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001-es BibID:	BIBFORM099656
Első szerző:	Münnich Ákos (matematikus)
Cím:	A real-time network-based approach for analysing best-worst data types / Münnich Ákos, Vargáné Karsai Emese, Nagy Jenő
Dátum:	2022
ISSN:	2662-9399
Megjegyzések:	Best-worst scaling is a widespread approach in market research used for collecting data on the needs and preferences of people. However, the current preparation of its design and the analysis of the data depends on complex statistical methods. One of the most commonly used models for estimating individual preference probabilities is the hierarchical Bayes model, which can only be applied after the data collection phase. This type of calculation needs more infrastructural background and a large sample to provide accurate estimations. Here, we introduce a new application that enables fast calculations and individual-level real-time estimations, which also has a great potential to ask additional questions depending on the respondent's answers during live interviews. Our network-based approach (integrating the PageRank algorithm) works well for online surveys, and it supports our dynamic and adaptive, real-time evaluation (DART) of best-worst data types, and results in more relevant decision making in marketing.
Tárgyszavak:	Bölcsészettudományok Pszichológiai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk adaptive decision making entropy maximum difference pagerank segmentation
Megjelenés:	SN Business & Economics. - 2 : 1 (2022), p. 1-24. -
További szerzők:	Vargáné Karsai Emese Nagy Jenő (1989-) (biológus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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