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001-es BibID:	BIBFORM118015
035-os BibID:	(WoS)001143951500001 (Scopus)85182442130
Első szerző:	Münnich Ákos (matematikus)
Cím:	Interview completed: the application of survival analysis to detect factors influencing response rates in online surveys / Ákos Münnich, Mátyás Kocsis, Mark C. Mainwaring, István Fónagy, Jenő Nagy
Dátum:	2024
ISSN:	2050-3318 2050-3326
Megjegyzések:	Marketing interviews are widely used to acquire information on the behaviour, satisfaction, and/or needs of customers. Although online surveys are broadly available, one of the major challenges is to collect high-quality data, which is fundamental for marketing. Since online surveys are mostly unsupervised, the possibility of providing false answers is high, and large numbers of participants do not finish interviews, yet our understanding of the reasons behind this pattern remains unclear. Here, we examined the possible factors influencing response rates and aimed to investigate the impact of technical and demographic information on the probability of interview completion rates of multiple surveys. We applied survival analysis and proportional hazards models to statistically evaluate the associations between the probability of survey completion and the technical and demographic information of the respondents. More complex surveys had lower completion probabilities, although survey completion was increased when respondents used desktop computers and not mobile devices, and when surveys were translated to their native language. Meanwhile, age and gender did not influence completion rates, but the pool of respondents invited to complete the survey did affect completion rates. These findings can be used to improve online surveys to achieve higher completion rates and collect more accurate data.
Tárgyszavak:	Társadalomtudományok Közgazdaságtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Online survey Completion probability Response rate Hazard ratio Optimisation Cox's regression Technical conditions Time-dependent processes
Megjelenés:	Journal of Marketing Analytics. - [Epub] (2024), p. 1-16. -
További szerzők:	Kocsis Mátyás (2001-) (alapokleveles matematikus) Mainwaring, Mark C. Fónagy István Nagy Jenő (1989-) (biológus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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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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