Technology Diffusion Research Articles

Towards a unified approach to prioritization of regenerative agricultural practices across cropping systems

Prioritizing field practices according to their contribution to regenerative efforts, in a structured and standardized manner, can enable efficient resource allocation and higher adoption rates. However, it is remarkably hampered by the diversity of production environments and their respective best management practices. This paper proposes prioritizing regenerative practices using a multicriteria assessment method informed by expert knowledge. To develop it, we mined and synthesized knowledge from eleven experts for three diverse cropping systems, showing that the prioritization of practices varies greatly across production environments. By integrating the multicriteria approach with a multi-indicator sustainability framework, we demonstrate how practice weights can be used to monitor sustainability efforts and outcomes of thirty vineyard farms in Maharashtra, India. The suggested prioritization approach can be used to standardize the assessment of the importance of regenerative practices in different cropping systems and be adopted in sustainable sourcing schemes of the agro-food industry.

Impact Of UPI Transaction on Purchasing Decision of Customers: A Survey in Chennai City

In today’s digital age, the Unified Payments Interface (UPI) has significantly transformed consumer purchasing behavior, making transactions more seamless and efficient. This study investigates the impact of UPI transactions on customers' purchasing decisions in Chennai, analyzing awareness, usage patterns, and the role of demographic factors. Data collected from 121 respondents reveal a high awareness and adoption rate of UPI, with a substantial portion of participants preferring UPI over cash for everyday purchases. Regression analysis indicates a strong positive correlation (r = 0.65, p < 0.01) between UPI transaction frequency and consumer purchasing activity. Additionally, age and income are shown to significantly influence UPI usage. The findings highlight UPI’s role in enhancing consumer convenience and security while promoting more frequent and impulsive buying behavior. The study offers insights for marketers and policymakers to leverage digital payment trends for improved consumer engagement.

Scalable Technology for Adolescents and Youth to Reduce Stress in the Treatment of Common Mental Disorders in Jordan: Protocol for a Randomized Controlled Trial

Background Young people in low- and middle-income countries encounter significant barriers to accessing mental health support due to various factors, including a substantial treatment gap and limited health care budgets allocated to mental health. Using innovative strategies, such as scalable digital self-help psychological interventions, offers a potential solution for improving access to mental health support. However, digital mental health interventions come with their own set of challenges, including issues related to low user engagement. Chatbots, with their interactive and engaging nature, may present a promising avenue for the delivery of these interventions. Objective This study aims to explore the effectiveness of a newly developed World Health Organization (WHO) digital mental health intervention, titled Scalable Technology for Adolescents and Youth to Reduce Stress (STARS). Methods A single-blind, 2-arm randomized controlled trial will be conducted nationally across Jordan. Participants will include 344 young adults, aged 18-21 years, currently residing in Jordan. Inclusion criteria are heightened levels of psychological distress as determined through the 10-item Kessler Psychological Distress Scale (≥20). Assessment measures will be conducted at baseline, 1-week post intervention, and 3-month follow-up. Following baseline assessments, eligible participants will be randomized to receive STARS or enhanced usual care. The primary outcomes are the reduction of symptoms of depression and anxiety (Hopkins Symptom Checklist, 25 subscales) at 3-month follow-up. Secondary outcomes include general functioning (WHO Disability Assessment Schedule 2.0), well-being (WHO-5 Well-Being Index), personal problems (Psychological Outcomes Profile), and agency (State Hope Scale subscale). Results The study was funded in January 2020 by the Research for Health in Humanitarian Crises Programme (Elhra) and recruitment for the trial started on July 16, 2023. As of November 15, 2023, we randomized 228 participants. Conclusions This trial intends to contribute to the growing digital mental health evidence base by exploring technological solutions to address global public health challenges. Given the widespread use of technology globally, even in resource-constrained settings, and the high adoption rates among adolescents and young individuals, digital initiatives such as STARS present promising opportunities for the future of mental health care in low- and middle-income countries. Trial Registration ISRCTN Registry ISRCTN10152961; https://www.isrctn.com/ISRCTN10152961 International Registered Report Identifier (IRRID) DERR1-10.2196/54585

SWOT-AHP Analysis of BIM Technology Utilization in the Japanese Construction Industry

Due to its advantages, Building Information Modeling (BIM) technology has been widely accepted and developed globally. BIM is transforming traditional construction methods and enabling the resolution of issues such as labor shortages, overwork, and rising costs, which the Japanese construction industry faces. In Japan, there is an active push to promote the application of BIM technology; however, its widespread adoption is encountering numerous challenges and barriers. Although BIM technology has been applied and developed globally, there is still a lack of systematic analysis on the specific challenges and strategies of its implementation in the Japanese construction industry. This study reviews the literature on the application of BIM worldwide and surveys stakeholders in the Japanese construction industry, examining strengths, weaknesses, opportunities, and threats. By integrating Analytic Hierarchy Process (AHP) and SWOT analysis, this research prioritizes these factors, assesses their impact on the diffusion of BIM technology, and provides in-depth analysis and insights for the promotion and development of BIM technology in Japan.

Spatial feasibility prediction of green hydrogen scale-up in China under decarbonization policies: Based on improved diffusion model

Hydrogen production from renewable electrolysis is a suitable transformative technology for driving a cleaner energy mix, mitigating climate change, and achieving sustainable development and has been given the color value definition of "green hydrogen". However, this low-carbon technology is still in its infancy, and the pathways for expanding market supply have not been analyzed. To address the problems of unclear factors influencing the diffusion of green hydrogen technology innovation, unclear technology diffusion laws, and inaccurate predictions of regional green hydrogen production capacity under the goal of decarbonization policy, this work constructs a spatial assessment framework of the feasibility of low-carbon technology development in response to climate change on the basis of the theory of technological innovation systems, the improved technology diffusion model, and the feasibility of climate change spatial concepts to analyze and predict the diffusion law and regional green hydrogen production capacity of hydrogen production by renewable energy under the goal of carbon neutrality. The results of the study on the development of renewable hydrogen technology in China show that it follows the law of diffusion of low-carbon technologies in both the formation and growth stages. The diffusion of green hydrogen technology in the formation stage is slow, and the regional green hydrogen capacity in the near term cannot meet the decarbonization policy target demand in 2030. Before the spatial inflection point of technology diffusion feasibility, the demand-pull of the phase decarbonization policy target dominated the stability and sustainability of the green hydrogen supply market. After the inflection point, the internal growth of technology diffusion plays a key role in market diffusion and ultimately reaches a saturation value under the joint regulation of policy and the market to realize a carbon-neutral target capacity demand in 2060. The uncertainty of diffusion model parameters leads to anomalies in the feasibility space of technology diffusion, and risks such as overcapacity or insufficient demand in some regions may occur. Overall, this work provides a systematic assessment system for analyzing and predicting the pathways and characteristics of expanding renewable hydrogen production in specific regions under decarbonization policy objectives.

Trending in the right direction: critical access hospitals increased adoption of advanced electronic health record functions from 2018 to 2023.

We analyzed trends in adoption of advanced patient engagement and clinical data analytics functionalities among critical access hospitals (CAHs) and non-CAHs to assess how historical gaps have changed. We used 2014, 2018, and 2023 data from the American Hospital Association Annual Survey IT Supplement to measure differences in adoption rates (ie, the "adoption gap") of patient engagement and clinical data analytics functionalities across CAHs and non-CAHs. We measured changes over time in CAH and non-CAH adoption of 6 "core" clinical data analytics functionalities, 5 "core" patient engagement functionalities, 5 new patient engagement functionalities, and 3 bulk data export use cases. We constructed 2 composite measures for core functionalities and analyzed adoption for other functionalities individually. Core functionality adoption increased from 21% of CAHs in 2014 to 56% in 2023 for clinical data analytics and 18% to 49% for patient engagement. The CAH adoption gap in both domains narrowed from 2018 to 2023 (both P < .01). More than 90% of all hospitals had adopted viewing and downloading electronic data and clinical notes by 2023. The largest CAH adoption gaps in 2023 were for Fast Healthcare Interoperability Resources (FHIR) bulk export use cases (eg, analytics and reporting: 63% of CAHs, 81% of non-CAHs, P < .001). Adoption of advanced electronic health record functionalities has increased for CAHs and non-CAHs, and some adoption gaps have been closed since 2018. However, CAHs may continue to struggle with clinical data analytics and FHIR-based functionalities. Some crucial patient engagement functionalities have reached near-universal adoption; however, policymakers should consider programs to support CAHs in closing remaining adoption gaps.

Zero Trust Architecture in Cloud Computing: A Paradigm Shift in Platform Engineering Security

This article explores the paradigm shift in platform engineering security brought about by Zero Trust Architecture (ZTA) in cloud computing environments. As organizations increasingly adopt cloud-native architectures and microservices, traditional perimeter-based security models prove inadequate for protecting complex, distributed systems. The article examines the core principles of ZTA, including robust identity and access management, least privilege access, micro-segmentation, continuous monitoring, and secure communication. It delves into the implementation strategies of ZTA in platform engineering, highlighting its benefits such as enhanced security, improved compliance, increased resilience against threats, adaptability to dynamic environments, and improved visibility. The article also addresses the challenges of implementing ZTA, including complexity, potential performance concerns, user experience considerations, cost implications, and the risk of vendor lock-in. Through an analysis of industry trends, adoption rates, and quantified benefits, the article demonstrates how ZTA addresses critical security challenges in modern cloud environments, ultimately enabling organizations to create more resilient, compliant, and adaptable platforms in the face of evolving cyber threats.

Parent experiences with genetic testing for pediatric hearing loss.

The purpose of the current study was to assess parent perceptions and experiences of genetic testing, as well as barriers for not undergoing testing in a sample of families of children with hearing loss. A 44-item questionnaire, The Parent Perception of Genetic Testing Questionnaire developed by the research study team was administered. Participants were recruited from a pediatric otolaryngology/audiology practice and social media. A total of 146 parents of children with hearing loss participated. Approximately 47.6% of the children in our sample underwent genetic testing, 44.8% did not, and 7.6% of parents were unsure. For those that did not undergo testing, reasons included: unaware (6.2%), not interested (8.9%), cost (8.9%), time (3.4%), fearful of results (2.7%), and never offered (15.1%). For those that did undergo testing, over half of the parents reported that they did not receive counseling before (55.1%) and 41.7% reported they received counseling after the testing. Furthermore, parents were confused about the results with 18.3% reporting they were Very Confused, 28.3% Somewhat Confused, 20% A Little Confused, and 33.3% Not Confused at All about the variant of uncertain or unknown significance. Notably, less than half of parents (43.4%) remembered what they were told about the mode of inheritance. Overall, our study highlighted the low adoption rate of genetic testing and lack of integration into standard of care for otology/audiology practices. Collaboration between hearing healthcare professionals and geneticists is warranted to determine how to reduce barriers to access while improving pre- and post-counseling.

Using a Random Controlled Trial to Explore the Impact of AI-Enabled Learning

Much is being made of AI’s role in learning. However, there have been few studies that evaluate AI’s ability to make learning more efficient or more effective and specifically the learner’s willingness to embrace and use AI-enabled learning tools over conventional learning methodologies. Working with a manufacturing company, we used a randomized control trial approach (matching pairs) to present two groups of employees with a course on design thinking, one presented via a traditional learning management system (LMS Group) and other through an AI enabled tool (AI Group), with the content being identical in order to compare completion rates, time to completion, learning outcomes (subject matter knowledge retained) and the learner’s perceptions of value and experience. We also were able to compare the AI enabled version of the course to the same version with the addition of a face-to-face instructional component. While we found that those participants in the AI Group completed the course in a shorter period of time, had better learning outcomes and expressed higher perceptions of course value, the AI Group significantly underperformed the LMS Group in terms of course commencement and completion rates. Our finding suggest that the adoption of AI enabled learning tools may follow similar patterns of adoption rates of new technologies -- and require educating learners regarding the use, benefits, value and limitations of new AI enabled tools before widespread acceptance and usage.

AI based predictive acceptability model for effective vaccine delivery in healthcare systems

Vaccine acceptance is a crucial component of a viable immunization program in healthcare system, yet the disparities in new and existing vaccination adoption rates prevail across regions. Disparities in the rate of vaccine acceptance result in low immunization coverage and slow uptake of newly introduced vaccines. This research presents an innovative AI-driven predictive model, designed to accurately forecast vaccine acceptance within immunization programs, while providing high interpretability. Primarily, the contribution of this study is to classify vaccine acceptability into Low, Medium, Partial High, and High categories. Secondly, this study implements the Feature Importance method to make the model highly interpretable for healthcare providers. Thirdly, our findings highlight the impact of demographic and socio-demographic factors on vaccine acceptance, providing valuable insights for policymakers to improve immunization rates. A sample dataset containing 7150 data records with 31 demographic and socioeconomic attributes from PDHS (2017–2018) is used in this paper. Using the LightGBM algorithm, the proposed model constructed on the basis of different machine-learning procedures achieved 98% accuracy to accurately predict the acceptability of vaccines included in the immunization program. The association rules suggest that higher SES, region, parents’ occupation, and mother’s education have an association with vaccine acceptability.

Strategic Technological Innovations Influencing Competitive Advantage Among Micro and Small Enterprises in Nairobi County, Kenya

Purpose: The ability of businesses to integrate information technology in their operations has been found to be an integral driver to their success. Through information technology, firms can utilize technological innovations to strengthen the uniqueness of their products in the market thus enhancing their competitive advantage. However, with limited evidence in the local context especially among the micro and small enterprises which continue to face a continuous decline, it warrants the study to assess how strategic technological innovations have been embraced among the small and micro enterprises and the role the innovations have played in enhancing the competitive advantage of the enterprises. Specifically, the study will examine the influence of enterprise’s IT Capabilities on competitive advantage among Micro and Small Enterprises (MSEs); determine the influence of Technological resources on competitive advantage among Micro and Small Enterprises (MSEs); explore the influence of adopted new technologies on competitive advantage among Micro and Small Enterprises; and establish the influence of Technological Processes and Products (TPP) on competitive advantage among Micro and Small Enterprises (MSEs) in Nairobi County, Kenya. The objectives will be anchored on dynamic capabilities theory, Schumpeter theory of innovation, technology acceptance model and diffusion theory of innovation. Methodology: Using a descriptive research approach, the study will collect and analyse both qualitative and quantitative data. This data was obtained from 386 micro and medium enterprises in Nairobi Central Business District, drawn from a population of 11,245 MSEs registered by the Nairobi City County in the CBD. A questionnaire was the main instrument of data collection, which was pilot-tested for validity and reliability. The collected data was analyzed using descriptive and inferential statistics. Findings: The findings were presented using frequency tables and graphs. In conclusion, the findings emphasize the critical role of technology in driving market expansion, customer engagement, and operational efficiency. The study highlights the importance of strategic support and intervention to address technological adoption challenges. By encouraging technological development and integration, policymakers and other stakeholders can assist MSEs in improving their operations, boosting the economy of Nairobi County. Investing in technological education and infrastructure is essential for MSEs to optimally benefit from TPP in the contemporary digital economy. Unique Contribution to theory, policy and practice: Training and development on how MSEs can use the platforms effectively, online marketing and selling needs to be promoted to help MSEs build their capacities. Therefore, when equipped with the appropriate knowledge and tools, the policymakers can help the MSEs to adopt e-commerce and increase their market access and productivity.

From challenges to opportunities: navigating the human response to automated agents in the workplace

Workers are increasingly embracing Artificial Intelligence (AI) to optimise various aspects of their operations in the workplace. While AI offers new opportunities, it also presents unintended challenges that they must carefully navigate. This paper aims to develop a deeper understanding of workers’ experiences with interactions with automated agents (AA) in the workplace and provide actionable recommendations for organisational leaders to achieve positive outcomes. We propose and test a simulation model that quantifies and predicts workers’ experiences with AA, shedding light on the interplay of diverse variables, such as workload, effort and trust. Our findings suggest that lower-efficiency AA might outperform higher-efficiency ones due to the constraining influence of trust on adoption rates. Additionally, we find that lower initial trust in AA could lead to increased usage in certain scenarios and that stronger emotional and social responses to the use of AA may foster greater trust but result in decreased AA utilisation. This interdisciplinary research blends a systems dynamics approach with management theories and psychological concepts, aiming to bridge existing gaps and foster the sustainable and effective implementation of AA in the workplace. Ultimately, our research endeavour contributes to advancing the field of human-AI interaction in the workplace.

The effects of climate change technology spillovers on carbon emissions across European countries

To unravel the challenges in the global diffusion of climate-friendly technologies, this investigation analyzes the diffusion of climate change-related technologies across countries. By using an unbalanced panel of selected European countries over the period 1990-2020, this investigation quantifies the carbon dioxide (CO2) emission effects of the diffusion of climate change-related technologies that are mediated by imports, geographical and technological proximity and free diffusion of technologies. In this study, the effects of domestic development of climate change-related technologies, population and affluence are also accounted for, and the emission effects are estimated using a fixed-effects panel model with instrumental variables. The instrumental variable for foreign technology spillovers is based on the technology support policies adopted in foreign countries. As expected, international spillovers of climate-friendly technologies are negatively linked to CO2 emissions, thus promoting emission reductions across the region. Importantly, emission reductions in Europe are more strongly influenced by international technology spillovers than by domestic innovation activities. Moreover, while all the analyzed technology diffusion channels appear relevant, the results are the most robust regarding import-mediated technology spillovers. Insights from this study support policy recommendations, especially in the trade policy context.

Small Farmer’s Perceptions of Climate Change and Adoption of Climate-Smart Practices: Evidence from Missouri, USA

Using survey data of Missouri small producers in 2022, this paper explores their perceptions of climate change and factors affecting their adoption of climate-smart practices. A synthesized climate change perception (CCP) index is developed to assess the effects of cognition and social demographics of producers on climate-smart practices (CSPs) adoption. The results show that 86.0% of participants have relatively high perceptions of climate change, and 82.0% have adopted at least one climate-smart practice; the higher the climate change perception index, the more likely the producers will adopt climate-smart practices. The findings encourage education on knowledge related to climate change, the relationship between climate change and agriculture, and how CSPs can reduce climate change. Specifically, we find specialty crop producers are more likely to adopt CSPs than other producers; and a one-unit increase in the CCP index can increase the CSP adoption rate by 4.61% for livestock producers and 2.61% for organic producers, suggesting relevant education programs can be tailored to the needs of livestock and organic farmers to improve their adoption rates of CPSs. Further, more resources are needed to support the farmers who reside in severely climate change – impacted areas for their sustainable production and climate sustainability.

Strategic deployment of hydrogen fuel cell buses and fueling stations: Insights from fleet transition models

Establishing new hydrogen value chains is challenging, requiring economies of scale and balanced supply-demand dynamics. Municipalities can mitigate this risk through government support and deployment strategies. This study analyzes Edmonton's transition to zero-emission buses (ZEBs), focusing on hydrogen fuel cell electric vehicles (HFCEVs) and hydrogen fueling stations (HFSs). Using scenario-based modeling and S-curve models for technology diffusion, we project the adoption of battery electric vehicles (BEVs) and HFCEVs. Deploying over 1000 ZEBs by 2040 is necessary to meet Net-Zero targets, with 310–760 HFCEVs required for the municipal bus inventory. This results in an estimated hydrogen demand of 6.2–14.5 t-H2/day and a reduction of 0.4–1.0 Mt-CO2 in tailpipe emissions by 2050. We use these scenario projections to develop a phased deployment strategy, optimizing fleet operations to reduce HFS costs by 50–60% from 8 to 9 C$/kg-H2 to 3–4 C$/kg-H2. The study underscores the importance of strategic planning and infrastructure investment in realizing net-zero goals, providing a model applicable globally.

Supply Chain Disruptions and Firm Performance: Proffering Feasible and Sustainable Solutions to Mitigating Futuristic Disruptions in the COVID-19 Pandemic Era

Recently, the two significant causes of supply chain disruptions globally are the Ukraine-Russia conflict and the COVID-19 pandemic. Concentrating on the Ukraine-Russia conflict, the crisis is said to substantially impact supply chains in five vital sectors: agriculture, chemicals, manufacturing, metals, and oil and gas. It is highly worrisome that there is a recorded significant reduction in the production rate for agriculture &amp; chemicals and a correspondingly significant reduction in demand for metals, oil &amp; gas. On the other hand, manufacturing has also witnessed a moderate decrease in production and demand. This research study is highly motivated by the historical record of extreme supply chain disruptions, which often translate to untold hardships for the government, firms, employees, and consumers. Furthermore, the current level of supply chain shortages has been termed worse off compared to historical levels during the Nixon administration in the early 70s. The broader objective of the research studies is to proffer feasible solutions to never-ending supply chain disruptions. The landscape is changing, negating the proponents and believers in certain pitfalls, especially unavailable data, few relevant talents and skills, and low adoption rate. The occurrence of significant events such as COVID-19, extreme weather-related events, other natural disasters, logistic failures, and tariff disputes have available and reliable data organizations can utilize in a range of analytical fact-finding projects.

An agent-based modeling approach for simulating solar PV adoption: A case study of Irish dairy farms

The agricultural sector faces increasing pressure to enhance energy efficiency in light of escalating electricity costs. This study aims to simulate the adoption of photovoltaic (PV) systems in Ireland’s dairy sector using an agent-based modeling (ABM) approach to facilitate PV uptake among dairy farmers. The model incorporates factors such as grid energy costs, annual electricity consumption, annual solar generation, PV cost, and maintenance expenses to predict PV adoption likelihood. Findings reveal that by 2022, about 2.41% of Irish dairy farmers had adopted PV systems, a figure only 0.41 pp higher than the actual observed rate, validating the ABM’s accuracy. Additionally, the research forecasts future adoption rates of PV systems among dairy farmers, demonstrating the efficacy of ABM in understanding and predicting renewable energy uptake in the dairy sector. These insights can inform policy suggestions to promote renewable energy adoption, ultimately enhancing energy efficiency and sustainability in dairy sector.

Insights for Managers About AI Adoption in New Product Development

OVERVIEW: The artificial intelligence (AI) revolution in new product development (NPD) has reached a critical juncture, demanding decisive action from business leaders. Our comprehensive study of key AI applications in NPD reveals a startling lack of adoption across industries, particularly in the United States. Despite the potential benefits, businesses also demonstrate weak intentions to adopt AI and low readiness regarding AI integration into NPD processes. Despite the low adoption rate, the study’s results offer a compelling picture of AI’s potential, including substantial positive impacts of AI use in key areas like product design, prototyping, testing, digital twin simulations, market analysis, and new product ideation. Benefits include accelerated development and markedly improved decision-making.

Using Statewide Transportation Planning Model to Forecast Demand for Electric Vehicle Charging at Stations Along Intercity Highways

The availability of Charging Stations (CSs) with adequate capacity is critical for the growth in ownership and usage of Electric Vehicles (EVs). In the United States, the state Departments of Transportation (DOTs) are responsible for planning CSs along the Interstate, national, and state highways. While the state DOTs have their Statewide Transportation Planning Models (STPMs), most of these models do not represent EVs internally. The state DOTs can only rely on the existing STPMs to forecast the demand for EV charging at specific CS locations in a state’s highway network. This research proposes a methodology that utilizes an existing STPM, EV and non-EV vehicle registration data, and hourly traffic volumes at strategic counting stations to predict the hourly demand for EV charging (charging port occupancy) at proposed CSs. This approach first estimates the volume of all passenger cars that will pass the vicinity of a CS along intercity highways, and identifies the Traffic Analysis Zones (TAZs) of their trip origins. Based on the EV adoption rates of the TAZs, EV range, and EV owner’s charging behavior, the methodology forecasts the volume of EVs that pass the vicinity of a CS and the number of EVs that will stop at the CS to charge every hour of the day. An example problem demonstrated the application of the methodology in the State of Texas. It not only forecasted the hourly occupancy at CSs along three major corridors, but also demonstrated how to determine the capacities of the CSs. The challenges of using the existing STPM and their solutions are discussed. This proposed methodology enables the state DOTs to forecast the demand for EV charging and the capacities at the proposed CSs along intercity highways, thus allowing the DOTs to apply for federal funds to accelerate the deployment of the CSs. (298 words)

Exploring the Role of quality management practices (QMS) in mitigating Construction Failures and building collapse

The construction industry in Nigeria faces critical challenges due to frequent building collapses, highlighting the necessity for robust Quality Management Systems (QMS). This study investigates the current state of QMS adoption and its effectiveness in mitigating building failures. Despite the recognised importance of QMS, the industry shows a low adoption rate, with only 34.8% of firms holding ISO certification and 60.9% lacking formal QMS frameworks. A significant barrier identified is insufficient top management support, impacting 71.4% of the companies surveyed. The study employs a mixed-methods approach, including a comprehensive literature review and a survey of 22 industry stakeholders across major Nigerian cities. This methodology allows for a detailed examination of QMS practices, highlighting the critical role of management commitment, communication, and training in enhancing quality outcomes. The research fills a gap by providing a holistic analysis of QMS issues across different regions, which have been previously understudied. The findings underscore the necessity for enhanced regulatory frameworks and standardised practices to ensure consistent quality management. The study contributes to the international discourse on construction quality by emphasising systemic improvements rather than merely technical fixes. It calls for further research into region-specific challenges and broader adoption of formal QMS practices to improve safety and reliability in Nigeria's construction sector.