Conceptual Model Research Articles

Examining consumers attitude and willingness to purchase organic products: Empirical evidence from emerging market

Consumers are increasingly interested in organic products. This study explores the correlation between consumer organic awareness (COA), environmental attitude (EA), health consciousness (HC), perceived expensiveness (PE), willingness to pay (WTP), attitude (ATT), organic purchase intention (OPI), and organic purchase behavior (OPB) towards organic food. The study examines how ATT and OPI mediate the association between the aforementioned factors and OPB. Data was acquired from 505 respondents by convenience sampling. This study employs Structure Equation Modeling (SEM) using SPSS AMOS 26 to investigate the hypotheses of the suggested conceptual model. The study indicated that variables like COA, EA, HC, WTP, ATT, and OPI have a substantial impact on the OPB. EC is the most significant predictor of ATT, OPI, and OPB in organic food buying, while PE had the least influence. The antecedents COA, EA, HC, and WTP have a strong indirect impact on OPB among respondents via ATT and OPI. Finally, theoretical and practical ramifications are explored.

Multiliteracies-translanguaging conceptual model framework for student agency in English as foreign language contexts

Multiliteracies-translanguaging conceptual model framework for student agency in English as foreign language contexts

Classification of IPB variety of cayenne pepper genotypes using physical characteristics during the growing period until harvest using machine learning

In agrarian sectors, the prevalence of adulteration from seed selection to final agricultural outputs is a significant concern. This deceptive practice not only spans across distinct plant species but also extends to varieties within the same species, as observed in chili cultivation. Addressing this issue, the current study aims to develop a machine learning-based classification model that discriminates between cayenne pepper genotypes of IPB varieties and others. The model harnesses a range of plant physiological parameters monitored from the growth phase through to harvest. These parameters include plant and dichotomous heights, age at flowering, age at harvest, fruit stalk length, chili dimensions, individual chili weight, total number of chilies per plant, and overall yield productivity. Employing a dataset of 45 plant samples—30 from IPB varieties and 15 from non-IPB varieties—this research evaluates four machine learning algorithms: Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN), Decision Tree (DT), and Random Forest (RF). To ascertain the robustness of the proposed model, the study also investigates the impact of varying data split ratios, including 90/10, 75/25, and 50/50, on model performance. Preliminary results indicate that the DT classifier, with an accuracy exceeding 80%, successfully differentiates the IPB cayenne pepper genotypes from other varieties. This promising conceptual model warrants further validation and enhancement through future research on a larger dataset, paving the way for its practical application in ensuring the authenticity of agricultural produce.

Development of a Conceptual Model to Identify and Rank Environmental and Health Costs of Work-Related Diseases Using Delphi Study and Analytic Network Process

Introduction: Improving health and the environment is one of the components of development, social welfare, and economic growth. Another influential factor in increasing health costs and reducing social welfare is work-related accidents and diseases, which impose high costs on individuals, industries, and the national economies of countries. Therefore, using multi-criteria decision-making methods, the present study provided a conceptual model to identify and rank work-related diseases’ environmental and health costs. Material and Methods: The present study was conducted in 2023. A classification model for the economic evaluation of environmental and health costs of occupational diseases was developed to achieve the study’s aim. In the current research, the Delphi method was used to identify health and environmental criteria, and the Analytic Network Process (ANP) was used to weight the sub-criteria. Finally, the cost of health and the environment was estimated based on the available information. Naft Tehran Hospital (NSHT) was also selected as a case study site. Results: The results showed that the drug and medical equipment cost factor, with a weight of 0.312 in the treatment sector, and the particular and infectious waste cost factor, with a weight of 0.085, were the most critical factors in the economic evaluation. Also, the parametric model results showed that 99.84% of the total costs are related to health costs, and 0.16% are related to environmental costs. In general, the results of this research showed that 61.3% of the costs of the health sector are related to the two sectors of medicine and medical equipment and the cost of service personnel, and 91.7% of the costs of the environmental sector are related to wastewater treatment and the cost of electricity consumption. Conclusion: This study presented a semi-quantitative model to estimate health and environmental costs caused by occupational diseases. The results can create a novel scientific insight into implementing control measures using the optimal point of cost-benefit parameters. Implementing this integrated model can be a practical and effective step in allocating resources and prioritizing interventions.

Conceptualizing mental health stigma in organizational settings: a sociolinguistic perspective

BackgroundSociolinguistic research on workplace mental health stigma is scarce and consequently, there are a lack of relevant conceptual models. Drawing on Goffman’s notion of stigma as a ‘language of relationships’, and Heller’s concept of ‘discursive space’, this paper offers a conceptual model of how stigma is produced and reinforced in workplace settings. Specifically, the model maps the complex discursive processes of mental health stigmatization through workplace discursive practices.MethodsThe model is empirically grounded and draws on 23 in-depth participant interviews with professional services employees in Hong Kong. Through a meta-discursive analysis of the employees’ experience in the workplace, the paper investigates how mental health stigma is produced in the workplace.ResultsConceiving the workplace as a discursive space, the model demonstrates that mental health stigma unfolds across three discursive layers, namely immediate encounters, organizational practices, and societal ideologies. Mediated by discursive practices, such as identity management, stigma is both produced and perpetuated across the three layers.ConclusionsThe paper provides a model for analyzing the production of mental health stigma through dynamic discursive activities in the workplace. By doing so, it offers a way to systematically map how stigma, brought about through discourse in organizational settings, can regulate both interpersonal relationships and resource allocation (such as career prospects).

Linking the Flow Regime of Papyrus‐Dominated Wetlands to Biologically Relevant Hydrologic Attributes

ABSTRACTThe dominant plant species in many African wetlands is Cyperus papyrus. Its adaptation to saturated and low‐oxygen conditions and its dense structure and height provide breeding and feeding grounds for unique flora and fauna. As a keystone species adapted to local hydrology, the flooding regime of papyrus offers the full range of hydrologic conditions and events essential to ecosystem health. However, no study has attempted to link papyrus wetlands' flow regimes to their biologically relevant hydrologic attributes. This study assesses hydrologic alterations of a papyrus wetland's flow regime due to rice irrigation. We develop a conceptual ecological model linking papyrus to hydrologic attributes to determine the consequences of changed environmental flow components (EFCs) on papyrus as a habitat. We find that agricultural water management considerably alters the magnitude, duration, timing and rate of change of EFCs, which could affect productivity (seed dispersal, germination and establishment; rhizome spreading; papyrus distribution across transects; and dispersal of floating mats) in papyrus wetlands. However, the effect on the papyrus wetlands' natural pulsed regime is negligible when the ratio of irrigated area to catchment area is no greater than 1:150. Overall, a better understanding of the threats of water diversion for agriculture is made by linking papyrus' flow regimes to biologically relevant hydrologic attributes. Knowledge of the roles of the various EFCs could provide opportunities for conserving and protecting papyrus wetlands, especially for systems at risk of altered flows.

Multi-Model Assessment of Climate Change Impacts on the Streamflow Conditions in the Kasai River Basin, Central Africa

The Congo River Basin is the second-largest watershed globally, flowing through nine countries before reaching the Atlantic Ocean. The Kasai River Basin (KARB), containing about one-fourth of Congo’s freshwater resources, plays a strategic role in sustaining navigation, food production, and hydroelectricity generation in Central Africa. This study applies a multi-model framework suited for data-scarce regions to assess climate change impacts on water availability in the KARB. Using two conceptual hydrological models calibrated with four reanalysis datasets and fed with bias-corrected outputs from 19 climate models under two representative climate pathways (RCPs), we project changes in the mean annual discharge ranging from −18% to +3%, highlighting the sensitivity of impact assessments to model and input data choices. Additionally, streamflow signatures (Q10, Q50, Q90) are projected to decline by approximately 9%, 18%, and 13%, respectively, under RCP 8.5. Annual hydropower potential is estimated to decrease by 14% and 5% under RCPs 4.5 and 8.5, respectively. These findings provide actionable insights for water management practices in the KARB, including guiding the development of adaptive strategies to optimize water allocation, mitigate risks of scarcity, and support sustainable agricultural and industrial activities in the region.

Hybrid Agile-Kanban frameworks for workflow adaptability: A proposed solution for innovation in project management

Agile and Kanban methodologies have become indispensable in modern project management, emphasizing workflow adaptability, iterative processes, and streamlined task prioritization to meet dynamic project requirements. However, traditional applications of these methodologies often face challenges such as workflow inefficiencies, misaligned task priorities, and bottlenecks caused by static configurations and manual adjustments. A hybrid approach that combines Agile methodologies with Kanban principles, including visualization tools and flow control, offers a promising solution to these challenges. This paper presents a comprehensive exploration of a Hybrid Agile-Kanban Framework (HAKF), designed to enhance project workflow optimization, team collaboration, and task execution. By integrating artificial intelligence (AI) for real-time feedback loops and dynamic task prioritization, the proposed framework addresses common bottlenecks and resource imbalances, enabling more efficient and scalable project management practices. Despite its transformative potential, challenges such as tool integration, scalability, and initial adoption barriers must be addressed for widespread implementation. To mitigate these issues, this paper introduces a conceptual HAKF model that seamlessly integrates with existing project management tools, providing actionable insights for optimizing workflows. This study lays a foundation for future research into hybrid methodologies, advancing the potential of Agile-Kanban integration to create more efficient and adaptive project management systems.

Assessing data variability in groundwater quality monitoring of contaminated sites through factor analysis and multiple linear regression models

Monitoring of long-term contaminant concentrations trends is essential to verify that attenuation processes are effectively occurring at a site. However, monitoring data are often affected by extreme variability which prevents the identification of clear concentration trends. The variability is higher in long-screened monitoring wells, which are currently used at many contaminated sites, although it has been known since the 1980s that monitoring data from long-screened wells can be biased. Understanding the factors that may influence the variability of monitoring data is pivotal. To this end, following hydrochemical conceptual modelling using a multi-method approach, the variability of hydrocarbon concentrations from fully screened monitoring wells was assessed over eleven years at a former oil refinery located in Northern Italy. The proposed methodology combined factor analysis with multiple linear regression models.Results pointed out a higher variability in hydrocarbon concentrations at the plume fringe and a lower variability at the plume source and core. 44–46 % of the total variability in measured hydrocarbon concentrations is due to “intrinsic plume heterogeneity”, related to the three-dimensional structure of a contaminant plume, which becomes thinner at the edge, creating a vertical heterogeneity of redox conditions at the plume fringe. This variability, expressed as increasing concentrations of sulfate and decreasing concentrations of methane, represents a background variability that cannot be reduced by improving sampling procedures. The remaining 56–54 % of the total variability may be due to the non-standardization of some purging and sampling operations, such as pump intake position, purging and sampling time/flow rates and variations in the analytical methods. This finding suggests that monitoring improvements in fully screened wells by standardizing all purging/sampling operations or using sampling techniques that can reduce the actual screen length (e.g., packers or separation/dual pumping techniques) would reduce data variability by more than half.

Research on Relationship Between Image Perception, Place Attachment and Tourists' Pro-environment Behavior

Ice and snow tourism, as a special way of tourism, is booming with the continuous development and utilization of ice and snow tourism resources in the world. China is rich in ice and snow tourism resources. In recent years, the ice and snow tourism industry has developed rapidly. However, problems such as ecological destruction and over-exploitation of resources have become increasingly prominent. In this context, pro-environment behavior has become an important means to alleviate the environmental pressure of ice and snow tourism destinations; At the same time, research on the relationship between the perceived image of ice and snow tourism destination, place attachment and tourists' pro-environment behavior has important theoretical value and practical significance for promoting the sustainable development of ice and snow tourism, enhancing tourists' environmental awareness and responsibility, and improving the image and attractiveness of ice and snow tourism destination. Based on the cognitive affective theory, place attachment, etc., and on the basis of literature research, this thesis proposes several hypotheses, constructs conceptual models, develops or cites corresponding measurement scales for the relationship among the four variables of cognitive image, emotional image, place attachment and pro-environment behavior, and takes Chongli Ski Resort as a case for empirical research. Using questionnaire survey data, the structural equation model of 264 valid questionnaires was analyzed to suggest the influence mechanism of cognitive image, emotional image and place attachment on tourists' pro-environment behavior. The findings are as follows: (1) Cognitive image has significant positive effects on affective image, place attachment and pro-environment behavior, and affective image and place attachment can mediate this process; (2) Emotional image has a significant positive impact on place attachment and pro-environment behavior, and place attachment can be used as a mediator to have a positive impact on them; (3) Place attachment has a significant positive effect on pro-environment behavior. As an emotional bond, it connects the emotional connection between tourists and tourist destinations. Accordingly, this study also puts forward the strategy to promote the tourists' pro-environment behavior in Chongli ski Resort in order to promote the sustainable development of ice and snow tourism. Including: (1) to enhance the attractiveness of ice and snow tourism; (2) Enhance the emotional connection between places and tourists; (3) Strengthen environmental awareness education. It is hoped that this study will have some reference value for the improvement of pro-environment behavior of tourists in other similar ice and snow tourism destinations.

Conceptual framework for AI-driven tax compliance in fintech ecosystems

This paper comprehensively reviews the integration of Artificial Intelligence (AI) into tax compliance processes within fintech ecosystems. It explores the theoretical foundations of AI technologies, such as machine learning and predictive analytics, and how they can automate tax reporting, auditing, and compliance monitoring. Conceptual models for AI-driven tax compliance are proposed, highlighting the potential for increased efficiency, accuracy, and cost reduction. The paper also examines challenges associated with AI adoption, including algorithmic biases, ethical concerns, data privacy issues, and regulatory hurdles. Strategies for overcoming these challenges and fostering broader adoption are discussed. Finally, the paper offers recommendations for fintech companies and policymakers, emphasizing the need for transparent AI models, bias mitigation, data privacy, and updated regulatory frameworks to ensure fair and effective AI-enabled tax systems.

Conserving cropland resilience space in alpine agro-pastoral ecotones: A quantitative study of Qinghai Province

CONTEXTInfluenced by natural conditions, geographic location and socioeconomics, Qinghai Province is a typical alpine agro-pastoral ecotone; additionally, its food production system cannot cope with many risks, and exploring the suitability of resilient cropland spaces and rational utilization paths is of practical significance. OBJECTIVEThe objectives of this study are to explore the influencing factors of the resilience space of cropland in the alpine agro-pastoral ecotone and to establish a method to quantify the quantity and quality of resilience space. Finally, recommendations for the development and utilization of cropland resilience space and cropland protection in Qinghai Province are presented. METHODSThis study takes cropland system resilience as the theoretical basis and creates a conceptual model of the factors influencing cropland resilience space. The Cropland Resilience Space Index (CRSI) is constructed from the three factors of suitability, scale and aggregation and is used to quantify the spatial suitability of cropland resilience in Qinghai Province. RESULTS AND CONCLUSIONSThe results revealed that there are 441,000 ha of cropland resilience space in Qinghai Province, low values of the suitability factor are distributed mainly in the edge areas of the Kunlun Mountains and the Qilian Mountains, and the scale factor and aggregation factor are generally high in the west and low in the east. The average CRSI value in Qinghai Province is 0.445, and that in Haixi Prefecture is 0.469, which is higher than the average value. Priority should be given to the development of cropland resilience space in the eastern part of Dulan County and Golmud city based on utilization sustainability and ecological stability. SIGNIFICANCEThis research will help enrich research methods for the sustainable utilization of cropland and help promote the transformation of cropland protection policies from rigid to resilient. The quantitative results of the cropland resilience space in the alpine agriculture and animal husbandry intertwined zone can provide a reference basis for rationally formulating the policies of cropland protection and resilience space development and utilization.

Machine learning in budget forecasting for corporate finance: A conceptual model for improving financial planning

This paper explores the transformative potential of machine learning (ML) in budget forecasting within corporate finance. It begins with an examination of the importance of accurate budget forecasting for financial planning and the limitations of traditional methods. Theoretical foundations of ML are then discussed, highlighting relevant techniques such as regression, time series analysis, and neural networks. A conceptual model for ML-driven budget forecasting is proposed, detailing its components, architecture, and integration into financial planning processes. The paper also addresses the expected benefits, including improved accuracy and efficiency, alongside the challenges such as data quality, ethical considerations, and organizational barriers. By navigating these challenges through robust data governance, ethical practices, and strategic implementation, organizations can significantly enhance their budget forecasting processes, leading to better financial outcomes and strategic decision-making.

Towards Supporting Satellite Design Through the Top-Down Approach: A General Model for Assessing the Ability of Future Satellite Missions to Quantify Point Source Emissions

Monitoring and accurately quantifying greenhouse gas (GHG) emissions from point sources via satellite measurements is crucial for validating emission inventories. Numerous studies have applied varied methods to estimate emission intensities from both natural and anthropogenic point sources, highlighting the potential of satellites for point source quantification. To promote the development of the space-based GHG monitoring system, it is pivotal to assess the satellite’s capacity to quantify emissions from distinct sources before its design and launch. However, no universal method currently exists for quantitatively assessing the ability of satellites to quantify point source emissions. This paper presents a parametric conceptual model and database for efficiently evaluating the quantification capabilities of satellites and optimizing their technical characteristics for particular detection missions. Using the model and database, we evaluated how well various satellites can detect and quantify GHG emissions. Our findings indicate that accurate estimation of point source emissions requires both high spatial resolution and measurement precision. The requirement for satellite spatial resolution and measurement precision to achieve unbiased emission estimation gradually decreases with increasing emission intensity. The model and database developed in this study can serve as a reference for harmonious satellite configuration that balances measurement precision and spatial resolution. Furthermore, to progress the evaluation model of satellites for low-intensity emission point sources, it is imperative to implement a more precise simulation model and estimate method with a refined mask-building approach.

Integrating predictive analytics into strategic decision-making: A model for boosting profitability and longevity in small businesses across the United States

The integration of predictive analytics into strategic decision-making has emerged as a transformative approach for small businesses in the United States, offering enhanced pathways to profitability and longevity. This study proposes a conceptual model that explores how predictive analytics can empower small businesses to make data-driven decisions, optimize resources, and mitigate risks. By leveraging advanced machine learning algorithms, data mining, and statistical techniques, small businesses can identify trends, predict customer behavior, and uncover growth opportunities. The proposed model focuses on integrating predictive analytics across key operational areas: financial planning, marketing, inventory management, and customer relationship management. In financial planning, predictive analytics enables more accurate forecasting of revenues and expenses, supporting budget optimization and investment decisions. In marketing, businesses can analyze consumer data to design targeted campaigns, improving conversion rates and customer retention. Inventory management benefits from predictive insights by anticipating demand patterns, reducing overstocking or stockouts. Moreover, customer relationship management is enhanced through personalized recommendations and proactive engagement strategies. This research emphasizes the accessibility of predictive analytics tools, particularly through cloud-based platforms, enabling small businesses to adopt these technologies without incurring substantial costs. Additionally, the study highlights challenges such as data quality, privacy concerns, and the need for technical expertise, proposing strategies to overcome these barriers. Case studies of small businesses that have successfully implemented predictive analytics demonstrate significant outcomes, including increased operational efficiency, reduced costs, and sustained competitive advantage. The findings underscore the importance of fostering a data-driven culture and investing in training to enhance employees’ analytical capabilities. Policymakers and industry stakeholders are encouraged to support initiatives that promote the adoption of predictive analytics in small businesses through funding, education, and technical assistance programs.

Vulnerability Evolution Mechanism of High-Speed Railway Construction System in the Goaf Site of Qinshui Coalfield

The main reason for serious consequences caused by disasters is “insufficient emergency preparedness and obvious system vulnerability”. To ensure the safe operation and maintenance of the high-speed railway in goaf sites and achieve effective hierarchical management, the vulnerability theory was introduced, based on the system approach, to explore the characteristics of hierarchical evolution. Firstly, based on the analysis of the internal and external disturbances of the high-speed railway construction system with underlying goaf areas, the vulnerability characteristic elements were defined, and a conceptual vulnerability model was proposed. Secondly, based on the literature research, accident cases, and standards and specifications, 25 factors affecting vulnerability were identified and a two-dimensional cross matrix was used to establish a vulnerability evaluation index system. Then, ISM was used to build hierarchical relationships among indicators, analyze the vulnerability characteristics of direct factors, indirect factors and essential factors, and a vulnerability evolution analysis framework was proposed. Finally, the Taiyuan–Jiaozuo high-speed railway project was selected for hierarchical and progressive vulnerability management. The study analyzed the coupling effect and evolution mechanism of various elements of the safety management system under external disturbances, identified the weak links of the high-speed railway system in the goaf site, and provided a decision-making basis for continuous dynamic optimization and emergency response technology, which complied with the strategic policy of sustainable development.

Investigating news coverage and circulation over time in a quantitative manner: the TARO framework

ABSTRACT Nowadays news spread all over the world at incredible speed, thanks to news channels and social media. This is of great value not only for readers, who benefit from a huge amount of available information, and publishers, who gain visibility and connections but also for scholars who can investigate publication processes and public debate in depth. This paper presents a framework for studying news circulation in quantitative and computational terms. The framework is called TARO and is able to capture how “the same news” has been treated by different outlets, in different languages and in different moments in time. The key contribution of TARO is the capability to collect and process hard data about online news publishing processes, and to be easily adaptable to multiple sources and target analyses. This article in particular presents the details of the whole TARO framework—including the core conceptual model and some recent extensions, together with a software implementation—and shows how TARO can be successfully used to compare strategies for publishing and re-publishing news over time.

A conceptual model of internal audit capability model (IACM) development to improve accountability of education funding management in private high schools in Semarang city

A conceptual model of internal audit capability model (IACM) development to improve accountability of education funding management in private high schools in Semarang city

Evaluating factors influencing visit intention towards birding tourism: an extended theory of planned behaviour perspective

ABSTRACT Birdwatching, also known as avitourism, has become the fastest-growing segment of ecotourism, which contributes significantly to socio-economic development as well as the protection and development of threatened natural areas around the world. Despite the potential benefits of avitourism, only a few countries have fully exploited such opportunities. Hence, this study aims to understand the factors influencing the intentions to visit avitourism destinations. To improve the conceptual model, this study extended the Theory of Planned Behaviour (TPB) by integrating environmental awareness, concern, knowledge and the Big-Five personality traits. A quantitative methodology via questionnaire was employed to gather data from 389 respondents across Malaysia. The results reveal that environmental awareness, environmental knowledge, openness to experience, introversion, and emotional stability significantly influence Malaysians’ intention to visit avitourism destinations. Notably, this study also highlights the demographic profile as a crucial factor influencing the visit intention towards avitourism. Older males with lower education and job positions are more likely to visit avitourism destinations than younger females with higher education and job positions. These insights are anticipated to assist tourism organisations, educators, and policymakers in devising strategies to foster avitourism amongst the Malaysian population. The limitations and future studies were also discussed in this study.

Young adults’ acceptance of data-driven personalized advertising: Privacy and Trust Equilibrium (PATE) model

Purpose This study aims to examine young social media users’ differential acceptance of data-driven ad personalization depending on the types of personal data used, and to propose and test the Privacy and Trust Equilibrium (PATE) model, a new conceptual model developed to explain the intertwined nature of the competing influences of platform-related factors (privacy concern, trust, and privacy fatigue) on acceptance of ad personalization. Design/methodology/approach A survey of 440 Instagram users aged 18–24 in Australia was conducted to examine the relationships between the three factors of the PATE model and acceptance of ad personalization utilizing overt vs covert data collection methods. Findings This study shows the highest level of acceptance for personalization using overtly collected data and the lowest for covert data. The results also support the PATE model, revealing the competing dynamics of how the platform-related factors shape consumers’ acceptance of data-driven ad personalization. Privacy concern discourages Instagram users from accepting personalized ads, while trust encourages them. When the pushing influence of privacy concern and the pulling influence of trust form equilibrium, generating cognitive dissonance, privacy fatigue seems to play a significant role in resolving the dissonance, leading to increased acceptance. Originality/value This study advances the understanding of how concurrent push–pull-resigning factors affect young consumers’ acceptance of data-driven ad personalization practices, expanding the scope of research on data-driven personalized advertising and privacy.