With the remarkable improvement in people’s socioeconomic living standards around the world, adolescent obesity has increasingly become an important public health issue that cannot be ignored. Thus, we have implemented its use in an attempt to explore the viability of scenario-based simulations through the use of a data mining approach. In doing so, we wanted to explore the merits of using a General Bayesian Network (GBN) with What-If analysis while exploring how it can be utilized in other areas of public health. We analyzed data from the 2017 Korean Youth Health Behavior Survey conducted directly by the Korea Centers for Disease Control & Prevention, including 19 attributes and 11,206 individual data points. Our simulations found that by manipulating the amount of pocket money-between $60 and $80-coupled with a low-income background, it has a high potential to increase obesity compared with other simulated factors. Additionally, when we manipulated an increase in studying time with a mediocre academic performance, it was found to potentially increase pressure on adolescents, which subsequently led to an increased obesity outcome. Lastly, we found that when we manipulated an increase in a father’s education level while manipulating a decrease in mother’s education level, this had a large effect on the potential adolescent obesity level. Although obesity was the chosen case, this paper acts more as a proof of concept in analyzing public health through GBN and What-If analysis. Therefore, it aims to guide health professionals into potentially expanding their ability to simulate certain outcomes based on predicted changes in certain factors concerning future public health issues.

For the sake of maintaining sustainable airport operations, biometric e-gates security systems started receiving significant attention from managers of airports around the world. Therefore, how to reduce flight passengers’ perceived resistance to the biometric e-gates security system became much more important than ever. In this sense, the purpose of this study is to analyze the factors which contribute to passenger’s resistance to adopt biometric e-gate technology within the airport security setting. Our focus lies on exploring the effects that perceived risks and benefits as well as user characteristics and propagation mechanisms had on causing such resistance. With survey data from 339 airport users, a support vector machine (SVM) model was implemented to provide a tool for classifying resistance causes correctly, and csQCA (crisp set Qualitative Comparative Analysis) was implemented in order to understand the complex underlying causes. The results showed that the presence of perceived risks and the absence of perceived benefits were the main contributing factors, with propagation mechanisms also showing a significant effect on weak and strong resistance. This study is distinct in that it has attempted to explore innovation adoption through the lens of resistance and in doing so has uncovered important complex causation conditions that need to be considered before service quality can be enhanced within airports. This study’s implications should therefore help steer airport managers in the right direction towards maintaining service quality while implementing sustainable new technologies within their current airport security ecosystem.

More and more these days, airport security is having to adapt to a greater number of passengers while keeping within finer margins of precision when it comes to clearing passengers for security clearance. Therefore, analyzing potential upgrades in the security process and adopting them in a way that does not impact passenger experience has become a key activity for airport managers. One viable technological solution that is not only effective but also efficient is biometric security. This technology allows for passengers to safely clear security based on their unique biometric features. Despite its promise, airports and passengers alike are slow to adopt its use. Additionally, there were few studies that revealed insights into passengers’ intentions toward repeat use of biometric security. Hence, in our novel attempts to uncover the underlying reasons, we implemented a study on passengers’ initial and repeat usage intention based on perceived benefits and risks of biometric security technology. Based on 327 survey responses, we employed the use of SEM to extract first- and second-order constructs before evaluating our hypotheses on a finally accepted model. To be aligned with the novel attempt of this research, the results showed that both the perceived benefits and risks have a significant impact on passenger’s initial and repeat use intention of biometric security. Therefore, for all practitioners engaged in sustainable airport management, strong consideration from this research should help in creating value for passengers while mitigating the risks of adopting biometric security within airport security settings.

It has been reported repeatedly that depression in middle-aged people may cause serious ramifications in public health. However, previous studies on this important research topic have focused on utilizing either traditional statistical methods (i.e., logistic regressions) or black-or-gray artificial intelligence (AI) methods (i.e., neural network, Support Vector Machine (SVM), ensemble). Previous studies lack suggesting more decision-maker-friendly methods, which need to produce clear interpretable results with information on cause and effect. For the sake of improving the quality of decisions of healthcare decision-makers, public health issues require identification of cause and effect information for any type of strategic healthcare initiative. In this sense, this paper proposes a novel approach to identify the main causes of depression in middle-aged people in Korea. The proposed method is the Sons and Spouses Bayesian network model, which is an extended version of conventional TAN (Tree-Augmented Naive Bayesian Network). The target dataset is a longitudinal dataset employed from the Korea National Health and Nutrition Examination Survey (KNHANES) database with a sample size of 8580. After developing the proposed Sons and Spouses Bayesian network model, we found thirteen main causes leading to depression. Then, genetic optimization was executed to reveal the most probable cause of depression in middle-aged people that would provide practical implications to field practitioners. Therefore, our proposed method can help healthcare decision-makers comprehend changes in depression status by employing what-if queries towards a target individual.

With information technology (IT) now showing advanced capabilities, many new services are being introduced to consumers of smartphones through the various available app stores. Moreover, the recent proliferation of such services related to information and communications technology has seen a momentous rise. Despite this trend, the ever-changing landscape of mobile IT services is creating a serious problem for businesses who are already experiencing fierce market conditions. Thus, in order to maintain the sustainability of an enterprise, it is necessary to make an adequate analysis of the success and failure factors of IT services in order to create a sustained competitive advantage. Considering 22 real IT service cases based on two platform models (merchant model and two-sided model) and through surveys submitted to 11 experienced entrepreneurs in IT services, we conducted a t-test analysis in order to first assess the success and failure factors of the IT service cases. Next, we performed a logistic regression analysis in order to find underlying relationships of our hypothesized model. The results showed that the participants identified 141 success and 101 failure factors in total with the t-tests, confirming that the distinction between success and failure of each IT service assessed was significant. Next, the results from the logistic regression showed which relationships were the best on the basis of the given platform model. Overall, this study was able to identify the main factors that have an influence on the success and failure of IT services based on two identified platform models. In doing so, this paper can help to inform future IT service entrepreneurs and researchers involved in developing new apps based on IT services by providing a guide to what factors need to be considered before going to market.