Emerging Science Journal (ESJ)
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Factors of Value Chain Affect Bank Efficiency
Get PDFThis paper aims to determine and estimate the factors in the value chain that affect the efficiency of state-owned commercial banks in Vietnam. The study employs a combination of qualitative techniques, such as conducting interviews with top executives, financial managers, chief accountants, and employee surveys, along with quantitative methods like ordinary least squares analysis. The outcomes reveal four critical segments within the value chain – the retail department, operation department, back office, and call center – all of which significantly influence the efficiency of the banks. Additionally, drawing upon value chain theory and Porter's model (1985), the author underscores the significance of implementing a robust value chain strategy. This strategic approach has the potential to substantially enhance the competitive advantages of state-owned commercial banks in Vietnam. Doi: 10.28991/ESJ-2023-07-06-010 Full Text: PD
Demystifying Tourists' Intention to Visit Destination on Travel Vlogs: Findings from PLS-SEM and fsQCA
Get PDFWith the advent of digital technologies (i.e., social media), tourism has evolved its marketing strategies. Even though published literature discusses the importance of tourism content on social media from various consumer perspectives, much more work must be done to examine how consumers make travel decisions based on tourism content. This study proposes a model for analyzing travel intent based on consumer motivations (e.g., novelty, entertainment, and relaxation) to watch social media travel videos. Consumers' travel intentions are influenced by trust and parasocial relationships. Through an online survey, 215 responses were collected and analyzed using a structural equation modeling (SEM) approach using Smart-PLS 3.0 and fuzzy set qualitative comparative analysis (fsQCA). In the study, relaxation ranked most highly among the three motivations for viewers to watch travel videos on YouTube for building parasocial relationships. In contrast, consumers seeking entertainment are more likely to form trust, which will result in consumers' intentions to travel. Based on intermediate solutions generated by the fsQCA, two causal configurations can be used to explain consumer travel decisions influenced by social media tourism content. The study also discusses theoretical and practical guidelines in depth. Doi: 10.28991/ESJ-2023-07-03-015 Full Text: PD
The Phenomena of Learning Loss Experienced by Elementary School Students during the Covid-19 Post Pandemic
Get PDFThe COVID-19 pandemic caused most students to experience learning loss after joining online learning for almost 2 years. Learning loss is a student's condition experiencing learning setbacks academically due to the factors of the non-sustainable educational process. The study aimed at analyzing the learning loss phenomenon experienced by elementary school students in the COVID-19 post-pandemic situation. The study used a descriptive qualitative approach. The population of the study was 401,321 distributed students, and the sample was gained through conducting two sampling techniques, such as random cluster sampling and accidental sampling, with a final sample of 1,104 students. The data collection methodologies conducted were interview, observation, and questionnaire. The questionnaire used contained 15 questions that had been validated before. The data analysis of the study was conducted interactively using 3 techniques, such as data reduction, data display, and drawing conclusions. The descriptive analysis was conducted by using the Guttman scale with the percentage statistic technique. The result of the study showed that there were learning loss phenomena in the post-pandemic situation, a condition seen from the researched dimensions in the low and medium categories. Moreover, a high level of learning loss was found in the rural area. This was observed from several things, such as the learning facilities provided, the parent's role, and the learning methodologies conducted. To resolve learning loss, we need to participate all parties in the learning process, which in this context means mending a good relationship with the community and parents to improve the learning quality, which is assisted with innovative learning models. Doi: 10.28991/ESJ-2023-SPER-014 Full Text: PD
Concentration of B-CG and sFlt-1 in Rattus Norvegicus Model of Preeclampsia with Swimming Exercise Treatment
Get PDFPreeclampsia (PE) is a life-threatening pregnancy complication for the mother and fetus. High concentrations of human chorionic gonadotrophin (hCG) and soluble fms-like tyrosine kinase-1 (sFLt-1) during pregnancy may have a role in the pathophysiology of PE. Swimming Exercise (SE) is one of the physical activities recommended for pregnant women and carries a minimal risk. This study is aimed at analyzing the interaction between the conditions of rats (normal and PE), the onset of PE (early onset and late onset), and the time of SE (SE 0 minutes; SE 5 minutes; SE 10 minutes) on the concentrations of B-CG and sFlt-1 in the Rattus norvegicus (R. norvegicus) model of PE. 72 R. norvegicus were included in this study and divided into 12 experimental groups (each group n = 6 individuals). R. norvegicus PE was prepared by inducing L-Nitro-Arginine-Methyl Ester (L-NAME) at a 75 mg/kg BW/day dose. The determination of PE was supported by the observation of differences in the values of urine protein (PU), urine glucose (GU), and urine leukocytes (LU) in R. norvegicus before and after injection of L-NAME. The three-factorial statistical test showed a significant interaction between the concentration of B-CG and the condition of R. norvegicus, the onset of PE, and the time of SE, with a p-value <0.001. The three-factorial statistical test also showed a significant interaction between the sFLt-1 concentration and the condition of R. norvegicus, the onset of PE, and the time of SE with p<0.05. The difference in the concentration of B-CG and sFLt-1 R. norvegicus in each treatment group was influenced by the condition of the rats (normal and PE), the onset of PE (early onset and late onset), and the time of SE (SE 0 minutes; SE 5 minutes; SE 10 minutes). Research related to SE on PE still needs to be continued to decide on recommendations on whether SE can be used as a preventive measure in complementary midwifery care for preventing and reducing symptoms of PE in pregnancy. Doi: 10.28991/ESJ-2023-07-03-021 Full Text: PD
An Intelligent Controller Based on LAMDA for Speed Control of a Three-Phase Inductor Motor
Get PDFThree-phase induction motors are widely used in the industrial field due to their low cost and robustness; therefore, it is essential to continuously develop new proposals that improve their behavior and response in applications where speed control is required. This paper proposes the development of an intelligent controller programmed in a PLC and interconnected with a three-phase induction motor through a VFD. The novel intelligent controller bases its operation on the LAMDA algorithm, which acts as a decision-making system based on the state of the error with respect to the speed reference and its derivative, obtaining a closed-loop controller. In addition, the VFD receives commands from the PLC to operate the motor at a constant voltage-frequency ratio in which flux remains constant. The proposed controller has been validated in two study cases: i) reference changes and ii) rejection of disturbances. The results obtained are promising and show a good performance of the LAMDA controller when compared qualitatively and quantitatively with the controller most commonly used in industrial systems, such as PID, and controllers with similar characteristics, such as fuzzy, based on Mamdani and Takagi-Sugeno inference. Doi: 10.28991/ESJ-2023-07-03-01 Full Text: PD
Adaptive Learning and Integrated Use of Information Flow Forecasting Methods
Get PDFThis research aims to improve quality indicators in solving classification and regression problems based on the adaptive selection of various machine learning models on separate data samples from local segments. The proposed method combines different models and machine learning algorithms on individual subsamples in regression and classification problems based on calculating qualitative indicators and selecting the best models on local sample segments. Detecting data changes and time sequences makes it possible to form samples where the data have different properties (for example, variance, sample fraction, data span, and others). Data segmentation is used to search for trend changes in an algorithm for points in a time series and to provide analytical information. The experiment performance used actual data samples and, as a result, obtained experimental values of the loss function for various classifiers on individual segments and the entire sample. In terms of practical novelty, it is possible to use the obtained results to increase quality indicators in classification and regression problem solutions while developing models and machine learning methods. The proposed method makes it possible to increase classification quality indicators (F-measure, Accuracy, AUC) and forecasting (RMSE) by 1%–8% on average due to segmentation and the assignment of models with the best performance in individual segments. Doi: 10.28991/ESJ-2023-07-03-03 Full Text: PD
A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration
Get PDFSome optimization problems are difficult to solve due to a considerable number of local optima, which may result in premature convergence of the optimization process. To address this problem, we propose a novel heuristic method for constructing a smooth surrogate model of the original function. The surrogate function is easier to optimize but maintains a fundamental property of the original rugged fitness landscape: the location of the global optimum. To create such a surrogate model, we consider a linear genetic programming approach coupled with a self-tuning fitness function. More specifically, to evaluate the fitness of the produced surrogate functions, we employ Fuzzy Self-Tuning Particle Swarm Optimization, a setting-free version of particle swarm optimization. To assess the performance of the proposed method, we considered a set of benchmark functions characterized by high noise and ruggedness. Moreover, the method is evaluated over different problems' dimensionalities. The proposed approach reveals its suitability for performing the proposed task. In particular, experimental results confirm its capability to find the global argminimum for all the considered benchmark problems and all the domain dimensions taken into account, thus providing an innovative and promising strategy for dealing with challenging optimization problems. Doi: 10.28991/ESJ-2023-07-04-01 Full Text: PD
Brand Experience on Brand Attachment: The Role of Interpersonal Interaction, Feedback, and Advocacy
Get PDFValue co-creation profoundly affects brand experience, human interaction rules, and customer behavior performance and is a recurring theme in research in the field of virtual brand communities. Studies have determined an influence model for value co-creation, but there is a dearth of research on the impacts of value co-creation activities on brand attachment. This study built a mediation theoretical research framework on value co-creation theory, brand experience, and brand attachment. The purpose of this study is to explore the significance of value co-creation activities on brand experience. Furthermore, this study intends to measure the impact of brand experience on brand attachment. Additionally, this study intends to investigate the mediation role of brand experience in the relationship between value co-creation and brand attachment. This study analyzed 512 data collected by structural equation modeling using registered users of the OPPO community as participants. The results of this empirical test show that the three dimensions of value co-creation (interpersonal interaction, feedback, and advocacy) have a positive effect on brand experience and that brand experience has a positive and significant impact on brand attachment and mediates the relationship between value co-creation and brand attachment. The findings of significance for management are the identification of factors that enhance value co-creation in virtual brand communities. Doi: 10.28991/ESJ-2023-07-04-014 Full Text: PD
Interval Estimation of the Dependence Parameter in Bivariate Clayton Copulas
Get PDFIn various disciplines, discerning dependencies between variables remains a crucial undertaking. While correlation measures like Pearson, Spearman, and Kendall provide insight into the degree of two-variable relationships, they fall short of revealing the intricate structure of dependencies between these variables. The Clayton copula, known for its flexible attributes, becomes instrumental in unveiling this dependency structure. This paper aims to advance knowledge by providing an explicit formula for creating Wald confidence intervals (CIs) for the dependence parameter in a bivariate Clayton copula, along with a mathematical derivation of the observed Fisher information. In comparison, we also propose likelihood CIs, whose performance we examine in simulation studies using both coverage probability and average length of CIs as performance indicators. Our findings reveal that in scenarios characterized by small sample sizes, likelihood-based CIs, despite their slightly more complex computational requirements, outperform Wald CIs, yielding a coverage probability more proximate to the nominal confidence level of 0.95. However, in situations involving large samples and a dependence parameter distant from zero, both Wald and likelihood-based CIs demonstrate comparable utility. For real-world data applications, the daily closing prices of two cryptocurrencies are analyzed using the proposed CIs. Doi: 10.28991/ESJ-2023-07-05-02 Full Text: PD
Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers
Get PDFAdvanced modern technology and the industrial sustainability theme have contributed to the implementation of composite materials for various industrial applications. Bio-composites are among the desired alternatives for green products. However, to properly control the performance of bio-composites, predicting their constituent properties is of paramount importance. This work introduces an innovative, evolving genetic programming tree model for predicting the mechanical properties of natural fibers for the first time based upon several inherent chemical and physical properties. Cellulose, hemicellulose, lignin, and moisture contents, as well as the Microfibrillar angle of various natural fibers, were considered to establish the prediction models. A one-hold-out methodology was applied for the training/testing phases. Robust models were developed utilizing evolving genetic programming tree models to predict the tensile strength, Young's modulus, and the elongation at break properties of the natural fibers. It was revealed that the Microfibrillar angle was dominant and capable of determining the ultimate tensile strength of the natural fibers by 44.7%, comparable to other considered properties, while the impact of cellulose content in the model was only 35.6%. This would facilitate utilizing artificial intelligence to predict the overall mechanical properties of natural fibers without exhausting experimental efforts and cost to enhance the development of better green composite materials for various industrial applications. Doi: 10.28991/ESJ-2023-07-06-02 Full Text: PD