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001-es BibID:	BIBFORM108479
035-os BibID:	(Cikkazonosító)13293 (Scopus)85135230203 (WOS)000836651200064
Első szerző:	Kutasi Kristóf (közgazdász)
Cím:	Understanding hesitancy with revealed preferences across COVID-19 vaccine types / Kristóf Kutasi, Júlia Koltai, Ágnes Szabó?Morvai, Gergely Röst, Márton Karsai, Péter Biró, Balázs Lengyel
Dátum:	2022
ISSN:	2045-2322
Megjegyzések:	Many countries have secured larger quantities of COVID-19 vaccines than their population is willing to take. The abundance and the large variety of vaccines created not only an unprecedented intensity of vaccine related public discourse, but also a historical moment to understand vaccine hesitancy better. Yet, the heterogeneity of hesitancy by vaccine types has been neglected in the existing literature so far. We address this problem by analysing the acceptance and the assessment of fve vaccine types. We use information collected with a nationally representative survey at the end of the third wave of the COVID-19 pandemic in Hungary. During the vaccination campaign, individuals could reject the assigned vaccine to wait for a more preferred alternative that enables us to quantify revealed preferences across vaccine types. We fnd that hesitancy is heterogenous by vaccine types and is driven by individuals` trusted source of information. Believers of conspiracy theories are more likely to evaluate the mRNA vaccines (Pfzer and Moderna) unacceptable. Those who follow the advice of politicians are more likely to evaluate vector-based (AstraZeneca and Sputnik) or whole-virus vaccines (Sinopharm) acceptable. We argue that the greater selection of available vaccine types and the free choice of the individual are desirable conditions to increase the vaccination rate in societies.
Tárgyszavak:	Társadalomtudományok Gazdálkodás- és szervezéstudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Scientific Reports. - 12 : 1 (2022), p. 1-10. -
További szerzők:	Koltai Júlia Szabó-Morvai Ágnes (1979-) (közgazdász) Röst Gergely Karsai Márton Bíró Péter Lengyel Balázs
Pályázati támogatás:	LP2018-2/2018 MTA OTKAFK131422 OTKA
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM098775
035-os BibID:	(cikkazonosító)16848 (WOS)000686768700106 (Scopus)85113195353
Első szerző:	Okunlola Oluyemi Adewole
Cím:	Spatial regression and geostatistics discourse with empirical application to precipitation data in Nigeria / Oluyemi A. Okunlola, Mohannad Alobid, Olusanya E. Olubusoye, Kayode Ayinde, Adewale F. Lukman, István Szűcs
Dátum:	2021
ISSN:	2045-2322
Megjegyzések:	In this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis. The linear regression model has been found inappropriate in this type of study. This motivates us to redefine its error structure to incorporate the spatial components inherent in the data into the model. Therefore, four spatial models emanated from the re-definition of the error structure. We fitted the spatial and the non-spatial linear model to the precipitation data and compared their results. All the spatial models outperformed the non-spatial model. The Spatial Autoregressive with additional autoregressive error structure (SARAR) model is the most adequate among the spatial models. Furthermore, we identified the hot and cold spot locations of precipitation and their spatial distribution in the study area.
Tárgyszavak:	Társadalomtudományok Közgazdaságtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Mathematics and computing Environmental social sciences Climate sciences
Megjelenés:	Scientific Reports. - 11 : 1 (2021), p. 1-14. -
További szerzők:	Alobid, Mohannad (1988-) (economist) Olubusoye, Olusanya E. Ayinde, Kayode Lukman, Adewale F. Szűcs István (1968-) (agrárközgazdász)
Pályázati támogatás:	EFOP-3.6.3-VEKOP-16-2017-00007 EFOP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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