficiency. Existing research indicates that trust may facilitate knowledge sharing, reduce uncertainty, and enhance coordination and decision-making abilities in hybrid teams, but these studies vary in their assessment of these effects across different industries and organizational structures. Additionally, while some research highlights the benefits of trust in virtual collaboration, others emphasize challenges related to trust asymmetry, digital communication barriers, and inconsistent managerial support.

Through a comprehensive analysis of existing studies, this review aims to identify potential gaps in the literature, particularly regarding the long-term impact of trust-based management on hybrid manufacturing workflows. Future research may need to address empirical validation across different manufacturing contexts and examine long-term evolution on trust dynamics and the interaction between trust and digital collaboration tools. This study provides a synthesis of existing research and identifies potential gaps for future explorationon shaping team performance in evolving industrial work environments.

ABSTRACT. The feasibility of alternatives in strategic level multi-criteria decision analysis problems is often ambiguous. Indeed, traditional evaluations rely on subjective evaluations of alternatives by a group of expert decision makers, with the aim to rank alternatives based on a matrix of criteria and weights discerned from Likert scale or pairwise comparison based questionnaires. Such approaches require researchers to define reference points for comparison which may not appropriately capture experts’ opinion about the feasibility of alternatives. For example, the best-worst method uses the distance between two reference points – the best and worst criteria – as a metric to rank alternatives based on decision makers’ preference. This does not always cover the entire range of feasible options, as reference points do not necessarily constitute the ideal or negative ideal scenario for decision makers. Inspired by the endowment effect, we propose a model which identifies these boundaries and uses them as reference points to evaluate strategic alternatives based on their feasibility. Specifically, we calculate decision-makers’ loss aversion about alternatives using the distance between alternatives and the ideal and negative ideal reference points, according to decision makers’ opinions. We then aggregate results across alternatives and criteria as a measure representative of feasibility. This approach can be used as a standalone measure when considering strategic decision making problems with single or few alternatives (e.g., whether to invest or not) or in addition to typical weighting methods for problems with numerous alternatives.

ABSTRACT. Digital technologies are transforming fresh produce retail by enabling real-time freshness tracking and direct consumer access to supply chain data. However, adopting these technologies comes at a high cost. It is therefore essential to determine whether an innovator—the first retailer to adopt supply transparency technology—gains a competitive edge over a follower, who adopts it only after the innovator, and under what conditions this advantage justifies the investment. In a duopoly with freshness-sensitive demand, we show that the innovator benefits from higher profits, greater consumer satisfaction, and improved demand fulfillment—particularly when consumers value transparency. Additionally, the innovator curbs more food waste through a market shrinkage effect, where the competitor’s market share contracts, reducing supply-demand mismatches. We further find that industry-wide adoption is more likely in less competitive markets, suggesting that grocery retail mergers could accelerate transparency adoption and enhance consumer demand satisfaction. Finally, we extend our analysis to cases involving freshness-based discounts and sequential adoption.

ABSTRACT. There is growing demand for an environmentally sustainable fashion supply chain from stakeholders to improve the working conditions in developing nations. Hence, SC actors must reconcile the environmental sustainability from two perspectives (waste management and pollution & emission) while also evaluating the role of digital tools in achieving these goals. This paper investigates how digital tools moderate the relationship between resilience practices and environmental sustainability. It focuses on four resilience constructs—visibility, agility, flexibility, and collaboration and several digital tools, which are commonly cited in the supply chain literature. This cross-sectional study collects data from 190 actors in the fashion supply chain in Bangladesh through a questionnaire. The PLS-SEM was conducted using Smart-PLS 4.0 to test the proposed hypotheses. Our findings particularly confirm that digital tools strengthen the impact of visibility on waste management of environmental sustainability. That means the greater adoption of digital tools for transparency among supply chain actors enhanced efficient waste management, remanufacturing, green supply chains, circular economy and natural conservation in fashion supply chain operations. Visibility through information sharing has been moderated by blockchain-based circular economy, AI-based waste management, and technology-based green supply chain design for environmental sustainability. However, our study did not find any other moderating effect of digital tools in connecting agility, flexibility, collaboration, and environmental sustainability. These mixed outcomes underscore the need for further confirmatory analyses, incorporating constructs across various industries. A key avenue for advancing the theoretical model is examining specific digital tools rather than considering digital tools as a collective entity.

ABSTRACT. For decades, autism was often viewed as a struggle with information and context. Neisser’s Perceptual Cycle Model (PCM) and research by Frith, Baron-Cohen, and Happé illustrate that autistic individuals frequently find it challenging to adjust their mental frameworks. They also face difficulties in adapting to various environments. These concepts influenced workplace policies, suggesting that autistic employees require ongoing intervention rather than strategic support. However, recent studies challenge this view. Research by Pellicano, Clark, and Van de Cruys demonstrates that autistic individuals possess unique ways of thinking. They do not lack cognitive flexibility. Their predictive models function distinctively, resulting in alternative decision-making strategies that many workplaces overlook. This study introduces the AI-Enhanced Predictive Cognitive Variability (AI-PCV) model. It merges AI cognitive profiling, heuristic decision-making analysis, and predictive processing to develop personalized job strategies for autistic individuals. The model employs AI analysis to identify each person’s cognitive strengths, facilitating job matching. Additionally, it enhances communication and automates workplace accommodations. Examining history reveals connections to Neisser’s background, challenging outdated deficit-based autism models. It provides a fresh perspective that links predictive processing concepts with AI-driven job strategies. AI-driven cognitive adaptation can help bridge the employment gap for autistic individuals, shifting the focus from fixing deficits toward integrating strengths. This shift impacts research on neurodiversity, job policies, and AI in cognitive modeling, paving the way for a more inclusive workplace.

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. In today's complex supply chains, companies must make strategic decisions frequently to remain competitive. Several software providers offer robust decision-making solutions to support businesses in this endeavor. Nevertheless, without adequate investment in digitalization and fostering a company culture that embraces this new paradigm by all stakeholders, it is challenging for the entire supply chain to fully leverage these software tools and make informed strategic decisions. This paper quantifies the optimal value of incentives to promote investments in digitalization based on existing contractual parameters through the lens of game theory, within a supply chain network comprising a manufacturing company, a Third-Party Logistics (3PL) provider and a last-mile logistics carrier. The primary objective of this paper is to design incentives that motivate 3PL and last-mile carriers to enhance their investment in digitalization, improving the overall logistic service level. Utilizing game theory models, we examine the interactions among supply chain actors, exploring various scenarios and their impact on the investment decisions of supply chain actors. The findings indicate that well-designed incentives can significantly increase the willingness of 3PL and last-mile carriers to adopt digital solutions, resulting in a more efficient and responsive supply chain. This research contributes to the understanding of how strategic incentives can foster technological advancements and operational excellence in modern supply chains.

ABSTRACT. Implementing circular economy principles in practice presents significant challenges. These include technical barriers related to the handling and upgrading of materials, as well as market-related obstacles stemming from underdeveloped and inefficient markets for circular materials. Circular material flows inherently increase the exchange of resources, thereby introducing new interdependencies. Substitutability and pricing are closely tied to the availability and quality of materials.In construction projects, high levels of interdependence exist among the various actors involved in relation to material and resource flows. Achieving circularity and ensuring a consistent supply of recyclable materials thus necessitate enhanced coordination among stakeholders. Accordingly, the purpose of this paper is to explore the coordination requirements in circular construction processes and to propose effective coordination mechanisms that support circular supply chain initiatives.

The focus is specifically on the supply side, aiming to ensure the availability of high-quality material volumes suitable for recycling by mineral wool producers. The study adopts a case study approach, examining the material flow of mineral wool within a residential construction project. Data were collected through interviews, direct observations, and analysis of internal project documentation. The findings provide a detailed account of the interdependencies characterizing material flows and underscore the importance of early-stage planning and strong interorganizational relationships as key coordination mechanisms. The paper contributes by identifying which coordination mechanisms various actors need to implement to maintain stable and efficient flows of circular materials in construction, decreasing present uncertainty of quality, quantity and location. Time uncertainty can be decreased through sharing of plans.

ABSTRACT. Understanding culture, supply contracts, supply chains, and business relationships is at the heart of any successful supplier-customer relationship. Often thought of as a modern managerial issue, we find a story in the Bible, to suggest that negotiating, or renegotiating, a contract for supply comes from a two thousand year old source. A central aspect of the Parable of the ‘Dishonest’ Manager, is the discussion and alteration of business contracts. The requirement arose in the context of a Manager of a large agricultural estate, located in Galilee, being accused of financial ineptitude. The Manager clears his name with the owner of the estate by changing the contractual terms of supply for olive oil and wheat, fundamental staples of an agricultural economy. Theological commentators have missed the importance of the scale of the oil and wheat to be supplied. I argue these are not small farmers, but large concerns. The Manager had to find markets for these suppliers, which could only be found in Imperial Rome, a vast city of 1 million people, which dominated the Mediterranean in the first four centuries AD. There was a sophisticated supply chain, with high volumes of product. I analyse the procurement and supply chain using modern procurement theory, including Kraljic’s Matrix, Lean Supply, and managing risk and resilience. The study highlights issues which are pertinent to any well-ordered supply chain - knowing your suppliers, having excellent relationships with customers and stakeholders, including those in logistics and finance, and having a well-ordered international financial system.

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. Guided by the dynamic capabilities theory, a quantitative research design was employed, collecting data from delivery firms operating in urban areas in Ghana. Structural Equation Modelling (SEM) was used to test the direct and indirect relationships among the variables. The results showed that urbanisation has a strong, significant effect on last-mile delivery. Furthermore, agile logistics capability partially mediates the relationship between urbanisation and last-mile delivery. Unlike previous studies that examine these variables in isolation, this research provided a holistic understanding of how agile logistics capabilities 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. Decision-makers at various levels are often required to make critical choices with limited information. However, most existing decision support frameworks prioritize data presentation rather than extracting actionable insights, limiting their effectiveness in guiding informed decision-making. A key challenge is overdependence on data without sufficiently integrating expert interpretation, resulting in too generalized or contextually irrelevant outputs. The outcome of the decision will rely only on the interpretation of the data. This paper investigates the impact of the narrative approach and machine learning on communication across different leadership levels, focusing on their role in enhancing information transfer and supporting the effective handover of topic-specific tacit knowledge and data. The research summarizes the publications that focus on the area and analyses the problem area through a transdisciplinary perspective. Relevant research articles and studies were gathered by querying major academic databases, including Scopus, Web of Science, and ScienceDirect from the past five years. The review identifies that narrative reporting practices are often used in accounting and sustainability-related reporting. It tends to show fewer charts and cumulated numbers and more analysis results based on the input, translated and preprocessed by subject matter experts. In the case of narrative reporting, there is a possibility of personalizing and adding a sentiment; this concept is referred to as Narrative Disclosure Tone (NDT). The findings indicated that expanding machine learning techniques and decision support systems (DSS) accelerate the development of innovative reporting methods while integrating experts' tacit knowledge with data.

ABSTRACT. For more than a century, public accounting firms have operated in stable organizational forms; traditional partnerships expanded to partnership networks with modified legal structures to limit liability. This, however, is rapidly changing. In North America and Europe, some public accounting firms are reorganizing to accommodate substantial investments from private equity firms. Private equity investors find the accountancy sector attractive due to stable revenue streams, the ability improve operational efficiency, and opportunities to scale through acquiring multiple smaller firms in the fragmented sector. However, the reorganization and ownership changes associated with private equity investment also introduce risks including the potential to compromise the quality of assurance services.

The unique role that auditors play in protecting investors and the integrity of capital markets warrants careful scrutiny of the implications of the evolving organizational forms in public accounting. Private equity investments can support increased operational efficiency and potentially help address current talent shortages. However, these opportunities are accompanied by challenges. Areas of concern include potential conflicts of interest, priority of profit over audit quality, concentration in the accounting and professional services industry, marginalization of assurance providers within public accounting firms, and erosion of public trust. Ultimately, new organizational forms are pushing the boundary between professions and businesses.

This paper provides descriptive evidence about the spread of private equity investments and new organizational forms in accounting firms. In addition, it analyzes potential implications for audit quality, investor confidence, the role of financial regulation, and the future of accountancy as a profession.

ABSTRACT. Abstract

This research develops a model of the post war US inflation experience. Quarterly time series macroeconomic data is taken from Federal Reserve Bank of St. Louis data facility, i.e. FRED, and is analyzed using graphical (histograms, time-line charts, scatterplots) and analytical methods (descriptive statistics, correlation and distributed lag correlations, as well as linear OLS and robust regressions. Percent change in Money Supply as a theoretical construct of the Monetary School of Economic Thought, Capacity Utilization, and Percent Change in Oil Prices are found to be statistically significant in the prediction of quarterly inflation with various lag-distributions. Unemployment Rate and Percent change in Real GDP, i.e. real economic growth, were found not to be significant.

The results both reinforce and refine the results of previous research and have profound implications for public policy. Consistent with the previous research, support for the Quantity Theory of Money, Supply Side Economics and Cost Push theory of inflation is advanced. Also, and consistent with the previous research, support for Demand Pull Inflation and the Phillips Curve Theory is denied.

This research improves upon previous research by increasing granularity from Annual down to Quarterly data. Modest improvement to predictive power is advanced with the primary contribution to identifying a more precise lag distribution of variable impact.

The analysis is conducted using S+, the advanced data modeling software.