The aim of this study is to determine the effects of information overload on consumer confusion in User-Generated Content (UGC) environments and to find whether consumers' final buying decisions are affected by the confusion. In this respect, consumer data gathered online was analyzed by means of Structural Equation Modeling (SEM) on the basis of the theoretical framework. In addition to model tests, a scale was developed to measure 'information overload' depending on UGC. The results revealed that depending on the quality of information created in UGC environments, consumers' perceptions of information overload and consequently their confused reactions are related. The most important dimension of the information overload was found to be the information processing capacity. The level of involvement, the level of internet self-efficacy, and the perceived usefulness of UGC were also related to the degree of information overload. Statistically meaningful relationships were found between perceived information overload and confusion, and this confusion had a negative effect on consumers' buying decisions, thus resulting in a decrease in purchasing.
Anahtar Sözcükler:e-hizmet kalitesi, e-memnuniyet, e-perakendecilik, e-ticaret, Çevrimiçi alışveriş. ÖZBu çalışmada internet perakendeciliğinde algılanan hizmet kalitesinin (e-hizmet kalitesi) müşteri memnuniyeti (ememnuniyet) üzerindeki etkisini tespit etmek ve bu etkinin farklı sektörlere göre nasıl değiştiğini ortaya koymak amaçlanmaktadır. Bu kapsamda iki farklı sektör (hazır giyim ve kitap) seçilmiş ve sektördeki en büyük iki rakip markanın müşterileri araştırma kapsamına alınmıştır. İnternet üzerinden anket yöntemi ile 590 kişiden elde edilen verilere regresyon analizi uygulanmıştır.Araştırmada e-hizmet kalitesi algısının e-memnuniyet üzerinde anlamlı bir etkiye sahip olduğu bulunmuştur. e-hizmet kalite algısında önemli unsurlardan olan "gizlilik" ve "teknik" boyutlarının, e-memnuniyeti açıklamakta anlamlı bir etkisinin olmadığı, "etkinlik", "işlem gerçekleştirme", "müşteri hizmetleri", "tasarım", ''eğlence" boyutlarının etkilerinin ise anlamlı olduğu bulunmuştur. En önemli etkinin ise "işlem gerçekleştirme" boyutunda olduğu, en az etkinin ise "tasarım" boyutunda olduğu saptanmıştır. Ayrıca e-hizmet kalitesinin e-memnuniyet üzerindeki etkilerinde sektörlere göre kısmi farklılıkların olduğu tespit edilmiştir.The Role of Service Quality on Customer Satisfaction in Internet Retailing: A Comparative Study of Apparel and Book IndustriesKeywords:e-service quality, e-satisfaction, e-retailing, e-commerce, Online shopping. ABSTRACTThe aim of this research is to investigate the effects of perceived e-service quality on e-satisfaction and to demonstrate how these effects differ in several business industries. In this context, customers of two competing brands from different sectors (apparel and books) were chosen as research sample. Research data that gathered from 590 consumers via internet survey was analyzed with regression analysis.Research results show that perceived e-service quality has a significant effect on e-satisfaction. Although "privacy" and "technical" dimensions are important factors for e-service quality, they have no significant effect on e-satisfaction while other factors which are "effectiveness", "execution of transactions", "customer relations", "design", "entertainment" have significant effect on customer satisfaction. "Execution of transactions" was found to be the most important in explaining e-satisfaction while "design" factor explains the least. Also the hypothesized effects in the research model for perceived e-service quality on e-satisfaction differ partially between selected industries.
The aim of this study is to explain differences between consumers' personal information disclosure to companies (IDC behavior) and self-disclosure in social media (SDM behavior) based on personality traits, privacy concern and types of disclosed personal information. The population consisted of consumers who are 18 and over, have one or more social media accounts, and live in Turkey. The data were collected via the online survey method and analyzed by structural equation modeling. As a result of the analyses, it was found that consumers' IDC behavior and SDM behavior differ from each other depending on the disclosed personal information. It was also found that the personality traits have direct and indirect effects on both consumers' personal information disclosure and their self-disclosure in social media, and the privacy concern was the main reason for indirect effects. Accordingly, each of these disclosure behaviors was affected by different personality traits, and the dominant traits shape them. In conclusion, it has been determined that the personality traits and privacy concern have significant roles in the differences between IDC behavior and SDM behavior.
Background The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. Methods Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann–Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. Results No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. Conclusion By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.
Bu çalışmanın amacı, tüketicilerin ikinci el alışveriş motivasyonlarının ve tüketici sinizminin çevrimiçi ikinci el alışveriş platformlarından ürün satın alma niyetleri üzerindeki etkisinin ve çevresel endişenin bu etki üzerindeki düzenleyici rolünün belirlenmesidir. Çevrimiçi ikinci el alışveriş platformlarından en az bir defa ürün satın almış 495 kişiden yüz yüze anket yöntemiyle elde edilen verilere hiyerarşik regresyon ve moderatör analizleri uygulanmıştır. Araştırma bulguları, tüketicilerin ikinci el satın alma motivasyonlarından sistem karşıtlığı, ekonomik motivasyon ve nostaljik haz ile tüketici sinizminin satın alma niyeti üzerinde anlamlı etkisinin olduğunu ortaya koymaktadır. Ayrıca çevresel endişe seviyesinin düzenleyici rolünün anlamlı olduğu belirlenmiştir. Buna göre sistem karşıtlığı, ekonomik motivasyon ve tüketici sinizminin satın alma niyeti üzerindeki etkilerini tüketicilerin çevresel endişe seviyesi farklılaştırmaktadır.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.