Advertising copies have been considered as a fundamental strategy for firms to continue sustainable marketing strategies. In order to provide the advancement of previous research and practical implication to marketers in the field for sustainable marketing strategy, this research tried to reveal the role of emotional and rational appeals as well as hidden heterogeneity of consumers in the appeal–value–trust–satisfaction–WOM framework. By applying the PLS-SEM and PLS-POS approach to 230 valid questionnaire samples, we could discover the role of appeals in the framework as well as three types of unobserved heterogeneity among the respondents. Both emotional and rational appeals had significant influences on the value–satisfaction–trust–WOM context. In addition, for hidden consumer traits in advertising copies, we revealed three types of consumer groups: Type 1, the consumer group with a rational orientation (n = 68); Type 2, the group with an emotional orientation (n = 74) and Type 3, the group with a utilitarian orientation. This research provided contributions by offering some insight into ways to establish sustainable marketing strategies in advertisements and to address unobserved heterogeneity consumers in advertising copy appeals.

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.

This study investigates the factors influencing users’ intention to use generative AI by employing a Bayesian network-based probabilistic structural equation model approach. Recognizing the limitations of traditional models like the technology acceptance model and the unified theory of acceptance and use of technology, this research incorporates novel constructs such as perceived anthropomorphism and animacy to capture the unique human-like qualities of generative AI. Data were collected from 803 participants with prior experience of using generative AI applications. The analysis reveals that social influence (standardized total effect = 0.550) is the most significant predictor of use intention, followed by effort expectancy (0.480) and perceived usefulness (0.454). Perceived anthropomorphism (0.149) and animacy (0.145) also influence use intention, but with a lower relative impact. By utilizing a probabilistic structural equation model, this study overcomes the linear limitations of traditional acceptance models, allowing for the exploration of nonlinear relationships and conditional dependencies. These findings provide actionable insights for improving generative AI design, user engagement, and adoption strategies.

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.

Emojis are prevalent in modern social media advertising. Despite this fact, little research can be found on its effects on consumer purchase intentions. This study seeks to examine purchase intentions in the context of Sponsored Ads on Facebook News Feeds, their perceived intrusiveness, and how the added factor of emoji presence can further affect consumer perception in order to suggest a pathway for establishing sustainable marketing strategies. We investigated the effect of emojis on consumers and then the extent to which ad personalization can attenuate intrusiveness to the point of influencing purchase intentions. In the empirical investigations (an online study) conducted, the study revealed several interesting findings. First, the emoji presence in Sponsored Ads on the News Feed did not prompt the users’ perceived intrusiveness. Second, the emoji use led to decreased purchase intentions. Third, the perceived intrusiveness did not mediate the relationship between emoji presence (vs. absence) and purchase intentions. Lastly, the emoji presence decreased perceived intrusiveness and also increased purchase intentions when consumers perceived ads to be less personalized. The findings of this research provide both theoretical and managerial implications of the effects of emojis, and the reasons as to why their usage affects the desired ad goals when used in Sponsored Ads on Facebook from the perspective of sustainable marketing.