Problem Definition Research Articles

The Effects of Policy-Interested Bureaucrats on State and Local Policymaking

From 1971 to 2023, 19 states, 24 counties, and 105 cities passed Source-of-Income Anti-Discrimination (SOI) laws, outlawing landlord discrimination against tenants receiving rental assistance. I present a theory of bureaucratic involvement in state and local policymaking, arguing that policy-interested local bureaucratic agencies—signaled by participation in a national membership organization—elevate mission-relevant policy problem definitions to the legislative agenda and motivate the passage of legislation. I present results from a Cox Proportional Hazards Model which demonstrates that SOI laws are significantly more likely to be enacted in cities, counties, and states associated with a public agency which is a member of the Council of Large Public Housing Authorities, even compared to jurisdictions associated with non-member Public Housing Authorities of similar sizes.

Civil society organizations as policy entrepreneurs: the practices of problem definition in the case of Myitsone Dam suspension of Myanmar

ABSTRACT The study explores the role of civil society organizations in Myanmar as policy entrepreneurs in the suspension of the Myitsone Dam project. It emphasizes their strategic use of framing and discursive practices within a non-democratic context. By analysing their involvement and strategies, the paper illustrates how civil society organizations define the problems that demand political attention and, by extension, influence policy change in restricted political environments. The findings contribute to the broader discourse on policy entrepreneurship, demonstrating its relevance across varying political landscapes.

Terapia daremna - problemy definicyjne

In my article, I discuss the definitional challenges concerning the concept of futile therapy and review and evaluate the strategies and definitional proposals that have appeared in the literature. Difficulties in adequately defining the concept of futile therapy are typical of the definitional problems encountered in defining medical notions, especially in relation to end-of-life procedures, where we are faced with a field of inadequately defined concepts with overlapping meanings and scopes. In addition to futile therapy, this conceptual field consists of ‘ordinary’ (proportionate) and ‘extraordinary’ (disproportionate) means, as well as the concept of the patient's best interests. In the Polish medical literature, this picture is further complicated by the use of the term ‘overzealous therapy’.

Workforce Configuration in Charity Settings: A Forward-Looking Approach

Problem definition: Volunteers, the primary workforce for many charities, represent a complex labor pool; they are unreliable and exhibit substantial heterogeneity in both performance and affinity to the organization. Additionally, many volunteers engage not only to contribute but also to immerse themselves in a volunteering experience that, if rewarding, can inspire them to become future donors. However, practical approaches to volunteer management commonly neglect these traits and the consequential impact that tactical decision-making can have on nurturing potential future donations. Methodology/results: Building on a previous study, we propose a forward-looking volunteer scheduling model that accounts for the heterogeneity among volunteers, mitigates both understaffing and overstaffing costs, and explicitly correlates individual time contribution with their monetary donations. We provide analytical solutions when the charity can reliably estimate distributions (e.g., uniform distribution) from data and suggest a distribution-free method to offer actionable insights where data are limited or uncertain. Managerial implications: At the strategic level, by viewing volunteers as potential donors, the optimal staffing strategy balances meeting the charity’s labor needs and maximizing volunteers’ satisfaction, as this satisfaction influences their likelihood of becoming future donors. We show that charities could avert substantial losses by adopting an integrative approach, thereby challenging conventional organizational structures that compartmentalize volunteer and donor management. Our model suggests that building robust data infrastructures can significantly advance the charity’s core mission. Paradoxically, efforts to increase labor productivity may inadvertently undermine this objective. At the operational level, we provide an Excel-based decision support tool and a decision-tree framework to navigate optimal policies, determining when and how a charity can rely on episodic (less reliable) volunteers. Our results confirm that reducing uncertainty in volunteer turnout benefits charities. However, we also find that when labor value is low, episodic volunteers are preferred, whereas formal (reliable) volunteers are favored when labor value is high. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0363 .

The Impact of the Opportunity Zone Program on Residential Real Estate

Problem definition: Opportunity zones (OZs) are designated census tracts in which real estate investments can gain tax benefits. Introduced by the U.S. Tax Cuts and Jobs Act of 2017, the goal of the OZ program is to foster economic development in distressed neighborhoods. In this paper, we investigate and optimize the OZ selection process and examine the impact of OZs by exploiting two data sets: a proprietary real estate data set that includes 36.1 million residential transactions spanning all 50 U.S. states and census-tract demographics data between 2010 and 2019. Methodology/results: We show that census tracts with higher poverty and unemployment rates were more likely to be selected. Counterintuitively, however, tracts with a higher average real estate price were also more likely to be selected. We then apply difference-in-differences, synthetic control, and matching techniques to rigorously assess the impact of the OZ program on two key real estate metrics: price and transaction volume. We find that the OZ program increased real estate prices by 4.03%–6.13% but do not observe a significant effect on the transaction volume. We also find that investors primarily targeted the high-end real estate market, namely, exhibiting a cherry-picking behavior. To better fulfill its intended societal and economic goals, we propose an optimization framework with fairness considerations for OZ assignment decisions. We show that the OZs assigned from our fairness-aware optimization formulation can better serve distressed communities and mitigate investors’ cherry-picking behavior. Managerial implications: Our paper underscores the importance of incorporating fairness in OZ designation to achieve a desirable real estate market reaction. Our large-scale empirical analysis provides a comprehensive assessment of the current government OZ assignment, and our fairness-aware optimization framework provides concrete recommendations for policy makers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0746 .

Implementing innovation in project-based learning in electro-mechanical engineering education

This study investigates the implementation of project-based learning (PBL) in electro-mechanical engineering education, emphasizing the modeling and application of an innovation process to enhance student engagement, motivation, and employability. Initially, the innovation process was systematically modeled using Structured Analysis and Design Technique (SADT) to manage and organize skills, knowledge, and resources effectively. This model was subsequently applied in an educational experience focused on highly technical aspects such as energy conversion modeling, mechanical improvements, material science, and efficiency analysis. These aspects were primarily applied in the context of permanent magnet synchronous machines employed in electric vehicle (EV) axles. The SADT modeling allowed for a clear delineation of the innovation phases, ensuring a structured approach to project execution. Statistical analysis of the PBL approach indicated significant improvements in student performance, with a 25% increase in problem-solving skills and a 30% enhancement in critical thinking abilities. The case study demonstrated that industry-oriented projects significantly boost student satisfaction, particularly in problem definition and solution identification, leading to a more engaging and effective learning experience.

Conceptual paradigms and theoretical problems in the medical definition of old age

Not available.

Full Quantity or Full Credit? Choosing the Right Buyback Policy for a Behavioral Retailer

Problem definition: Despite extensive research on buyback contracts in supply chain management, little attention has been given to a direct comparison between a traditional full-quantity, partial-credit (FQ) buyback contract and an alternative full-credit, partial-quantity (FC) contract, especially when considering the behavioral biases of the retailer. This study aims to fill this research gap by exploring how these biases influence contract performance and offer practical guidelines for suppliers. Methodology/results: Through controlled laboratory experiments and structural model estimation, we show that the ordering decisions made by human retailers are jointly affected by three behavioral biases—loss aversion, prospective accounting, and psychological cost of inventory error—and these biases differ in magnitude between the two contracts. Incorporating these biases into a behavioral retailer’s utility function, we then analytically examine which buyback policy the supplier should choose. When the wholesale price is exogenously given, the supplier should adopt the FC contract for high-margin (low-cost) products. Conversely, when the supplier has the power to set both the wholesale and buyback prices, the FC contract is generally more beneficial for low-margin products. Finally, when the product cost is sufficiently high, a wholesale price contract may be advantageous over buyback policies. Managerial implications: The managerial implication of this paper is significant for suppliers who have to choose between different buyback policies. The paper suggests that the supplier should carefully consider not just the economic, but also the behavioral dimensions when making contract decisions, particularly when dealing with a retailer influenced by behavioral biases. Specifically, the choice between FQ and FC contracts is influenced by two key environmental factors: the production cost of the product and the nature of wholesale pricing. Understanding these elements allows suppliers to be flexible in contract choices and design appropriate return policies for their downstream supply chain partners. Funding: P. Yu was supported by the National Natural Science Foundation of China [Grants 72371038 and 72033003] and the Fundamental Research Funds for the Central Universities [Grant 2024CDJSKPT16]. T. Feng was supported by the National Natural Science Foundation of China [Grant 72131004]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0574 .

Innovative Approach: Leveraging Design Thinking for Enhancing Knowledge Sharing in Knowledge Intensive Firms

Purpose: This research delves into the intricate challenges of knowledge management (KM) within technical departments of knowledge-intensive firms (KIFs), with a specific focus on preserving critical knowledge amidst employee turnover. Recognizing the four main components of KM—people, process, content/IT, and strategy—and emphasizing the paramount importance of individuals in KM alongside the human-centric approach of design thinking (DT), the study aims to enhance KM processes, particularly knowledge sharing (KS), utilizing DT to fortify organizational success. Methodology: This study utilizes mixed method action research, which includes in-depth interviews and observations, immersion and questionnaire to collect employee insights. Thematic analysis is utilized to identify key challenges. Employing the DT framework, the research progresses through problem definition, ideation, prototyping, testing, and iterative refinement stages, following the structured steps of DT. Findings: Thematic analysis identifies persistent KM challenges, encompassing individual, organizational, and technology barriers. These challenges are effectively addressed through innovative solutions developed with DT principles, leading to tangible enhancements in KM processes. Iterative cycles of prototyping and testing further refine these solutions, particularly in facilitating KS among employees. Originality: This research demonstrates the efficacy of DT in addressing KM challenges within KIFs. Through prioritizing human-centric problem-solving, DT's iterative and learning-oriented nature boosts organizational efficiency and stimulates innovation. The direct engagement of end-users in solution design further highlights the cost-effectiveness of this approach, ultimately enhancing KM outcomes. Contribution: The study presents solutions tailored to the unique challenges of KM, transferable not only within the technical department but also across KIFs. Given the prevalence of unsuccessful KM implementations, the human-centric ethos of DT emerges as a pragmatic and economical approach for diagnosing and mitigating KM obstacles. Nonetheless, the study highlights the adaptability of DT in addressing specific KM issues, offering a pathway for other departments to tackle their KM challenges effectively.

Stochastic Resource Allocation Problems: Minmax and Maxmin Solutions

Problem definition: Min-max or max-min objective criteria naturally arise in many resource allocation applications, especially when the attendant goal involves distribution of a limited resource across multiple subsystems, locations, or activities. Each has a different consumption rate of the resource. In this setting, the decision maker is interested in optimizing the system performance, which can be measured in terms of the smallest or largest order statistic across subsystems. Examples include the allocation of health resources to maximize the first runout time or to minimize the maximum delay. Methodology/results: In the literature, there are no unified theoretical and computational results on the optimal allocations for these problems. Thus, in this paper, we develop key theoretical properties of the attendant stochastic resource allocation problems, providing a technical condition for the objective functions to have favorable properties, upon which we derive novel optimal allocation rules with clear interpretation. Managerial implications: The proportional allocation rule can be interpreted as a robust solution when the full distribution of the interarrival time is unknown. It generally has poor performance, especially when the resource consumption rates vary across locations, which happens regularly in practice. Our allocation rule is exact and should be used for the allocation of scarce resources—that is, when the number of resources per location is limited, which is often encountered in healthcare settings.

From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.

Hematoxylin- and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology has enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs by using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages that have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of tumors high in MSI. Moreover, we provide an open-source code base, which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge.

Optimal Salesforce Compensation with General Demand and Operational Considerations

Problem definition: We investigate the optimal salesforce compensation scheme in the context of private information and unobservable actions, considering common operational factors encountered in practice, including inventory costs, contractible versus censored demand information, and controlled versus delegated ordering. Methodology/results: Based on an agency model with general demand and cost functions, we derive optimality conditions for implementable contracts that can achieve the second-best outcome in all scenarios. The contracts are in the forms of a menu with linear compensation for demand or sales, incorporating inventory costs. Moreover, the contracts feature adjustments in compensation corresponding to the ordering level if it is delegated. Managerial implications: Our study reveals that, under reasonably mild conditions, optimal salesforce contracts can still maintain relatively simple forms, even when confronted with common operational factors and generalized demand and cost functions. However, the contracts must be tailored to suit the operational settings. Intriguingly, neither the loss of demand information nor the delegation of inventory decisions would compromise system efficiency at optimum. Funding: H. Song is partially supported by the Key International Cooperation and Exchange Projects of the NSFC [Grant W2411062] and the Foundation for Innovative Research Groups of the NSFC [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0400 .

Supplying Cash-Constrained Retailers: Understanding Shopkeeper Behavior at the Bottom of the Pyramid

Problem definition: As the largest retail channel in the world, nanostores are the source of income for millions of shopkeepers in developing countries. Being cash constrained, nanostore shopkeepers face the challenge of deciding how much of their available cash to retain to support their families and how much to invest in acquiring products from their suppliers, who visit the nanostore with different frequencies and offer products with different margins. We study how shopkeepers deal with this challenge while seeking to make their nanostores financially sustainable. Methodology/results: We conduct an empirical study to explain how suppliers’ visit frequency influences shopkeepers’ ordering behavior. The study integrates more than 29 million orders placed with a multinational manufacturer. Results show that visit frequency adjustments by the supplier cause nanostores to experience a significant discontinuity in their orders such that reduced order frequency leads to significantly lower orders per unit of time. Building on this insight, we ran two experimental studies that make use of theoretical benchmarks to explore how shopkeepers’ ordering decisions are influenced by supplier visit frequency, product margin, and shopkeepers’ cash constraints. We analyze how and when shopkeepers deviate from the theoretical benchmarks and show how the available budget moderates the relationship between suppliers’ visit frequencies and shopkeepers’ order volumes, normalized by unit of time. Our results give support to the theory of diversification bias and show how this bias might bring additional benefits to suppliers offering low-margin products. Finally, we propose a behavioral model that explains how shopkeepers’ decisions might be driven by their preference toward inventory (i.e., leftover versus shortage aversion) and toward cash (i.e., liquidity preferences). Managerial implications: Our findings suggest that suppliers should deploy high-frequency visits to cash-constrained nanostores regardless of the margin that their products provide for shopkeepers. For low-margin suppliers, this mitigates shopkeepers’ tendencies to reduce the order volume per unit of time following a visit frequency decrease. For high-margin suppliers, high-frequency visits mitigate shopkeepers’ overdiversification bias and liquidity preference. Financial and digital schemes that reduce the need for high-frequency visits may lead to substantial efficiency gains. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0362 .

The Networked Data Lab: lessons from four years of analytical collaboration on common problems in health on social care

ObjectiveThe Networked Data Lab (NDL) is a UK-wide network of analytical teams embedded in the health and care system. Each year, we support five local partners to acquire and link new health and social care datasets on a different topic. The NDL provides insights from this linked data to help UK policymakers improve health and care, leveraging public, patient, and stakeholder engagement to prioritise topics of shared local interest. Approach Now in its fourth year, the NDL has commissioned a Learning Study (LS) to evaluate progress towards its aims. This employs a theory-based approach to test and assess key assumptions about programme impact through a Theory of Change. For the study, semi-structured interviews with analysts and stakeholders at the five NDL sites were conducted to identify factors influencing the programme’s impact. ResultsDrawing on interim LS findings, we will describe the types of impact the programme has achieved, and some of the key barriers and enablers to analytical collaboration across geographic and organisational boundaries. Themes include senior sponsorship; stakeholder input into problem definition; information governance and tools and skills. Themes will be illustrated with case studies drawn from the different NDL topic areas, including children and young people’s mental health and unpaid carers. Conclusions and ImplicationsThe Networked Data Lab shows that data linkages can provide unique insights that local service planners can act on, but highlights a complex set of conditions that need to be met to enable impactful analytics using real-world health and care data.

Social Learning and Content Quality Under Polarization

Problem definition: This paper studies how polarization influences content consumption and production on digital platforms that monetize consumer engagement. Specifically, we consider a content that advocates a particular position on a divisive issue. Consumers with polarized preferences toward the content’s position are sequentially exposed to the content. Initially, consumers are uncertain about the content quality, but they have the opportunity to learn about it using aggregate consumption metrics and other informative signals provided by the platform. Methodology/results: Using a stylized model, we find that under polarization, social learning based on consumption metrics can mislead consumers to perceive low-quality content as higher quality, even in the long run. Consequently, content providers may decrease their effort to improve content quality. These effects are amplified for more polarizing issues, especially when the content’s position is “mainstream” (i.e., aligned with the majority of the population). Our results thus provide a potential explanation for the proliferation of low-quality, polarizing content on platforms. Managerial implications: We offer normative guidance for content platforms seeking to enhance content quality, including offering consumers information to aid the social learning mechanism and appropriately selecting audience to mitigate the echo-chamber effect. Additionally, we propose payment schemes based on content popularity as an effective means to encourage content providers to improve quality. Funding: This work was supported by the Hong Kong Research Grants Council [General Research Fund 16502920]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0533 .

Strategic Cross-Subsidization in Healthcare Capitation Programs: Evidence from Medicare Advantage

Problem definition: This study identifies a resource misallocation problem in Medicare Advantage (MA), the United States’ largest healthcare capitation program, which may result in discrepancies between patients’ health status and the healthcare resources allocated to them. Methodology/results: Utilizing a large commercial insurance database with claims from more than 2 million MA enrollees, this research investigates the allocation of MA capitation payments. By exploiting an exogenous policy shock on MA capitation payments through a difference-in-difference design, we find empirical evidence of an illegal practice known as “cross-subsidization.” This practice involves MA health plans strategically reallocating portions of the capitation payments intended for one group of patients to spend on another group of patients. Additionally, we show that this cross-subsidization practice is associated with the risk selection problem in MA, where low-risk patients are more likely to enroll in MA compared with high-risk patients. Managerial implications: This research unveils a previously undocumented healthcare resource misallocation problem, that is, strategic cross-subsidization. This practice is explicitly prohibited by law in the United States due to its heightened effect on the undesired risk selection within capitation programs, where health plans cherry-pick profitable enrollees through strategic benefit designs. Our study has direct practical implications as it underscores the need for greater transparency in MA claims data to enable the Centers for Medicare & Medicaid Services to more effectively administer the MA program. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0637 .

A survey on temporal knowledge graph embedding: Models and applications

Knowledge graph embedding (KGE), as a pivotal technology in artificial intelligence, plays a significant role in enhancing the logical reasoning and management efficiency of downstream tasks in knowledge graphs (KGs). It maps the intricate structure of a KG to a continuous vector space. Conventional KGE techniques primarily focus on representing static data within a KG. However, in the real world, facts frequently change over time, as exemplified by evolving social relationships and news events. The effective utilization of embedding technologies to represent KGs that integrate temporal data has gained significant scholarly interest. This paper comprehensively reviews the existing methods for learning KG representations that incorporate temporal data. It offers a highly intuitive perspective by categorizing temporal KGE (TKGE) methods into seven main classes based on dynamic evolution models and extensions of static KGE. The review covers various aspects of TKGE, including the background, problem definition, symbolic representation, training process, commonly used datasets, evaluation schemes, and relevant research. Furthermore, detailed descriptions of related embedding models are provided, followed by an introduction to typical downstream tasks in temporal KG scenarios. Finally, the paper concludes by summarizing the challenges faced in TKGE and outlining future research directions.

LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models.

In clinical science and practice, text data, such as clinical letters or procedure reports, is stored in an unstructured way. This type of data is not a quantifiable resource for any kind of quantitative investigations and any manual review or structured information retrieval is time-consuming and costly. The capabilities of Large Language Models (LLMs) mark a paradigm shift in natural language processing and offer new possibilities for structured Information Extraction (IE) from medical free text. This protocol describes a workflow for LLM based information extraction (LLM-AIx), enabling extraction of predefined entities from unstructured text using privacy preserving LLMs. By converting unstructured clinical text into structured data, LLM-AIx addresses a critical barrier in clinical research and practice, where the efficient extraction of information is essential for improving clinical decision-making, enhancing patient outcomes, and facilitating large-scale data analysis. The protocol consists of four main processing steps: 1) Problem definition and data preparation, 2) data preprocessing, 3) LLM-based IE and 4) output evaluation. LLM-AIx allows integration on local hospital hardware without the need of transferring any patient data to external servers. As example tasks, we applied LLM-AIx for the anonymization of fictitious clinical letters from patients with pulmonary embolism. Additionally, we extracted symptoms and laterality of the pulmonary embolism of these fictitious letters. We demonstrate troubleshooting for potential problems within the pipeline with an IE on a real-world dataset, 100 pathology reports from the Cancer Genome Atlas Program (TCGA), for TNM stage extraction. LLM-AIx can be executed without any programming knowledge via an easy-to-use interface and in no more than a few minutes or hours, depending on the LLM model selected.

Israel and Its Elephants: Problems of Definition, Narrative, and Analogy in Discussing Antisemitism

Israel and Its Elephants: Problems of Definition, Narrative, and Analogy in Discussing Antisemitism

THE APPLICATION OF SWAN IDEA SOURCE ON BRIDAL FASHION

Swans are the most loyal animals that only mate once in a lifetime. The purpose of this research is to find out the process of making and the finished result of bridal fashion with a swan as the source of the idea. This research method uses a three-stage design process approach. This method consists of three stages of research: problem definition and research, creative exploration and implementation. The design direction was determined in the initial stage, and in the second stage, a moodboard was made. The implementation stage involves applying a swan feather and manipulating fabric decoration as the centre of attention on the bridal outfit. The process of manipulating fabric starts with preparing copper wire, segment fabric and brocade fabric, then cutting to form strands of swan feathers and sewn with balut stitch on all sides to unite the three materials. The results of this research show bridal fashion with feather-shaped manipulating fabric decoration, with two designs of men's and women's bridal fashion. The final result is the style of Muslim clothing, according to the provisions of Islamic law, intended to cover parts of the body that are inappropriate to be shown to the public. The primary material is tille sequins and satin maxmara lining with the appropriate size, but it does not show the shape of the woman's body so that it can be worn for Muslim women wearing hijab.