Hybrid Methodology Research Articles

Evaluation of constraints for investment in NOx emission technologies: case study on Greek bulk carrier owners

PurposeThe maritime industry is the transport mode that contributes most to air pollution. The International Maritime Organization (IMO) identified the reduction of air pollution by ships as a crucial issue. Since 1 January 2020, ships have had to adopt strategies and new technologies to eliminate air pollution. However, ship compliance with nitrate oxide (NOx) emission restrictions is more challenging. This paper aims to identify shipowners' challenges in investing in new technologies.Design/methodology/approachThis paper applied a hybrid methodology combining a survey, a balanced scorecard and fuzzy analytic hierarchy process (F-AHP) to identify and evaluate constraints and weights in investment decision-making for NOx technologies. A survey was carried out to validate constraints.FindingsA survey was carried out, representing 5.1% of Greek-owned ships by deadweight capacity. The findings provide a weighted list of seven crucial technical and economic constraints faced by ship operators. The constraints vary from ship retrofit expenditure to crew training and waste management. Additionally, NOx emission technologies were compared. It was found that liquefied natural gas is the preferred investment option for the survey participants compared with selective catalytic reduction, exhaust gas recirculation and batteries.Originality/valueSeveral studies have dealt with the individual technical feasibility of NOx reduction technologies. However, apart from technical feasibility for a shipowner, the selection of a NOx technology has several managerial and safety risks. Therefore, the originality of this paper is to reveal those constraints that have a higher weight on shipowners. With this cost-benefit approach, investment challenges for ship operators are revealed. Policymakers can benefit from the results of the employed methodology.

A federated learning model for integrating sustainable routing with the Internet of Vehicular Things using genetic algorithm

A distributed machine learning technique called federated learning allows numerous Internet of Things (IoT) edge devices to work together to train a model without sharing their raw data. Internet of Vehicular Things (IoVT) are an important tool in smart cities for moving objects, such as knowing the traffic patterns, road conditions, vehicle behavior, etc. To enhance traffic management and optimize routes, federated learning, and IoT must work jointly, which may achieve sustainable development goals (SDG) in many ways. This research outlines a system for federated learning in vehicular networks in smart cities. The suggested architecture considers the difficulties presented by such situations’ restricted network connectivity, privacy issues, and security concerns. The framework employs a hybrid methodology integrating federated learning on a centralized server with local training on individual cars. The proposed framework is assessed based on a real-world dataset from a smart city through IoT devices. The findings demonstrate that the suggested method successfully increases model accuracy while preserving the confidentiality and security of the data. In this investigation, we incorporated the Federated Learning model, which can fetch all the information between arbitrary nodes and derive the Traffic, Fuel Cost, Safety, Parking Cost, and Transportation cost for a better routing approach. The suggested framework can be utilized to increase the effectiveness of the transportation system, decrease congestion in smart cities, and improve traffic management. We employ an improved genetic algorithm (iGA) with generation-dependent even mutation to tackle the emission in the smart environment.

CNN-TCN: Deep Hybrid Model Based on Custom CNN with Temporal CNN to Recognize Sign Language

Sign language plays a pivotal role as a primary means of communication for individuals with hearing and speech impairments. Given their limited auditory and vocal communication abilities, these individuals heavily rely on visual cues, such as body language and hand gestures, to convey their emotions and thoughts in everyday social interactions. Sign language primarily consists of characters (letters) and numerals (numbers). This study introduces an innovative hybrid methodology for automated sign language identification, combining Temporal Convolutional Neural Network (TCNN) and a Custom Convolutional Neural Network (CCNN). The effectiveness of this system was rigorously evaluated using three distinct benchmark datasets that encompass isolated letters and digits. These datasets are comprehensive and publicly accessible resources covering both British and American sign languages. The proposed CNN-TCN model comprises various phases, including data collection, preprocessing (involving labeling, normalization, and frame extraction), feature extraction using CCNN, and sequence modeling through TCNN. The experimental results clearly demonstrate the remarkable performance of the proposed system, with accuracy, precision, recall, and F1 scores reaching impressive levels of 95.31%, 94.03%, 93.33%, and 93.56%, respectively, across the three diverse datasets. These outcomes serve as compelling evidence of the CNN-TCN method’s viability and effectiveness in the realm of sign language recognition.

Use of GIS tools, enhanced by FFT filtering methods, to detect blurred craters in Synthetic Digital Elevation Models, to improve their location and morphological characterisation

Filtered Geomorphic Reference Models (FGRMs), extracted from High-Pass Filters based on the Fast Fourier Transform (HPF-FFT), provide a semi-automated and objective detection of a wide range of geoforms and geomorphic situations (flat areas, slopes, depressions, or valley bottoms). The method is sensitive enough to reveal numerous inflection points that enable the identification of subtle changes in relief polarity, resulting in accurate representations of real land depressions. The effectiveness of this method has been demonstrated in lunar landscapes, utilizing altimetry extracted from high-precision Planetary Digital Elevation Models (PDEMs) to construct crater inventories with well-defined FGRMs. However, when analysing lunar environments with closely-spaced geomorphic features, the FGRMs display blurring and generalization effects, reducing their capability to identify all existing crater depressions. This limitation controls the ability of FGRMs, to generate reliable crater inventories. This study examines such effects in three experiments. Using Synthetic Digital Relief Models (SDRMs) constructed by means of a MATLAB script two experiments are performed. In Experiment_1 different scenarios of proximity between aligned craters are analysed. The marks, or footprints, formed by craters of identical size and shape (i.e., circular bowl-shaped depressions with central peaks) are considered, resulting in simplified PDEMs. Various scenarios of crater centre separation (proximity) and the presence of internal features within craters are also analysed. Experiment_2 validates the method with different proximities, locations and crater sizes. Experiment_3 validates the approach in a sampled area of LRO lunar PDEM corresponding to Zelinskiy crater (Mare Ingenii area). To address these blurring effects, a hybrid methodology is employed, generating FGRMs extracted from HPF-FFT, in conjunction with other GIS and remote sensing tools, such as singular point zoning, aspect-slope zoning, and edge-limits zoning. As a result, the correspondence between the FGRMs and the synthetic forms is examined, aiding the comprehension of the graphical response provided by filtering in these cratered areas. The methodology demonstrates that FGRMs derived from HPF-FFT are sensitive to capturing connections between shapes as soon as the crater features start to merge, while blurring becomes evident when the craters are located at a proximity of <20 model units. Additionally, the results highlight the method's utility in accurately locating crater footprints and morphologically characterizing inner crater features, even when crater centres are closely spaced. This approach effectively mitigates blurring effects and enables the precise localization of crater marks and their internal features. The validations carried out in complex relief and high precise DEMs provide a use in real situation.

Social Sustainability Risk Assessment of Fresh Produce Supply Chain in China under Computer Technology and Digital Transformation

This paper investigates the social sustainability risks in China’s fresh produce supply chain amid ongoing digital transformation. As computer technologies reshape supply chain operations, understanding and mitigating associated social risks become paramount. This research aims to provide a comprehensive assessment of these risks, examining factors such as data and cyber security, inequality, and ethical sourcing concerns. Ten social risk criteria are identified through literature review and expert opinion. In a Sichuan case study, by integrating hybrid methodology Fuzzy Failure Modes and Effects Analysis (FMEA) and Fuzzy VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje), Data Privacy and Security Risks (SR1) stands out as the most critical risk criterion in the supply chain with the highest Q value, followed by Ethical Use of Technology (SR4) and Digital Dependency (SR5). This paper offers insights into how stakeholders can navigate the intersection of digitalization and social sustainability to foster more resilient and equitable fresh produce supply chains.

Shaping the Future of Destinations: New Clues to Smart Tourism Research from a Neuroscience Methods Approach

In the context of the technological era, the smart tourism construct serves as a bridge between human and the artificial worlds, combining social sciences and neurosciences. This study aims to explore smart tourism through neuroscientific methods in order to shape the future of tourism destinations, using a hybrid methodology combining bibliometric techniques and content analysis. The findings reveal the integration of diverse scientific domains, highlighting a transdisciplinary approach. They offer clear evidence that neuroscientific methods in smart tourism integrate multiple areas of scientific knowledge, surpassing disciplinary boundaries. “Destination” stands out alongside “emotion”, “visual attention” and eye tracking (ET). The collaboration network reveals the emergence of a new school, called neurotourism in the 21st century, formed mainly by actors and organizations from the Global North, evoking the need to include the Global South in the research scenario. The predominant methods include ET, heart rate (HR), and electroencephalography (EEG), suggesting triangulation with traditional methods for robust results. Virtual reality emerges as the primary immersive technology, promising insights when integrated with neurosciences. This study’s practical and theoretical contributions guide smart tourism strategies and enhance destination experiences through neuroscientific methods, addressing a gap in the scientific literature while advancing ontological and epistemological understanding.

AUTOMATED ANAEMIA DETECTION FROM CONJUNCTIVA IMAGES : A MACHINE LEARNING APPROACH FOR ANDROID APPLICATION

Anaemia is a common global disorder condition in which the red blood cell count is lower than normal. Traditional diagnostic methods often prove costly, invasive, and inaccessible, leading to delays in treatment and severe consequences. This study explores non-invasive techniques leveraging smartphone technology for efficient anaemia detection. Some researcher investigated that eye’s conjunctiva analysis can a viable alternative, considering its rich blood vessel network and sensitivity to anaemia indicators, independent of skin color. Utilizing smartphone cameras, the study establishes a robust correlation between the color of the conjunctiva and anaemia status, offering a promising avenue for non-invasive diagnosis. Employing a hybrid methodology, the study integrates You Only Look Once (YOLO) version 8 for efficient object detection, achieving a mean average precision of 96% in extracting Regions of Interest (ROI) from conjunctiva images. Subsequently, K-Nearest Neighbors (KNN) classification of features extracted from these ROI’s attained an 83% accuracy rate in determining anaemia status. Furthermore, the study aims to extend its impact by developing an Android application using the Flutter framework. This application integrates the established YOLO and KNN approaches, enabling real-time anaemia detection through smartphone cameras. Such a tool holds the potential to facilitate early evaluations in resource-constrained regions, enabling timely diagnosis and intervention, thus mitigating the adverse effects of untreated anaemia.

Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning.

In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms-namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm-with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning's potential to boost cybersecurity measures in rapidly evolving threat environments.

Assessing the Academic Performance of Turkish Universities in 2023: A MEREC-WEDBA Hybrid Methodology Approach

Research and reporting on university rankings serve as valuable tools for students in evaluating universities and understanding their current performance status. Within academic literature, university rankings are established using diverse criteria across various domains, each carrying varying degrees of importance. This study adopts a multi-criteria decision-making (MCDM) perspective to analyze the academic performance ranking of Turkish Universities in 2023. Data sourced from the 2023 reports of sixty-one universities from Times Higher Education (THE) serve as the basis for this research, with THE indicators—teaching, research, citations, industry income, and international outlook—considered as primary research criteria. The Method based on the Removal Effects of Criteria (MEREC) method is employed to ascertain criterion weights, while the Weighted Euclidean Distance-Based Approach (WEDBA) method is utilized for university ranking. The study identifies "citations" as the criterion of highest significance. Notably, the top-performing universities in the ranking include Çankaya University, Fırat University, and Bahçeşehir University. Furthermore, by comparing the rankings from this study with THE university rankings, the research offers tailored suggestions for universities. This study underscores the importance of deriving criterion weights from university performance datasets rather than relying on fixed weights, facilitating a more nuanced approach to university rankings. Moreover, it presents THE performance rankings for sixty-one Turkish universities, offering valuable insights for strategic planning within the university sector.

A hybrid method of system dynamics and design of experiments for investigating the economic and environmental indicators of electricity industry

Electricity plays a pivotal role in the socio-economic development of nations. However, heavy reliance on fossil fuels for electricity generation, as observed in Iran, poses significant environmental challenges. This study proposes a novel hybrid methodology that combines system dynamics modeling and Design of Experiments (DOE) to examine economic and environmental indicators within Iran's electricity sector. The system dynamics model delineates four key subsystems: consumption, production, CO2 emissions, and power trade. By integrating DOE into this framework, various economic and environmental metrics are assessed for the year 2040. Through a comprehensive analysis of variable impacts on these indicators, optimal levels are identified to achieve favorable outcomes. Notably, variables such as the allocation coefficient of export income to capacity development and electricity export price emerge as critical determinants. Due to economic, environmental, and economic-environmental indicators, the most appropriate level of allocation of export income towards capacity development is estimated at 30, 10, and 20 percent, respectively. The study recommends allocating 80 % of the capacity development budget to renewable energy sources and 20 % to thermal power plants to optimize future conditions. In business as usual, the Export CO2 emission damage to export income index will be 0.19. In implementing the proposed scenario, according to the economic-environmental index, this value will decrease and reach 1.73E-06, which indicates the improvement of electricity export from the economic-environmental dimension. This research underscores the importance of balancing economic prosperity with environmental sustainability in electricity industry planning and policy formulation.

The importance of citizen participation in improving comfort and health in obsolete neighbourhoods affected by energy poverty

This study analyses comfort and health in obsolete neighbourhoods in regions with high rates of energy poverty. The Polígono San Pablo in the city of Seville, Spain has been chosen as a case study.The research aims to highlight the importance of including participatory processes in the analysis of human rights issues, such as energy poverty. A hybrid and interdisciplinary methodology is presented which incorporates a citizen participation process. The aim is to use this methodology to identify how an obsolete building stock and lack of access to a basic energy service may be affecting people's health.The main results obtained show the need to include citizen consultation to identify shortcomings and potentialities in obsolete neighbourhoods, especially in terms of energy education. This is expected to contribute to the creation of more effective local energy policies to address the multidimensional problem of energy poverty.

The implementation of RBL-STEAM with pictorial riddle and augmented reality in improving students' historical thinking skill

The purpose of this study is to analyze how the integration of the RBL-STEAM learning model with the Pictorial Riddle method, supported by Assemblr-Edu media, can enhance students’ historical thinking skills in world history courses. This research employs a hybrid methodology, combining qualitative and quantitative approaches. A pretest-posttest nonequivalent group design was utilized, where two groups received different educational interventions. The experimental group was exposed to the RBL- STEAM model integrated with the Pictorial Riddle technique and Assemblr-Edu media, while the control group received only the standard RBL-STEAM instruction. The integration of RBL-STEAM with the Pictorial Riddle technique and Assemblr-Edu media significantly enhances students' abilities to critically analyze, contextualize, and interpret historical events and sources. This approach not only fosters deeper engagement and understanding but also demonstrates a marked improvement in historical thinking skills compared to traditional RBL-STEAM methods. Additionally, student experiences were further analyzed using NVivo software, and Smart-PLS was employed to explore the relationships between RBL-STEAM variables and historical thinking skills. The analysis of the post-test data using SPSS and the independent samples t-test revealed a significant Sig value of 0.015 (&lt;0.05). This indicates a statistically significant difference in the historical thinking skills scores between the experimental and control groups, with the experimental group showing superior outcomes. The results of the study indicate that implementing the RBL-STEAM model of teaching through the use of pictorial riddle and its integration with the Assemblr-Edu augmented reality learning media may enhance students' historical thinking abilities.

Comparing hybrid method simulations to hydrogen dispersion and explosion tests

With an increase in the production and storage of hydrogen, it is likely that an increased number of explosions in which hydrogen plays a central role will occur unless a step change increase in safety is seen. Modelling releases of hydrogen and explosions arising from the delayed ignition of premixed gas clouds of hydrogen is carried out today using analytical and numerical approaches and most recently, using a hybrid methodology with the commercially available software exploCFD. Hybrid simulation methodology relies on the calculation of an analytically derived source term which is then used as an input for a 2D numerical simulation. In this paper we further our earlier validation work and test the ability of the hybrid methodology to predict flammable gas dispersion and subsequent explosions arising from hydrogen releases. Hydrogen has some particularly unique properties; it is the lightest of fuels – meaning masses involved are very small, and it has the highest laminar burning velocity – an order of magnitude greater than most hydrocarbons. The hybrid methodology is compared against previously documented 3D corner tests and dispersion tests. Excellent agreement between overpressure prediction and the reported experimental data for explosion against equivalence ratio is observed. The data from the hydrogen jet release simulations also compare extremely well to the previously reported experimental data. The results show that it is difficult to justify the use of far more laborious, time and cost-intensive purely numerical methods, or the use of largely uncertain more crude methods still widely used today.

Construction of a bibliometric dataset on gender studies

Feminist discussions are reflected in scientific production.(1) During the 1970s, various lines of research from different disciplines were institutionalized, conforming gender studies.(2,3) While "gender studies" often denotes a field, it can be understood as an umbrella term for a diversity of approaches. From other disciplines, a gender perspective can be adopted, recognizing the constitution of power relations between genders and considering their concrete expressions in different areas. Given the diffusion of this approach in multiple disciplines, its bibliometric approach presents difficulties. New computational tools can contribute to identifying publications linked to gender studies even when they are indexed as part of other disciplines. In this work, we applied bibliometric techniques and natural language processing to the Dimensions database to build a dataset of scientific publications that allows us to analyze gender studies and their influence on other disciplines.(4–6) This is achieved through a hybrid methodology that combines core journals and keywords in titles. We obtained a set of more than 1.5 million publications, in English, Spanish, French, and Portuguese, published between 1970 and 2020. It allows characterizing gender studies according to citations and collaborations, the participation of institutions, countries and regions, or the evolution of certain concepts or theories. The methodology used would be suitable for studying other cases that exceed disciplinary limits, such as schools of thought or political movements that have become scientific disciplines

An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection.

In clinical decision-making for chronic disorders like chronic kidney disease, high variability often leads to uncertainty and negative outcomes. Deep learning techniques have been developed as useful tools for minimizing the chance and improving clinical decision-making. Moreover, traditional techniques for chronic kidney disease recognition frequently the accuracy is compromised as it relies on limited sets of biological attributes. Therefore, in the proposed work, a combination of deep radial bias network and the puma optimization algorithm is suggested for precised chronic kidney disease classification. Initially, the accessed data undergo preprocessing using Spectral Z score Bag Boost K-Means SMOTE transformation, which includes robust scaling, data cleaning, balancing, encoding, handling missing values, min-max scaling, and z-standardization. Feature selection is then conducted using the hybrid methodology of Role-oriented Binary Walrus Grey Wolf Algorithm to choose discriminative features for improving classification accuracy. Then, Auto Encoder with Patch-Based Principal Component Analysis is employed for dimensionality reduction to minimize the processing time. Finally, the proposed classification method utilizes deep radial bias and the puma optimization search algorithm for effective chronic kidney disease classification. The introduced scheme is tested on two datasets: the risk factor prediction of chronic kidney disease dataset and chronic kidney disease dataset, which provides accuracies of 99.02%, and 99.15%, respectively. Experiments demonstrate that the proposed model identifies chronic kidney disease more accurately than the existing approaches.

A Hybrid Semi-Automated Workflow for Systematic and Literature Review Processes with Large Language Model Analysis

Systematic reviews (SRs) are a rigorous method for synthesizing empirical evidence to answer specific research questions. However, they are labor-intensive because of their collaborative nature, strict protocols, and typically large number of documents. Large language models (LLMs) and their applications such as gpt-4/ChatGPT have the potential to reduce the human workload of the SR process while maintaining accuracy. We propose a new hybrid methodology that combines the strengths of LLMs and humans using the ability of LLMs to summarize large bodies of text autonomously and extract key information. This is then used by a researcher to make inclusion/exclusion decisions quickly. This process replaces the typical manually performed title/abstract screening, full-text screening, and data extraction steps in an SR while keeping a human in the loop for quality control. We developed a semi-automated LLM-assisted (Gemini-Pro) workflow with a novel innovative prompt development strategy. This involves extracting three categories of information including identifier, verifier, and data field (IVD) from the formatted documents. We present a case study where our hybrid approach reduced errors compared with a human-only SR. The hybrid workflow improved the accuracy of the case study by identifying 6/390 (1.53%) articles that were misclassified by the human-only process. It also matched the human-only decisions completely regarding the rest of the 384 articles. Given the rapid advances in LLM technology, these results will undoubtedly improve over time.

A 2.5D hybrid SBM-MFS methodology for elastic wave propagation problems

This paper proposes a novel hybrid methodology that combines the singular boundary method (SBM) and the method of fundamental solutions (MFS) for the computational simulation of elastic wave propagation. Particularly, the methodology aims to address radiation or scattering problems of longitudinally invariant systems in the wavenumber–frequency domain involving boundaries with intricate geometries. The approach uses the SBM to deal with the complex parts of these geometries and the MFS for the smooth ones. The method is studied in the framework of three case studies involving longitudinally infinite cavities in a homogeneous full-space with circular, square-shaped and five-cusped hypocycloid cross-sections. These three examples are selected to assess the accuracy and robustness of the hybrid SBM-MFS approach in comparison with alternative modelling strategies. The comparisons show that the proposed method inherits the accuracy of the MFS while keeping the robustness of the SBM when dealing with complex geometries. The method is found to be computationally more efficient than the SBM or the boundary element method (BEM). Moreover, the hybrid approach naturally mitigates the effect of fictitious eigenfrequencies, a feature that neither conventional versions of the SBM nor the BEM have.

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

Innovative modeling of monolayer puckered arsenene: Bridging quantum mechanics and finite element analysis

Current study presents a novel hybrid approach combining finite element modeling and density functional theory calculations to investigate the mechanical properties of monolayer puckered arsenene. The multiscale analysis in this study leverages finite element analysis as a distinctive approach, complementing the nano‐scale capabilities of density functional theory and molecular dynamics by overcoming limitations faced by these two methods in representing complex scenarios. Furthermore, finite element analysis demonstrates computational efficiency for larger structures, making it suitable for systems where atomistic simulations may be impractical. This hybrid methodology offers a unique framework for accurately predicting key properties, including elastic modulus and buckling force, by synergistically integrating the strengths of both computational techniques. In addition to demonstrating the effectiveness of our approach in accurately capturing material behavior, our findings shed light on fundamental aspects of nanoscale mechanics, with implications for various applications in nanotechnology, materials science, and structural engineering. By providing a deeper understanding of the mechanical response of 2D materials, our research contributes to advancing the field of nanoscale materials engineering and informs the design of innovative nanostructures with tailored mechanical properties.

Customising Hybrid project management methodologies

Despite the growing popularity of Hybrid project management methodologies (combining Plan-driven and Agile methodologies), research guiding project managers on integrating various methodologies into their project management approach is limited. This gap in understanding managers’ choices and their implications is important as mismatches between the project and its methodology can adversely affect project success. This paper examines how project managers customize Hybrid project management methodologies by selecting aspects of both Agile and Plan-driven approaches. An abductive approach grounded in both theory and practice was used, drawing on Contingency and Coordination Theories together with empirical data including interviews, document analysis and observations from 10 case studies. Results suggest two key characteristics of projects that impact project managers’ decisions when customizing project management methodologies: Team type and project goal. The paper advances our knowledge on how practices from various project management methodologies can be integrated by method or by phase into a Hybrid model.