Search results

The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results.

This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance.

The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs.

Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.

This paper aims to examine the existence of a transparency gap between voluntary external sustainability reporting and internal sustainability performance of an organisation arising from the operationalisation of transparency as an instrumental tool.

This study combined an analysis of a firm’s sustainability report (secondary data) with a qualitative case study data (primary data comprising interviews, meetings and internal documents) to understand how the Global Reporting Initiative (GRI) sustainability reporting guidelines are applied in practice.

By comparing what is reported with a range of primary case study data, this study finds evidence of transparency gaps, particularly in terms of the quality of measurement of sustainability performance, the materiality of issues covered and the completeness of the report. This study posits that voluntary disclosures following the GRI guidelines (transparency technique) shape the external expression of acceptable corporate behaviour (transparency norm) that is nevertheless at odds with actual behaviour or performance.

The findings indicate the importance of mandatory sustainability reporting requirements that facilitate accountability to all key stakeholders and that are externally assured and enforced. Such requirements might take the form of standards that put boundaries on judgement and address material sustainable development impacts and that are accompanied by implementation guidance. Non-financial assurance practices must be developed to cover adherence to reporting principles and processes.

Transparency gaps that result from voluntary disclosure guidelines or standards being used to imply a transparency norm may undermine accountability for the impacts of the organisation and hinder alignment of business models and corporate strategies with sustainable development.

The paper contributes to a theoretical understanding of transparency as a form of self-regulation and has implications for the further development of sustainability reporting standards.

Coal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.

The proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.

The ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.

The proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.

This is an acceptable fact that firms put efforts to maximize shareholders wealth but there is growing demand that firms are also accountable to various stakeholders associated directly or indirectly with the firms' business activities. Investors now evaluate firm's performance not only from financial perspective but also consider environment, social, and governance (ESG) factors when taking investment decision. ESG is not visible in firm's annual financial reports but investors do not deny its significance when valuing firms. There are increasing interests in ESG by communities, professionals, and government bodies, and all are interested to keep it as part of firms' regular activity and have to relate it with firm performance and efficiency that affects firm value. Still, there are difficulties in integration of ESG factors into investment decision-making, but efforts are being put to overcome all the issues. Firms which consider ESG are in a good position to achieve their long-term financial goals as they are likely to attract capital, lower borrowing costs, mitigate risks, and maximize shareholders value.

Crucial transition of the Indian residential building sector into a low-emission economy require an in-depth understanding of the potentials for retrofitting the existing building stock. There are, however, limited studies that have recognised the interdependencies and trade-offs in the embodied energy and life cycle impact assessment of retrofit interventions. This research appraises the life cycle assessment and embodied energy output of a residential building in India to assess the environmental implications of selected retrofit scenarios.

This study utilises a single case study building project in South India to assess the effectiveness and impact of three retrofit scenarios based on life cycle assessment (LCA) and embodied energy (EE) estimates. The LCA was conducted using SimaPro version 9.3 and with background data from Ecoinvent database version 3.81. The EE estimates were calculated using material coefficients from relevant databases in the published literature. Monte Carlo Simulation is then used to allow for uncertainties in the estimates for the scenarios.

The three key findings that materialized from the study are as follows: (1) the retrofitting of Indian residential buildings could achieve up to 20% reduction in the life cycle energy emissions, (2) the modification of the building envelope and upgrading of the building service systems could suffice in providing optimum operational energy savings, if the electricity from the grid is sourced from renewable plants, and (3) the production of LEDs and other building services systems has the highest environmental impacts across a suite of LCA indicators.

The retrofitting of residential buildings in India will lead to better and improved opportunities to meet the commitments in the Paris Climate Change Agreement and will lead to enhanced savings for building owners.

This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms.

This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning.

Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables.

Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.

Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.

This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.

In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.

Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.

This paper aims to explore the open government data initiative in the Food Protection program area within the New York State’s Department of Health to assess the impacts of opening data in terms of data quality and public value. An ecosystem lens is used to explore the dynamics of actors and their interactions, the processes involved in the program and the consequences such interplay brought forth to data quality.

The data were collected through 15 semistructured interviews with multiple stakeholders from different sectors, such as county officials, administrators and technicians, food sanitarians, data journalists and restaurant owners. At the analysis stage, the ecosystem perspective helped to capture the big picture of the open data actor interrelationships within this community regarding the food service inspections datasets.

Prior research suggests that open data initiatives enhance data quality. However, this study shows how opening data can adversely affect the quality of data. Results are explained by competing dynamics and conflicting interests among open data actors, undermining the expected public value from open data initiatives.

The findings are in contrast with the mainstream open data literature and helps open data scholars to anticipate some currently unexpected results of open data initiatives. Limitations include potential biases associated to interpretation of interview data and that the results are based on a single case study.

This study makes governments and policymakers alert about the possibility of similar open data byproducts and unwanted outcomes and helps them to design more effective open data policies, hence gaining higher economic advantage while lowering costs of open data initiatives.

Detailed open data and open data case studies through the ecosystem perspective are still scarce and can enrich discussions about open data policy design and refinement in the public sector. The data used for this research are not used in any prior papers, and to the best of the authors’ knowledge, this is the first study to identify such adverse effects of data quality that have been reported.

The effects of big data in this present age are highly significant, and big data have become more applicable to society. Big data technology has been adopted by many, and its applications are utilized at national, organizational, and industry levels. This transformation of industries due to big data is changing working practice in academia, business, the humanitarian sector, and government, as they offer insights and positive effects across all sectors, making legal, economic, political, social, and ethical impacts in our world and producing innovation, efficiency, better decision-making, and a greater return on investments. This paper reviews the social implications, risks, challenges, and present and future opportunities of big data.

This study aims to investigate the main drivers of private saving in Egypt (2005–2020).

It employs an autoregressive distributed lag (ARDL) approach for quarterly data on private saving, lagged private saving, real gross domestic product (GDP) growth, public saving, inflation, real interest rate, money supply, current account deficit and unemployment.

Private saving in Egypt displays persistency and public saving depresses private saving in the short run and long run. Real interest rate, inflation and unemployment have negative and statistically significant impacts on private saving in the short run and long run. The current account deficit displays a negative effect on private saving but is significant only in the short run. Other incorporated variables, like real GDP and money supply, are not statistically significant. This could be attributed to the high consumption rather than saving motive of the Egyptian population and their tendency to rely more on other informal saving channels.

Findings are of policy relevance as unleashing the determinants of private saving guides policymakers in formulating the appropriate sustainable development policies. It also assists in identifying the main obstacles hindering the promotion of private saving and hence major areas for policy intervention, like financial inclusion, poverty eradication, employment generation and structural reforms.

This study contributes to the literature: (1) it tackles private saving figure rather than aggregate saving figure that is covered by similar studies due to lack of consistent data, (2) given the relatively low quality, unavailability and inconsistency of data on private saving in developing countries, investigating the determinants of private saving should be carried out on an individual country basis which is done by this study, (3) this study fulfills the gap in literature related to the lack of up-to-date studies on private saving in Egypt and (4) it relies on quarterly data that could produce more reliable results.