Immersion in cheers: Experiencescape, customer inspiration, and drink statues in craft beer tourism using PLS-SEM, IPMA, and ANN

SEM-ANN-based approach to understanding students’ academic-performance adoption of YouTube for learning during Covid

Blockchain technology has applications beyond cryptocurrencies, notably in marketing through blockchain-based loyalty programmes (BBLPs). This study examines key factors for their implementation using importance-performance map analysis (IPMA) and necessary condition analysis (NCA), based on an adapted TAM3 model. The key endogenous variables include behavioural intention to use (BEINT), perceived usefulness (USEF), and perceived ease of use (EASE). The model suggests that USEF mediates the effects of trust (TRUST) and price-value (PVAL) on BEINT, while EASE mediates self-efficacy (SEFFIC) and perceived enjoyment (PENJ). Structural equation modelling reveals that USEF, EASE, PVAL, SEFFIC, and PENJ significantly impact BEINT. TRUST influences USEF but not BEINT directly, and social norms (SNORM) are not significant. IPMA analysis identifies USEF as the critical variable for BBLP acceptance, with EASE and PENJ being key influencers, followed by PVAL. NCA and IPMA findings show that all variables, except SNORM, can act as constraints to increasing BEINT. However, TRUST and SEFFIC exceed the necessary threshold, indicating they are not actual constraints. Conversely, USEF, EASE, and PENJ act as bottlenecks, with USEF being crucial for BEINT improvement, while EASE and PENJ enhance perceived usefulness. Strengthening these factors can drive BBLP adoption.

There are various reasons why vaccine fear has resulted in public rejection. Students have raised concerns about vaccine effectiveness, leading to hesitation when it comes to vaccination. Vaccination apprehension impacts students' perceptions, which has an impact on the acceptability of an e-learning platform. As a result, the goal of this study is to look at the post-acceptance of an e-learning platform using a conceptual model with several factors. Every variable makes a unique contribution to the e-learning platform's post-acceptance. In the current study, TAM variables were combined with additional external factors such as fear of vaccination, perceived routine use, perceived enjoyment, perceived critical mass, and self- efficacy, all of which are directly associated with post-acceptance of an e-learning platform. Here, a hybrid conceptual model was used to evaluate the newly widespread use of e-learning platforms in this area in this study in the UAE. In the past, empirical investigations primarily used Structural Equation Modeling (SEM) analysis; however, this study used a developing hybrid analysis approach that combines SEM with deep learning–based Artificial Neural Networks (ANN). This study also employed the Importance–Performance Map Analysis (IPMA) to determine the significance and performance of each element. Through the findings, it was found that fear of vaccination, perceived ease of use, perceived usefulness, perceived routine use, perceived enjoyment, perceived critical mass, and self-efficiency all had a significant impact on students' behavioral intention to use the e-learning platform for educational purposes. It was also shown in the analysis of ANN as well as IPMA that the perceived ease of use of the e-learning platform is the most important indicator of post-acceptance. The proposed model, in theory, provides appropriate explanations for the elements that influence post-acceptance of the e-learning platform in terms of internet service factors at the individual level. In the practical sense, these findings will help decision-makers and practitioners in higher education institutions identify the factors that should be given extra care and plan their policies accordingly. The ability of the deep ANN architecture to identify the non-linear relationships between the factors involved in the theoretical model has been determined in this research. The implication offers extensive information about taking effective steps to decrease the fear of vaccination among people and increase vaccination confidence among teachers and educators and students, consequently impacting society.

Purpose The purpose of this paper is to introduce the importance-performance map analysis (IPMA) and explain how to use it in the context of partial least squares structural equation modeling (PLS-SEM). A case study, drawing on the IPMA module implemented in the SmartPLS 3 software, illustrates the results generation and interpretation. Design/methodology/approach The explications first address the principles of the IPMA and introduce a systematic procedure for its use, followed by a detailed discussion of each step. Finally, a case study on the use of technology shows how to apply the IPMA in empirical PLS-SEM studies. Findings The IPMA gives researchers the opportunity to enrich their PLS-SEM analysis and, thereby, gain additional results and findings. More specifically, instead of only analyzing the path coefficients (i.e. the importance dimension), the IPMA also considers the average value of the latent variables and their indicators (i.e. performance dimension). Research limitations/implications An IPMA is tied to certain requirements, which relate to the measurement scales, variable coding, and indicator weights estimates. Moreover, the IPMA presumes linear relationships. This research does not address the computation and interpretation of non-linear dependencies. Practical implications The IPMA is particularly useful for generating additional findings and conclusions by combining the analysis of the importance and performance dimensions in practical PLS-SEM applications. Thereby, the IPMA allows for prioritizing constructs to improve a certain target construct. Expanding the analysis to the indicator level facilitates identifying the most important areas of specific actions. These results are, for example, particularly important in practical studies identifying the differing impacts that certain construct dimensions have on phenomena such as technology acceptance, corporate reputation, or customer satisfaction. Originality/value This paper is the first to offer researchers a tutorial and annotated example of an IPMA. Based on a state-of-the-art review of the technique and a detailed explanation of the method, this paper introduces a systematic procedure for running an IPMA. A case study illustrates the analysis, using the SmartPLS 3 software.

A conceptual framework for determining metaverse adoption in higher institutions of gulf area: An empirical study using hybrid SEM-ANN approach

Comparison of prediction models for adverse outcome in pediatric meningococcal disease using artificial neural network and logistic regression analyses

Artificial neural network (ANN) analysis methods have led to more sensitive diagnosis of myocardial infarction and improved prediction of mortality in breast cancer, prostate cancer, and trauma patients. Prognostic studies have identified early clinical and radiographic predictors of mortality after intracerebral hemorrhage (ICH). To date, published models have not achieved the accuracy necessary for use in making decisions to limit medical interventions. We recently reported a logistic regression model that correctly classified 79% of patients who died and 90% of patients who survived. In an attempt to improve prediction of mortality we computed an ANN model with the same data. To determine whether an ANN analysis would provide a more accurate prediction of mortality after ICH when compared with multiple logistic regression models computed using the same data. Analyses were conducted on data collected prospectively on 81 patients with supratentorial ICH. Multiple logistic regression was used to predict hospital mortality, then an ANN analysis was applied to the same data set. Input variables were age, gender, race, hydrocephalus, mean arterial pressure, pulse pressure, Glasgow Coma Scale score, intraventricular hemorrhage, hydrocephalus, hematoma size, hematoma location (ganglionic, thalamic, or lobar), cisternal effacement, pineal shift, history of hypertension, history of diabetes, and age. The ANN model correctly classified all patients (100%) as alive or dead compared with 85% correct classification for the logistic regression model. A second ANN verification model was equally accurate. The ANN was superior to the logistic regression model on all objective measures of fit. ANN analysis more effectively uses information for prediction of mortality in this sample of patients with ICH. A well-validated ANN may have a role in the clinical management of ICH.

The craft beer movement and craft beer tourism are a new global phenomenon that has reached various parts of the world. However, the literature on craft beer tourism mostly focuses on traditional origins of craft beer in Western countries—the US, Australia, and European countries. This research note illustrates how a study of the Thai craft beer movement and craft beer tourism could contribute to the existing body of knowledge. The consumption of non-Western people in non-Western places has been underrepresented in the literature of food and beverage tourism. The craft beer movement has spread to Thailand via urban middle-class Thais who brought the passion for and knowledge of home brewing from the West to Thailand. Brewing lessons, brewery visits, and craft beer events/ festivals have functioned as community building activities for Thai craft beer enthusiasts as well as the main craft beer distribution channel. Craft beer consumption continues to grow despite the Thai alcoholic production law that prohibits home brewing. For future studies, different craft beer tourism activities in Thailand should be analyzed for 1) the adoption of the experience economy framework, 2) the formation of the consumption community, 3) the roles of various stakeholders who differentially contribute to and benefit from craft beer tourism activities, and 4) the role of foreign tourists in the development of craft beer tourism in Thailand.

This research aims to evaluate the impact of knowledge management (KM) factors, including acquisition, sharing, application, and protection on students’ behavioral intention to use m-learning. Unlike the previous m-learning empirical studies which primarily relied on structural equation modeling (SEM) analysis, this research employs an emerging hybrid analysis approach using SEM and artificial neural network (ANN) based on deep learning. This research also applies the importance-performance map analysis (IPMA) to identify the importance and performance of each factor. Through the means of an online survey, a total of 319 IT undergraduate students have participated in the study. The findings indicated that all KM factors have significant positive impacts on m-learning adoption except knowledge sharing. The analysis of both ANN and IPMA revealed that knowledge protection is the most crucial predictor of behavioral intention. Theoretically, the proposed model has provided ample explanations concerning what impacts the behavioral intention to use m-learning from the perspective of KM factors at the individual level. Practically, the results will enable decision-makers and practitioners in higher educational institutions to determine which factors should be given more importance than others and strategize their policies accordingly. Methodologically, this research ascertains the capability of the deep ANN architecture in determining the non-linear relationships among the factors in the theoretical model.

Smart electricity meters measure, control, analyze, and predict the amount of electricity used. Do the same for water and gas power. Automatically svae this monitoring data to the energy provider, for billing and tracking services. In developed countries, there has not been a consensus to accept the use of smart electricity meters, in addition to the benefits mentioned above, there are many possible risks when using smart meters. This paper examines information technology system (IS) related factors and engineering model related factors following technical readiness such as optimism, innovation insecurity, and discomfort. Accompanying that is the expectation of a smart meter, for the Vietnamese people’s intention to continuously use smart meters. The oriented approach is applied to evaluate the intention model of continuous use of smart meters, through the survey results of 500 answer samples of Vietnamese people. We propose to use a 2-layer research model to analyze the results of the user survey about the smart meter system. Most of the previous studies on smart meter systems focused on analyzing the impact of factors affecting applications, using single-step Structure Equation Modeling (SEM). The purpose of this study based on the Technology Acceptance Method (TAM) theory, describes the Artificial Neural Network (ANN) method to perform indepth analysis, yielding more accurate results than the SEM model. The study measures the relationship between the readiness for new technologies (optimision, innovation, discomfort, and insecurity). Technology acceptance (Perceived ease of use, Perceived usefulness). Expectations confirmed and Information systems acceptance (service quality, system quality, and information quality). This paper outlines the research model of the multi-analysis approach by combining Partial Least Squares Structure Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. First, the PLS-SEM model evaluates the factors affecting the intention to use the smart meter system. Second, ANN ranks the impact factors of important predictors from the PLS-SEM model. The findings from the PLS-SEM and ANN approach research model confirm the results obtained from PLS-SEM by ANN. In addition, ANN performs linear and non-linear relational modeling with high prediction accuracy compared with the SEM model. In addition, an Importance Performance Map Analysis (IPMA) analyzes the results accurately for factors’ important performance.

Artificial neural network (ANN) analysis is a new technique in NMR spectroscopy. It is very often considered only as an efficient "black-box' tool for data classification, but we emphasize here that ANN analysis is also powerful for data quantification. The possibility of finding out the biochemical rationale controlling the ANN outputs is presented and discussed. Furthermore, the characteristics of ANN analysis, as applied to plasma lipoprotein lipid quantification, are compared to those of sophisticated lineshape fitting (LF) analysis. The performance of LF in this particular application is shown to be less satisfactory when compared to neural networks. The lipoprotein lipid quantification represents a regular clinical need and serves as a good example of an NMR spectroscopic case of extreme signal overlap. The ANN analysis enables quantification of lipids in very low, intermediate, low and high density lipoprotein (VLDL, IDL, LDL and HDL, respectively) fractions directly from a 1H NMR spectrum of a plasma sample in < 1 h. The ANN extension presented is believed to increase the value of the 1H NMR based lipoprotein quantification to the point that it could be the method of choice in some advanced research settings. Furthermore, the excellent quantification performance of the ANN analysis, demonstrated in this study, serves as an indication of the broad potential of neural networks in biomedical NMR.

Epidemiological studies have shown an association between global warming, air pollution, and allergic diseases. Several air pollutants, including volatile organic compounds, formaldehyde, toluene, nitrogen dioxide (NO2 ), and particulate matter, act as risk factors for the development or aggravation of atopic dermatitis (AD). We evaluated the impact of air pollutants and weather changes on AD patients. Sixty AD patients ≥5years of age (mean age: 23.5±12.5years), living in the Campania Region (Southern Italy), were followed for 18months. The primary outcome was the effect of atmospheric and climatic factors on signs and symptoms of AD, assessed using the SCORAD (SCORing Atopic Dermatitis) index. We measured mean daily temperature (TOD), outdoor relative humidity (RH), diurnal temperature range (DTR), precipitation, particulate with aerodynamic diameter≤10μm (PM10 ), NO2 , tropospheric ozone (O3 ), and total pollen count (TPC). A multivariate logistic regression analysis was used to examine the associations of AD signs and symptoms with these factors. An artificial neural network (ANN) analysis investigated the relationships between weather changes, environmental pollutants, and AD severity. The severity of AD symptoms was positively correlated with outdoor temperatures (TOD, DTR), RH, precipitation, PM10 , NO2 , O3 , and TPC. The ANN analysis also showed a good discrimination performance (75.46%) in predicting disease severity based on environmental pollution data, but weather-related factors were less predictive. The results of the present study provide evidence that weather changes and air pollutions have a significant impact on skin reactivity and symptoms in AD patients, increasing the severity of the dermatitis. The knowledge of the single variables proportion on AD severity symptoms is important to propose alerts for exacerbations in patients with AD of each age. This finding represents a good starting point for further future research in an area of increasingly growing interest.

BackgroundThis study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches.Methods and MaterialsWe analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared.ResultsUnivariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses.ConclusionThe prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset.

Multiple serum tests were performed on archival samples from patients who participated in trials to assess the ProstaScint scan staging ability. Traditional statistical analysis as well as artificial neural network (ANN) analysis were employed to evaluate individual patients and the group as a whole. The results were evaluated so that each factor was tested for prognostic value. Data obtained from serum tests, bone scans, and ProstaScint scans were evaluated by traditional statistical methods and ANN to determine the individual value in clinical staging of prostate cancer. Two hundred seventy-five patients (180 postprostatectomy, 95 intact prostate) with prostate cancer (14 with distant metastases) were available for analysis. Data available included: clinical state (remission or progression), most recent clinical TNM stage, bone scan, and ProstaScint scan. Serum was tested for prostate-specific membrane antigen(PSMA), prostate-specific antigen(PSA), free PSA (fPSA), and complexed PSA (cPSA). Additional calculations included percent free PSA, and percent complexed PSA. Spearman individual statistical assessment for traditional group evaluation revealed no significant factors for T-stage. The free PSA and complex PSA had a significant association with node (N)-status. The distant metastases (M) stage correlated well with the bone scan and clinical stage. ANN analysis revealed no significant T-stage factors. N-stage factors showed a 95% sensitivity and 49% specificity. These factors included the presence or absence of a prostate, PSA serum levels, bone scan, and ProstaScint scans as major associated indicators. ANN analysis of the important variables for M-stage included ProstaScint scan score, and PSA levels (total, percent complexed, percent free, and fPSA). These factors were associated with a 95% sensitivity and 15% specificity level. Two hundred seventy-five patients receiving treatment for prostate cancer were evaluated by ANN and traditional statistical analysis for factors related to stage of disease. ANN revealed that PSA levels, determined by a variety of ways, ProstaScint scan, and bone scan, were significant variables that had prognostic value in determining the likelihood of nodal disease, or distant disease in prostate cancer patients.

In this study, Artificial Neural Networks (ANN) analysis is used to predict the compression strength of polypropylene fibre mixed concrete. Polypropylene fibre admixture increases the compression strength of concrete to a certain extent according to mix proportion. This proportion and homogenous distribution are important parameters on compression strength. Determination of compression strength of fibre mixed concrete is significant due to the veridicality of capacity calculations. Plenty of experiments shall be completed to state the compression strength of concrete which have different fibre admixture. In each case, it is known that performing the laboratory experiments is costly and time-consuming. Therefore, ANN analysis is used to predict the 7 and 28 days of compression strength values. For this purpose, 156 test specimens are produced that have 26 different types of fibre admixture. While the results of 120 specimens are used for training process, 36 of them are separated for test process in ANN analysis to determine the validity of experimental results. Finally, it is seen that ANN analysis predicts the compression strength of concrete successfully.