Structured Component Analysis Research Articles

Structural analysis and comparison of components of oil filter

The primary objective of this research endeavor is to investigate the structure and characteristics of commonly employed materials in oil filters, with the aim of optimizing the overall structural density and durability of said filters. In the present study, a selection of diverse materials, including Polyester, was made for the purpose of filter paper fabrication, as opposed to the conventional utilization of glass fiber material. This investigation aimed to conduct a comparative analysis of the filter properties exhibited by these distinct materials. The model was generated utilizing Autodesk Fusion 360, a computer-aided design (CAD) software widely employed in various industries for its advanced modeling capabilities. To enhance the structural integrity of the cover assembly, we opted to incorporate titanium alloy into our design. The oil filter plays a crucial role in the maintenance of a car engine by effectively eliminating contaminants and impurities. As an integral component of the engine, the oil filter assumes a position of utmost significance. To enhance filtration efficiency and mitigate the accumulation of oil residues, it is imperative to address several key factors: chemical compatibility, high-temperature resistance, high strength, and the implementation of graduated filtration techniques. These considerations are essential for optimizing the performance and longevity of filtration systems. Through the implementation of structural analysis and computational fluid dynamics (CFD) simulations, it has been determined that the chosen materials exhibit distinct structures and properties when employed in practical applications. Furthermore, it is worth noting that the streamlined oil flow within the polyester pleats of the system exhibits enhanced efficiency and consistency, thereby contributing to an overall improvement in its efficiency and durability. The analyses have been conducted utilizing the ANSYS WORKBENCH Software.

X-ray structural analysis of the BaO and TiO2 starting compounds and initial mechanochemical activation

The aim of this research was to obtain an initial characterization of the BaO and TiO2 components, as well as to conduct preliminary research into the possibility of activating the initial components in order to obtain barium titanate. Chemical and X-ray structural analysis of the initial components was performed. In order to determine the possibility of applying a mechanochemical activator and the activation time in which the first intermediate compounds were formed by the activation of the starting substances, the system was activated for 30 minutes. After this, an X-ray structural analysis was performed and certain changes in the crystal lattice of the starting components were made. X-ray structural analysis showed that during grinding, amorphization of the initial components occurred, which is the first stage in the process of obtaining titanate, as well as the presence of a small amount of barium titanate, which is indicating the presence of an intermediate compound in the process of finally obtaining barium titanate.

Population whole-genome resequencing reveals the phylogenetic relationships and population structure of four Hunan typical tea landraces

Hunan located at the evolutionary transitional zone edge of the Yunnan-Guizhou plateau, tea germplasms are abundant. At present, although morphological, enzymological and molecular markers have been used to study the genetic relationship of these tea germplasms. However, due to the complexity of the genetic background of tea germplasm resources, the evolution history and taxonomy of Hunan tea germplasm is still unclear. In this study, four Hunan tea landraces from the evolutionary transitional zone were collected, including 9 'Chengbu Dongcha' (CBDC), 8 'Jianghua Kucha' (JHKC), 11 'Rucheng Baimao' (RCBM) and 8 'Anhua Yuntai' (AHYT). We used whole-genome re-sequencing to identify genetic variation, phylogenetic placement, and population structure among the four tea landraces and then scanned the genome for regions related to population potential selective. Across the samples, we identified an average of 1.38 million (M) SNPs and 6.82 × 10³ INDELs. Based on the high-quality SNPs, we performed neighbor-joining phylogenetic tree, population structure, and principal component analysis, all of which supported that the 36 tea germplasms could been divided into two groups, CBDC and AHYT were clustered into a group, and RCBM and JHKC were clustered into another group. With CBDC, JHKC, and RCBM as control, 407, 380, and 147 selected genes were identified in AHYT, respectively, were in the top 5% of F_ST and Log₂^Ratioθπ scores. The identified genes are mainly involved in stress tolerance, biosynthesis of important secondary metabolites, adaptability, and yield. These results will be helpful in the classification of tea germplasms and investigating selection–related genes.

Entrepreneurship education for women through project-based flipped learning: The impact of innovativeness and risk-taking on course satisfaction

PURPOSE: The primary aim of this research is to explore the correlation between learners’ characteristics and the perceived value and satisfaction associated with Project-Based Flipped Learning (PBFL) methodologies. A secondary objective involves investigating how these PBFL methodologies can be employed to enhance the quality of entrepreneurship education for women. METHODOLOGY: During the first semester of 2018, a total of 80 students enrolled in the Communication Society class were engaged in a longitudinal study, involving bi-weekly online surveys prior to the semester’s conclusion. The survey instruments utilized Likert-scale measurements, with a 5-point scoring system. The data acquired was subsequently analyzed using structural equation modeling, which facilitated the examination of both the pre- and post-change scores and the structural properties of their relationships with overall course satisfaction. In terms of statistical evaluation, the study employed Generalized Structured Component Analysis (GSCA), a powerful component-based SEM technique, thus ensuring a robust and academically rigorous interpretation of the data. FINDINGS: Our research sought to understand the effects of learners’ characteristics, specifically innovativeness and risk-taking, on course satisfaction in Project-Based Flipped Learning (PBFL). We found that female learners’ innovativeness positively influenced their perception of the project’s entertainment and educational value, which in turn increased preference for PBFL and course satisfaction. Interestingly, risk-taking did not significantly influence perceived project value, which provides insights into the role of personality traits in learning outcomes. IMPLICATIONS: Our study invigorates entrepreneurship education theory by highlighting the key role of learner innovativeness in PBFL course satisfaction, urging a nuanced examination of personality traits in educational contexts. Further, we question the established importance of risk-taking, necessitating a critical reassessment in this domain. These pivotal theoretical contributions challenge prevailing assumptions, enrich scholarly discourse, and open new avenues for research. On the practical side, our findings emphasize the imperative of fostering innovativeness in women’s entrepreneurship education. These insights underscore the need for a strategically tailored, creative learning environment, with the potential to enhance learner engagement and satisfaction significantly. In sum, our research generates transformative theoretical insights and provides actionable strategies for improving the practice of entrepreneurship education. ORIGINALITY AND VALUE: Our research presents a novel approach to fostering women entrepreneurs in the media sector through PBFL. This unique focus on the intersection of gender, media entrepreneurship, and PBFL distinguishes our study from existing literature. Furthermore, our findings offer educators invaluable guidance for enhancing female entrepreneurship education, thereby enriching the pedagogical landscape of this domain.

Organizational Culture and Welfare, Its Influence on Work Motivation, Job Satisfaction and Performance of Civil Servants in the Government of Tuban Regency

The State Civil Apparatus (ASN) as a government apparatus determines the success of administering Government for national goals and regional governments in the Tuban district, which is required to carry out accounting services. It is hoped that the Regional Government will massively implement the organizational culture "Tuban Bumi Wali" and increase income improvement allowances for ASN to improve performance. At the same time, it has not been able to improve SAKIP, so this study aims to measure how much influence organizational culture and welfare have on performance through intervening variable work motivation and job satisfaction. This study also aims to discuss in more depth the conditions of the Regional Government of Tuban Regency regarding "organizational culture and welfare, their influence on work motivation, job satisfaction and the performance of the state civil apparatus in the Tuban regency government". The research method used in this study is associative quantitative with an approach based on the philosophy of positivism. This associative quantitative research aims to examine the interplay between variables. This research tested the Component-Based Structure Equation Model (SEM) using the Generalized Structured Component Analysis (GeSCA) program. The results showed that the coefficients on the path of organizational culture, work motivation, and ASN performance was significant so that the indirect effect of 0.306 was significant, as well as the coefficient on the path of welfare, job satisfaction and ASN performance was tested significantly so that the indirect effect of 0.185 was significant. Keywords: Tuban, Government, Welfare, Work, Organizational DOI: 10.7176/PPAR/13-1-02 Publication date: January 31 st 2023

DAMPAK ADANYA PENGARUH KEPUTUSAN ETIS BAGI AUDITOR EKSTERNAL

The ethical dilemma that an auditor faces when carrying out an ethical decision-making process that includes decisions between limiting quality. IAI gives sanctions to KAPs that violate auditing standards, so there is a crisis of confidence for auditor careers in Indonesia. This study aims to obtain empirical evidence of the influence of audit experience, professional commitment, ethical orientation and time budget pressure on external auditors' ethical decision making. Surabaya Public Accounting Firm is an object of observation. Auditors at 47 KAP Surabaya in 2020 are the population of this study. The research sample consisted of 82 external auditors. This research method uses Simple Random Sampling. Generalized Structured Component Analysis (GeSCA) is the technique of this research. The output results of this study explain that audit experience, professional commitment, ethical orientation and time budget pressure influence external auditors' ethical decision making.
 Keywords: Audit, Commitment, Ethics, Budget, Ethics.

Structural Equation Modelling (SEM) in Research: Narrative Literature Review

The structural equation modelling (SEM) method has stronger predicting power than path analysis and multiple regression because SEM is able to analyze at the deepest level the variables or constructs studied. This literature review aimed to describe the use of structural equation modelling in research. In general, SEM can be used to analyze research models that have several independent (exogenous) and dependent (endogenous) variables, as well as moderating or intervening variables. SEM provides several benefits and advantages for researchers, including building research models with many variables, examining variables or constructs that cannot be observed or cannot be measured directly (unobserved), testing measurement errors (measurement errors) for observed variables or constructs ( observed) and confirmatory factor analysis. Broadly speaking, SEM methods can be classified into two types, namely covariance-based structural equation modelling (CB-SEM) and variance or component-based SEM (VB-SEM), which includes partial least squares (PLS) and generalized structured component analysis (GSCA). This literature review aimed to describe the use of structural equation modelling in research.

Material damage integration approach for efficient modelling of high cycle fatigue

The recognition of the risk of fatigue failure and its study, particularly fatigue crack growth (FCG) behaviour of engineering materials, is not neoteric, and the majority of approaches for investigating the problem are empirical and dated. With recent computational advances, the cohesive zone modelling (CZM) approach for FCG analysis has become popular especially amongst researchers owing to its flexibility of use particularly within the finite element framework. However, the use of the CZM for explicit cycle-by-cycle high cycle FCG analysis of real structural components is still largely computationally prohibitive. Thus, this study presents a novel material integration (MI) approach to accelerate fatigue crack propagation within the cyclic cohesive zone modelling (CCZM) framework. Using a bilinear cohesive law, the proposed technique is compared with the Linear Extrapolation (LE) technique and assessed for three models of different bulk-interface element discretisation and deformations. The results show that the MI technique offers a more consistent approximation to the accelerated fatigue damage computation and, more importantly, better convergence characteristics for the different models under tension, mixed mode and bending deformations. These outcomes underline the computational benefits of the proposed MI technique in assessing the FCG behaviour of real structural components within the CCZM framework.

Disturbed hippocampal intra-network in first-episode of drug-naïve major depressive disorder.

Complex networks inside the hippocampus could provide new insights into hippocampal abnormalities in various psychiatric disorders and dementia. However, evaluating intra-networks in the hippocampus using MRI is challenging. Here, we employed a high spatial resolution of conventional structural imaging and independent component analysis to investigate intra-networks structural covariance in the hippocampus. We extracted the intra-networks based on the intrinsic connectivity of each 0.9 mm isotropic voxel to every other voxel using a data-driven approach. With a total volume of 3 cc, the hippocampus contains 4115 voxels for a 0.9 mm isotropic voxel size or 375 voxels for a 2 mm isotropic voxel of high-resolution functional or diffusion tensor imaging. Therefore, the novel method presented in the current study could evaluate the hippocampal intra-networks in detail. Furthermore, we investigated the abnormality of the intra-networks in major depressive disorders. A total of 77 patients with first-episode drug-naïve major depressive disorder and 79 healthy subjects were recruited. The independent component analysis extracted seven intra-networks from hippocampal structural images, which were divided into four bilateral networks and three networks along the longitudinal axis. A significant difference was observed in the bilateral hippocampal tail network between patients with major depressive disorder and healthy subjects. In the logistic regression analysis, two bilateral networks were significant predictors of major depressive disorder, with an accuracy of 78.1%. In conclusion, we present a novel method for evaluating intra-networks in the hippocampus. One advantage of this method is that a detailed network can be estimated using conventional structural imaging. In addition, we found novel bilateral networks in the hippocampus that were disturbed in patients with major depressive disorders, and these bilateral networks could predict major depressive disorders.

Population structure and genetic diversity characterization of soybean for seed longevity.

Seed longevity is an important trait in the context of germplasm conservation and economics of seed production. The identification of populations with high level of genetic variability for seed longevity and associated traits will become a valuable resource for superior alleles for seed longevity. In this study, Genotyping-by-sequencing (GBS)-single nucleotide polymorphism (SNP) approach, simple sequence repeats (SSR) markers and agro-morphological traits have been explored to investigate the diversity and population structure of assembled 96 genotypes. The GBS technique performed on 96 genotypes of soybean (Glycine max (L.) Merrill) resulted in 37,897 SNPs on sequences aligned to the reference genome sequence. The average genome coverage was 6.81X with a mapping rate of 99.56% covering the entire genome. Totally, 29,955 high quality SNPs were identified after stringent filtering and most of them were detected in non-coding regions. The 96 genotypes were phenotyped for eight quantitative and ten qualitative traits by growing in field by following augmented design. The STRUCTURE (Bayesian-model based algorithm), UPGMA (Un-weighed Pair Group Method with Arithmetic mean) and principal component analysis (PCA) approaches using SSR, SNP as well as quantitative and qualitative traits revealed population structure and diversity in assembled population. The Bayesian-model based STRUCTURE using SNP markers could effectively identify clusters with higher seed longevity associated with seed coat colour and size which were subsequently validated by UPGMA and PCA based on SSR and agro-morphological traits. The results of STRUCTURE, PCA and UPGMA cluster analysis showed high degree of similarity and provided complementary data that helped to identify genotypes with higher longevity. Six black colour genotypes, viz., Local black soybean, Kalitur, ACC Nos. 39, 109, 101 and 37 showed higher seed longevity during accelerated ageing. Higher coefficient of variability observed for plant height, number of pods per plant, seed yield per plant, 100 seed weight and seed longevity confirms the diversity in assembled population and its suitability for quantitative trait loci (QTL) mapping.

Comparison of structural analysis and principle component analysis for leakage prediction on superheater in boiler

<span>Leakage is one of the failures which commonly happens in boiler operation. Moreover, a continuous unsettled anomaly in a boiler could lead to leakage failure. An algorithm has been developed to predict the failure, consisting of three general procedures: feature selection, followed by hierarchical clustering, and naïve Bayes classification. The hierarchical clustering changes unlabeled data into labeled data, and naïve Bayes classification calculates the probability to justify anomaly occurrence. Meanwhile, this research focused on the effect of the feature selection method on the result of leakage prediction. Two different feature selection methods, namely the structural analysis and the principal component analysis (PCA), were deployed separately and then compared. The result showed that leakage prediction using the structural analysis method gave 13 hours 40 minutes of prediction time, and the PCA method gave 25 hours of prediction time. However, the PCA feature selection method caused more false alarms than feature selection with structural analysis, which only triggered five false alarms a week before leakage. Moreover, the structural analysis offered better traceability than PCA to understand the leakage occurrence.</span>

Enacting Alternating Least Square Algorithm to Estimate Model Fit of Sem Generalized Structured Component Analysis

Structural Equation Modeling (SEM) is a statistical modeling technique that combines three methods, namely factor analysis, path analysis and regression analysis to test a theoretical model in social science, psychology and management. Covariance-based SEM is a parametric SEM that must meet several parametric assumptions such as, multivariate normally distributed data, large sample sizes and independent observations, so that, variance-based SEM was developed to overcome the problem of covariance SEM, namely the Generalized Structured Component Analysis (GSCA) method. This study aims to implement the GSCA method on factors data that are expected to have an effect on the level of behavioral intention towards online food delivery services and to examine the significance of the mediating variable on the structural relationship. The results of hypothesis testing with a 95% confidence level showed that the quality of convenience motivation, prior online purchase experience, and attitude towards online food delivery services had a significant effect on behavioral intentions towards online food delivery services. The fit value is above 0, 523 which indicates that the model is able to explain around 52,3% of the variation of the data. Furthermore, the hedonic motivation variable has a significant effect on convenience motivation. Post usage usefulness and prior online purchase experience variables significantly affected the attitudes towards online food delivery services. The proposed model using GSCA achieves a much better result (good fit) compared with the previous model using Confirmatory Factor Analysis (CFA) with marginal fit.

GENETIC DIVERSITY OF ALPINIA MALACCENSIS (BURM.F.) ROSCOE (ZINGIBERACEAE) IN JAVA ISLAND, INDONESIA

Alpinia malaccensis (Burm.f.) Roscoe is a rare plant in Java, Indonesia, that produces essential oils. Its sustained decline causes great concern for future uses. Therefore, the study of its diversity in its natural habitat to predict future survival needs serious focus. The present study aims to determine the genetic diversity of A. malaccensis in Java based on ISSR markers. The sampling of a total of 11 populations yielded 33 accessions, which underwent amplifying using 12 ISSR primers. Using the molecular data analyzed their relationships, population structure, and genetic diversity. The UPGMA cluster, structure, and principal component analysis (PCA) demonstrated that the accessions divide in three groups, a result that correlates with their geography in Java. Group I consisted of var. malaccensis populations, while groups II and III comprised the var. nobilis populations, which proved truer among populations of var. nobilis. The genetic diversity category of A. malaccensis showed moderate based on Nei's genetic diversity (h = 0.2892) and Shannon's information index (I = 0.4438). The coefficient of genetic differentiation (GST) and molecular variance among the populations displayed higher within the individual population, which revealed a significant genetic variance among the 11 populations of A. malaccensis. The latest results confirm that ISSR markers can help distinguish the genotypes of A. malaccensis.

IS UNDERSTATEMENT A CONCEPT OR A CATEGORY?

The aim of the study is to define the British phenomenon “understatement”. The relevance of this research is due to the desire to better understand the English character, which still remains a mystery to representatives of other cultures due to a special historically established, indirect, veiled, and sometimes ambiguous tradition of communication. Understatement directly serves to create this ambiguity, causing enormous difficulties in communicating both foreigners with the British and the British themselves among themselves. The main methods of research are: descriptive and structural methods, component and definitional analysis. The results of the study showed that understatement represents a nationally specific communicative category which contains the attitudes and rules of speech behavior of the British and participates in the regulation of the communicative process. Understatement has a certain structure and includes verbal and non-verbal means of expressing communicative content aimed at harmonious, conflict-free communication. The theoretical significance lies in a clear definition of the British phenomenon “understatement”, in identifying its speech strategies, linguistic and extralinguistic ways of expression. The practical significance is expressed in using the results of the research in lecture courses of the theory of intercultural communication, stylistics and general linguistics.

Assessment of Genetic Diversity and Discovery of Molecular Markers in Durian (Durio zibethinus L.) in China

Durian (Durio zibethinus L.) is a crop of economic and health importance globally. Efforts are being made to revamp China’s only successful commercial-scale durian plantations in Hainan; however, their genetic base is unknown. Therefore, the present study was undertaken to assess the genetic base and population structure of 32 genotypes in durian plantation sites in Hainan, China, and develop simple sequence repeat (SSR) markers by whole genome sequencing through restriction site-associated DNA sequencing technology to facilitate germplasm conservation and breeding. The results from identity by state (IBS), phylogenetic tree, population structure, and principal component analysis grouped the 32 genotypes into two clusters/sub-populations. Based on IBS, genotypes in Cluster I are largely duplicated genotypes; however, results from the model-based population structure demonstrated that most of the genotypes in Sub-population II shared a common genetic background with those in Sub-population I/Cluster I. The results revealed that the core durian collection in the plantation sites in Hainan include D24, D101, MSW, JH, D163, HFH, and NLX-5. In addition, we developed a total of 79,178 SSR markers with varied lengths and amplicon sizes. The genetic diversity and population structure reported in this study will be useful for durian conservation and utilization. In addition, the discovered and developed SSR markers will lay the foundation for molecular breeding via marker-assisted selection, quantitative trait loci mapping, and candidate gene discovery and validation.

PSYCHOLINGUISTIC ANALYSIS OF ASSOCIATIVE FIELDS OF WORD-STIMULUS ДРУГ (FRIEND) ACCORDING TO THE DATA OF DIRECTED ASSOCIATIVE EXPERIMENTS

Introduction The associative approach to the study of the meaning of the word is nota new one in psycholinguistics, however, having received a positive assessment of leading scientists,it still remains in the priority areas of semantics research next to the parametric, feature, prototypeand situational approaches. Scientists use it both for revealing the ethnocultural specificity ofthe image of the world of representatives of various ethnoses, and for a comprehensive analysisof the meaning of the word of a certain language. We used both perspectives of research in ourprevious scientific studies. In this article we present the results of directed associative experiments,that were held at the beginning of the XXI century in Ukraine. Purpose. The purpose ofthe study is to reveal the structure of the associative value by the example of the associative fieldsof the stimulus FRIEND in the Ukrainian language. Methods. The main methods of researchare descriptive, structural (component analysis), associative experiment (directed), “associativegestalt”. Results. The psycholinguistic analysis demonstrates the structure of associative fieldof the stimulus FRIEND, obtained as a result of conducting of the directed associative experimentsas for the selection of definitions and comparisons. The placement of zones and their filling showthe main association strategies that we consider as specific components leading in determiningof the associative meaning of the word, in the formation of images of the linguistic consciousnessof Ukrainian respondents. Conclusion The study of the meaning of the word on the materialof lexicographic sources and a number of associative experiments that were applied to a specificword-stimulus is justified and necessary for the comprehensive identification of semantics.

Detection of Vascular Inflammation and Oxidative Stress by Cotinine in Smokers: Measured Through Interleukin-6 and Superoxide Dismutase.

PurposeSmoking is a significant risk factor in developing cardiovascular disease pathogenesis through oxidative stress and inflammation mechanisms. This study used cotinine as a biomarker of nicotine exposure levels in the body, which was associated with levels of Interleukin-6 (IL-6) and Superoxide Dismutase (SOD) as markers of oxidative stress and vascular inflammation. The research aimed to analyze the effect of cotinine levels on the expression of IL-6 and SOD.MethodsThis study used a cross-sectional design on 200 subjects, consisting 100 smokers and 100 non-smokers. Cotinine levels, IL-6 expression, and SOD were measured from the blood serum of each subject using the Enzyme-Linked Immunosorbent Assay (ELISA) method. Then the data were analyzed using Generalized Structured Component Analysis (GSCA).ResultsThere was a significant effect of cotinine levels on the reduction of SOD mediated by IL-6 (CR = 4.006). Cotinine levels also increased IL-6 mediated by SOD (CR = 4.292). The structural model shows that higher cotinine levels will increase IL-6 expression, and conversely, SOD expression will decrease.ConclusionHigh cotinine levels cause an increase in the inflammatory process and oxidative stress in the vasculature of smokers, which is characterized by high IL-6 expression and low SOD expression.

Analysis of Covid-19 Risk Perception Survey Result Using Generalized Structured Component Analysis

The capital city of Indonesia, Jakarta, became the province with the highest number of Covid-19. Response this situation, LaporCovid-19 collaborate with the Social Resilience Lab, Nanyang Technological University conducted a survey to measure how Jakarta residents perceive the risk of Covid-19 from May 29 to June 20 2020. Factors of risk perception are variables that cannot be measured directly, so they are analyzed used a Structural Equation Modeling (SEM) approach, namely Generalized Structured Component Analysis (GSCA). The Likert scale used can be considered as interval or ordinal depending on the point of view of the theory built. Therefore, this study will compare the GSCA method with the nonlinear GSCA and evaluate six variables, namely risk perception, knowledge, information, health behavior , social capital, and economy. Evaluation of the overall model showed that the nonlinear GSCA model can explain the diversity of qualitative data better than the GSCA model with FIT > 0.9. Based on GSCA nonlinear model, information has significantly influence of knowledge, economy and social capital have a real reciprocal relationship, along knowledge and risk perception have significantly influence of health behavior.

Farmer to Farmer Extension Approach to increase Coffee Farmers' Food Security

The purpose of this study was to determine what factors influence the food security of coffee farmers, primarily through the Farmer-to-Farmer approach (or independent extension officer) and formulate a model of food security for coffee farmer families. This approach uses a quantitative approach with survey research methods. The research was conducted in Malang Regency, East Java Province, especially in the Districts of Ampelgading, Sumbermanjing Wetan, Trirtoyudo and Dampit. This study's population was 2,622 coffee farmers with a total sample size of 96 people selected using the Propositional random sampling technique. Data were analyzed using Structural Equation Modeling (SEM) based on the variant Generalized structured component analysis (GSCA). The results showed that the Family Farmers' Food Security was more dominantly influenced by independent Extension Officers' Role. Meanwhile, the role of independent extension officers is more dominantly influenced by the role of civil servant extension officers. The conclusion of this study is to increase the role of Independent Extension Workers in increasing the food security of coffee farmers. It is necessary to increase the role of independent extension officer, especially in providing consultation to farmers.

Low‐carbon tourism for island destinations: A crucial alternative for sustainable development

AbstractThe impact of climate change has been evidenced in several tourist destinations, and triggered concerns on the destination development. Low‐carbon tourism has become a national, if not, global agenda that can be used to mitigate the climate change impact caused by the tourist destinations. To respond to this timely agenda and the United Nation World Tourism Organisation's (UNWTO) callout, this study establishes and verifies important components and attributes of low‐Carbon destinations, particularly on island destination, which are still unexamined in the literature. Taking on the perspective of tourists, this study is driven by Stimulus–Organism–Response (S‐O‐R) theory which is a consolidative theoretical framework that integrates environmental input (external), emotional status (internal) and behavioural responses to explain actual behaviours of low‐carbon tourists. Integrated generalised structured component analysis (IGSCA) and multigroup analysis were performed on 1808 travellers who posed different degrees of psychological fear of the COVID‐19 pandemic. During COVID‐19, health and safety risks have become a critical concern; therefore, this study further explores the moderating effect of risk from the perspective of the low‐ and high‐perceived risk travellers, before identifying the attitude‐behaviour gaps of these two groups. The study provides theoretical insights into low‐carbon tourism experience at the island destinations and offers useful managerial implications on low‐carbon destination development.