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This paper aims to investigate the influence of sustainable enterprise resource planning (S-ERPs) systems implementation on sustainability performance.

A 1 × 2 experiment was conducted, involving a sample of 72 professional accountants enrolled in MPA, MBA and DBA programs at two prominent Egyptian universities. Simple linear regression was used to analyze the data.

The results reveal positive relationships between the implementation of S-ERPs and economic, environmental and social sustainability performance.

Considering the research methodology used, which relies on a laboratory experiment design; nevertheless, empirical data derived from a quasi-experiment conducted in a real-world context would offer valuable insights into the existing literature.

For manufacturing sector managers, the results offer value as organizations can benefit from S-ERP adoption in the internal and external integration of sustainability functions. The findings also provide decision-makers in the manufacturing context, particularly in emerging countries, with tangible reasons to consider S-ERP adoption for holistic sustainability benefits including waste management, resource consumption reduction and management of sustainable supply chain complexities. Further, the findings provide valuable insights for ERP vendors on how they can develop their ERP packages to align with software sustainability criteria.

This study is among the few that experimentally investigates the influence of S-ERPs implementation on sustainability performance within the manufacturing sector, especially in an emerging context such as Egypt. This unique contribution provides valuable insights into the complex connection between technology adoption and sustainability outcomes.

This paper aims to investigate the impact of integrating Sustainable Enterprise Resource Planning (S-ERP) systems and lean manufacturing (LM) practices on sustainability performance, especially in Egypt as an emerging country.

The authors carried out an experimental study with a sample of 144 professional accountants of MPA, MBA and DBA students at two of the top universities in Egypt.

The results provide significant evidence that the integration of S-ERP systems and LM practices implementation improve sustainability performance. However, there is no significant evidence that S-ERP adoption contributes to the success of LM practices implementation.

Because of the chosen research approach, this study is limited to use of a laboratory experiment design. Empirical evidence based on quasi experiments on a field setting would add value to the current literature.

Findings provide practical insights for the manufacturing sector managers into the benefits of integrating S-ERP systems and LM practices for sustainability performance improvement (e.g. reducing cost and waste, increasing operational efficiency). For ERP vendors, findings highlight how ERP vendors introduce “enablers” that incorporate LM best practices into their ERP systems and also how those vendors conform to the software sustainability criteria in the design of ERP applications.

Contrary to previous studies that addressed the individual impact of S-ERP systems and LM practices on performance, this paper experimentally gives an indication of the impact of concurrent implementation of S-ERP and LM practices on sustainability performance, especially in developing countries.

This study aims to explore the interaction of strategic knowledge management (SKM) and innovation on the performance of large manufacturing firms (LMFs) in Malaysia.

This study used a quantitative approach in investigating this interaction. Smart partial least-squares analysis was performed to test the hypotheses.

It was observed that administrative innovation, process innovation and product innovation were effective drivers of LMF performance. It was also ascertained that SKM has no moderating effect on the product innovation relationship with performance, although it does moderate the relationships between LMF performance and administrative innovation and process innovation, respectively.

The main limitation of this study is its focus on Malaysian LMFs. It nevertheless contributes to the literature by extending understanding of SKM and innovation dimensions from multi-faceted perspectives. As this is largely ignored in the literature, the study paves the way for additional research.

The findings may be used as guidelines for chief executive officers, particularly on the way SKM and innovation can be developed for enhanced LMF performance, in the context of South Asian countries.

To the best of the authors’ knowledge, this is the first empirical work to confirm the main drivers of SKM, including in the analysis the effect of administrative innovation, process innovation and product innovation and performance, in the context of the manufacturing sector. In support of an original conceptual model, the insights contribute to the literature on innovation, LMFs, SKM and emerging economies.

The purpose of this study is to develop a condition assessment (CA) model for a building's indoor 21 environments and to improve the building's asset management process.

The methodology is based on dividing the building into spaces, which are the principal evaluated elements based on the building's indoor environmental quality (IEQ). An evaluation scheme was prepared for the identified factors and the analytical hierarchy process (AHP) technique was used to calculate the relative weight of each space inside the building as well as the contribution of each IEQ factors (IEQFs) in the overall environmental condition of each space inside the building. The multi-attribute utility theory (MAUT) was then applied to assess the environmental conditions of the building as a whole and its spaces. An educational building in Canada was evaluated using the developed model.

Each space type was found to have its own IEQFs weights, which confirms the hypothesis that the importance and allocation of each IEQF are dependent on the function and tasks carried out in each space. A similar indoor environmental assessment score was calculated using the developed model and the building CA conducted by the facility management team; “89%” was calculated, using K-mean clustering, for the physical and environmental conditions.

IEQ affects occupants' assessment of their quality of life (QOL). Despite the existence of IEQ evaluation models that correlate the building's IEQ and the occupants' perceived indoor assessments, some limitations have led to the necessity of developing a comprehensive model that integrates all factors and their sub-criteria in an assessment scheme that converts all the indoor environmental factors into objective metrics.

The purpose of this research is to focus on the evaluation of heritage buildings' sustainability. BIM modeling was necessary for the design of the sustainability assessment model for Heritage Buildings (SAHB). Using ArchiCAD®, energy simulations were performed for two case studies (Murabba Palace, Saudi Arabia, and Grey Nuns Building, Canada), and the developed model was validated through sensitivity analysis.

Heritage buildings (HBs) are unique and must be preserved for future generations. This article focuses on a sustainability assessment model and rating scale for heritage buildings in light of the need for their conservation. Regional variations were considered in the model development to identify critical attributes whose corresponding weights were then determined by fuzzy logic. Data was collected via questionnaires completed by Saudi Arabian and Canadian experts, and Fuzzy TOPSIS was also applied to eliminate the uncertainties present when human opinions are involved.

Results showed that regional variations were sufficiently addressed through the multi-level weight consideration in the proposed model. Comparing the nine identified factors that affect the sustainability of HBs, energy and indoor environmental quality were of equal weight in both case studies.

This study will be helpful for the design of a globally applicable sustainability assessment model for HBs. It will also enable decision-makers to prepare maintenance plans for HBs.

Heritage buildings are significant for their historical and architectural value. Due to the lack of rating systems designed specifically for heritage buildings, it is essential to develop and validate a heritage building assessment tool that considers its specific characteristics. The purpose of this study is to provide an extensive review of research on Sustainability of Heritage Buildings (SHBs).

This review highlights methodologies applied in SHBs research and analyzes major global rating systems in order to identify their deficiencies for SHBs assessment. A systematic review was employed and articles from the top 10 high impact factor journals were studied. Twelve major global rating systems and their assessment criteria were identified.

Significant variability was observed among the assessment tools since each tool assesses several criteria, factors and indicators that fit its local context. Part of this variability can also be seen in the rating scales, threshold values and accreditation titles. As a result, the final sustainability ranking for a given building cannot be compared among the 12 rating systems. Most importantly, these systems fail to analyze some factors such as energy that are considered important with respect to heritage building assessment.

Since no specific rating system could be identified in this review as the most appropriate for heritage buildings, a new sustainability assessment tool that is specific to heritage buildings should be developed. Such a tool will enable facility managers to evaluate and improve the sustainability of their heritage buildings while preserving them.

This paper aims to offer a wider examination of the research concerning entrepreneurship characteristics in the Middle East and North Africa (MENA) region via a review of recent studies relevant to this topic. Research publications concerning entrepreneurship within the MENA region evidence growing interest in this field of study, with the potential to boost and drive future economic development and growth. This focus within entrepreneurship research is because of the economic development in the region, which is becoming increasingly important for policymakers and businesses.

The author performed a systematic literature review to produce robust information about entrepreneurship in the MENA region, followed by a thematic analysis to identify key research themes within each category.

Despite the growth in entrepreneurship research in the MENA region, research on certain factors is lacking. An analysis of 271 studies published between 2009 and 2019 identifies 9 main research categories, within which 30 themes have attracted significant academic attention. Female entrepreneurship and gender, youth entrepreneurship and entrepreneurship behaviour and orientation are the three key categories influencing perspectives on entrepreneurship in the MENA region. This study highlights research gaps and provides recommendations to guide future research on the sustainable development of entrepreneurship in the MENA region.

This paper highlights trends in entrepreneurship research amongst scholars within the MENA region and suggests paths for future research efforts.

The construction industry is facing an enormous number of challenges due to continuous advancements in construction technologies and techniques. Hence, construction management theories have to confront critical newly issues concerning market globalization and construction innovations. The key factor to address these challenges is to ameliorate the competitive abilities of the competing construction firms. In this context, measuring competitiveness of construction firms is an efficacious approach to amplify their competitive growth and profitability. To this end, the purpose of this research paper is to design a three-tier multi-criteria decision making model for competitiveness assessment and benchmarking of construction companies, meanwhile tackling a wide range of essential factors and attributes that covers broad aspects of the present competitive market.

In the first tier, four new pillars (4P) of competitiveness assessment are introduced for construction firms, namely, organization performance, project performance, environment and client and innovation and development. These pillars are able to aid in construction firms’ management on both long and short term basis. Hence, 21 key competitive factors and eighty key competitive criteria are identified, incorporated and analyzed in this research study. The second tier encapsulates carrying out a questionnaire survey in the Canadian and Vietnamese market to garner two main sets of information. The first set of information incorporates responses of the pairwise comparisons between competitiveness factors and criteria. The second set involves gathering utility scores pertinent to each competitiveness criteria. The developed model then leverages the use of analytical hierarchy process to scrutinize the relative importance priorities of competitiveness factors and criteria. The third tier of the developed model encompasses the use of multi-attribute utility theory to compute competitiveness scores for construction companies through blending criteria’ relative importance weights alongside their respective utility functions. In addition, the third tier comprises conducting a sensitivity analysis to derive the most important criteria influencing the overall competitiveness of construction companies. The developed model is tested and validated using three case studies; one construction company from Canada and two construction companies from Vietnam.

Results demonstrated that the developed model has a potential to render a synthesized and methodical performance evaluation for the competitive ability of a given construction company. Furthermore, it was found that Vietnamese companies are more considerate towards pillars pertaining to environment and client while Canadian companies are more attentive towards innovation and development. The outcome of sensitivity analysis revealed that effectiveness of cost management highly affects the competitive ability of Vietnamese companies while effectiveness of cost management exhibits the most significant influence on the competitive of Canadian companies.

The developed model can benefit construction companies to understand their competitiveness in their market and diagnose their strengths and weaknesses. It is also can be useful in efficient utilization of their limited resources and development of sustainable and long-term strategic plans strategic plans, which consequently leads to maintaining better position in their dynamic business markets.

Literature review manifests that reported competitiveness assessment models and practices are not able to address present challenges, technologies and developments in construction market.

It is now well-established that good water quality is associated with economic prosperity, reduced incidence on public health and the good functioning of the various ecosystems found in our environment. Water contamination is mostly related to both diffused (agricultural lands and geologic rock degradations) and point sources of pollution. Mauritius has many water resources which depend solely on precipitation for their replenishment. Water parameters which are of relevance include total dissolved solids (TDS), temperature, pH, electrical conductivity, turbidity, dissolved oxygen, dissolved and particulate organic carbon and major cations and anions. The traditional methods of analysis for these parameters are mostly using electrical and optical methods (probes and sensors in the field), while chemical titrations, Flame AAS and High-Performance Liquid Chromatography techniques are carried out in the laboratory. Image Classification techniques using neural networks can also be used to detect the presence of contaminants in water. In addition to basic water quality parameters, the field sensors range have been extended to cover important major ions and can now be integrated with Artificial Intelligence (AI)-based models for the prediction of variations in water quality to better protect human health and the environment, reduce operation costs of water and wastewater treatment plant unit processes.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.