Data identification of determinants affecting the adoption of sustainable construction: The perspective of residential building developers
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ABSTRACT:
SUBMITTER: Mahat N
PROVIDER: S-EPMC8603009 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background:E-health technology applications are essential tools of modern information technology that improve quality of healthcare delivery in hospitals of both developed and developing countries. However, despite its positive benefits, studies indicate that the rate of the e-health adoption in some developing countries is either low or underutilized. This is due in part, to barriers such as resistance from healthcare professionals, poor infrastructure, and low technical expertise among others. Objective:The aim of this study is to investigate, identify and analyze the underlying factors that affect healthcare professionals decision to adopt and use e-health technology applications in developing countries, with particular reference to hospitals in Nigeria. Methods:The study used a cross sectional approach in the form of a close-ended questionnaire to collect quantitative data from a sample of 465 healthcare professionals randomly selected from 15 hospitals in Nigeria. We used the modified Technology Acceptance Model (TAM) as the dependent variable and external factors as independent variables. The collected data was then analyzed using SPSS statistical analysis such as frequency test, reliability analysis, and correlation coefficient analysis. Results:The results obtained, which correspond with findings from other researches published, indicate that perceived usefulness, belief, willingness, as well as attitude of healthcare professionals have significant influence on their intention to adopt and use the e-health technology applications. Other strategic factors identified include low literacy level and experience in using the e-health technology applications, lack of motivation, poor organizational and management policies. Conclusion:The study contributes to the literature by pinpointing significant areas where findings can positively affect, or be found useful by, healthcare policy decision makers in Nigeria and other developing countries. This can help them understand their areas of priorities and weaknesses when planning for e-health technology adoption and implementation.
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Project description:These data support the research article "Improving energy savings from a residential retrofit policy: a new model to inform better retrofit decisions" - (Mac Uidhir et al., 2019) [1]. This article presents 3 data sources which are utilised in conjunction with a detailed energy system model of the residential sector to explore policy pathways for residential retrofitting. Data is collected from the Central Statistics Office (CSO) and the Sustainable Energy Authority of Ireland (SEAI). The first SEAI dataset is compiled for Ireland in compliance with the EU Energy Performance of Buildings Directive (EPBD) [2]. Data is collected using the Dwelling Energy Assessment Procedure (DEAP) [3]. DEAP is used to produce energy performance certificates known as Building Energy Ratings (BER). A BER indicates a buildings energy performance across a 15-point energy efficiency scale, rated alphabetically from A1 to G, in units of kWh/m2 year. A BER is required for new buildings and the rent or sale of existing dwellings - therefore the database has consistently grown in size since its inception in 2006. The BER database contains 735,906 records of individual dwellings. The database includes detailed building fabric information across a range of different building types, year of construction, Main/Secondary space/water heating fuels, heating system efficiency, ventilation method and structure type (Insulated concrete form, Masonry, Timber or Steel Frame). The second SEAI dataset (PWBER) contains aggregated pre and post BER information for a sample of 112,007 dwellings retrofitted during the period 2010-2015; this database contains mean energy efficiency improvement (kWh/m2 year) for a range of retrofit combinations as they apply to nine distinct building archetypes. The third CSO dataset is compiled from census data, representing the frequency of building types by year of construction.
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Project description:This article is a description of data related to the research article entitled "Factors influencing e-Procurement adoption in the Nigerian Building Industry" (Ibem et al., 2016) [1]. The data were derived via a questionnaire survey involving 213 participants comprising architects, quantity surveyors, builders, construction/project managers, and engineers working in consulting and contracting firms, private client organisations and government agencies. The survey was conducted in Nigeria between June and November 2015. The data set contains responses on the levels of awareness and extent of use of the different e-Procurement technologies, the barriers to the uptake as well as the factors influencing e-Procurement adoption by organizations in the Nigerian building industry. The survey data and results of the analysis are made available in this article for further use by researchers; and for the purpose of improving understanding of the key findings of the survey.
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Project description:Waste utilization is crucial for achieving sustainability in the construction industry. This article provides data that demonstrate the possibility of industrial waste utilization in high volumes to enhance cementitious composites. This dataset could help researchers understand the cement paste composites' behavior in the fresh and hardened state. The data were obtained from the physical, mechanical, and durability tests of laboratory-produced samples. The composites were tested at 7, 28, and 90-days of hardening, according to the American Society for Testing and Materials (ASTM) standards. The obtained values were then evaluated using the DataFit curve fitting (nonlinear regression) and data plotting software. Additionally, two spreadsheets were created to help researchers calculate their weights and calculate the bulk specific gravity, water absorption, and porosity values. The detailed information related to the data described here can be found in "High-volume marble substitution in cement-paste: towards a better sustainability" [1].
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Project description:This data article presents an expert system and econometric entropy-based informatics model for residential building project for cost judgment and decisions in residential building project. The data was obtained using purposive sampling technique to select projects completed between 2009 and 2011in Lagos state Nigeria, the project were examined for their cost centres. Also, As-built cost of one thousand(1000) samples of trained As-built cost of residential building projects trained with Neural network with Levenberg Marqua after being adjusted and modified with econometric factors like inflation index, cost entropy and entropy factor to stabilized the data and were used to form and train neural network used. Probability technique was used to generate risk impact matrix and influence of entropy on the cost centres. A parametric model similar to hedonic models was generated using the utility parameters within the early and late elemental dichotomy. The model was validated through comparative analysis of the econometric loading attributes using Monte Carlo technique of SPSS software extracting the contingency coefficient. The data of the model can provide solution to the problems of knowing the cost implication of a future project and also enable a builder or contactor load cost implication of an unseen circumstance even on occasion of deferred cost reimbursement.
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Project description:The urban building stock dataset consists of synthetic input and output data for the energy simulation of one million buildings. The dataset consists of four different residential types, namely: terraced, detached, semi-detached, and bungalow. Constructing this buildings dataset requires conversion, categorization, extraction, and analytical processes. The dataset (in .csv) format comprises 19 input parameters, including advanced features such as HVAC system parameters, building fabric (walls, roofs, floors, door, and windows) U-values, and renewable system parameters. The primary output parameter in the dataset is Energy Use Intensity (EUI in kWh/(m2*year)), along with Energy Performance Certificate (EPC) labels categorized on an A to G rating scale. Additionally, the dataset contains end-use demand output parameters for heating and lighting, which are crucial output parameters. jEPlus, a parametric tool, is coupled with EnergyPlus and DesignBuilder templates to facilitate physics-based parametric simulations for generating the dataset. The dataset can be a valuable resource for researchers, practitioners, and policymakers seeking to enhance sustainability and efficiency in urban building environments. Furthermore, dataset holds immense potential for future research in the field of building energy analysis and modeling.
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Project description:This paper reports data describing development projects for new buildings according to construction costs in North-East Italy. A survey was carried out on local companies undertaking new residential development projects in two Italian regions (Veneto and Lombardy). The aim of this survey was to record new real estate construction projects, collecting both technical and socio-economic cost features. It is extremely difficult to collect such data for the Italian real estate construction sector, due to its lack of transparency, so that the novelty for the Italian scenario is the dataset itself. Another interest perspective of this survey is that socio-economic characteristics were also recorded; they are often studied in urban economics, but are usually related to property purchase prices and values, not to construction costs. The data come from an analysis of Canesi and Marella regarding the relationship between the trend of construction costs and the socio-economic conditions of the reference setting, such as the mean years of schooling of the workforce, housing market trends, and average per capita income.
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Project description:Integrating social client relationship management (CRM 2.0) in the built environment can enhance the relationship between construction organizations and client towards sustaining a long and lasting collaboration. The data exploration analyzed the e-readiness of contracting and consulting construction firms in the uptake of CRM 2.0 and the barriers encountered in the adoption of the modern business tool. The targeted organizations consist of seventy five (75) construction businesses operating in Lagos State which were selected from a pool of registered contracting and consulting construction firms using random sampling technique. Descriptive statistics of the e-readiness of contracting and consulting construction firms for CRM 2.0 adoption and barriers limiting its uptake were analyzed. Also, inferential analysis using Mann-Whitney U statistical and independent sample t-test was performed on the dataset obtained. The data generated will support construction firms on the necessity to engage in client social relationship management in ensuring sustainable client relationship management in the built environment.
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Project description:In order to improve the quality and performance of healthcare services, healthcare information technology is among the most important technology in healthcare supply chain management. This study sets out to apply and test the Unified Theory of Acceptance and Use of Technology (UTAUT), to examine the factors influencing healthcare Information Technology (IT) services. A structured questionnaire was developed and distributed to healthcare representatives in each province surveyed in Thailand. Data collected from 400 employees including physicians, nurses, and hospital staff members were tested the model using structural equation modeling technique. The results found that the factors with a significant effect are performance expectancy, effort expectancy and facilitating conditions. They were also found to have a significant impact on behavioral intention to use the acceptance healthcare technology. In addition, in Thai provincial areas, positive significance was found with two factors: social influence on behavioral intention and facilitating conditions to direct using behavior. Based on research findings, in order for healthcare information technology to be widely adopted and used by healthcare staffs in healthcare supply chain management, the healthcare organizational management should improve healthcare staffs' behavioral intention and facilitating conditions.
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