Empirical Case Study Research Articles

Contiguity of underutilized lands: Dynamic simulation taking their temporary uses into account

In Japan, shrinking densely built-up cities face the dual issue of lacking open spaces and increasing underutilized lands, such as vacant lots, lots with vacant houses, and parking lots. These unused land patches can be temporarily repurposed as open spaces for evacuation and recreation. However, identifying such clump is methodologically challenging. To address this issue, lot geometry is utilized. The study thus aims to investigate the frequency and size of contiguous underutilized lands, called the contiguity of underutilized lands, at a specific time point and under their temporary uses. A densely built-up area in Kobe city, Japan, was selected for the empirical case study. A comparison with simulation results shows that the observed static contiguity of underutilized lands tends to be more substantial than a uniformly random distribution. It shows a certain feasibility of a temporary use policy conducted in the case site. Specifically, when considering the temporary uses of underutilized lands, the maximum area of contiguous temporary open spaces is 583 m2, meeting the area requirement for a redevelopment project in Kobe. Utilizing parking lots can further extend the maximum area up to 945 m2. Nevertheless, policy makers need to promote the joint development of privately owned lots facing a wide roadway, as these are unlikely to become temporary open spaces. This study contributes not only to providing new methods for land use change simulation using lot geometry to analyse the contiguity of underutilized lands under their temporariness but also to demonstrating the feasibility and limitations of a temporary use policy.

An Empirical Case Study GeoAI Usage for Sustainability Scoring of University Campuses

UI GreenMetric (GM) ranking system is a methodology to score and rank the university campuses in several categories. The campuses are measured according to their various sustainability indicators in this methodology. The base of this research to study on how we can use geoAI to calculate GM Setting & Infrastructure (SI) score. By this empirical case study to have ideas connected with the relation of score point and geospatial metrics by recognized of google map geotiff images of university campuses. To develop a geoIA methodology with KooMap cloud service in the purpose of sustainability scoring would provide a practical, accurate and pretty objective algorithm for a smart campus evaluation. In the way of transforming to smart city these applications and utilizations of geoAI are the digital trends of new era.

Revisiting the change curve: A rigorous examination and three case studies prompting a re-evaluation of a timeless concept

BACKGROUND: Numerous models of the so called ‘change curve’ exist, yet there is a paucity of scholarly inquiry regarding empirical substantiation, practical implementation, and general applicability. OBJECTIVE: This study explores the change curve, aiming to examine whether and how the various phases of the curve manifest in a business context, thus assessing their relevance, which represents a novel approach. METHODS: The analysis, employing a mixed-methods approach of qualitative and quantitative elements, is inductive and interpretative, focusing on exploring and understanding the complexity of the cases. Data collection of this exploratory research was conducted utilizing a multi-faceted approach that included online surveys, semi-structured interviews, on-site observations through Gemba Walks. RESULTS: A pivotal discovery arises from the validation of the four phases of the change curve. Irrespective of disparate project contexts, individuals manifest akin responses within their respective phases of the change curve, amenable to effective facilitation through judicious interventions. These interventions, derived and systematized from empirical case studies for the first time, underscore their potential utility in guiding individuals through the change process. CONCLUSIONS: The validation of the four phases and their pragmatic utility warrant further exploration in subsequent scholarly inquiries, aiming to ascertain their robustness across diverse organizational contexts.

How can a regional innovation system meet circular economy challenges? Conceptualization and empirical insights from Germany

Abstract Promoting the circular economy (CE) increasingly draws on regional approaches. But in particular the potentially supportive role of regionally provided innovations requires more exploration. This paper suggests an expanded categorization of innovation demands for the regional CE and integrates them into an enriched conceptualization of the challenge-oriented regional innovation system (CORIS). How the proposed framework can guide regional analysis and strategy building is then illustrated by the empirical case study of the German ‘Circular City’ Aachen. Structuring obtained information according to CORIS components reveals which requirements are met, but also which innovation deficiencies should be tackled locally.

Assessing law students in a GenAI world to create knowledgeable future lawyers

ABSTRACT Assessing law students has always been a challenging task, but the introduction of Generative Artificial Intelligence (GenAI), such as ChatGPT, compounds the problems already caused by increased student numbers, contract cheating and budget cuts in universities. As GenAI rapidly develops, legal educators must find ways to accommodate, and even incorporate, GenAI into their curricula and assessments so that law graduates can understand its capabilities and limitations within legal practice. Simultaneously, many jurisdictions, including Australia, have legislative obligations to deliver law graduates who satisfy legal knowledge-based “eligibility” requirements for admission into practice. This article introduces a knowledge framework for managing GenAI in legal education consisting of three pillars: Substantive Legal Knowledge, GenAI Ethics Knowledge, and GenAI System Knowledge. The authors argue this framework can assist legal educators in designing optimal assessments in an AI-disrupted world. The article employs the knowledge framework to examine the experiences and views of Australian law students’ engagement with GenAI outputs in completing a compulsory legal ethics assessment in 2023. This empirical case study demonstrates that effective assessment design incorporating GenAI can enhance law student and graduate outcomes despite the ongoing challenges for legal educators and the profession associated with GenAI.

EFL Writer Identity Construction in Academic Writings: A Systematic Review

English, as the most widely used language in academic publications, poses great challenges for novice EFL researchers not only in the language competence, but also in constructing authorial identity in academic writing because the codes for the construction of writer identity is implicit. This paper reviews the research on identity construction in academic writing among EFL writers published in key applied linguistics journals between 2000 and 2024, and summarizes the current research status of writer identity construction in academic writing and delves into the characteristics and influencing factors of identity construction. Through in-depth analysis of the theoretical framework and the characteristics of the EFL writers, we have selected representative papers, including empirical studies, theoretical discussions, and case studies, to ensure the comprehensiveness and accuracy of this review.The study found that the construction of writer identity in academic writing is a complex and multifaceted process. It is not only influenced by the English language proficiency of the writer but also by a combination of factors such as individual experiences, academic background, and sociocultural factors. These factors collectively shape the writer’s identity and recognition by others in the academic community, thereby affecting their status and influence in the academic field. This paper aims to provide valuable references and inspirations for future research on academic writing and writer identity construction.

Untapping the potential of mobile location data: The opportunities and challenges for retail analytics

Smartphone technology has created a burgeoning mobile location data (MLD) marketplace. MLD has been adopted by many retailers to track consumer behaviours at a granular level across space and time. This paper provides a critical empirical case study approach to generate spatiotemporal MLD-based insights using raw smartphone data at five super-regional shopping centres. The analysis and visualization of MLD-based metrics provide the basis to rethink how we approach retail analytics and location decision-making. Many challenges need to be addressed to leverage the full potential of MLD, including data bias, spatial and temporal accuracy and significant consumer privacy concerns.

A hybrid spatiotemporal distribution forecast methodology for IES vulnerabilities under uncertain and imprecise space-air-ground monitoring data scenarios

The weak spots in an integrated energy system that may jeopardize the overall reliability call for timely and efficient Inspection and Maintenance (I&M). One core step is the reasonable allocation and deployment of limited I&M personnel or apparatus to the regions or periods with higher event risks, which requires a pinpoint spatiotemporal distribution forecast of future vulnerabilities. This paper presents a hybrid forecast methodology, the Saliency-Rough Fuzzy Utility Pattern recognition ensemble, in light of space-air-ground multi-source-heterogeneous input data. A parallel learning architecture is established and identifies the critical components with higher yields to enhance efficiency. Accordingly, more reasonable quantitative and qualitative evaluations can be carried out concurrently. Potential imprecise and uncertain data scenes are handled in quantitative assessments, both the failure hazard path sets and survival function likelihood boxes are incorporated in the designed relative path-Fussell Vesely Saliency (rp-FVS) model; and in qualitative analyses, the underlying perilous components can be distinguished via a combination of the variable precision-rough model. The rp-FVS-based fuzzy inference logic configures all membership functions identically according to components’ impacts. These two parts are integrated into the rough-fuzzy Utility Measure to discover concealed component-vulnerability interconnection patterns. Finally, an empirical case study is conducted for validation.

Harmonizing Intelligence: A Holistic Approach to Bias Mitigation in Artificial Intelligence (AI)

Artificial intelligence (AI) is transforming the way we interact with data, leading to a growing concern about bias. This study aims to address this issue by developing intelligent algorithms that can identify and prevent new biases in AI systems. The strategy involves combining innovative machine-learning techniques, ethical considerations, and interdisciplinary perspectives to address bias at various stages, including data collection, model training, and decision-making processes. The proposed strategy uses robust model evaluation techniques, adaptive learning strategies, and fairness-aware machine learning algorithms to ensure AI systems function fairly across diverse demographic groups. The paper also highlights the importance of diverse and representative datasets and the inclusion of underrepresented groups in training. The goal is to develop AI models that reduce prejudice while maintaining moral norms, promoting user acceptance and trust. Empirical evaluations and case studies demonstrate the effectiveness of this approach, contributing to the ongoing conversation about bias reduction in AI.

Machine learning-based causal inference for evaluating intervention in travel behaviour research: A difference-in-differences framework

Causal inference with the difference-in-differences (DID) framework is popular in identifying causal effects with observational data and has started to be applied in recent travel behaviour studies. Most relevant transportation research adopts the conventional linear parametric DID model, which is known to be inflexible and restrictive. This study applies non-parametric DID estimators facilitated by machine learning (ML) models for causal inference in a variety of data scenarios. Semi-parametric and doubly robust estimators are established and integrated with the ML-based cross-fitting pipeline. Simulation studies and empirical case studies are conducted to showcase the ability of ML-based DID to detect causal effects from both simulated and real-world datasets. Results suggest that the proposed methods outperform conventional DID models in all data scenarios. Light working models are generally preferred over hyperparameter-dependent ones for their comparable performance, lower computational burden, and higher levels of compatibility to real-world empirical analysis. Empirical case studies also demonstrate how the proposed DID method could be applied to evaluate the impacts of various interventions on travel behaviour in different contexts. The present study adds to the existing travel behaviour literature by leveraging machine learning algorithms and non-parametric estimators to the impact evaluation of external interventions on travel characteristics and expanding the application of causal inference approaches in transportation research.

Smart and Sustainable Energy Consumption: A Bibliometric Review and Visualization

This paper presents a comprehensive bibliometric review and visualization of smart and sustainable energy consumption, delving into the challenges and opportunities of developing renewable and non-renewable energy sources. The study examines research trends and emerging themes about integrating smart solutions and sustainable energy resource consumption. The analytical methods used involve thoroughly analyzing empirical data, case studies, and review papers to map the research landscape. The results highlight dominant research topics, influential authors, and publication timelines in this field. The review identifies the key challenges in harnessing renewable and non-renewable energy sources, including the need for reliable energy sources, energy storage systems, and smart grid technologies. The paper concludes with insights into the most effective practices for promoting smart and energy-efficient methods while emphasizing the complexity of sustainable energy solutions.

Adaptive evolution and dynamic mechanism of resort socioecological system in tourism cities: The case of Qinhuangdao, China

Resort socioecological systems (SESs) have changed significantly worldwide because of rapid urbanisation and tourism development. Exploring the dynamics and driving mechanisms of resort SES at different stages is beneficial for understanding urban adaption to future pressure, creating elaborate strategies for tourism innovation, and achieving sustainable resort development. Therefore, this study proposed the Adaptive-TALC model as a novel analytical framework for resort evolution, using Qinhuangdao Resort as an empirical case study. The results revealed that 1) from 1979 to 2023, Qinhuangdao tourism resort had been gradually transformed from a simple socioecological system based on natural resource utilization to a complex adaptive system integrating natural and human resource utilization, 2) governance systems played a pivotal role in resort evolution, and business utilized digital technology to innovate resort products, thereby accelerating system renewal and reorganisation, and 3) within the context of panarchy, Aranya's holiday products serve as a model for local businesses to improve and innovate their products and ultimately promote Qinhuangdao's sustainable development. This study contributes to theories of tourist destination evolution by providing a novel framework for the cognition of the tourism resource sustainable utilization in resorts and provides insights into innovative approaches such as nature-cultural hybrid solutions, adaptive urban planning, and digital tourism, enabling tourism cities to enhance their adaption and mitigate potential risks.

Analysis and Prediction of Stock Price using HMM and Facebook’s Prophet Computational Models

In recent times, the utilization of Statistical and Machine Learning techniques has gained prominence in the realm of financial data analysis. These methods are applied to various types of financial data, encompassing textual information, numerical data, and graphical representations. This study aims to compare the performance of two prominent forecasting methods, Hidden Markov Models and Facebook’s Prophet in the context of stock price prediction. Assessing the predictive accuracy, interpretability, and adaptability of both approaches through empirical experiments and case studies sheds light on their respective advantages and limitations. These experiments demonstrate that the predicted stock prices are in closer proximity to the actual price when compared to using a single data source. Furthermore, the achieved MAPE are 0.01, 0.025 and respectively, outperforming conventional methodologies. Our validation of effectiveness extends to real-world datasets encompassing the NIFTY50 Index. These findings offer valuable insights for researchers and practitioners seeking effective strategies for stock price prediction.

Environmentality in (food) sustainability education: an analysis of a pedagogic intervention in Italy

ABSTRACT This article applies a governmentality perspective to approach education for sustainability. First, we outline governmentality through a Foucauldian lens and consider the emergence of environmentality in Italian education. Next, we analyse education for sustainability teaching and learning dynamics. Focusing on a pedagogic intervention carried out in a hotel-management vocational high school, we explore how school programming, classroom interactions, teachers, and environmental experts conceptualise food sustainability, influencing the pupils' view. Precisely, by analysing the ‘From farm to fork' programme – the empirical case study we have investigated – three pedagogic processes are detected: ‘framing', ‘politicising', and ‘normalising'. Our argument is that they work in tandem to problematise the socio-ecological implications of eating consumption choices and to guide students towards the neoliberal solution of sustainability. Implications of this research discuss how neoliberalism influences pedagogic activities, especially as it relates to establishing environmentally responsible action and shaping the ethics of the sustainable citizen.

INCREASING OR DECREASING FRUGALITY: THE CONNECTION BETWEEN DIGITALISATION AND FRUGAL INNOVATION

Within wider debates on sustainability and digitalisation, frugal innovation (FI) scholars largely assume that digital technologies are important for FI in realising more sustainable outcomes. However, very few studies interrogate this causality. To tackle this challenge, we connect FI with digitalisation. Thereto, we conceptualise digital technologies as and within FIs and discuss three frugality dimensions to analyse three empirical case studies of digitally-enabled FIs. We use these cases to introduce new nuances on how digitalisation affects frugality and scaling. Our results unveil that digitalisation can increase frugality by enabling more accessible and affordable solutions through new flexible funding schemes and pay-as-you-go models. However, our evidence also implies decreasing frugality by an increase in end-user costs and digital exclusion. Likewise, increasing frugality through complexity reduction seems mainly to benefit intermediaries and frugal innovators themselves, whereas benefits of digitalisation in terms of complexity reduction for end-users seem to be limited. Digitalisation can even increase complexity for end-users, thus suggesting decreasing frugality. Finally, just like with non-digital innovations, scaling of digitally enabled innovations is dependent on the quality of logistical infrastructure and local adaptation practices. Moreover, scaling of digitally-enabled innovations is limited to users in regions with a proper ICT infrastructure.

Risk reduction through managed retreat? Investigating enabling conditions and assessing resettlement effects on community resilience in Metro Manila

Abstract. Managed retreat, a key strategy in climate change adaptation for areas with high hazard exposure, raises concerns due to its disruptive nature, vulnerability issues and overall risk in the new location. On-site upgrading or near-site resettlement is seen as more appropriate and effective compared to a relocation far from the former place of living. However, these conclusions often refer to only a very limited set of empirical case studies or do not sufficiently consider different context conditions and phases in resettlement. Against this background, this paper examines the conditions and factors contributing to community resilience of different resettlement projects in Metro Manila. In this urban agglomeration reside an estimated 500 000 informal households, with more than 100 000 occupying high-risk areas. In light of the already realized and anticipated climate change effects, this precarious living situation exposes families, already socio-economically vulnerable, to an increased risk of flooding. The response of the Philippine government to the vexing problem of informal dwellers has been large-scale resettlement from coasts, rivers and creeks to state-owned sites at urban fringes. However, only very few resettlement projects could be realized as in-city projects close to the original living space. The study employs a sequential mixed-method approach, integrating a large-scale quantitative household survey and focus group discussions (FGDs) for a robust comparison of resettlement types. Further, it reveals community-defined enabling factors for managed retreat as climate change adaptation strategy. Results indicate minor variations in well-being conditions between in-city and off-city resettlement, challenging the expected impact of a more urban setting on resilience. Instead, essential prerequisites for resettlement involve reduced hazard exposure, secure tenure and safety from crime. Beyond these essential conditions, social cohesion and institutional support systems emerge as significant influencers for the successful establishment of well-functioning new settlements. With this findings, the study contributes to the expanding body of literature on managed retreat, offering a comprehensive evaluation based on extensive datasets and providing entry points for the improvement of retreat as a climate change adaptation strategy.

Reconceptualising Transparency in Journalism: Thinking Through Secrecy and PR Press Releases in News Cultures

ABSTRACT This article re-examines debates about transparency in journalism by using a sociological framework that analyses how transparency is held in a dynamic tension with secrecy. I use a specific empirical case study as the grounding for my analysis and then proceed to expand its scope to consider significant developments in transparency in the media. I take as my case study the relationship between UK journalists and public relations (PR) practitioners. Specifically, I analyse the ways in which press releases are shaped by PR practitioners, targeted at journalists, and are taken up by journalists in a UK news media context in which such “information subsidies” may be becoming ever more prevalent. Reframing transparency as one element in a compound phenomenon (the secrecy–transparency dynamic), I argue that practices of both transparency and secrecy are not merely situated within social contexts but are active in creating society and social relations. This approach pays close attention to how power operates in this shifting dynamic and offers new challenges for thinking about journalism’s role in society.

Teaching advanced topics in econometrics using introductory textbooks: The case of dynamic panel data methods

We show how to use the introductory econometrics textbook by Stock and Watson (2019) as a starting point for teaching and studying dynamic panel data methods. The materials are intended for undergraduate students taking their second econometrics course, undergraduate students in seminar-type courses, independent study courses, capstone, or thesis projects, and beginning graduate students in a research methods course. First, we distill the methodological core necessary to understand dynamic panel data methods. Second, we design an empirical and a theoretical case study to highlight the capabilities, downsides, and hazards of the method. The empirical case study is based on the cigarette demand example in Stock and Watson (2019) and illustrates that economic and methodological issues are interrelated. The theoretical case study shows how to evaluate current empirical practices from a theoretical standpoint. We designed both case studies to boost students’ confidence in working with technical material and to provide instructors with more opportunities to let students develop econometric thinking and to actively communicate with applied economists. Although we focus on Stock and Watson (2019) and the statistical software R, we also show how to modify the material for use with another introductory textbook by Wooldridge (2020) and Stata, and highlight some possible further pathways for instructors and students to reuse and extend our materials.

Role of Social Determinants in Reshaping the Public Health Policies: A Comprehensive Study

This comprehensive study delves into the pivotal role of social determinants of health (SDOH) in shaping public health policies. Social determinants encompass a wide range of social, economic, and environmental factors that significantly influence health outcomes and disparities. By analyzing the intricate interplay between social determinants and public health policies, this study aims to elucidate the mechanisms through which societal factors influence population health and well-being. Drawing upon empirical evidence, case studies, and critical analysis, the study explores the impact of social determinants on health disparities, health behaviors, healthcare access, and quality. Furthermore, it examines the challenges and barriers in addressing social determinants within public health policy frameworks, including systemic inequalities and data measurement issues. The study also identifies strategies for integrating social determinants into public health policies, including policy recommendations, collaborative approaches, and community empowerment initiatives. Ultimately, this study underscores the imperative of adopting a socioecological approach to public health policymaking, one that recognizes and addresses the underlying social determinants to promote health equity and improve population health outcomes. Keywords: Social Determinants, Reshaping, Public Health Policies, Comprehensive Study

Resilience dimensions in health system performance assessments, European Union.

To explore the definition and operationalization of resilience in health system performance assessments in European Union countries. We conducted multiple empirical case study analyses. We identified relevant cases through a literature review from 2014 to 2023 using Google Scholar and through a snowball technique to retrieve additional information. We included only documents that explicitly mentioned resilience in health system performance assessments. We performed a content analysis to identify common patterns in defining resilience. The final sample consisted of six countries: Belgium, Croatia, Czechia, Estonia, Ireland and Italy. Each country adopted a distinct approach to conceptualizing resilience, with countries prioritizing specific aspects based on lessons learnt from the coronavirus disease 2019 (COVID-19) pandemic. Some countries focused on maintaining essential health-care services and protecting vulnerable groups. Other countries prioritized management capacity, staff preparedness, digital health utilization and strengthening of primary health care. Content analysis revealed six resilience definitions derived from the key performance indicators: addressing unmet needs and maintaining outcomes; protecting vulnerable groups; acquiring and using resources; having trained and prepared staff in place; using digital health; and strengthening primary health care. Integration of resilience into the health profiles of European Union countries preceded its inclusion in national health system performance assessments, the latter of which became more prominent after the COVID-19 pandemic. Variations in interpretations within health system performance assessments reflect differences in indicators and policy responses.