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Due to their innovative treatment, Unconventional Arterial Intersection Designs (UAIDs) have been developed to alleviate congestion at conventional signalized intersections, in an effort towards the sustainable development of crowded capitals. A methodological framework for economic assessment, however, has not been investigated properly for such designs, particularly under mixed traffic environments. This article aims to outline a methodological framework that can be followed for the socio-economic assessment of such designs. A cost–benefit analysis approach was developed to figure out the different determinants of costs and benefits of an overpass interchange (as a widespread treatment) and two selected UAIDs (as alternative measures). The two studied UAID schemes in this article are Continuous Flow Intersection (CFI) and Restricted Crossing U-Turn (RCUT). Seeking credible results, a set of three signalized intersections in downtown Cairo, Egypt was selected as a proof-of-concept for the developed method. PTV-VISSIM, a simulation-based platform, was utilized to estimate the benefits gained by road users. Our research objectives were to identify, evaluate, and compare the economic feasibility of the different alternatives. Compared to the overpass intersection, we found that the CFI and RCUT designs ensure higher economic efficiency, while mitigating congestion at conventional signalized intersections under heterogeneous traffic conditions.

Practicing new economic geographies necessarily entails a critical re-evaluation of research methodologies because of its different substantive research foci. In this article, I examine some methodological implications of the recent refiguring of the \"economic\" in economic geography. Some key features of new economic geographies include understanding the social embeddedness of economic action, mapping shifting identities of social actors, and exploring the role of material and discursive contexts in shaping economic behavior. I argue that practitioners of new economic geographies can no longer rely exclusively on established \"scientific\" methodology for empirical research and data analysis. Instead, I argue for a process-based methodological framework through which we employ complementary methodological practices (e.g., tracing actor networks and in situ research) and triangulation, not only to explore the microfoundations of economic action, but also to generate, in a reflexive manner, theoretical insights from the multiscalar dimensions of economic action.

Background Although the benefits of using methodological frameworks are increasingly recognised, to date, there is no formal definition of what constitutes a ‘methodological framework’, nor is there any published guidance on how to develop one. For the purposes of this study we have defined a methodological framework as a structured guide to completing a process or procedure. This study’s aims are to: (a) map the existing landscape on the use of methodological frameworks; (b) identify approaches used for the development of methodological frameworks and terminology used; and (c) provide suggestions for developing future methodological frameworks. We took a broad view and did not limit our study to methodological frameworks in research and academia. Methods A scoping review was conducted, drawing on Arksey and O’Malley’s methods and more recent guidance. We systematically searched two major electronic databases (MEDLINE and Web of Science), as well as grey literature sources and the reference lists and citations of all relevant papers. Study characteristics and approaches used for development of methodological frameworks were extracted from included studies. Descriptive analysis was conducted. Results We included a total of 30 studies, representing a wide range of subject areas. The most commonly reported approach for developing a methodological framework was ‘Based on existing methods and guidelines’ (66.7%), followed by ‘Refined and validated’ (33.3%), ‘Experience and expertise’ (30.0%), ‘Literature review’ (26.7%), ‘Data synthesis and amalgamation’ (23.3%), ‘Data extraction’ (10.0%), ‘Iteratively developed’ (6.7%) and ‘Lab work results’ (3.3%). There was no consistent use of terminology; diverse terms for methodological framework were used across and, interchangeably, within studies. Conclusions Although no formal guidance exists on how to develop a methodological framework, this scoping review found an overall consensus in approaches used, which can be broadly divided into three phases: (a) identifying data to inform the methodological framework; (b) developing the methodological framework; and (c) validating, testing and refining the methodological framework. Based on these phases, we provide suggestions to facilitate the development of future methodological frameworks.

The optimization of production processes has always been one of the cornerstones for manufacturing companies, aimed to increase their productivity, minimizing the related costs. In the Industry 4.0 era, some innovative technologies, perceived as far away until a few years ago, have become reachable by everyone. The massive introduction of these technologies directly in the factories allows interconnecting the resources (machines and humans) and the entire production chain to be kept under control, thanks to the collection and the analyses of real production data, supporting the decision making process. This article aims to propose a methodological framework that, thanks to the use of Industrial Internet of Things—IoT devices, in particular the wearable sensors, and simulation tools, supports the analyses of production line performance parameters, by considering both experimental and numerical data, allowing a continuous monitoring of the line balancing and performance at varying of the production demand. A case study, regarding a manual task of a real manufacturing production line, is presented to demonstrate the applicability and the effectiveness of the proposed procedure.

Purpose Confusion and conflation about the components of research continue to hinder how scholars position, design, and communicate their research. In response, this article aims to clarify the components of research and how they can be effectively illustrated and written through the lens of frameworks. Design/methodology/approach This article adopts a conceptual approach to achieve its purpose by integrating allied literature and drawing on established theory-building heuristics to articulate a five-part research framework, with each part accompanied by a set of criteria (what it is, what it is not, what to do, and what to evaluate). Findings This article introduces the notion of research framework as a framework of frameworks comprising theoretical, conceptual, contextual, methodological, and impact frameworks. The article clarifies the distinct role of each component, sets out criteria for what each framework is and is not and what it should do and be evaluated on, and shows how alignment across the five parts connects explanation, scope, design, and stakeholder-relevant outcomes into a coherent whole. An exemplar on brand switching and brand loyalty in consumer goods traded across markets illustrates how this integrated framework can organize research questions, justify design choices, and foreground scholarly, managerial, and societal implications. Originality/value The notion of research framework introduced herein this article is an original conceptualization that should help both emerging and established researchers better position their research. More specifically, the article advances a framework of frameworks that differentiates and links theoretical, conceptual, contextual, methodological, and impact frameworks and, in doing so, addresses common confusion and conflation among them. The resulting criteria and visual tools enable authors, reviewers, and trainers to design, evaluate, and teach research in a more coherent and consistent way across business and trade domains.

The UK Medical Research Council's Guidance on Developing and Evaluating Complex Interventions (MRC GDECI) outlines a 4-phase framework for structuring research programs on interventions: development, feasibility, evaluation, and implementation. However, it provides limited practical direction on how researchers should select which phases to conduct or determine when and whether to progress between phases. This gap is particularly challenging in the context of digital health interventions (DHIs), given their fast-paced and rapidly evolving nature. This scoping review examined the research phases conducted, how researchers progressed through them, and the intervention characteristics associated with overall program structure and duration in DHI research, to inform the design of future research programs. We searched PubMed, Embase, CINAHL, PsycINFO, and ClinicalTrials.gov to identify complex DHIs promoting health among adolescents and young adults, implemented between 2017 and 2026, for which at least 2 phases of the MRC GDECI were reported, including the evaluation phase. For each eligible intervention, all related protocols, preprints, and published articles were retrieved to reconstruct the full research program. For each program, we analyzed the presence of each research phase, its organization (ie, phase arrangements), and the mechanisms guiding progression between phases (ie, progression mechanisms). Phase-specific and overall program durations were recorded. A total of 31 research programs, covering 31 interventions and reported in 130 articles, were included. Development, feasibility, evaluation, and implementation phases were reported in 26, 23, 31, and 7 research programs, respectively. Three types of phase arrangements were identified: sequential, iterative, and overlapping. Progression mechanisms between phases included automatic progression, conditional progression based on researchers' appraisal of findings without prespecified criteria, and progression based on predefined quantitative criteria. Six main research program structures were observed, combining phase arrangements and progression mechanisms. Iterative arrangements were most common, observed in 22 research programs, followed by overlapping (n=10) and strictly sequential structures (n=7). Most progressions relied on researchers' appraisal of findings without prespecified criteria. Justifications for phase iteration, omission, or progression decisions were rarely reported. The median program duration was 5.8 (IQR 3.8-6.6) years (n=13). Based on these findings, a novel 4-step operational framework and visualization tools were developed to guide the design and planning of DHIs, highlighting key considerations for each step, as well as the strengths, limitations, and risks associated with each phase arrangement and progression mechanism. This scoping review is the first to systematically examine phase arrangements and progression mechanisms in DHI research programs. Beyond descriptive reporting, it provides a conceptualization of research program structures and offers a flexible operational framework to support the concrete implementation of the MRC GDECI. Greater explicitness in decisions about program structure may enhance methodological rigor, reduce research waste, and improve the integrity and reproducibility of interventions. PROSPERO CRD42023401979; https://tinyurl.com/mvc265y3.

Purpose Due to rapid digitalization, the emergence of the “phygital” environment, which blends physical and digital experiences, creates unique challenges for researchers. This paper aims to introduce an interpretivist methodological framework designed to understand consumer behavior in phygital environments. The framework enables an in-depth exploration of the contextual factors, subjective experiences, personal emotions and social networks that influence consumer behavior in this space. Design/methodology/approach The framework was developed after a thorough literature review of the phygital environment and interpretivist research landscape. Consistent with the phygital transformation theory, this approach allows researchers to go beyond the limitations of purely quantitative methods, gaining a deeper understanding of consumer behavior in phygital environments. The framework is organized into four meticulously designed pillars, each focusing on specific aspects of research and using distinct data collection and analysis approaches. Findings The systematic framework facilitates exploration of various dimensions of consumer experiences in phygital settings through qualitative research techniques. Uncovering the richness of contextual factors, subjective meanings, consumer experiences and social interactions within the phygital environment yields meaningful insights into consumer decision-making and preferences. These insights help marketers craft better phygital marketing strategies. Originality/value This interpretivist framework presents a unique approach for researchers hoping to investigate consumer behavior in phygital environments. It offers deep insights and understanding of this largely unexplored space, contributing to the evolving body of knowledge in phygital studies.

In the last decade, applying supervised machine learning (SML) has become increasingly popular in the information systems (IS) field. However, SML results rely on many different data-preprocessing techniques, algorithms, and ways to implement them, which has contributed to an inconsistency in the way researchers have documented their SML efforts and, thus, the degree to which others can reproduce their results. In one sense, we can understand this inconsistency given the goals and motivations for SML applications vary and the research area’s rapid evolution. However, for the IS research community, the inconsistency poses a big challenge because, even with full access to the data, researchers can neither completely evaluate the SML approaches that previous research has adopted or replicate previous research results. Therefore, in this paper, we provide the IS community with guidelines for comprehensively and rigorously conducting and documenting SML research. First, we review the literature concerning steps and SML process frameworks to extract relevant problem characteristics that researchers should report and relevant choices that they should make in applying SML. Second, we integrate these characteristics and choices into a comprehensive “Supervised Machine Learning Report Card (SMLR)” that researchers can use in future SML endeavors. Third, we apply this report card to a set of 121 relevant papers published in renowned IS outlets between 2010 and 2018 and demonstrate how and where these papers’ authors could have improved their documentation and, thus, how and where researchers can better document their SML approaches in the future. Thus, with this work, we help researchers more completely and rigorously apply and document SML approaches and, thereby, enable researchers to more deeply evaluate and reproduce/replicate results in the IS field.

Policy analysis is essential to improving the rationality and adaptability of policies. Traditional policy analysis easily generates biased results due to different individual perspectives and personal experiences. Text mining emerges as an efficient way, but is not widely used in urban greening policy analysis. Moreover, existing policy studies are mostly limited to topic categorization, and there is a lack of systematic policy text analysis and real-time policy tracking. Here, we constructed a multidimensional dynamic policy analysis framework for systematic evaluation of urban greening policies by introducing AI big models and text mining. With Wuhan as an example, the framework was used to analyze the evolution of policy topics, distribution of annual topics, and spatial and temporal changes in greening indicators. Moreover, the framework supports real-time tracking and in-depth interpretation of policies, and the results can be presented through visualization scenarios. Analysis with the framework revealed variations of greening policies in Wuhan over the past 15 years, such as transformation from basic greening to ecological remediation and policy focus shift from flower planning to wetland protection. This methodology marks a new paradigm for intelligent policy evaluation, and significantly improves the efficiency and accuracy of policy formulation and implementation in smart cities.

GLOFs, driven by climate change-induced glacier melt, present a serious threat to downstream communities in mountainous regions. The present study addresses this pressing issue by employing a systematic methodology to analyze scientific literature on GLOF risk management and hazard mitigation. The research synthesizes key insights through thematic analysis, focusing on GLOF hazard mitigation, hazard and risk mapping, and the development of a methodological framework for managing GLOF hazards and risks. It evaluates structural measures such as spillways and diversion canals, and non-structural measures, including strategic land-use planning and community-based strategies. The study highlights the importance of Early Warning Systems (EWS), GLOF risk management skills, and knowledge transfer, emphasizing integration with climate change adaptation. It also discusses facilitating factors like policy legislation, institutional support, and addressing knowledge disparities. While the physical aspects of GLOFs have been widely explored, research on their social impacts is relatively limited, leaving a crucial gap in understanding the full extent of these disasters. The findings of the study offer a comprehensive resource for policymakers, planners, and disaster management professionals, providing a holistic approach to mitigating GLOF risks and enhancing resilience in vulnerable regions. This research is poised to significantly contribute to effective GLOF hazard mitigation and climate change adaptation strategies.