Earticle

This study investigates how product quality and delivery quality influence customer satisfaction and repurchase intention in the context of Chinese online shopping platforms, focusing on Korean consumers. Amid the rapid expansion of cross-border e-commerce, understanding how consumers perceive product and delivery quality has become increasingly important. The study reveals that delivery quality significantly enhances customer satisfaction, while product quality plays a pivotal role in driving repurchase intention. Customer satisfaction, in turn, strongly influences repurchase behavior. Furthermore, while consumers with higher perceived risk or price sensitivity tend to respond more strongly to product quality, the differences across consumer segments were not statistically significant. These findings underscore the distinct roles of product and delivery quality in shaping consumer responses and offer practical implications for e-commerce businesses seeking to strengthen customer loyalty. Theoretically, the study contributes to the growing body of knowledge on consumer behavior in cross-border e-commerce by clarifying the relative impacts of product and delivery quality. Practically, it suggests that businesses should prioritize delivery excellence to boost satisfaction and tailor product strategies to meet the expectations of more cautious or price-conscious consumers.

Machine learning algorithms are innovatively transforming the field of business, attracting deepened interest from researchers. In this project, we review marketing research and develop machine learning methods useful in building marketing strategies. We provide an overview of machine learning methods, and compare them with statistical methods that marketing researchers have traditionally used. Machine learning methods can be used to process large-scale data, providing flexible analysis models and yielding solid predictive performance. We present an integrative conceptual framework to extract insights from large-scale tracking, and network data to represent descriptive, causal, and inferential analyses. Customer purchase journeys with decision support capabilities can connect the machine learning methods to marketing theories and human insights. The specific applications of machine learning methods in many marketing segments and their contribution for marketing sectors have been validated. The proposed methods can be applied to analyze dynamic mechanism of marketing data with diverse customer features.

Coffee franchise companies have recently strengthened their value collaboration to practice ESG management and are working to increase their favorability for the brand among young consumers who highly appreciate value consumption. This study examined the perception of Generation Z towards the coffee franchise brands who is strongly emerging as a loyal consumer group, and found out whether these positive evaluation influence price fairness and intention to pay additional price. To test the hypothesis for the study, data from 152 consumers who have used coffee franchises of Generation Z were analyzed using a structural equation model. The results showed that environmental and governance activities among ESG activities had a positive effect on the brand image of coffee franchises. On the other hand, it was found that social activities had no effect. In addition, it was found that the brand image had a positive effect on price fairness and additional price payment intention. Furthermore, price fairness was also found to have a positive effect on the intention to pay additional prices. It means that Generation Z pursuing value consumption highly appreciates fairness in price by forming a good brand image through environmental protection activities, win-win cooperation with franchisees and ethical management of franchisors, which leads to value consumption.

Animal detection and classification are crucial for effective wildlife management (WM) and reducing risks associated with animals related road accidents and attacks. Previous attempts trained the models using imbalanced data with fewer representative features and baseline models without improvement. This paper presents a new dataset of five animal classes captured in various poses, lighting conditions, and intraclass variations. The standard coupled detection head of the YoloV4 algorithm faces limitations when performing simultaneous classification and localization due to shared parameters and inputs. To address this issue, we propose a decoupled detection head (DDH) that handles these tasks separately, improving performance. We conducted extensive experiments using the proposed dataset. We found that the optimal backbone features marginally improve the performance of the modified network compared to state-of-the-art (SOTA) works in the subject domain. Our work contributes by addressing the limitations of the standard YoloV4 algorithm and proposing a new dataset for researchers to use in future studies.

다양한 정보기술과 스마트폰이 보급되어 온라인과 오프라인의 경계가 없어지고 사용자들이 오프라인과 온라인에 대해 구별없이 쇼핑을 하게 되면서, 두 채널의 장점을 채택한 O2O서비스가 나타났다. 본 연구는 기존의 연구를 토대 로 O2O서비스에 대한 소비자의 기술준비도와 O2O서비스 특성요인들을 도출하여, 이런 기술준비도 요인(낙관성, 혁 신성)과 O2O서비스 특성(즐거움)이 O2O서비스의 고객만족에 어떤 영향을 미치는지, 고객만족이 고객충성도에 어떤 영향을 미치는지를 살펴보고자 한다. 분석결과, O2O서비스에 대한 소비자의 기술준비도 중 낙관성과 혁신성은 모두 고객만족에 긍정적인 영향을 미치 는 것으로 나타났다. 반면, O2O서비스의 특성인 즐거움은 고객만족에 긍정적인 영향을 미치지 않은 것으로 나타났다. 이는 O2O서비스에 대한 소비자의 기술준비도 중 낙관성과 혁신성이 높을수록 고객만족이 높아짐을 알 수 있다. 채택된 요인의 영향 정도를 살펴보면 낙관성, 혁신성 순으로 고객만족에 영향을 미치는 것으로 나타났다. 또한 O2O 서비스에서 고객 만족도가 고객충성도에 긍정적인 영향을 미치는 것으로 나타났다. 따라서 새로운 기술에 대한 난관 성과 혁신성인 높은 소비자집단을 중심으로 마케팅전략의 수립이 이루어져야 고객만족과 고객충성도가 높아짐을 확 인할 수 있다.

The purpose of this research is to discover the structural factors of technology readiness indices of O2O service users and O2O service’s characteristics from previous researches, and then explore whether technology readiness indices(optimism, innovativeness) and O2O service’s characteristics(funny) have an impact on consumer satisfaction, and consumer satisfaction on consumer loyalty focusing on the Chinese market. This research has a significant impact on successful maintenance and management of an O2O service enterprise. Accordingly, a variety of practical and viable operational strategies are proposed. Data were collected from Chinese consumers who purchased products through O2O service. The research has gone through a basic statistical analysis primarily. Hypothesises were set and tested using statistical analysis of the questionnaire data. The reliability was tested using Cronbach’s alpha coefficient. In addition, the validity was analysed using Factor Analysis. What is more, the 5 variables were analysed using regression analysis in our research, and our research hypotheses were verified. The analysis results are summarized as follows. This research finds that among the technology readiness indices’ optimism, innovativeness have positive effect on consumer satisfaction. But the O2O service’s characteristics(funny) have not significant effect on consumer satisfaction. As the results demonstrated, the consumer satisfaction increases along with technology readiness indices’ optimism, innovativeness. What is more, the findings also illustrate that the degree of impact on consumer satisfaction decreases one after another in the order of technology readiness indices’ optimism, innovativeness. And consumer satisfaction have positive impact on consumer loyalty.