Department of Decision Sciences

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https://hdl.handle.net/20.500.14078/22

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Now showing 1 - 20 of 101

A review of practices for adjusting exam scores and a proposed nonlinear scaling method
(2025) Orchard, Ryan
Advanced practices for summative exam development and post-exam analysis are proven to be effective but aren’t always practical, and, even when these are applied to some degree, exams remain inherently imperfect measures of student ability. Instructors may thus deem it necessary to adjust overall exam scores to account for aspects of an exam that may have been ill-suited to some or all students, and often these adjustments are made in an ad hoc and/or uninformed manner. This paper reviews reasons and methods for adjusting exam scores and proposes a new method that was developed organically from observation, reflection, and literature consultation. The scaling method considers that some of the underlying reasons for adjusting exam scores may affect certain sets of students more than others and seeks to incorporate this proposition while also avoiding weaknesses of other methods.
Cost-sharing mechanisms for product quality improvement in supply chains
(2022) Chakraborty, Tulika; Mukherjee, Arka
In today's dynamic and competitive modern business environment, the performance of a supply chain is increasingly driven by quality improvement and effective coordination mechanisms. The rising costs associated with innovation or quality enhancement force companies to collaborate within the supply chain to maintain a competitive edge. This chapter explores coordinating contracts in the context of product quality to incentivize quality improvement and align the objectives of the supply chain. Specifically, we focus on the cost-sharing contracts and how they relate to the existing quality models, employing both static and dynamic game-theoretic frameworks. Our objectives are: (1) to introduce the concept of quality within the supply chain context, (2) to analyze how different contract structures influence supplier-manufacturer relationships, optimize profit sharing, and mitigate risks associated with quality failures, (3) to provide different industrial examples of how firms can excel in quality management, and finally (4) to explore the future research agenda in this particular area of the supply chain. Our chapter aims to build a bridge between academics and practitioners in operations management, marketing, operations research, and industrial engineering in the context of supply chain contracts. Furthermore, our chapter emphasizes the strategic interplay among stakeholders in competitive and disruptive frameworks.
Dynamic pricing and advertising decisions to mitigate the negative spillover effect of a product recall
(2025) Mukherjee, Arka; Chauhan, Satyaveer S.
Severe loss of reputation and its lasting effect on a firm’s sales are the consequences of a product harm crisis. Research shows that a product recall can result in a loss of goodwill for the firm responsible for the recall and its rival, especially when the rival is from the same country of origin. Alternatively, rival firms from different countries of origin may benefit from the recall. Firms often adjust pricing and advertising decisions in the post-recall period to mitigate a product recall’s harmful effect on profit. In our study, we investigate the negative spillover effect of a product recall on a firm. Using a differential game-theoretic framework, we analyze how pricing and advertising decisions for a firm and its rival differ in the pre-crisis and the post-crisis regimes. We also examine the impact of the crisis on firms’ profits. Based on our analysis, we highlighted suitable strategies for different recall impacts and likelihoods. We also find that the focal firm’s market leadership may not always be profitable during a product recall. Our findings highlight the importance of crisis likelihood and damage on which the firms’ decisions depend.
Pricing and AI technology adoption decisions in a competitive supply chain network
(2025) Ma, Jun; Dong, June; Tu, Yiliu
This paper develops an equilibrium model to investigate pricing and technology upgrade decisions in the context of supply chain versus supply chain competition. The model considers a two-echelon supply chain where manufacturers and retailers make decisions on pricing, output, and artificial intelligence technology upgrades. Demand is influenced by both price and service level, and technology upgrades implicitly enhance customer utility by improving the service level. These interdependencies across the supply chain significantly impact overall profitability. Consequently, pricing and technology upgrade decisions must be optimized collectively rather than in isolation. Three key features of this model are highlighted: First, our model is built upon the context of supply chain versus supply chain competition, rather than the context of firm versus firm competition. Second, it integrates pricing and technology upgrade decisions to enhance the overall performance of a supply chain network. The interplay between pricing strategies, technological advancements, and customer utility is analyzed to improve supply chain efficiency and competitiveness. Third, the model assumes that heterogeneous customers are sensitive to both price and service level, with technology upgrades playing a pivotal role in shaping customer preferences. We utilize variational inequalities to characterize the equilibrium conditions of the supply chain, enabling robust analysis and decision-making. This study provides a novel framework for understanding pricing and technology upgrade strategies in a competitive supply chain network, offering actionable insights for decision-makers seeking to optimize supply chain performance and enhance customer satisfaction in competitive markets.
The dual path of digitalization: evaluating the impact of bank-FinTech collaborations on standardized and customized service performance
(2025) Cao, Ting; Kristal, Murat; Huang, Xiaowen; Chi, Maomao; Tu, Yiliu Paul
This study explores bank-FinTech collaboration in retail banking, focusing on digitalizing existing and new service offerings. Leveraging primary data from Canadian banks and credit unions, we categorize digital services into high- and low-standardized services and offer empirical support for the interplay among digitalization efforts, operational agility, FinTech collaboration, and service performance. Results show that digitalizing existing services enhances performance across both categories, while digitalizing new services only enhances high-standardized service performance. Interestingly, collaborating with FinTech firms positively impacts low-standardized services, especially in banks with high operational agility, yet offers limited additional benefits for the ongoing digitalization of existing services.
Managerial self-references in corporate disclosures: an analysis of MD&A
(2024) Hao, Yamin; Mao, Ying; Wang, Xiaojia
This study examines strategic pronoun usage in the Management Discussion and Analysis (MD&A) section of annual reports. Through automated textual analysis of a large sample of MD&As, we find that managers of firms with higher earnings growth tend to use more self-inclusive pronouns (e.g., “we,” “us,” and “our”) and fewer self-exclusive words (e.g., “the company”). This self-referential language pattern is associated with a higher likelihood of future financial restatements. Our findings contribute to the literature on corporate narrative disclosures and identify a potential new indicator of financial misstatements. The results have implications for investors, analysts, auditors, and regulators.
AI and VR: shaping the next generation of adaptive learning and development programmes
(2024) Enstroem, Rickard; Bhawna, Bhawna; Kumar, Dinesh; Suthar, Nidhi; Taherdoost, Hamed; Madanchian, Mitra
This chapter explores the transformative potential of integrating Artificial Intelligence (AI) with Virtual Reality (VR) in developing adaptive learning and development (L&D) programs. Traditional L&D methodologies are increasingly inadequate in the face of rapidly changing business environments. AI and VR technologies offer unprecedented opportunities to personalize learning experiences, enhance engagement, and improve outcomes. This chapter provides a comprehensive overview of current trends, applications, challenges, and future directions of AI and VR in L&D. Key findings emphasize the role of these technologies in fostering continuous learning cultures, addressing individual learner needs, and enhancing organizational effectiveness. Practical insights and case studies are included to guide HR professionals in leveraging AI and VR for innovative and effective L&D programs.
Decoding the modern supply chain management professional: the industry’s voice
(2025) Enstroem, Rickard; Kang, Parminder Singh
This study examines the evolving nature of supply chain management (SCM) in response to increasing complexity and the expanding scope of competencies required of SCM professionals. It lays the groundwork for developing a comprehensive competency framework aligned with current industry needs.
Work integrated learning
(2026) Enstroem, Rickard
This chapter explores Work Integrated Learning (WIL) as an educational approach that blends academic learning with practical work experience to enhance employability and develop transversal competencies. WIL’s foundations in experiential learning emphasize structured work placements, mentorship, and reflective practices. It highlights different WIL models, including internships and project-based learning, while addressing challenges such as logistical and regulatory barriers. The chapter also examines current trends like remote collaboration and virtual WIL.
Curriculum development
(2026) Enstroem, Rickard
Curriculum development is the process of establishing the structure and content that equip students with knowledge and competencies to meet academic, professional, and societal demands. This chapter examines the foundational theories, processes, and frameworks that guide curriculum development in higher education. It addresses the role of stakeholders in aligning curricula with institutional priorities, educational trends, and the needs of learners. Future directions in curriculum development are considered, focusing on adaptive technologies, competency-based learning, and strategies for integrating transversal competencies within scalable frameworks.
Academic misconduct
(2026) Enstroem, Rickard; Benson, Lyle
This chapter provides an overview of common forms of academic misconduct and examines the personal, institutional, and systemic factors that contribute to such behaviors. It explores strategies for detecting and preventing misconduct, with a focus on the role of technology, faculty oversight, and cultural initiatives. The broader impact of academic misconduct on students, institutions, and society is also addressed, highlighting the importance of creating a culture of academic integrity through comprehensive policies and educational interventions.
E-learning
(2026) Enstroem, Rickard; Bhawna, Bhawna
E-learning leverages digital technologies and learning platforms to deliver educational content that is flexible, scalable, accessible, and interactive. In higher education, it supports several delivery modalities, including online, hybrid, and face-to-face formats. This chapter examines the theoretical principles underpinning e-learning, its foundational design components, and its role in supporting learner engagement and personalization. It highlights persistent challenges like digital equity and academic integrity and explores emerging trends, including artificial intelligence and immersive technologies. The chapter addresses these themes and provides insights into how e-learning reshapes higher education to meet evolving pedagogical needs.
Enterprise education
(2026) Enstroem, Rickard
Enterprise education equips individuals with the attributes, competencies, and behaviors necessary to navigate and succeed in diverse professional environments. This chapter explores the historical evolution, core components, and methodologies of enterprise education. Enterprise education bridges the gap between formal education and real-world applications by integrating experiential learning and work readiness frameworks. The chapter also highlights effective classroom strategies and measurement tools that assess the impact of enterprise education on student outcomes.
A text mining study of competencies in modern supply chain management with skillset mapping
(2025) Kang, Parminder Singh; Enstroem, Rickard; Bhawna, Bhawna; Bennett, Owen
This study explores the skills and competencies required by modern supply chain management professionals, focusing on the shift toward advanced technological capabilities. We analyze job advertisements from a prominent Canadian employment platform using web scraping, natural language processing, and machine learning techniques, including Latent Dirichlet Allocation and Term Frequency-Inverse Document Frequency. The findings reveal that job postings primarily emphasize traditional operational skills such as logistics, inventory control, and customer relationship management. However, there is a noticeable underrepresentation of advanced technological competencies, such as machine learning, data analytics, and automation, which are increasingly critical in today's supply chain environment. This gap highlights the need for greater alignment between job market demands and supply chain management's evolving digital transformation landscape. The study identifies key themes, including technical, managerial, and soft skills integration, emphasizing adaptability, data literacy, and strategic decision-making. The results suggest a misalignment between the competencies highlighted in job advertisements and the skills necessary for managing the complexities of a digitalized supply chain. This research offers practical recommendations for industry leaders to refine hiring strategies, academic institutions to modernize curricula, and job platforms to better showcase emerging skill requirements. Addressing this gap is essential to equip supply chain professionals with the tools and expertise to meet the challenges of a technology-driven future.
Exploring the impact of customized academic technology resources on first-year university students’ digital competency: preliminary results
(2025) Leung, Mavis; dos Santos Nogueira de Góes, Fernanda; Hesemeier, Susan
In response to the growing demand for digital competency among university students, this study examines the effectiveness of customized academic technology resources in enhancing first-year students’ technology skills within a Canadian post-secondary context. Many incoming students struggle with essential academic technology due to limited digital literacy or unfamiliarity with specific tools. To bridge these gaps, we developed tailored resources to support their learning. This research employs a pre- and post-test comparison study to assess the impact of these resources. An intervention group with access to the materials is compared to a control group without access. By measuring changes in students’ technology skills and confidence, the study highlights the potential benefits of targeted support and discusses implications for improving students’ transitions to university.
Hospitality for prime consumers and others under the retail management
(2024) Mukherjee, Arka; Bhattacharya, Sandipa; Sarkar, Mitali; Sarkar, Biswajit
Despite concerns about social welfare over its green consumption, retail management is an emerging prime consumer service as hospitality that promotes sustainability towards their consumers. The study emphasizes the retailing policy to the prime consumers concerning global competitiveness and perspectives. It addresses the changing perception of purchasing necessary products of prime consumers due to greening commercialization. The main findings reveal that the subjective norm perceives consumers' service, effectiveness, agreeableness, and intentions correspond to the major interventions of consumers primarily. Effects on the computational results and performances are measured through the efficiency of the product concerning the case studies by undertaking the fairness concern. The investigation highlights several solutions and associations with the rebate on governmental subsidies towards the subjective norm in retailing. A 2% discount achieves 65% commercial benefit by transforming the conventional policies analytically. Interestingly, the empirical solutions explore which policy is better from the edge at an economic attractiveness to the prioritization of the exclusive prime consumers' preference. It determines the stability of how the conservative and aggressive retailing strategies integrate into the potential structure of the system. A smart and proper policy eliminates the deficiency in retailing by promoting a considerable influence based on the analytical and numerical studies to the industry practitioners more than previous. The implications of current hospitality for consumers research provide valuable insights for retail managers in maximizing trading operations and efforts to encourage environmentally responsible purchasing decisions.
Bridging the gap: a systematic analysis of circular economy, supply chain management, and digitization for sustainability and resilience
(2024) Kang, Parminder Singh; Bhawna; Sharma, Sanjeev Kumar
The primary objective of this research paper is to conduct a comprehensive and systematic literature review (SLR) focusing on Sustainable Supply Chain Management (SSCM) practices that promote Circular Economy (CE), sustainability, and resilience through adopting emerging digital technologies. A SLR of 130 research articles published between 1991 and 2023 was used to analyze emerging trends in CE, supply chain management (SCM), and digitalization. This study meticulously examined research publication patterns, the intricate themes explored, influential scholars, leading countries, and substantial scientific contributions that have shaped this multifaceted domain. This paper contributed to the collective understanding of how SSCM practices, driven by the principles of CE and empowered by the adoption of digital technologies, foster sustainability, resilience, and innovation within contemporary SCs. The research findings presented herein are primarily based on an analysis of the current literature from only Scopus and Web of Science (WoS) databases, which may restrict the generalizability of implementing these results. Based on this study, organizations and practitioners can assess the maturity of their SCM practices, gauge the resilience and digitalization levels of their SCs, and align them with academic literature trends. This enables practitioners to bridge the gap between scholarly advancements and real-world SCM implementation. Through its systematic review, the study provides a structured literature review that offers a collective understanding of SSCM practices driven by CE principles and empowered by digital technologies. This understanding enables sustainability, resilience, and innovation within contemporary SCs, benefiting academicians and practitioners.
Enhancing supply chain resilience through supervised machine learning: supplier performance analysis and risk profiling for a multi-class classification problem
(2025) Kang, Parminder Singh; Bhawna, Bhawna
Purpose This paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature. Design/methodology/approach This research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations. Findings The study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service. Research limitations/implications The findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties. Practical implications The practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly. Social implications The study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains. Originality/value Unlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.
On the effectiveness of option contracts under supply disruption
(2023) Son, Joong Y.
This paper studies the effectiveness of implementing option contracts for the procurement of seasonal products subject to short selling season, demand uncertainty, and supply-side disruption. The research intends to show how profitability and product availability can be enhanced both locally and globally by combining the long-term wholesale price contract and option contracts. Using the newsvendor model, the paper aims to identify business settings with respect to disruption parameters, demand uncertainty, and the option pricing under which the use of option contracts could improve supply chain performance. The main contribution of this research is that the effectiveness of option contracts is investigated under the impact of the supply-side disruption in addition to the demand uncertainty. The option contract-based portfolio procurement displays significant performance enhancement in terms of both the retailer profitability and the reduction in the lost sales quantity when supply-side disruptions prevail. The study of the procurement management subject to seasonal disruption can be readily applied to numerous business situations where the disruption can lead to devastating impacts such as the insufficient or untimely supply of COVID-19 vaccines with limited shelf life.
Service 4.0: Technology-enabled customer-centric supply chains
(2024) Kang, Parminder Singh; Wang, Xiaojia; Son, Joong Y.; Jat, Mohsin
This book presents a systematic framework for Service 4.0, including service digitization, digitalization, and digital transformation, which is an integral part of Supply Chain 4.0 in coping with complex, dynamic, and interdependent systems. It provides a comprehensive state-of-the-art review of digital technologies to support Service 4.0 and Supply Chain 4.0, and discusses important pillars of customer-centric supply chain models. It then explains the role of big data in customer-centric service-based supply chains and links the different types of data needed to promote end-to-end transparency and value co-creation activities to promote these key pillars. Moreover, the book introduces practical models to support analytics for customer-centric supply chains and sheds light on how the industry practically uses existing models to promote service co-creation. A chapter of a case study on women's clothing e-commerce reviews and demonstrates how various data visualization and text mining methods can be used to uncover meaningful insights within the review data. The book is intended to help students and researchers quickly navigate through various technologies and future research directions in the areas of Service 4.0 and Supply Chain 4.0. It is also a valuable read for practitioners in this field.

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