Different multidimensional exploratory techniques in classifying variables into qualitative criteria of spare parts selection for passenger cars
Artykuł w czasopiśmie
MNiSW
10
Lista B
| Status: | |
| Autorzy: | Longwic Rafał, Lotko Aleksander, Lotko Małgorzata |
| Rok wydania: | 2016 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 31 |
| Wolumen/Tom: | 10 |
| Web of Science® Times Cited: | 1 |
| Bazy: | Web of Science |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 1 września 2016 |
| Abstrakty: | angielski |
| The aim of the paper was (1) to compare cluster analysis and factor analysis applied in the classification of variables into quality criteria of spare parts selection for passenger cars and (2) to create a metamodel taking into account the similarities and differences between the results of the carried out analyses. To collect empirical data, a survey questionnaire was used. It was built on the basis of literature overview concerning quality management. Data was processed with the use of multi-dimensional exploratory techniques: cluster analysis and factor analysis. A theoretical implication is a proposed metamodel, which joins the results of both cluster and factor analysis. A practical implication is a possibility of taking an advantage on the obtained results when planning, designing, manufacturing, distributing, selecting and selling spare parts for passenger cars. Paper contribution is the use of exploratory data analysis techniques in the research area and the proposal of the metamodel formalizing quality criteria of spare parts selection for passenger cars. The research showed, that classifications of variables obtained with the use of two multi-dimensional exploitation techniques are different although there are distinct common elements. When using cluster analysis, the following clusters were identified: marketing, economy and utility one (arranged in accordance with the order of linking). While when using factor analysis, the following factors were discovered: utility, marketing, availability and cost factor (arranged in descending way in accordance with the explained variance) |
