Recent Articles

Sarthak Patel, Jinit Patel  ·  International Journal of Computer Science and Artificial Intelligence  ·  15 Jul 2026

The market for AI software-development tools has expanded faster than the frameworks used to evaluate it, leaving practitioners to choose among code-completion assistants, AI-native integrated development environments (IDEs), and terminal-native agents on the basis of headline price or benchmark rank — neither of which predicts realised value. This paper makes two contributions. First, it develops the Cost–Methodology–Fit (CMF) framework, an analytical model that treats tool selection as the alignment of a team's dominant workflow with a tool's interaction paradigm and billing structure, grounded in the established SPACE model of developer productivity [1]. Second, drawing on a systematic documentary comparison of leading tools (verified June 2026) and on the conflicting experimental literature — a controlled trial reporting a 55.8% task speed-up [2] against a randomised trial of experienced developers reporting a net slowdown [3] — it derives the central claim that AI-tool value is workflow-contingent, not tool-intrinsic. Because documentary comparison cannot establish causal productivity effects, the paper additionally specifies a reproducible mixed-methods evaluation protocol that adopting organisations can run to measure fit in their own context. We report the framework and protocol, not new empirical outcomes, and state this scope explicitly. Findings indicate the market has bifurcated by billing model, that capability is increasingly a model-level rather than tool-level property, and that hybrid tool stacks are a rational response to fit-contingency.

Mr. MALLE LINGAMAIAH  ·  International Journal of Technology and Emerging Research  ·  14 Jul 2026

This paper presents the development of a multifunctional grid-connected hybrid energy system (HES) integrating solar photovoltaic (PV) modules, a wind energy conversion system (WECS), and battery energy storage. To address the inherent drawbacks of conventional maximum power point tracking (MPPT) algorithms such as delayed response and suboptimal tracking under fast-varying environmental conditions a Mamdani type fuzzy logic-based MPPT controller is proposed. This controller adaptively modulates the duty cycles of individual DC–DC boost converters, enabling optimal power extraction from both PV and wind sources under dynamic operating conditions. The hybrid energy coordination is implemented without requiring linearized system models, thereby increasing control robustness against parameter variations and environmental uncertainties. Grid interfacing is accomplished using a phase-locked loop (PLL) to ensure synchronization in frequency and phase, while an LCL filter is employed at the point of common coupling (PCC) to attenuate high-frequency switching harmonics, thereby improving power quality in accordance with grid codes. The proposed methodology enhances system dynamic response, stabilizes power output, and facilitates seamless energy sharing among multiple sources. Time-domain simulations conducted in MATLAB/Simulink validate the effectiveness of the proposed control scheme, demonstrating improved system stability, reduced harmonic content, and efficient energy regulation. The novelty of this work lies in the adaptive fuzzy logic-driven coordination of hybrid renewable sources in real time, enabling superior performance, high efficiency, and compliance with grid power quality standards in a hybrid energy system environment.

Rakshitha M M, Dr. VijayKumar  ·  International Journal of Economics and Business Management  ·  14 Jul 2026

Artificial Intelligence (AI) has become an integral component of modern recruitment by enabling organizations to automate candidate sourcing, resume screening, and selection processes. While AI-driven recruitment systems improve efficiency and decision-making, they also introduce significant concerns regarding algorithmic bias, fairness, transparency, and accountability. This study presents a conceptual and systematic review of contemporary literature published between 2020 and 2026 to examine the nature, sources, and implications of algorithmic bias in AI-based recruitment systems. The review synthesizes multidisciplinary evidence from Human Resource Management, Artificial Intelligence, Business Analytics, Organizational Behaviour, and AI Ethics to identify key factors influencing fair hiring practices. The findings indicate that algorithmic bias primarily arises from biased training data, model design, proxy variables, and organizational implementation practices, potentially leading to discriminatory recruitment outcomes and reduced workforce diversity. The study further highlights the importance of explainable AI, ethical governance, human oversight, and continuous bias monitoring in promoting transparent and accountable recruitment systems. Particular attention is given to the emerging Indian context alongside global developments in AI governance. The study proposes a conceptual framework linking algorithmic bias, transparency, organizational trust, and recruitment outcomes while providing practical recommendations for HR professionals and organizations seeking to implement responsible AI-driven recruitment and support equitable hiring practices.

Tadiparti.Venu, Pakki Bhargava Shanmukha Sai, Lotheti Pravalika, choppa Durga Prasad, Khaspa Nikhita  ·  International Journal of Technology and Emerging Research  ·  14 Jul 2026

AI Legal Buddy is an AI-powered legal information assistant designed to help users understand Indian laws in a simple and accessible way. The system allows users to enter their legal queries through text or voice input. The frontend is developed using React, TypeScript, Tailwind CSS, and Vite, providing a responsive and user-friendly interface. The backend is implemented using Supabase Edge Functions, which process user queries. In this project, we are using RAG (Retrieval- Augmented Generation) to retrieve relevant legal data from stored database and then generate the response based on that data. This helps in giving more accurate and meaningful answers instead of general responses. The system analyzes the query, identifies related Indian Acts and Sections, checks the seriousness of the issue, and provides structured responses with useful steps. The system also supports multiple languages to improve accessibility for users from different regions. By combining modern web technologies with RAG-based approach, the project aims to make legal information more understandable and easy to access for common people.

Isah Abubakar Ipemida, Momoh Iliyasu Onimisi, Prof. Henry Ohiani Ohize, Yusuf Idris  ·  International Journal of Electrical and Electronics Engineering  ·  13 Jul 2026

This paper examined the quality of electric supply in Nigeria, especially the North Central region, and proffered a modern approach to improving the quality using Flexible Alternating Current Transmission System (FACTS) devices. Electrical energy is a product that should possess the proper quality requirements to satisfy the social, economic, and technical needs of society. The Nigerian transmission grid system was modeled and simulated in a PSAT environment as a 35-bus system. Thyristor Control Series Compensator (TCSC) is among other FACTS devices that are capable of minimizing transmission line losses and increasing the voltage profile of the buses. Genetic Algorithm (GA) optimization technique has been deployed as an effective method of placing TCSC on the Nigerian 330 kV Grid System, to control power flow and improve bus voltages.

Evans Asante, Binji Seidu Zakaria  ·  International Journal of Arts, Culture and Creative Studies  ·  13 Jul 2026

Existing scholarship on African divination has focused overwhelmingly on its religious, therapeutic and social functions, leaving open the question of whether such rituals possess codified dramaturgical structure. This article addresses that gap through an ethnographic case study of the Tiboaar divination ritual performed by the Bikpakpaam (Konkomba people) of Ghana. Working from Schechner's Performance Theory and Turner's Ritual Theatre Theory, the analysis identifies six performative elements embedded in the ritual, namely theme, plot, character, dialogue, technical elements and audience, and shows that its plot follows a five-part structure moving from the arrival of the diviners through rising action, climax, falling action and an artistic denouement. Read alongside the ritual origins of Athenian and English Renaissance theatre, and against anthropological objections to analysing ritual through dramatic categories, the Tiboaar is best understood not as equivalent to Greek dramatic form but as evidence of a parallel and independent theatrical evolution within Konkomba funerary practice. The article concludes by considering the implications of this codification for African theatre historiography and for the documentation of indigenous performance as living cultural heritage.

Siyaka Hassan Eromi, Samuel Olonikawu  ·  International Journal of Education, Pedagogy and Psychology  ·  13 Jul 2026

During Corona Virus Disease 2019 (COVID-19) lockdown, citizens were directed to stay in door in other to break the chain of transmission of the pandemic. The only mode of communication available to the outside world during the lockdown is digital communication via telephone, computer, information communication technology (ICT) devices and other multi-media learning technologies and devices. This research’ Impact of multi-media technologies to learning on secondary education during covid-19 lockdown in selected secondary school in FCT, Abuja’ explore challenges militating against using e-learning and m-learning during COVID-19 lockdown by conducting investigative study among the students and teachers to assess their readiness to use of multi-media for teaching and learning by schools during COVID-19 lockdown. This research work is a survey research that used random sampling to obtain data from the sampled population of 225 out of the total study population of 5567 that comprises students and teachers across the nine (9) selected secondary schools with 4 publics and 5 private secondary schools in FCT, Abuja. Findings revealed that e-learning and m-learning were effectively used while m-learning was used by the majority of the students with 81.8% used it for learning. It was concluded that, the challenges that were raised need to be addressed to improve the state of learning via multi-media technologies in both private and public schools. This will in return bridge the gap between current state of things when we compare both the private and public schools in terms of multi-media facilities to deliver online and broadcasting media learning.

Kassahun Tafese Keneni  ·  International Journal of Economics and Business Management  ·  13 Jul 2026

This study investigates the impact of internal bank-specific and external macroeconomic factors on the profitability of commercial banks in Ethiopia. A panel FMOLS regression model was applied. Ten years of data were gathered from audited financial statements of six selected banks, the National Bank of Ethiopia, and World Bank sources. The findings indicate that the mean values of profitability for the banks, measured by ROA and NIM, indicate a “very healthy” condition, while ROE indicates a “healthy” condition. The CAR has a positive and significant impact on the ROA, ROE, and NIM, highlighting the importance of capital reserves in maintaining profitability and financial stability. GDP shows mixed results for profitability indicators. It positively influences ROE and NIM but negatively impacts ROA, indicating inefficient asset utilization during economic expansion. IFL has a positive and significant impact on ROE and NIM, suggesting that the banks may adjust interest rates during inflationary periods to maintain profitability. Although the LLPTL has no statistically significant effect on profitability, strong credit risk management remains crucial to maintaining financial stability. The Granger tests reveal that IFL has a unidirectional impact on CAR and ROA. GDP and IFL exhibit a bidirectional relationship. The study forwarded the recommendations to the banks’ managements and the policy regulating authorities. The management required reinforcing minimum capital requirements to maintain stable financial operations and risk resilience. The policymaking authorities continued to enhance economic development to mitigate the negative impact of GDP on ROA, while closely monitoring inflation as a crucial factor in preventing its adverse effects on profitability.

Zakir Ahmed Ansari, Dr. Tariq Siddiqui  ·  International Journal of Technology and Emerging Research  ·  13 Jul 2026

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in monitoring the operational health of modern wind turbines by continuously collecting large volumes of sensor data. Efficient analysis of this data is essential for early fault detection, predictive maintenance, and reliable turbine operation. However, anomaly detection in SCADA environments remains challenging due to data imbalance, noise, nonlinear relationships, and dynamic operating conditions. Traditional machine learning approaches often suffer from limited generalization capability and may fail to achieve a balanced trade-off between precision and recall. To address these challenges, this paper proposes a Hybrid Machine Learning Framework for SCADA-based anomaly detection in wind turbine systems using ensemble learning. The proposed framework integrates Isolation Forest, One-Class Support Vector Machine (OCSVM), and Deep Autoencoder models to capture complementary anomaly characteristics from operational data. The outputs of these base models are further combined using an AdaBoost-based stacking architecture to improve classification robustness and anomaly detection performance. Experiments were conducted on a publicly available wind turbine SCADA dataset containing more than 50,000 operational samples and multiple turbine health parameters. The proposed hybrid framework was evaluated using Precision, Recall, F1-score, Area Under Curve (AUC), and confusion matrix analysis. Experimental results demonstrate that the proposed model significantly outperforms standalone approaches, achieving a Recall of 0.8702, F1-score of 0.7118, and AUC of 0.9678. Furthermore, the framework substantially reduces false negative predictions, making it highly suitable for predictive maintenance applications. The findings indicate that integrating machine learning, deep learning, and ensemble learning techniques provides a robust and effective solution for intelligent anomaly detection in industrial SCADA systems.

Zainuddin  ·  International Journal of Education, Pedagogy and Psychology  ·  13 Jul 2026

The COVID-19 pandemic has intensified a longstanding debate in Indonesian education policy: whether it is more important to widen access to schooling or to improve the quality of learning. This article examines the relationship between two major policy instruments in Indonesian education—free schooling and nutritional support, specifically the Free Nutritious Meals Program (Makan Bergizi Gratis, MBG). Using a systematic literature review combined with interpretive policy analysis, the study draws on evidence from Scopus- and Web of Science-indexed international journals, reports from major global institutions, and Sinta 1- and Sinta 2-accredited national journals. The findings are notable: free schooling has successfully increased enrollment, particularly among children from low-income families, but has not automatically translated into better learning outcomes. School feeding programs, by contrast, show measurable impact—students attend more regularly, concentrate better in class, and achieve higher scores—although the effectiveness of these programs depends heavily on implementation quality. This article rejects the framing that pits access against quality, arguing that the two are not mutually exclusive. Drawing on human capital theory, social protection scholarship, and systems-based policy analysis, the article proposes an integrated policy model that simultaneously expands access, delivers nutritional intervention, and raises education quality. Three elements are emphasized: cross-sectoral coordination, data-driven monitoring, and mutually reinforcing policy design. These findings are relevant not only to Indonesia but also to other developing countries facing similar challenges

ZAID MUSTAFA ALVI  ·  International Journal of Law, Politics and Governance  ·  13 Jul 2026

The United States is the world's largest arms exporter and a professed champion of international human rights. This article identifies a critical analytical gap: the arms transfer legal framework—the Leahy Laws, Section 502B of the Foreign Assistance Act, and the Arms Export Control Act—is architecturally designed to produce the appearance of human rights conditionality while preserving executive discretion to transfer weapons regardless of rights violations. Through legal analysis, historical case studies of Yemen and Gaza, and institutional critique, this article demonstrates that non-enforcement is a structural design feature, not a malfunction, and proposes a framework for genuine reform.

Dr. Paresh Ilasariya, Manjula Ilasariya  ·  International Journal of Technology and Emerging Research  ·  13 Jul 2026

Artificial Intelligence (AI) is reshaping the way institutions collect, organize, and deliver information. Libraries – public, academic, and special – are uniquely positioned as information stewards, literacy educators, and community access points. This paper synthesizes recent research and professional guidance to examine the opportunities (service automation, discovery, personalization), risks (backdoors, misinformation, privacy, digital exclusion), and strategic responses (AI literacy training, governance, partnerships). We propose a mixed-methods research design to evaluate AI implementation in libraries and present practical recommendations for librarians who aim to preserve library values while using AI responsibly. Key Word: AI is transforming academic research and academic workflows; libraries should respond by blending AI tools with stewardship and critical instruction.

CYRIL K.S, BASIL VARGHESE , JAIJITH A, YADHU KRISHNA, Dr Krishna Kumar P R  ·  International Journal of Technology and Emerging Research  ·  13 Jul 2026

With the rapid expansion of internet usage and web-based services, cyber threats such as phishing, malware distribution, and malicious URL attacks have significantly increased. Attackers often exploit IP-based patterns and URL structures to bypass traditional security mechanisms. This project focuses on identifying URL-based attacks using IP data analysis combined with machine learning techniques to improve detection accuracy and cybersecurity resilience. The system analyzes URLs by extracting features such as IP address patterns, domain behavior, request frequency, and URL structure. By leveraging IP intelligence and classification models, the system can distinguish between legitimate and malicious URLs in real time. This approach enhances traditional URL filtering mechanisms by incorporating behavioral and network-level insights. The proposed solution aims to provide a scalable, efficient, and automated detection system capable of preventing cyber threats before they reach end users. The system can be deployed as a web-based application or integrated into network security tools, contributing to safer browsing environments and improved threat intelligence systems. Keywords: URL Detection, Cybersecurity, IP Analysis, Phishing Detection, Machine Learning, Network Security

Diya Panwar, Garima Agarwal, Hritik Chaudhary, Vardan Yadav  ·  International Journal of Technology and Emerging Research  ·  13 Jul 2026

The rapid growth of academic and workplace stress has significantly increased mental health concerns among students and professionals. Traditional therapeutic models lack real-time monitoring, personalization, and scalable accessibility. Existing digital platforms often provide static recommendations without integrating structured mood analytics, secure data management, and intelligent conversational assistance. This paper presents RELieF, a MERN stack-based AI powered mental health monitoring and emotional support system. The proposed framework integrates daily mood tracking, graphical analytics, conversational AI assistance, and gamification mechanisms within a secure and scalable web architecture. The frontend is developed using React.js, while the backend employs Node.js and Express.js with MongoDB Atlas for cloud-based data storage. JWT-based authentication ensures secure access and data privacy. Experimental evaluation conducted on structured test cases demonstrates consistent performance with an overall accuracy of 93.33%, high engagement consistency, and effective visualization of emotional patterns. The proposed system provides a modular, scalable, and user-centric approach toward digital mental wellness.

Gizachew Ayele  ·  International Journal of Law, Politics and Governance  ·  12 Jul 2026

ABSTRACT This paper examines the relationships between urban transport governance, accessibility and spatial economic development in Ethiopia with a focus on spillover effects in secondary cities. It grounded in spatial economic theory and accessibility frameworks, the paper integrates empirical evidence from Ethiopian cities and comparable developing country contexts. Using qualitative analytical approach based on a total of approximately 40 theoretical and empirical studies were systematically reviewed, together with key policy reports and institutional publications, the study finds that transport investments generate significant positive spillover such as market integration, labor mobility, and productivity achievements ,but these effects are uneven due to centralized governance , weak institutional coordination and infrastructure disparities. The study conclude that while, effective urban transportation governance and transportation accessibility have a major impact on spatial economic development, regional disparities in Ethiopia still caused by poor institutional coordination and unequal accessibility. To promote balanced sustainable regional growth, authorities should enhance institutional coordination, strength investment in secondary cities, and expand integrated accessibility based transportation planning. By connecting theory with empirical observations and recommending policy paths for attaining balanced regional development, the work adds to the body of literature.

Manikanta R  ·  International Journal of Economics and Business Management  ·  12 Jul 2026

This study examines the economic contributions generated by household death-related activities in urban India and provides one of the first multi-sector empirical analyses of the country's mortality-driven economy. Despite millions of deaths occurring annually and triggering substantial economic activity across formal and informal sectors, the economic dimensions of mortality remain underexplored in Indian literature. The study utilizes primary survey data collected from 283 vendors across nine sectors in Bengaluru, including funeral services, transport, healthcare, flower markets, hospitality, religious services, textile supply, printing and media, and legal and financial services. Descriptive statistics, Pearson correlation analysis, and ordinary least squares (OLS) regression were employed to examine demand patterns, revenue determinants, and sectoral interdependencies. The findings reveal that the surveyed sectors collectively generate approximately ₹4.40 crore in monthly revenue, demonstrating the significant scale of mortality-linked economic activity. Funeral services recorded the highest average vendor revenue, while transport services generated the largest aggregate sectoral revenue. Regression models showed strong explanatory power, with adjusted R-squared values ranging from 0.87 to 0.98. The study identifies three distinct economic archetypes within the death economy: volume-driven sectors, value-driven sectors, and balanced sectors. The findings contribute to the emerging field of death economics by establishing a sectoral taxonomy, quantifying mortality-related economic circulation, and offering policy recommendations for household financial protection, market formalization, and service-sector development in India.

Onubaye Onimisi Shaibu, Siyaka Hassan Eromi  ·  International Journal of Mechanical and Mechatronics Engineering  ·  12 Jul 2026

Radiators play a critical role in determining vehicle efficiency and continue to see improvements in heat dissipation capabilities while shedding weight. Radiators have shrunk from heavy copper brass designs to lightweight aluminum radiators with intelligent cooling. These intelligent cooling designs are achieved through innovative technologies such as fin optimization, nanofluids, computational fluid dynamics (CFD) modeling, and additive manufacturing. Advancements in artificial intelligence (AI) assisted radiator design and integrated sensor technologies will enable radiators of the future to become adaptive platforms with the intelligence to react and respond to changing conditions. Electric vehicles (EV) and hybrid electric vehicles (HEV) will use modular radiators capable of providing dynamic cooling based on the real time heat load of each subsystem. Marelli has developed a smart EV thermal management module called iTMM. Another way engineers are developing radiators at a faster pace is through AI algorithms paired with 3D printing that allows for user customizable radiator geometries. Integrated sensor networks within radiators allow for real time condition monitoring, predictive maintenance, and even active manipulation of cooling channels to control heat transfer. Research is also being conducted outside of the automobile industry in areas such as eco-friendly machining, resource efficient convective cooling, and machine learning based predictive control to reduce energy consumption and associated emissions. Developments in scalable organic photovoltaics and printable flexible graphene supercapacitors are other examples of how thermal management intersects with improving environmental impacts.

Onubaye Onimisi Shaibu, Dr. Sadiq Ibrahim Ogu, Abdullahi Muhammad Dutsun, Abdulrasheed Onimisi Muhammed  ·  International Journal of Mechanical and Mechatronics Engineering  ·  11 Jul 2026

The study provides an in-depth analysis of the technological advancements in modern machining and its impact on the global industry. It traces the evolution from traditional subtractive manufacturing processes to emerging trends like AI-based predictive maintenance, adaptive machining, hybrid manufacturing, sustainable machining, and nanomachining for next-generation electronics and biomedical devices, including advancements in off-world manufacturing and space-ready materials. Challenges such as skill gaps in the workforce, affordability and accessibility of cutting-edge technologies, and the influence of developing nations on Industry 4.0 and 5.0 are explored. Results indicate that innovations in materials science, automation, and sustainable practices contribute significantly to precision, efficiency, and environmental sustainability in machining. The research highlights the importance of maintaining craftsmanship in the age of automation. Machining is positioned as a pivotal force and a reflection of the global shift towards a more integrated, adaptive, and sustainable industrial future.

Dr.Surya Teja Nakka, Mrs. Shyamala. B, Mrs.Prathiba. S  ·  International Journal of Medical and Health Sciences  ·  11 Jul 2026

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, necessitating precise diagnostic interventions. Ambulatory electrocardiography, specifically Holter monitoring, serves as a cornerstone for detecting transient cardiac arrhythmias, silent myocardial ischemia, and evaluating anti-arrhythmic therapies over prolonged periods. Staff nurses and nursing students are directly responsible for patient preparation, device initialization, lead placement, and patient education. Inadequate proficiency in managing these systems frequently compromises data validity, introducing artifacts and reducing patient compliance. This study aimed to evaluate and compare the baseline knowledge levels regarding the importance, technical application, troubleshooting, and patient instruction requirements of Holter monitoring systems among staff nurses and final-year nursing students. A quantitative descriptive cross-sectional design was executed. A sample of 150 participants was recruited via purposive sampling, evenly split between registered staff nurses (n = 75) working in critical/cardiac care areas and final-year nursing students (n = 75) from a selected tertiary care hospital and affiliated nursing college in Bengaluru, India. Data were collected using a validated 25-item Structured Knowledge Questionnaire covering key domains of Holter monitoring. Analysis was conducted via descriptive and inferential statistics using SPSS version 26.0. The overall findings indicated that 48% of the total participants possessed moderate knowledge, 34% had inadequate knowledge, and only 18% demonstrated adequate knowledge. Staff nurses exhibited a significantly higher mean knowledge score of 16.4 (SD 3.2) compared to nursing students who scored 11.8 (SD 2.9), validated by an independent t-test (95% CI [3.62, 5.58]; t = 9.21, df = 148, p < 0.001). Notable systemic knowledge deficits were observed in both groups regarding skin preparation, artifact troubleshooting, and patient diary documentation rules. While clinical exposure provides staff nurses with an advantage, a profound deficit in comprehensive knowledge persists across both cohorts. Integrating dedicated ambulatory telemetry modules into nursing curricula and mandatory clinical workshops for staff is critical to optimizing diagnostic accuracy in cardiovascular care.

Dr. Yemane Gidey Gebrerufael, Gebremicael Tesfay, Tesfay Gebremikael  ·  International Journal of Economics and Business Management  ·  10 Jul 2026

Tourism destinations in post-conflict and post-pandemic contexts face significant challenges in restoring visitor demand and rebuilding destination image. Promotion plays a critical role in remarketing destinations, particularly in regions with rich cultural and historical resources but weakened market confidence. This study examines the role of promotional strategies in remarketing tourism destination demand in the Central and Eastern zones of the Tigray region, Ethiopia. Using a mixed-methods approach, primary data were collected from 384 domestic tourists through structured questionnaires and complemented by key informant interviews conducted at major destination sites, including Axum, Yeha, Wukro, and Gheralta. Quantitative data were analyzed using descriptive statistics and multiple regression analysis, while qualitative data were thematically analyzed. The findings reveal that promotional mix elements-advertising, public relations, personal selling, sales promotion, and direct marketing-have a statistically significant and positive effect on tourism destination demand. Advertising and public relations emerged as the most influential tools in shaping destination awareness and revisit intentions. The study highlights the importance of integrated, digitally driven, and experience-oriented promotional strategies to restore confidence and stimulate domestic tourism demand. The findings contribute to tourism marketing literature in post-conflict destinations and provide practical implications for destination marketing organizations, policymakers, and tourism stakeholders in Ethiopia and similar contexts.