Systematic Framework Research Articles

Developing complex thinking through a systemic framework for water education: a case study on the domestic water cycle in French curricula

This study explores systemic approaches in water education, focusing on the Domestic Water Cycle (DWC) as a complex educational topic. Water education aims to cultivate responsibility for water resources, engage individuals in water-related issues, and promote sustainable behaviours. Understanding water’s complexity is paramount, and systemic approaches offer a promising way to enhance this understanding. This research develops a comprehensive systemic tool for curriculum designers, educators and teachers to implement water education projects effectively. It also analyses the representation of DWC in official documents related to compulsory education in France, focusing on cycles 1–4. Using qualitative content analysis, the study examines these documents in depth. A four-step methodology was developed to create a systemic diagram of the DWC, incorporating various approaches as guiding principles. This diagram was then used to analyze educational programs. Findings reveal progressive development in DWC treatment across educational levels. Cycles 1 and 2 include some DWC elements, but the overall flow is not identified. In Cycle 3, several elements and relationships are not explicitly included. However, Cycle 4 exhibits a stronger systemic approach, addressing DWC complexity. This study demonstrates that integrating a systemic approach and utilizing a systemic tool can provide valuable support in teaching complexity effectively. Furthermore, it suggests this approach can help students develop essential skills such as problem-solving, understanding interrelationships, and making informed decisions across various domains, including environmental studies.

Buckling prediction based on joint opening and safe temperature estimation in jointed plain concrete pavements

Concrete pavement buckling is a significant distress that disrupts traffic flow and results in costly repairs, issues that are increasingly exacerbated by global warming. Addressing these issues, this study develops a systematic framework for assessing jointed plain concrete pavement (JPCP) by integrating temperature and moisture gradients, pavement structures, and material properties into mechanical-based models for predicting buckling potential. Specifically, a two-stage analysis is developed to analyze buckling development based on the slab deformation and the slab-base interface, including joint closure assessment and upheaval buckling prediction. Through the established model, parametric studies are conducted to quantitatively identify key factors influencing buckling. Moreover, the developed models are applied to pavement sections in various climate regions and validated through comparative analysis with field data from the Long-Term Pavement Performance (LTPP) database to confirm model accuracy. Additionally, a case study using pavement buckling data from Wisconsin between 2014 and 2020 demonstrates the model's practical application. As the results, it is found that temperature increase, the percentage of incompressible materials, and the coefficient of thermal expansion are critical for joint opening changes, while the coefficient of thermal expansion, elastic modulus, and slab thickness are crucial to post-buckling performance. Regression equations are provided to estimate safe temperature increases as a recommended buckling temperature limit. Furthermore, the predicted joint opening shows reasonable alignment with joint gauge measurement in the field recordings from the LTPP database. Finally, the predicted buckling events are reasonably compared with field recordings for a Wisconsin pavement section from 2014 to 2020. In summary, by considering pavement structural parameters, material properties, and weather conditions, this comprehensive model provides a reasonable tool for predicting and mitigating JPCP buckling in different climate conditions.

Minimum environmental flow assessment: a fuzzy TOPSIS decision-making system for selecting the best approach

The literature has explored various methods for assessing minimum environmental flow. Implementing holistic approaches proves to be prohibitively expensive and impractical for many small and medium projects. Hence, desktop and cost-effective methods are commonly employed without an integrated decision-making system to justify the assessed values. This study introduces a systematic decision-making framework aimed at selecting the most suitable method for assessing the actual needs of river habitats. Employing a fuzzy technique known as the Order Preference Similarity to the Ideal Solution (FTOPSIS), the study considers factors such as physical, thermal, and dissolved oxygen habitat suitability, maximum habitat area, and water demand loss function to determine the most appropriate method among established ones, including the Tennant method, flow duration curve analysis method, wetted perimeter method, and physical habitat simulation method. The results prioritize physical habitat simulation, wetted perimeter by slope method, and flow indices of 70%, 75%, and 80% by flow duration curve analysis method as the optimal approaches for assessing minimum environmental flow. This proposed decision-making system offers a viable platform to explore the applicability of existing cost-effective methods for assessing minimum environmental flow. It also serves as an effective mechanism for reducing negotiations among stakeholders by comprehensively considering all relevant aspects in the environmental management of river ecosystem requirements.

Resilience in agri-food supply chains: a framework for risk assessment and strategy development

ABSTRACT The COVID-19 pandemic has underscored the critical importance of resilience in the agri-food supply chain (AFSC) to ensure a reliable, uninterrupted, and disruption-free food supply for the ever-increasing global population. This study aims to comprehensively rank the multitude of risks faced by the AFSC, which have predominantly been examined in isolation. It develops a strategic framework for mitigating these risks by implementing appropriate resilient strategies. The study employs novel hybrid multi-criteria decision-making techniques, including the fuzzy analytical hierarchy process (F-AHP), fuzzy technique for order preference by similarity to ideal solution (F-TOPSIS), and fuzzy quality function deployment (F-QFD), to prioritise risks, assign weights, and rank various resilient strategies. The study contributes to the existing literature by proposing a systematic and robust strategic framework to facilitate decision-making processes in AFSC resilience management. It advances theoretical understanding and provides practical insights and recommendations for enhancing resilience in AFSC management.

Analyzing user ideologies and shared news during the 2019 argentinian elections

The extensive data generated on social media platforms allow us to gain insights over trending topics and public opinions. Additionally, it offers a window into user behavior, including their content engagement and news sharing habits. In this study, we analyze the relationship between users’ political ideologies and the news they share during Argentina’s 2019 election period. Our findings reveal that users predominantly share news that aligns with their political beliefs, despite accessing media outlets with diverse political leanings. Moreover, we observe a consistent pattern of users sharing articles related to topics biased to their preferred candidates, highlighting a deeper level of political alignment in online discussions. We believe that this systematic analysis framework can be applied to similar scenarios in different countries, especially those marked by significant political polarization, akin to Argentina.

FinTech and consumers: a systematic review and integrative framework

PurposeThis review aims to present the state of the art regarding the impact of financial technology (FinTech) on financial inclusion and its implications for consumers and institutions in terms of accessibility, usage and quality. An integrated framework is developed to illustrate the primary thematic areas for future research.Design/methodology/approachWe performed a systematic literature review (SLR) to summarize and synthesize existing research published in peer-reviewed academic journals. Forty-two eligible studies were identified from the Web of Science database and a cross-reference search.FindingsThe results suggest that FinTech promotes financial inclusion for consumers and businesses by increasing the accessibility, usage and quality of financial products. We present a multidisciplinary integrative framework that links the three dimensions of financial inclusion (i.e. access, usage and quality) to financial technology. Finally, we propose several avenues for future research.Originality/valueTo the best of the author’s knowledge, this is the first SLR on how FinTech is associated with the accessibility, usage and quality of financial products. We provide an integrative framework for understanding the topic with implications in different fields.

A methodology for planning, implementation and evaluation of skills intelligence management - results of a design science project in technology organisations.

The evolving labour market requirements amidst digital transformation necessitate robust skills intelligence for informed decision-making and adaptability. Novel technologies such as Big Data, Machine Learning, and Artificial Intelligence have significant potential for enhancing skills intelligence. This study bridges the gap between theory and practice by designing a novel software artefact for skills intelligence management. With its systematic framework for identifying skills intelligence elements, an assessment instrument, and an implementation methodology, the artefact ensures a thorough approach to skills intelligence management. The artefact was demonstrated in 11 organisations. Feedback collected from interviews, focus group sessions, and observations (N = 19) indicated that the artefact is a feasible starting point for implementing or systematising skills intelligence management. Participants suggested improvements but concurred that the systematic approach enhances skills intelligence data collection and quality. The study shows that the artefact facilitates the application of advanced technologies in skills intelligence management. Additionally, it contributes a set of principles for effective skills intelligence management, fostering a broader conversation on this critical topic. Participants' feedback underscores the artefact's potential and provides a basis for further refinement and application in diverse organisational contexts.

Populist autocratization and populist electoral autocracies: towards a unified conceptual framework

AbstractAlthough the research subjects of populism and autocratization studies are interconnected, the full potential of combining their focuses—the dynamic elements of political systems and institutional dimensions—into a comprehensive and systematic conceptual framework remains underexploited. This study makes a stride in this direction by reviewing and connecting these areas through the concepts of populist autocratization and populist electoral autocracies. These concepts aim to deepen our understanding of the nature of contemporary autocratization and the stability of autocracies. While the study is primarily theoretical-conceptual in nature, it also engages with methodological and operationalization considerations, and outlines a brief research agenda to demonstrate the empirical applicability of the analytical framework and lays the foundation for future research.

Automatic Identification of Sunken Oil in Homogeneous Information Perturbed Environment through Fusion Image Enhancement with Convolutional Neural Network

With the development of the marine oil industry, leakage accidents are one of the most serious problems threatening maritime and national security. The spilt crude oil can float and sink in the water column, posing a serious long-term threat to the marine environment. High-frequency sonar detection is currently the most efficient method for identifying sunken oil. However, due to the complicated environment of the deep seabed and the interference of the sunken oil signals with homogeneous information, sonar detection data are usually difficult to interpret, resulting in low efficiency and a high failure rate. Previous works have focused on features designed by experts according to the detection environments and the identification of sunken oil targets regardless of the feature extraction step. To automatically identify sunken oil targets without a prior knowledge of the complex seabed conditions during the image acquisition process for sonar detection, a systematic framework is contrived for identifying sunken oil targets that combines image enhancement with a convolutional neural network (CNN) classifier for the final decision on sunken oil targets examined in this work. Case studies are conducted using datasets obtained from a sunken oil release experiment in an outdoor water basin. The experimental results show that (i) the method can effectively distinguish between the sunken oil target, the background, and the interference target; (ii) it achieved an identification accuracy of 83.33%, outperforming feature-based recognition systems, including SVM; and (iii) it provides important information about sunken oil such as the location of the leak, which is useful for decision-making in emergency response to oil spills at sea. This line of research offers a more robust and, above all, more objective option for the difficult task of automatically identifying sunken oils under complex seabed conditions.

Development of Ancient Khmer Stone Castle Ontology

This research delves into the development of ontology, a crucial aspect of knowledge organization system studies. The primary goal is to establish a semantic search system tailored to the Khmer stone castles of the Greater Mekong Subregion (GMS), specifically addressing the challenge of semantic gaps. This paper details a novel approach to developing ontologies for Khmer stone castles in the GMS to enhance our understanding of these countries' rich and ancient cultural heritage. A systematic four-step theoretical framework for ontology development was adopted, utilizing the Hozo Ontology Editor to create the ontologies by used the Wizard for documenting ontologies (WIDOCO) framework for web ontology platform. This methodology plays a key role in differentiating relevant from irrelevant data, thus significantly improving the efficiency of semantic searches. Central to this study is developing a comprehensive ontology encompassing ten primary classes: castle, location, country, Khmer art, art type, castle size, castle type, material, religion, and condition. This ontology is foundational for developing a semantic search system designed explicitly for the Khmer stone castles in the GMS. The insights from this research offer significant implications for advancing semantic search systems in cultural heritage, providing a robust framework for future studies in this field.

Sensitivity analysis-driven machine learning approach for groundwater quality prediction: Insights from integrating ENTROPY and CRITIC methods

Precise estimation of water quality is essential for managing water pollution effectively and improving water management practices. Although, numerous recent investigations have documented inconsistencies in conventional water quality indexing methods owing to inherent subjective nature of its computation. Therefore, this study introduced a novel approach that integrates ENTROPY and CRITIC methods and applied sensitivity analysis-driven machine learning (ML) algorithms for groundwater quality prediction. The aim was to enhance prediction accuracy and robustness while gaining valuable insights into hydrochemical processes influencing groundwater quality. Six ML models, i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN), Linear Regression Model (LRM), Regression Tree (RT), Gaussian Process Regression (GPR), and Boosted Regression Tree (BRT) were applied to forecast water quality across Tamil Nadu, India. This study addresses a significant gap, as systematic ML framework has been lacked to evaluate and predict drinking water quality across the same state, covering 619 monitoring sites. Calibration of the proposed Integrated Water Quality Index (IWQI) models was conducted using 75 % of the dataset (465 training wells) and validated with 25 % (154 monitoring wells), considering 13 physicochemical parameters. Rigorous statistical evaluations employing metrics such as RMSE, MSE, and MAE were performed to identify most effective prediction model, accounting for both uncertainty and predictive performance. This study suggest that ANN model is the most suitable, exhibiting the lowest uncertainty with R2 = 1 and the best predictive performance statistics, including RMSE (training = 0.0264, testing = 0.5638), MSE (training = 0.0007, testing = 0.3179), and MAE (training = 0.0083, testing = 0.0729). Sensitivity analysis identified electrical conductivity (EC) as highest responsive input parameter, while fluoride (F−) showed the lowest sensitivity. The robustness of the ANN model was evident, maintaining accuracy even with perturbations up to 10 %, thereby enabling precise IWQI score forecasting across all monitoring stations.

Research on the construction of intangible cultural heritage corridors in the Yellow River Basin based on geographic information system (GIS) technology and the minimum cumulative resistance (MCR) model

Objectively and accurately identifying the spatial structure and protection scope of intangible cultural heritage and constructing intangible cultural heritage corridors are crucial for the comprehensive systematic protection of intangible cultural heritage and the synergistic development of the region. However, the current research on intangible cultural heritage is limited to the protection and development of intangible cultural heritage in specific locations or specific areas. Thus, systematic and holistic research perspectives are relatively limited. Therefore, this study employs geographic information system spatial analysis and the minimum cumulative resistance model to construct an intangible cultural heritage corridor in the Yellow River Basin. This study aims to establish a systematic protection method and framework for intangible cultural heritage. The results show the following: (1) The intangible cultural heritage in the Yellow River Basin has a large-scale centralized distribution and small-scale scattered distribution, which provides an important spatial basis for the construction of intangible cultural heritage corridors. (2) Overall, intangible cultural heritage corridors can be more effectively constructed in the eastern region of the Yellow River Basin than in the western region, with 84.6% of the area being suitable and 15.4% being unsuitable. (3) Based on the suitability analysis, the "18 + N" corridor system of intangible cultural heritage in the Yellow River Basin, distributed across the eastern, central and southern regions, is constructed. The major corridor has a suitable width of 60–100 km, a total length of 11,935 km, and an area of 625,976 km2–919,942 km2, and can connect 634–711 intangible cultural heritage sites in series. On this basis, this study proposes a multilevel construction system for intangible cultural heritage corridors in the Yellow River Basin that integrates the "network structure–spatial scope" and "element–axis–region" levels. This approach culminates in a pattern of intangible cultural heritage protection and development in the Yellow River Basin characterized by "connecting points into lines, distributing in groups, and linking regions." This study reveals that combining geographic information system spatial analysis tools with a minimum cumulative resistance model effectively identifies potential heritage corridor networks and clarifies the hierarchical relationships of heritage element protection in the study area. This approach provides a reference model for the comprehensive protection and systematic development of intangible cultural heritage in the Yellow River Basin. Furthermore, the effectiveness and universality of this framework make it applicable to the protection and development of other similar international heritage areas.

Shedding Light on the Organizational Growth Puzzle: A Systematic Review and Integrative Framework

Shedding Light on the Organizational Growth Puzzle: A Systematic Review and Integrative Framework

Reliable attitude control integrating reaction wheels and embedded asymmetric magnetorquers for detumbling CubeSats

The advent of CubeSats, compact satellites with a standardized form factor, has disrupted traditional space research paradigms, fostering innovation and collaboration across academic institutions and industries. Following deployment, CubeSats typically employ magnetorquer rods to initiate the detumbling sequence, gradually reducing the angular velocity before transferring control to reaction wheels for complete spin neutralization. However, this conventional approach entails a substantial spacecraft space requirement, necessitating an alternative and disruptive methodology. Furthermore, considering that approximately 50% of Attitude Determination and Control Subsystems (ADCS) failures are attributed to faults related to the moving parts in reaction wheels, strategies are taken into consideration to address a worst-case scenario of reaction wheels failure. This paper introduces a disruptive approach that uses diverse geometries and a non-unity track width ratio in PCB-integrated magnetorquers with reaction wheels for comprehensive control. We demonstrate the effectiveness of various coil configurations through a series of extensive simulations and establish a systematic framework to select optimal hybrid designs tailored to specific mission requirements.

‘A huge political experiment’ – exploring the political brand positioning of political parties in Jersey from an internal-external perspective’

ABSTRACT A political brand aims to project a differentiated and identifiable position in the minds of voters. However, there is limited understanding on the envisaged and realised positioning of political ‘party’ brands particularly in under-explored contexts. Therefore, this study investigates the brand positioning of four political ‘party’ brands from an insider (politician) and outsider (voter) perspective in the context of the British Crown Dependency of Jersey. Adopting a qualitative approach, this study conducted semi-structured interviews with politicians-candidates from all four political parties in Jersey and focus group discussions with young voters 18–24 years. Thematic analysis was adopted as part of the analytical strategy. It was revealed internal stakeholders created clear positioning for their political brands grounded on values and visual identity cues rather than grounded on distinct policies. However, the brand positioning of the three of the four political party brands were largely unclear from the standpoint of young voters. The study has implications for academics-practitioners beyond politics. More specifically, this study presents ‘the Political Brand Positioning Toolkit’. The toolkit developed from existing theory and empirical findings represents a systematic framework, which provides guidance on how to position new or existing brands and strategically manage a brand’s envisaged and realised position.

An application of machine learning to classify food waste interventions from a food supply chain perspective

PurposeThe purpose of this paper is to demonstrate the efficacy of machine learning (ML) in managing natural language processing tasks, specifically by developing two ML models to systematically classify a substantial number of food waste interventions.Design/methodology/approachA literature review was undertaken to gather global food waste interventions. Subsequently, two ML models were designed and trained to classify these interventions into predefined supply chain-related groups and intervention types. To demonstrate the use of the models, a meta-analysis was performed to uncover patterns amongst the interventions.FindingsThe performance of the two classification models underscores the capabilities of ML in natural language processing, significantly enhancing the efficiency of text classification. This facilitated the rapid and effective classification of a large dataset consisting of 2,469 food waste interventions into six distinct types and assigning them to seven involved supply chain stakeholder groups. The meta-analysis reveals the most dominant intervention types and the strategies most widely adopted: 672 interventions are related to “Process and Operations Optimisation”, 457 to “Awareness and Behaviour Interventions” and 403 to “Technological and Engineering Solutions”. Prominent stakeholder groups, including “Processing and Manufacturing”, “Retail” “Government and Local Authorities” and “NGOs, Charitable Organisations and Research and Advocacy Groups”, are actively involved in over a thousand interventions each.Originality/valueThis study bridges a notable gap in food waste intervention research, a domain previously characterised by fragmentation and incomprehensive classification of the full range of interventions along the whole food supply chain. To the best of the authors’ knowledge, this is the first study to systematically classify a broad spectrum of food waste interventions while demonstrating ML capabilities. The study provides a clear, systematic framework for interventions to reduce food waste, offering valuable insight for practitioners in the food system, policymakers and consumers. Additionally, it lays the foundation for future in-depth research in the food waste reduction domain.

AI-enhanced "Two-thirds Guidelines" for Lipolifting: Addressing Multiple Hallmarks of Facial Aging.

Facial aging involves complex changes such as volume loss, ligament weakening, and skin quality alterations. The "two-thirds guidelines" emerge as a novel strategy to combat these aging signs, drawing from an extensive analysis of 2800 facial fat grafting procedures conducted over two decades. Guided by facial lipolifting data, including patient age, fat type (microfat and nanofat), and injection depth, this study devises a systematic framework for multilayer fat rejuvenation and ligament restoration. The two-thirds guidelines advocate injecting two-thirds of the patient's age for microfat and one-third for nanofat, with specific injection codes for lower, middle, and upper facial regions. A prospective study involving 400 patients confirms the efficacy of the two-thirds guidelines. However, applicability may vary for patients outside SD ranges, particularly concerning facial proportions and body mass index. Patients within the golden ratio range (1.4-1.9) report high satisfaction rates and a 50% fat graft uptake, with minimal complications. For patients outside this range, an artificial intelligence (AI) program was implemented. The two-thirds guidelines offer a comprehensive approach to facial rejuvenation, addressing volume loss, ligament weakening, and skin quality. They are applicable in early aging stages, promising enduring and natural outcomes while mitigating effects of weight fluctuations. These guidelines provide a safe, replicable, and adaptable approach to facial fat grafting, either standalone or in combination with facelift techniques, with minimized overfilling risks. A dataset obtained from 2800 patients serves as the foundation for developing an AI program tailored to aid doctors in diagnosing and treating similar cases.

ClinGen Hereditary Cardiovascular Disease Gene Curation Expert Panel: Reappraisal of Genes associated with Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is an inherited cardiac condition affecting ~1 in 500 and exhibits marked genetic heterogeneity. Previously published in 2019, 57 HCM-associated genes were curated providing the first systematic evaluation of gene-disease validity. Here we report work by the ClinGen Hereditary Cardiovascular Disorders Gene Curation Expert Panel (HCVD-GCEP) to reappraise the clinical validity of previously curated and new putative HCM genes. The ClinGen systematic gene curation framework was used to re-classify the gene-disease relationships for HCM and related syndromic entities involving left ventricular hypertrophy. Genes previously curated were included if their classification was not definitive, and if the time since curation was >2-3 years. New genes with literature assertions for HCM were included for initial evaluation. Existing genes were curated for new inheritance patterns where evidence existed. Curations were presented on twice monthly calls, with the HCVD-GCEP composed of 29 individuals from 21 institutions across 6 countries. Thirty-one genes were re-curated and an additional 5 new potential HCM-associated genes were curated. Among the re-curated genes, 17 (55%) genes changed classification: 1 limited and 4 disputed (from no known disease relationship), 9 disputed (from limited), and 3 definitive (from moderate). Among these, 3 (10%) genes had a clinically relevant upgrade, including TNNC1, a 9th sarcomere gene with definitive HCM association. With new evidence, two genes were curated for multiple inheritance patterns (TRIM63, disputed for autosomal dominant but moderate for autosomal recessive; ALPK3, strong for autosomal dominant and definitive for recessive). CSRP3 was curated for a semi-dominant mode of inheritance (definitive). Nine (29%) genes were downgraded to disputed, further discouraging clinical reporting of variants in these genes. Five genes recently reported to cause HCM were curated: RPS6KB1 and RBM20 (limited), KLHL24 and MT-TI (moderate), and FHOD3 (definitive). We report 29 genes with definitive, strong or moderate evidence of causation for HCM or isolated LVH, including sarcomere, sarcomere-associated and syndromic conditions.

Evolving Software Architecture Design in Telemedicine: A PRISMA-based Systematic Review.

This article presents a systematic review of recent advancements in telemedicine architectures for continuous monitoring, providing a comprehensive overview of the evolving software engineering practices underpinning these systems. The review aims to illuminate the critical role of telemedicine in delivering healthcare services, especially during global health crises, and to emphasize the importance of effectiveness, security, interoperability, and scalability in these systems. A systematic review methodology was employed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. As the primary research method, the PubMed, IEEE Xplore, and Scopus databases were searched to identify articles relevant to telemedicine architectures for continuous monitoring. Seventeen articles were selected for analysis, and a methodical approach was employed to investigate and synthesize the findings. The review identified a notable trend towards the integration of emerging technologies into telemedicine architectures. Key areas of focus include interoperability, security, and scalability. Innovations such as cognitive radio technology, behavior-based control architectures, Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) standards, cloud computing, decentralized systems, and blockchain technology are addressing challenges in remote healthcare delivery and continuous monitoring. This review highlights major advancements in telemedicine architectures, emphasizing the integration of advanced technologies to improve interoperability, security, and scalability. The findings underscore the successful application of cognitive radio technology, behavior-based control, HL7 FHIR standards, cloud computing, decentralized systems, and blockchain in advancing remote healthcare delivery.

Building linguistic bridges: Pakistan's forward-thinking strategy for foreign language learning

ABSTRACT This article provides insight into Pakistan's proactive strategy for developing linguistic ties by improving proficiency in Chinese as a foreign language. Interpretive phenomenological analysis (IPA) is used to closely examine the participants’ lived experiences. A purposive sampling technique is adopted to choose the samples for this research. Semi-structured interviews of sixteen professionals are done to gather the primary information needed to comprehend the participants’ viewpoints on learning Chinese. The study focuses on the objectives and challenges associated with acquiring the Chinese language from the perspective of individuals in Pakistan and has provided valuable insights into individuals’ attitudes and behaviours about the acquisition of the Chinese language, hence contributing to developing a systematic framework for Chinese language learning. The findings aregrouped under three sections according to the conceptual framework (motivations, amenities, and challenges) of Chinese language acquisition and highlight the need for creative approaches, specialised teacher preparation, and suitable materials to promote efficient Chinese language acquisition and get beyond linguistic and cultural barriers. The findings of this study will aid language institutions and policymakers in planning strategies for foreign language learning and acquisition.