Quality Management Strategies Research Articles

A longitudinal study on the bacterial quality of baby spinach cultivated in Arizona and California.

In the U.S., most spinach is produced in Arizona (AZ) and California (CA) seasonally; typically, spinach is cultivated in the Yuma, AZ, area during the winter and in the Salinas, CA, area during the summer. As the bacterial quality of baby spinach can influence consumer acceptance of the product, it is important to assess whether the bacterial quality of baby spinach can vary between spinach-growing regions. The findings of this study provide insights that could be used to support region-specific quality management strategies for baby spinach. Our results also highlight the value of further evaluating the impact of growing region and preharvest temperature on the bacterial quality of different produce commodities.

Enhancing spatial modeling and risk mapping of six air pollutants using synthetic data integration with convolutional neural networks

Air pollution poses significant risks to human health and the environment, necessitating effective air quality management strategies. This study presents a novel approach to air quality management by integrating an autoencoder (AE) with a convolutional neural network (CNN) algorithm in Tehran city of Iran. One of the primary and vital problems in deep learning is model complexity, and the complexity of a model is affected by data distribution, data complexity, and information volume. AE provide a helpful way to denoise input data and make building deep learning models much more efficient. The proposed methodology enables spatial modeling and risk mapping of six air pollutants, namely, particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). For air pollution modelling, data from a spatial database containing the annual average of six pollutants from 2012 to 2022 was utilized. The model considered various parameters influencing air pollution: altitude, humidity, distance to industrial areas, NDVI (normalized difference vegetation index), population density, rainfall, distance to the street, temperature, traffic volume, wind direction, and wind speed. The risk map accuracy was assessed using the area under the receiver operating characteristic (ROC) curve for six pollutants. Among them, NO2, PM10, CO, PM2.5, O3, and SO2 exhibited the highest accuracy with values of 0.964, 0.95, 0.896, 0.878, 0.877, and 0.811, respectively, in the risk map generated by the CNN-AE model. The findings demonstrated the CNN-AE model’s impressive precision when generating the pollution risk map.

Enhancing spatial modeling and risk mapping of six air pollutants using synthetic data integration with convolutional neural networks

Air pollution poses significant risks to human health and the environment, necessitating effective air quality management strategies. This study presents a novel approach to air quality management by integrating an autoencoder (AE) with a convolutional neural network (CNN) algorithm in Tehran city of Iran. One of the primary and vital problems in deep learning is model complexity, and the complexity of a model is affected by data distribution, data complexity, and information volume. AE provide a helpful way to denoise input data and make building deep learning models much more efficient. The proposed methodology enables spatial modeling and risk mapping of six air pollutants, namely, particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). For air pollution modelling, data from a spatial database containing the annual average of six pollutants from 2012 to 2022 was utilized. The model considered various parameters influencing air pollution: altitude, humidity, distance to industrial areas, NDVI (normalized difference vegetation index), population density, rainfall, distance to the street, temperature, traffic volume, wind direction, and wind speed. The risk map accuracy was assessed using the area under the receiver operating characteristic (ROC) curve for six pollutants. Among them, NO2, PM10, CO, PM2.5, O3, and SO2 exhibited the highest accuracy with values of 0.964, 0.95, 0.896, 0.878, 0.877, and 0.811, respectively, in the risk map generated by the CNN-AE model. The findings demonstrated the CNN-AE model’s impressive precision when generating the pollution risk map.

Bottom-up assessment of air quality management strategies in Vijayawada, India: Emissions inventory, atmospheric capacity, and policy scenarios

A bottom-up approach evaluates air quality management strategies in Vijayawada, India, a rapidly urbanizing city facing declining air quality. Detailed emission inventories and dispersion modeling assess pollutant concentrations (PM10, PM2.5, SO2, NOx) under various policy scenarios for 2025 and 2030. Business-as-usual (BAU) scenarios reveal the inadequacy of current regulations for PM, SO2, and NOx control. While BS-VI standards effectively reduced NOx emissions from vehicles, overall trends indicate a diminishing atmospheric capacity to assimilate pollutants. Alternative scenarios (ALTs) incorporating sector-specific controls project emission reductions (12–15 % PM10, 19–24 % PM2.5, 24–48 % SO2, 19–32 % NOx) and are expected to maintain pollutant levels below national ambient air quality standards. This integrated approach offers valuable insights for policymakers to develop sustainable air quality strategies aligned with SDGs. The study emphasises the need to consider potential trade-offs between environmental benefits and resource demands, such as increased electricity demand for electric vehicles (SDG 7) and land use competition from urban greening (SDG 11). This study aids in identifying strategies that maximise environmental benefits while minimising trade-offs, fostering sustainable urban development. The research's novelty lies in its integration of scenario-based analysis with atmospheric capacity evaluation, providing a robust framework for air quality management in rapidly urbanising regions.

Machine learning for cyanobacteria inversion via remote sensing and AlgaeTorch in the Třeboň fishponds, Czech Republic

Cyanobacteria blooms in fishponds, driven by climate change and anthropogenic activities, have become a critical concern for aquatic ecosystems worldwide. The diversity in fishpond sizes and fish densities further complicates their monitoring. This study addresses the challenge of accurately predicting cyanobacteria concentrations in turbid waters via remote sensing, hindered by optical complexities and diminished light signals. A comprehensive dataset of 740 sampling points was compiled, encompassing water quality metrics (cyanobacteria levels, total chlorophyll, turbidity, total cell count) and spectral data obtained through AlgaeTorch, alongside Sentinel-2 reflectance data from three Třeboň fishponds (UNESCO Man and Biosphere Reserve) in the Czech Republic over 2022–2023. Partial Least Squares Regression (PLSR) and three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were developed based on seasonal and annual data volumes. The SVM algorithm demonstrated commendable performance on the one-year data validation dataset from the Svět fishpond for the prediction of cyanobacteria, reflected by the key performance indicators: R2 = 0.88, RMSE = 15.07 μg Chl-a/L, and RPD = 2.82. Meanwhile, SVM displayed steady results in the unified one-year validation dataset from Naděje, Svět, and Vizír fishponds, with metrics showing R2 = 0.56, RMSE = 39.03 μg Chl-a/L, RPD = 1.50. Thus, Sentinel data proved viable for seasonal cyanobacteria monitoring across different fishponds. Overall, this study presents a novel approach for enhancing the precision of cyanobacteria predictions and long-term ecological monitoring in fishponds, contributing significantly to the water quality management strategies in the Třeboň region.

Estimating black carbon levels using machine learning models in high-concentration regions

Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as Nitrogen oxides (NOx) and Ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.

Bottom-Up Assessment of Air Quality Management Strategies in Vijayawada, India: Emissions Inventory, Atmospheric Capacity, and Policy Scenarios

A bottom-up approach evaluates air quality management strategies in Vijayawada, India, a rapidly urbanizing city facing declining air quality. Detailed emission inventories and dispersion modeling assess pollutant concentrations (PM10, PM2.5, SO2, NOx) under various policy scenarios for 2025 and 2030. Business-as-usual (BAU) scenarios reveal the inadequacy of current regulations for PM, SO2, and NOx control. While BS-VI standards effectively reduced NOx emissions from vehicles, overall trends indicate a diminishing atmospheric capacity to assimilate pollutants. Alternative scenarios (ALTs) incorporating sector-specific controls project emission reductions (12-15% PM10, 19-24% PM2.5, 24-48% SO2, 19-32% NOx) and are expected to maintain pollutant levels below national ambient air quality standards. This integrated approach offers valuable insights for policymakers to develop sustainable air quality strategies aligned with SDGs. The study emphasises the need to consider potential trade-offs between environmental benefits and resource demands, such as increased electricity demand for electric vehicles (SDG 7) and land use competition from urban greening (SDG 11). This study aids in identifying strategies that maximise environmental benefits while minimising trade-offs, fostering sustainable urban development. The research's novelty lies in its integration of scenario-based analysis with atmospheric capacity evaluation, providing a robust framework for air quality management in rapidly urbanising regions.

Enhancing Prime Services through Total Quality Management Strategies: Impacts on the Digital Transformation Process at Islamic Universities

Enhancing Prime Services through Total Quality Management Strategies: Impacts on the Digital Transformation Process at Islamic Universities

Enhancing Prime Services through Total Quality Management Strategies: Impacts on the Digital Transformation Process at Islamic Universities

Total Quality Management (TQM) is a management approach aimed at enhancing the quality of products, services, and organizational processes. In educational administration, TQM involves applying these principles to boost the efficiency and effectiveness of the education system. This study investigates whether TQM implementation improves digital transformation initiatives and service delivery in educational settings. Using a quantitative approach with the Structural Equation Model (SEM), this research measures and analyzes the relationships between TQM, Digital Transformation, and Prime Services. The study population comprises students from the Faculty of Tarbiyah who have completed at least three semesters, providing them with sufficient experience to assess TQM's effectiveness. Nonprobability quota sampling is used to select respondents based on predetermined criteria. The findings reveal that all reflective indicator variables in the outer model are valid, with high reliability demonstrated by composite reliability and Cronbach's alpha values. The inner model testing shows that exogenous variables can be explained by endogenous variables, including TQM (X1), Digital Transformation (X2), and Prime Services (X3). The results indicate that TQM significantly impacts digital transformation and the enhancement of prime services in education. Recommendations for educational administrators include strengthening TQM principles, increasing investment in digital technology, and focusing on prime services to students. These steps can improve operational efficiency, educational quality, and responsiveness to student needs in the digital era.

Analysis of Water Quality Prediction in the Yangtze River Delta under the River Chief System

Water quality prediction is essential for effective water resource management and pollution prevention. In China, research on predictive analytics for various water bodies has not kept pace with environmental needs. This study addresses this gap by conducting a comprehensive analysis and modeling of water quality monitoring data from multiple distributed water bodies specifically within the Yangtze River Delta. Using a novel approach, this paper introduces a distributed water quality prediction system enhanced by a CNN-LSTM joint model. This model synergistically combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks to robustly extract and utilize spatiotemporal data, thereby significantly improving the accuracy of predicting dynamic water quality trends. Notably, the excellent predictive performance of the joint model enables its prediction results to achieve RMSE and MAPE as low as 1.08% and 6.8%, respectively. Empirical results from this study highlight the system’s superior predictive performance. Based on these findings, this paper offers targeted recommendations for water quality monitoring, treatment, and management strategies tailored to the specific needs of the Yangtze River Delta. These contributions are poised to aid policymakers and environmental managers in making more informed decisions.

Trend Analysis and Spatial Source Attribution of Surface Ozone in Chaozhou, China

Surface ozone (O3), a critical air pollutant, poses significant challenges in urban environments, as exemplified by the city of Chaozhou in southeastern China. This study employs a novel combination of trend analysis and spatial source attribution techniques to evaluate the long-term dynamics of surface ozone and identify its sources. Utilizing the Kolmogorov–Zurbenko (KZ) filter and percentile regression, we analyzed the temporal trends of daily maximum 8 h moving average ozone (MDA8 O3) concentrations from 2014 to 2023. Our analysis revealed a general long-term downward trend in MDA8 O3 values alongside notable monthly fluctuations, with peak concentrations typically occurring in October and April. Additionally, the percentile regression analysis demonstrated a significant downward trend in MDA8 O3 concentrations across nearly all percentiles, with larger decline rates at higher percentiles, highlighting the effectiveness of local and regional O3 management strategies in Chaozhou. The changes in MDA8 O3 concentrations were mainly influenced by the short-term component, contributing 62.2%, while the contribution of the long-term fraction is relatively small. This suggests a significant influence of immediate meteorological conditions and transient pollution events on local O3 levels. To further elucidate the origins of high O3 concentrations, trajectory cluster analysis, trajectory sector analysis (TSA), and potential source contribution function (PSCF) analysis were conducted. The trajectory cluster analysis revealed that the northeast air mass was the main transport air mass in Chaozhou during the study period, accounting for 39.1% of occurrences. The northeast cluster C with medium-distance trajectories corresponds to higher concentration of O3, which may be the main transport pathway of O3 pollution in Chaozhou. TSA corroborates these findings, with northeast sectors 1, 2, and 3 accounting for 50.3% of trajectory residence time and contributing 52.2% to O₃ levels in Chaozhou. PSCF results further indicate potential high O₃ sources from the northeast, especially in autumn. This comprehensive analysis suggests that Chaozhou’s elevated O3 levels are influenced by both regional transport from the northeast and local emissions. These findings offer crucial insights into the temporal dynamics of surface O3 in Chaozhou, paving the way for more effective and targeted air quality management strategies.

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis.

Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynamics at high spatial resolution is critical for public health and policymaking. This study analyses 194,682 georeferenced SARS-CoV-2 RT-PCR tests from March 2020 and April 2022 in the canton of Vaud, Switzerland. We characterized five distinct pandemic periods using metrics of spatial and temporal clustering like inverse Shannon entropy, the Hoover index, Lloyd's index of mean crowding, and the modified space-time DBSCAN algorithm. We assessed the demographic, socioeconomic, and environmental factors contributing to cluster persistence during each period using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to consider non-linear and spatial effects. Our findings reveal important variations in the spatial and temporal clustering of cases. Notably, areas with flatter epidemics had higher total attack rate. Air pollution emerged as a factor showing a consistent positive association with higher cluster persistence, substantiated by both immission models and, to a lesser extent, tropospheric NO2 estimations. Factors including population density, testing rates, and geographical coordinates, also showed important positive associations with higher cluster persistence. The socioeconomic index showed no significant contribution to cluster persistence, suggesting its limited role in the observed dynamics, which warrants further research. Overall, the determinants of cluster persistence remained across the study periods. These findings highlight the need for effective air quality management strategies to mitigate air pollution's adverse impacts on public health, particularly in the context of respiratory viral diseases like COVID-19.

Sources of acellular oxidative potential of water-soluble fine ambient particulate matter in the midwestern United States

Ambient fine particulate matter (PM2.5) is associated with numerous health complications, yet the specific PM2.5 chemical components and their emission sources contributing to these health outcomes are understudied. Our study analyzes the chemical composition of PM2.5 collected from five distinct locations at urban, roadside and rural environments in midwestern region of the United States, and associates them with five acellular oxidative potential (OP) endpoints of water-soluble PM2.5. Redox-active metals (i.e., Cu, Fe, and Mn) and carbonaceous species were correlated with most OP endpoints, suggesting their significant role in OP. We conducted a source apportionment analysis using positive matrix factorization (PMF) and found a strong disparity in the contribution of various emission sources to PM2.5 mass vs. OP. Regional secondary sources and combustion-related aerosols contributed significantly (> 75 % in total) to PM2.5 mass, but showed weaker contribution (43–69 %) to OP. Local sources such as parking emissions, industrial emissions, and agricultural activities, though accounting marginally to PM2.5 mass (< 10 % for each), significantly contributed to various OP endpoints (10–50 %). Our results demonstrate that the sources contributing to PM2.5 mass and health effects are not necessarily same, emphasizing the need for an improved air quality management strategy utilizing more health-relevant PM2.5 indicators.

A virtual sensing approach to enhancing personalized strategies for indoor environmental quality and residential energy management

This study presents a consumer-centric approach on Demand-Side Management (DSM) for residential energy systems, aiming to align cost-efficient energy strategies with the individual's preferences on operational time-flexibility for residential electric appliances, thermal comfort, and indoor air quality. The optimization framework innovatively incorporates forecasts of the Indoor Air Quality (IAQ) index and thermal comfort levels using virtual sensing technology to predict their day-ahead values, which are furtherly integrated as constraints in the optimization problem. A dual-objective approach is employed, balancing the minimization of electricity costs all whilst increasing consumer's satisfaction. The approach underlines the critical role of consumer participation in DSM and illustrates how the integration of smart home technologies can lead to reductions in energy consumption and cost savings. This paradigm not only promotes consumer engagement in energy management but also showcases the potential of intelligent home systems to optimize energy usage while maintaining personalized comfort standards.

Optimizing Sampling Strategies for Estimating Riverine Nutrient Loads in the Yiluo River Watershed, China

Accurately estimating nutrient loads is crucial for effective management and monitoring of aquatic ecosystems. This study evaluated the uncertainty in different sampling frequencies and calculation methods for estimating total nitrogen (TN) and total phosphorus (TP) loads in the Yiluo River watershed, a tributary of the Yellow River in China. Using daily TN and TP concentration data from 2019 to 2020, we conducted a bootstrapping analysis to evaluate the accuracy of nine different load estimation methods at different sampling frequencies. Our results showed that Method 3 (M_3, constant concentration interpolation) and Method 7 (M_7, flow-weighted concentration method), when used with a biweekly sampling frequency, had the lowest Standard Deviation of the Percentage errors (STD) (7.70% and 8.60% for TN, 12.0% and 18.8% for TP, respectively) and Mean Relative Error (MRE) values (0.078% and −1.60% for TN, 0.305% and 2.33% for TP, respectively) on an annual scale. For monthly TN and TP load estimates, M_7 can control the MRE within ±20% at a biweekly sampling frequency. Furthermore, the uncertainty in TN and TP load estimates was generally larger during the summer months (June–September), emphasizing the important role of storm events in nutrient export. Extreme events (&lt;10% of the time) contributed approximately 50% of the annual nutrient loads. The findings of this study provide a scientific basis for optimizing water quality monitoring schemes and management strategies in agricultural watersheds.

Advancing air quality forecasting in Abu Dhabi, UAE using time series models

This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi’s Vision 2030.

Assessing Tunisia's urban air quality using combined lichens and Sentinel-5 satellite integration.

In Tunisia, urban air pollution is becoming a bigger problem. This study used a combined strategy of biomonitoring with lichens and satellite mapping with Sentinel-5 satellite data processed in Google Earth Engine (GEE) to assess the air quality over metropolitan Tunis. Lichen diversity was surveyed across the green spaces of the Faculty of Science of Tunisia sites, revealing 15 species with a predominance of pollution-tolerant genera. The Index of Atmospheric Purity (IAP) calculated from the lichen data indicated poor air quality. Spatial patterns of pollutants sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and aerosol index across Greater Tunis were analyzed from Sentinel-5 datasets on the GEE platform. The higher values of these indices in the research area indicate that it may be impacted by industrial activity and highlight the considerable role that vehicle traffic plays in air pollution. The results of the IAP, IBL, and the combined ground-based biomonitoring and satellite mapping techniques confirm poor air quality and an environment affected by atmospheric pollutants which will enable proactive air quality management strategies to be put in place in Tunisia's rapidly expanding cities.

Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM2.5) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Good Statistical Monitoring: A Flexible Open-Source Tool to Detect Risks in Clinical Trials

Abstract Background Risk-based quality management is a regulatory-recommended approach to manage risk in a clinical trial. A key element of this strategy is to conduct risk-based monitoring to detect potential risks to critical data and processes earlier. However, there are limited publicly available tools to perform the analytics required for this purpose. Good Statistical Monitoring is a new open-source solution developed to help address this need. Methods A team of statisticians, data scientists, clinicians, data managers, clinical operations, regulatory, and quality compliance staff collaborated to design Good Statistical Monitoring, an R package, to flexibly and efficiently implement end-to-end analyses of key risks. The package currently supports the mapping of clinical trial data from a variety of formats, evaluation of 12 key risk indicators, interactive visualization of analysis results, and creation of standardized reports. Results The Good Statistical Monitoring package is freely available on GitHub and empowers clinical study teams to proactively monitor key risks. It employs a modular workflow to perform risk assessments that can be customized by replacing any workflow component with a study-specific alternative. Results can be exported to other clinical systems or can be viewed as an interactive report to facilitate follow-up risk mitigation. Rigorous testing and qualification are performed as part of each release to ensure package quality. Conclusions Good Statistical Monitoring is an open-source solution designed to enable clinical study teams to implement statistical monitoring of critical risks, as part of a comprehensive risk-based quality management strategy.

The Nexus of Quality Management and Construction Organisations' Performance in Urban Ghana: The Moderating Effect of Information Technology Integration

Effective quality management strategies improve and sustain organisations' competitiveness in the current business environment by incorporating information technology. The primary aim of this research is to investigate the moderating influence of information technology on the connection between quality management strategies and the performance of construction firms in Accra, Ghana. The study utilized a sample of 255 construction firms in the Accra- Metropolis. The data collected underwent analysis using inferential and descriptive statistics. The results indicated a favorable impact of quality management strategies on the performance of construction companies. Furthermore, a positive correlation was found between the integration of information technology (IT) and the performance of construction firms. Nevertheless, there was an insignificant correlation of the moderator (information technology integration) on the link between quality management strategies and the performance of construction companies. This could be attributed to the absence of skills and expertise, as well as the absence of a modernized information technology system that integrates communication and quality management processes in construction organizations in Ghana. Subsequent studies should concentrate on the barriers to integrating IT into quality management processes in construction firms in developing nations.