Project Success Research Articles

Re-visit project success: evaluation of visitor perspectives in the Sungai Melaka flood mitigation project

Purpose This interdisciplinary study re-evaluates the Sungai Melaka flood mitigation project’s impact on community success in Malaysia’s UNESCO World Heritage site, broadening the definition of success considering historical and cultural significance. This study aims to revisit success metrics, focusing on the project’s influence on people’s interactions, cultural well-being, social cohesion and heritage preservation. Design/methodology/approach This study explored visitors’ reviews of their experience visiting Sungai Melaka in several data collection stages. The first data collection involved a Web survey posted on Facebook to capture diverse perspectives of the population and grasp the core strand of knowledge to focus on the second data collection, the questionnaire. The final stage involved interviews to generate rich qualitative data. Findings The findings of this study revealed that the impact exceeded tangible outcomes, as the project impacted people’s interactions and practices. This research assesses cultural benefits, incorporating these indicators into evaluating cultural ecosystem services by capturing local visitors’ and communities’ perceptions, preferences and behaviours. The findings found that other project success attributes include identities, capabilities and experience, further defining learning, health, economics, connection to nature and symbolism. Originality/value This research explores the meaning of project success beyond the traditional metrics by capturing success from the users’ perspectives and people’s interaction and their impact on culture and well-being. The cultural ecosystem services framework used in this study is applied to explore the interactions between people and the facility and its effect on the people.

Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed.

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. Four maturity stages (color turning stage, coloring stage, maturity stage, and fully ripe stage) of Cerasus Humilis fruit were examined using hyperspectral images (895–1700 nm) alongside data collection on SSC and firmness. These samples were divided into a calibration set and a validation set with a ratio of 3:1 by sample set partitioning based on the joint X-Y distances (SPXY) method. The original spectral data was processed by a spectral preprocessing method. Multiple linear regression (MLR) and nonlinear least squares support vector machine (LS-SVM) detection models were established using feature wavelengths selected by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and two combined downscaling algorithms (UVE-SPA and UVE-CARS), respectively. For SSC and firmness detection, the best models were the SNV-SPA-LS-SVM model with 18 feature wavelengths and the original spectra-UVE-CARS-LS-SVM model with eight feature wavelengths, respectively. For SSC, the correlation coefficient of prediction (Rp) was 0.8526, the root mean square error of prediction (RMSEP) was 0.9703, and the residual prediction deviation (RPD) was 1.9017. For firmness, Rp was 0.7879, RMSEP was 1.1205, and RPD was 2.0221. Furthermore, the optimal model was employed to retrieve the distribution of SSC and firmness within Cerasus Humilis fruit. This retrieved information facilitated visual inspection, enabling a more intuitive and comprehensive assessment of SSC and firmness at each pixel level. These findings demonstrated the effectiveness of hyperspectral imaging technology for determining SSC and firmness in Cerasus Humilis fruit. This paves the way for online monitoring of fruit quality, ultimately facilitating timely harvesting.

European Climate Policy in the Context of the Problem of Methane Emissions from Coal Mines in Poland

This paper presents a thorough examination of methane capture from Polish coal mines, contextualized within the framework of the European Union’s (EU) climate policy objectives. Through a strategic analysis encompassing the interior of coal mines, the surrounding environment, and the macro environment, this study elucidates the complex dynamics involved in methane emissions and capture initiatives. The key findings include a declining trend in absolute methane emissions since 2008, despite fluctuations in coal extraction volumes, and a relatively stable level of methane capture exceeding 300 million m3/year since 2014. The analysis underscores the critical role of government support, both in terms of financial incentives and streamlined regulatory processes, to facilitate the integration of methane capture technologies into coal mining operations. Collaboration through partnerships and stakeholder engagement emerges as essential for overcoming resource competition and ensuring the long-term success of methane capture projects. This paper also highlights the economic and environmental opportunities presented by methane reserves, emphasizing the importance of investment in efficient extraction technologies. Despite these advancements, challenges persist, particularly regarding the low efficiency of current de-methanation technologies. Recommendations for modernization and technological innovation are proposed to enhance methane capture efficiency and utilization.

MEREC-WISP(S) Integration Extended with Fermatean Fuzzy Set for Requirement Prioritization

Software projects need efficient requirement prioritization. Time, budget, and quality often limit these projects. MCDM techniques help balance conflicting criteria. However, they struggle to rank options due to multiple parties. Current MCDM methods have drawbacks, like poor uncertainty management. This thesis presents a new technique, WISP-S, for requirement prioritization in software development. The dynamic WISP(S) approach and the MEREC method are merged. Fermatean fuzzy numbers manage qualitative data and uncertainty. This technique surpasses the restrictions of current MCDM methods. It offers an effective way to rank software requirements within limits. The research consists of two separate stages. The first phase introduces an enhanced MEREC technique. It’s designed to compute the objective weights of each criteria within Fermatean Fuzzy Sets (FFS). By leveraging FFS features, this expansion improves the current MEREC technique. It allows a more thorough examination of criteria weights. The second phase integrates the WISP(S) method with the suggested generalized weighted Fermatean fuzzy aggregated operator and MEREC technology. This integration allows ranking and evaluating alternatives in a prioritization context. The study offers a robust strategy to prioritize alternatives. It considers both qualitative and uncertain data by integrating two novel methods. The approach’s validity and robustness are confirmed through comparisons with existing models and sensitivity analysis. A real-world case study demonstrates the proposed approach. The WISP-S approach is a novel technique for integrated Fermatean fuzzy information-based decision-making. It can handle complex decision-making problems in software development. This approach could improve the success of software projects. It addresses the shortcomings of existing MCDM methods.

‘My life is not to be called a failure’: how family reputation in the origin affects return considerations among South Asian migrants in the Gulf

ABSTRACT Families are an important factor to consider when trying to understand if and when labor migrants decide to return to the origin. Research has mainly focused on how families provide incentives to return. During the COVID-19 pandemic, however, stories about return migration decisions among male South Asian migrants in the Gulf reveal that families can exert substantial pressure on migrants to stay abroad, even during times of crisis. Using in-depth interviews with maigrants in the UAE, returnees and family members between 2020 and 2022, we find that social expectations of migrants to maintain or build family reputation through actions abroad, in the context of broader communities of origin, is a key factor in deterring migrants from returning. Migrants and their families in the origin often seek to continue signaling a successful migration project, particularly by telling stories of entrepreneurial success or of being embedded in the destination through strong and resourceful networks. Returning early from a migration project, even during the pandemic, can be interpreted as a failure and risks negatively impacting the family's reputation. We observe that those who return early, despite family pressure to stay, face significant social sanctions from family and community.

Planning for complex decommissioning projects in Australia – Gippsland Basin case study

Over more than 50 years, safe and successful installation projects have enabled progressive development and expansion of the Gippsland Basin Joint Venture off the coast of Victoria, Australia. This has resulted in a highly integrated network of 19 offshore platforms which are now starting to progressively reach the end of their productive life and detailed planning for the first phase of decommissioning projects has increased in intensity. Decommissioning of these assets is planned to take place in a number of campaigns. The Campaign 1 Decommissioning Project includes the removal of up to 13 platforms and is likely to be the largest single project of its kind in the World. Experience of similar decommissioning projects in Australia is very limited, resulting in unprecedented challenges across many aspects of this project. This requires creative, innovative and adaptable solutions to be developed to enable an efficient execution methodology. This paper will explore the strategies, challenges and successes involved with the early planning and development phase of the Gippsland Basin Joint Venture, Campaign 1 Decommissioning Project. Key insights, from contracting, to execution planning and stakeholder engagement will be discussed, promoting sharing across the industry and improving efficiency for future decommissioning projects in Australia.

Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network

The study of user perception and interaction with applications is referred to as user experience, or UX. The intricacy and versatility of software products, from requirements engineering to product functionality are well recognized. UX evaluations are often depends on prototypes, but it's important to consider the semantics embedded in software requirements to ensure project success. In this manuscript, Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network (SRE-UEDM-BDA-CBTCN) is proposed. The input data are collected from Requirements dataset. The collected data are given to the Convolution-Bidirectional Temporal Convolutional Network (CBTCN) to Design Modeling of Big Data Analysis user experience based on the dataset. In general, CBTCN does not express any adaption of optimization techniques for determining the ideal parameters to accurate Design user experience. Hence, African Vultures Optimization Algorithm (AVOA) is proposed in this work to improve the weight parameter of CBTCN. The proposed model is implemented and the efficiency is evaluated utilizing some performance metrics like accuracy, precision, specificity, sensitivity and F1-Score. The proposed SRE-UEDM-BDA-CBTCN method provides 28.46%, 21.34 and 33.81% higher accuracy, 22.88%, 26.52% and 34.63% higher Precision and 28.46%, 21.34 and 33.81% higher specificity compared with the existing techniques like Holistic big data integrated artificial intelligent modeling to improve privacy and safety in data management of smart cities (AIM-BDI-SDM), Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach (MHA-UED-MLA) and Towards Measuring User Experience based on Software Requirements (TM-UEB-SR).

Readiness for Implemented Sustainable Procurement in Indonesian Government Construction Project

Procurement is the process of obtaining goods and services in a construction project and is a crucial point for the success of a design and build (DB) project. The success of a DB project has an effect on work performance metrics such as cost, quality, and time. Therefore, this research aims to develop a readiness model for implementing sustainable procurement in a DB project with a financing system in the state/regional budget (APBN/APBD). By using previous literature reviews, this research adopted a mixture of qualitative and quantitative methods. Specifically, the qualitative method was conducted using in-depth interviews, and conclusions were drawn using the Delphi method and focus group discussion (FGD). Meanwhile, the quantitative method was used to analyze secondary data from the current DB project in order to examine sustainable procurement. The projects examined were spread across the country and had a value of at least 100 billion. Consequently, the results showed that various factors influenced sustainable procurement in the DB management project. In addition, this research impacted better procurement management in the DB project based on APBN/APBD funding, thereby increasing project productivity and innovation, as well as other beneficial values to stakeholders. Typically, the result could be used as a readiness model for implementing sustainable procurement in a DB project with APBN/APBD funding schemes, serving as a guide for construction management in the future and being useful for decision making on government projects.

“Stumbling blocks” in the translation of technical documentation

The objective of this study is to clarify the notion of “technical documentation” and explore the challenges and strategies involved in translating this type of text. Technical documentation is crucial in helping customers understand how to use the product effectively that is why it should be well-structured, allowing users to find the information they need quickly and easily. Examples of technical writing include technical manuals, operating instructions, scientific books, monographs, academic papers and articles, and patent translations. However, several obstacles can impede the translation process, potentially resulting in inaccuracies or misunderstandings. Common translation difficulties include untranslatable words, multiple-word meanings, differences in grammatical and syntactic systems, technical and specialized terminology, and “false friends.” The process of translation of such texts is intricate, involving the transfer of information from one language to another while considering cultural, linguistic, and contextual factors. While translating technical texts, the translator may encounter various obstacles, such as linguistic challenges, source text issues, expertise gaps, and realia. Improving the effectiveness of translating technical documentation involves a combination of best translation practices. Effective communication and collaborative efforts are crucial elements for project success. It is also important for professional translators to be vigilant and use the right resources to avoid common pitfalls and deliver high-quality. By combining these strategies, you can enhance the efficiency and accuracy of translating technical documentation, ensuring that the information is effectively communicated to a global audience.

Using Optimized Spectral Indices and Machine Learning Algorithms to Assess Soil Copper Concentration in Mining Areas

Soil copper (Cu) contamination in mining areas poses a serious threat to the surrounding environment and human health. Timely determination of Cu concentrations is crucial for the ecological protection of mining areas. Hyperspectral remote sensing technology, with its non-destructive monitoring advantages, is essential for monitoring soil Cu pollution and achieving sustainable agricultural development. Using the hyperspectral technique for assessing soil Cu concentration, four machine learning models (support vector regression (SVR), random forest (RF), partial least squares regression (PLSR), and artificial neural network (ANN)), combined with three types of input variables (the full-band, sensitive bands, and optimized spectral indices (Opt-TBIs)) were employed. The hyperspectral reflectance of 647 soil samples from an abandoned tailings mine in western Inner Mongolia, China was collected. The sensitive bands were extracted using the successive projections algorithms (SPA), and 12 Opt-TBIs were selected. Results showed that the regions with higher soil Cu concentration extracted by SPA and Opt-TBIs were concentrated in the red edge and near-infrared regions. Compared with the full spectrum and SPA-sensitive bands, models based on Opt-TBIs successfully predicted soil Cu concentrations. The Opt-TBIs-RF model provided higher accuracy in estimating soil Cu among the four models. Using only four Opt-TBIs as input variables, the model maintained a stable performance in estimating Cu concentrations in different mining areas (R2Val = 0.72, RPDVal = 1.90). In conclusion, Opt-TBIs as input variables demonstrate good predictive capabilities for soil Cu concentrations in the study area, providing a basis for the formulation of sustainable strategies for soil reclamation and environmental protection in Inner Mongolia.

Success factors of real estate crowdfunding projects: Evidence from Spain

AbstractReal estate crowdfunding is a relatively new alternative financing and investing method. This research aims to identify factors which might increase investors' willingness to participate in real estate crowdfunding campaigns. We analyse 195 lending-based real estate crowdfunding campaigns from four Spanish platforms. Project success is measured by duration, i.e. the time required to reach the funding target. We assess the impact of the funding target, the annual return, the loan duration, several risk-related metrics and the minimum investment amount. We find that the higher the funding target and the minimum investment amount, the longer it takes to reach the target. We document that investors prefer projects where the maturity of the loan is shorter. We also find that construction-type projects reach the funding target faster than other type of fundraising goals. At the same time, we do not find any association between the annual return or risk-related metrics and project success. To assure successful fundraising, real estate crowdfunding platforms should prioritize those real estate projects which are highly popular among investors (i.e. construction-type projects with short maturity). Real estate developers, in turn, should crowdfund projects which are demanded by the crowdinvestors and use their traditional financing methods for the remaining projects.

Semesters Simplified: A Comprehensive Approach to Academic Management

The "Semesters Simplified" project addresses challenges in academic management at Dr. A.P.J. Abdul Kalam Technical University (AKTU) by introducing a dedicated website and mobile application. This technological initiative aims to streamline information dissemination, enhance communication, and foster an engaging learning environment for both students and educators within semester-based academic structures. Grounded in educational technology principles, the project prioritizes user engagement, personalized learning, and inclusivity. The chosen technology stack employs modern tools for front-end and back-end development, ensuring a seamless and responsive user experience. The methodology encompasses needs analysis, user-centric design, development, testing, deployment, and continuous improvement. The proposed facilities, spanning software and hardware requirements, are crucial for the project's success. The literature review provides a solid foundation, aligning the project with established principles and best practices in educational technology. Ultimately, "Semesters Simplified" aspires to contribute significantly to the advancement of academic management, fostering accessibility, engagement, and inclusivity at AKTU.

Effect of Financial Resources Management on Project Success in Rwanda; Case of Green Gicumbi Project

Effect of Financial Resources Management on Project Success in Rwanda; Case of Green Gicumbi Project

A DEA Game Cross-Efficiency Model with Loss Aversion for Contractor Selection

Evaluating and selecting appropriate contractors is critical to the success of specific construction projects in the building industry. Existing approaches for addressing this problem are unsatisfactory due to the ignorance of the multi-dimensional nature of the evaluation process and inappropriate consideration of existent risks. This study presents a DEA game cross-efficiency model with loss aversion for evaluating and selecting specific contractors. The competitiveness of the evaluation process is modeled using game theory with respect to the adoption of the cross-efficiency model. The attitude of the decision maker toward risks is tackled with the use of loss aversion, which is a phenomenon formalized in prospect theory. As a result, the proposed approach can adequately screen available contractors through prequalification and adequately consider the attitude of the decision maker toward risks, leading to effective decisions being made. An example is presented to demonstrate the applicability of the proposed model in evaluating and selecting appropriate contractors for specific construction projects. The results show that the proposed model is effective and efficient in producing a unique solution for contractor selection through appropriate modeling of the multi-dimensional contractor selection process and adequate consideration of the competition between the contractors and the attitude of the decision maker toward risks in practical situations.

Co-Creating a Seniors’ Meeting Place: A Prototype Pop-Up Installation on a Popowice Housing Estate in Wrocław, Poland

Citizens worldwide are increasingly engaged in co-creation processes. It can be applied to meet the needs of seniors regarding their living arrangements or activities in public spaces. This article presents a fragment of research aimed at identifying problems and needs faced by older adults (aged 65 and older) in the Popowice neighborhood in Wrocław, Poland, and co-creating solutions with them. Using the co-creation method—both qualitative and quantitative research—a series of activities were conducted (workshops, interviews, surveys) during five phases of the Living Lab, culminating in the creation of a physical prototype. Intervention through a physical prototype allowed for verifying the validity of the conclusions drawn in the earlier part of the study, serving as an activator and accelerator of neighborhood life. The research suggests that the co-creation process and the prototype can influence the social activity of seniors. Recommendations for future co-design processes with older people include establishing relationships with stakeholders and beneficiaries, which is essential for the success of co-creation projects, and involving participants in directly creating solutions to increase their engagement and willingness to collaborate. In the face of aging societies, co-creation can be a useful method for creating tailored solutions to meet users’ needs.

Working From Within: The Rural Community Participatory Design Framework.

This article describes the development of the rural community-based participatory design framework to guide healthcare design teams in their integration of rural community and clinical voice during the planning, design, and construction of a healthcare facility. Rural communities are facing an alarming rate of healthcare facility closures, provider shortages, and dwindling resources, which are negatively impacting population health outcomes. A prioritized focus on rural care access and delivery requires design teams to have a deeper understanding of the contextual considerations necessary for a successful healthcare facility project, made possible through engagement and partnership with rural dwelling community members and healthcare teams. The rural community participatory design framework is adapted from the rural participatory research model, selected due to its capture of key concepts and characteristics of rural communities. Underpinning theories included rural nursing theory and theory of the built environment. The framework encompasses healthcare facility project phases, key translational concepts, and common traits across rural communities and cultures. As a middle-range theoretical framework, it is being tested in a current healthcare project with a Critical Access Hospital in Montana to facilitate design team and stakeholder collaboration. The rural community participatory design framework may be utilized by design teams as a means of familiarization with rural cultures, norms, values, and critical needs, which relate to meaningful design. The framework further enables design teams to critically appraise best practices of stakeholder engagement throughout the project lifecycle.

Decarbonizing Affordable Housing in New York City

Buildings account for about 30% of global greenhouse gas emissions. Retrofitting existing buildings for electrification and energy efficiency is a necessary first step toward building decarbonization. For the City of New York to reach its ambitious decarbonization goals, the city must create market conditions to allow rapid scaling up of deep energy retrofits. RiseBoro Community Partnership delivered one such innovative deep energy retrofit project called Casa Pasiva. This case study reveals ways in which affordable housing developers are uniquely positioned with many supporting policies in New York to become early adopters of deep energy retrofits, given their need to improve both business operations and tenant health. However, future projects still face steep obstacles ranging from funding gaps, a lack of familiarity with new building technologies, and conservative investors. RiseBoro’s successful Casa Pasiva pilot project identifies many of the remaining challenges related to deep energy retrofit capital projects as well as further opportunities to scale necessary retrofits across the New York affordable housing market. Readers will learn about options for and obstacles to implementing and scaling up deep energy retrofits in the affordable housing industry.

The Knowledge Acquisition Process in Interorganizational Projects: A Multiple-Case Study of Infrastructural Projects in Pakistan

Knowledge acquisition is pivotal for delivering successful interorganizational projects where multiple organizations are involved. The phenomenon, however, has been the subject of very few comprehensive empirical studies. Hence, this article sets out to understand how knowledge is acquired, particularly in the context of interorganizational projects. It explores the knowledge acquisition process at different levels, including the individual, team, organizational, and interorganizational levels. For this purpose, metro train and metro bus projects in Pakistan were examined. A multicase study approach is employed in this article; semistructured interviews and archival documents are included in the empirical data. Within and across case analysis is used for analyzing the data. The findings present the knowledge acquisition process, including types of knowledge (i.e., explicit and tacit knowledge) and multiple levels such as individuals, teams, organizations, and the interorganizational level. By examining knowledge acquisition in interorganizational projects, this article contributes to the literature on knowledge-based theory.

Unleashing the Power of Django for Construction Materials Rental

In the realm of construction equipment rental, efficient management of resources and seamless access to construction materials are paramount for project success. This paper explores the utilization of Django, a high-level Python web framework, to revolutionize the construction equipment rental industry. By harnessing the power of Django, this study proposes a comprehensive platform that streamlines the process of renting construction equipment and accessing necessary materials. The platform integrates features such as user-friendly interfaces, robust inventory management systems, real-time tracking, and secure payment gateways to enhance user experience and optimize operational efficiency. Through a combination of Django's flexibility, scalability, and rich ecosystem of plugins, this solution aims to address the challenges faced by construction companies in managing equipment rentals and material procurement. Furthermore, the paper discusses the technical implementation of Django within the context of construction equipment rental, highlighting its potential to transform traditional workflows and propel the industry towards digital innovation. This study specify the adoption of Django in construction equipment rental has the potential to unleash a new era of efficiency, transparency, and productivity, thereby driving the industry towards greater success and competitiveness in the modern era