Purpose The purpose of this paper is to explore the relationship among risk aversion, brand trust, brand affect, attitudinal loyalty and behavioral loyalty for low involvement day-to-day use of personal care products. Design/methodology/approach To achieve the above-stated objective, a theoretical model was tested using structural equation modeling. Before undertaking the analysis, preliminary analysis techniques such as the common method bias social desirability bias reliability and validity analysis were also assessed. Findings The results indicate that, for low involvement products, risk adverse consumers do not purchase a brand based only on trust. Risk aversion is also positively associated with attitudinal loyalty. When it comes to the relationship between brand trust and brand affect, it has been concluded that brand trust has had an important impact on brand affect. In this study, it has been found that attitudinal loyalty has a positive and strong impact on behavioral loyalty. This paper explains that due to the lack of trust, certain risk adverse customers are sticking with a particular brand. Originality/value Most of the brand loyalty research has been performed on high involvement products, whereas very limited research is available on low involvement day-to-day use products (i.e. personal care products), in particular where the consumption period of the product is less than a month. This kind of research is very rare, and this study has been done to fill this gap using rigorous data analysis.
Internet of Things is the integration of a variety of technologies. The Internet of Things incorporates transparently and impeccably large number of assorted end systems, providing open access to selected data for digital services. Internet of things is a promising research in commerce, industry, and education applications. The abundance of sensors and actuators motivates sensing and actuate devices in communication scenarios thus enabling sharing of information in Internet of Things. Advances in sensor data collection technology and Radio Frequency Identification technology has led large number of smart devices connected to the Internet, continuously transmitting data over time.In the context of security, due to different communication overloads and standards conventional security services are not applicable on Internet of Things as a result of which the technological loopholes leads to the generation of malicious data, devices are compromised and so on. Hence a flexible mechanism can deal with the security threats in the dynamic environment of Internet of Things and continuous researches and new ideas needs to be regulated periodically for various upcoming challenges. This paper basically tries to cover up the security issues and challenges of Internet of Things along with a brief introduction on Internet of Things, its elements and components such as Radio Frequency Identification, Wireless Sensor Network and Near Field Communication.
Coronavirus Disease 2019 (COVID-19) is an evolving communicable disease caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which has led to a global pandemic since December 2019. The virus has its origin from bat and is suspected to have transmitted to humans through zoonotic links. The disease shows dynamic symptoms, nature and reaction to the human body thereby challenging the world of medicine. Moreover, it has tremendous resemblance to viral pneumonia or Community Acquired Pneumonia (CAP). Reverse Transcription Polymerase Chain Reaction (RT-PCR) is performed for detection of COVID-19. Nevertheless, RT-PCR is not completely reliable and sometimes unavailable. Therefore, scientists and researchers have suggested analysis and examination of Computing Tomography (CT) scans and Chest X-Ray (CXR) images to identify the features of COVID-19 in patients having clinical manifestation of the disease, using expert systems deploying learning algorithms such as Machine Learning (ML) and Deep Learning (DL). The paper identifies and reviews various chest image features using the aforementioned imaging modalities for reliable and faster detection of COVID-19 than laboratory processes. The paper also reviews and compares the different aspects of ML and DL using chest images, for detection of COVID-19.
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.