Heterogeneity Measures in Customer Satisfaction Analysis
Abstract In this paper we deal with the problem of identifying a valid way to characterize heterogeneity in the analysis of customer satisfaction observing the phenomenon through a new perspective. In the literature, the variability of a Customer Satisfaction index is measured by the standard deviat...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Valentini, Pasquale [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2011 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of classification - New York, NY : Springer, 1984, 28(2011), 1 vom: 16. Feb., Seite 38-52 |
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| Übergeordnetes Werk: |
volume:28 ; year:2011 ; number:1 ; day:16 ; month:02 ; pages:38-52 |
| Links: |
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| DOI / URN: |
10.1007/s00357-011-9075-y |
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SPR004452127 |
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| 520 | |a Abstract In this paper we deal with the problem of identifying a valid way to characterize heterogeneity in the analysis of customer satisfaction observing the phenomenon through a new perspective. In the literature, the variability of a Customer Satisfaction index is measured by the standard deviation or the coefficient of variation. In this way, heterogeneity among customers may be masked. To overcome this drawback, we provide a new approach to the construction of a multi-dimensional measure of heterogeneity of the Customer Satisfaction index not depending on the choice of a particular heterogeneity index. The approach is based on heterogeneity profiles which lead to a more detailed description of heterogeneity than alternative measures. Moreover, a latent class model is used for classifying individuals into distinct groups based on responses to a set of items. Once groups are formed, Customer Satisfaction researchers can make conclusions about the level of satisfaction and the characteristics of groups in terms of heterogeneity. | ||
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| 700 | 1 | |a Antonio Gattone, Stefano |4 aut | |
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10.1007/s00357-011-9075-y doi (DE-627)SPR004452127 (SPR)s00357-011-9075-y-e DE-627 ger DE-627 rakwb eng Valentini, Pasquale verfasserin aut Heterogeneity Measures in Customer Satisfaction Analysis 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2011 Abstract In this paper we deal with the problem of identifying a valid way to characterize heterogeneity in the analysis of customer satisfaction observing the phenomenon through a new perspective. In the literature, the variability of a Customer Satisfaction index is measured by the standard deviation or the coefficient of variation. In this way, heterogeneity among customers may be masked. To overcome this drawback, we provide a new approach to the construction of a multi-dimensional measure of heterogeneity of the Customer Satisfaction index not depending on the choice of a particular heterogeneity index. The approach is based on heterogeneity profiles which lead to a more detailed description of heterogeneity than alternative measures. Moreover, a latent class model is used for classifying individuals into distinct groups based on responses to a set of items. Once groups are formed, Customer Satisfaction researchers can make conclusions about the level of satisfaction and the characteristics of groups in terms of heterogeneity. Response pattern (dpeaa)DE-He213 Heterogeneity (dpeaa)DE-He213 Heterogeneity profile (dpeaa)DE-He213 Latent class model (dpeaa)DE-He213 Di Battista, Tonio aut Antonio Gattone, Stefano aut Enthalten in Journal of classification New York, NY : Springer, 1984 28(2011), 1 vom: 16. Feb., Seite 38-52 (DE-627)253769558 (DE-600)1459289-7 1432-1343 nnns volume:28 year:2011 number:1 day:16 month:02 pages:38-52 https://dx.doi.org/10.1007/s00357-011-9075-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2011 1 16 02 38-52 |
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10.1007/s00357-011-9075-y doi (DE-627)SPR004452127 (SPR)s00357-011-9075-y-e DE-627 ger DE-627 rakwb eng Valentini, Pasquale verfasserin aut Heterogeneity Measures in Customer Satisfaction Analysis 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2011 Abstract In this paper we deal with the problem of identifying a valid way to characterize heterogeneity in the analysis of customer satisfaction observing the phenomenon through a new perspective. In the literature, the variability of a Customer Satisfaction index is measured by the standard deviation or the coefficient of variation. In this way, heterogeneity among customers may be masked. To overcome this drawback, we provide a new approach to the construction of a multi-dimensional measure of heterogeneity of the Customer Satisfaction index not depending on the choice of a particular heterogeneity index. The approach is based on heterogeneity profiles which lead to a more detailed description of heterogeneity than alternative measures. Moreover, a latent class model is used for classifying individuals into distinct groups based on responses to a set of items. Once groups are formed, Customer Satisfaction researchers can make conclusions about the level of satisfaction and the characteristics of groups in terms of heterogeneity. Response pattern (dpeaa)DE-He213 Heterogeneity (dpeaa)DE-He213 Heterogeneity profile (dpeaa)DE-He213 Latent class model (dpeaa)DE-He213 Di Battista, Tonio aut Antonio Gattone, Stefano aut Enthalten in Journal of classification New York, NY : Springer, 1984 28(2011), 1 vom: 16. Feb., Seite 38-52 (DE-627)253769558 (DE-600)1459289-7 1432-1343 nnns volume:28 year:2011 number:1 day:16 month:02 pages:38-52 https://dx.doi.org/10.1007/s00357-011-9075-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2011 1 16 02 38-52 |
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Valentini, Pasquale |
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Valentini, Pasquale misc Response pattern misc Heterogeneity misc Heterogeneity profile misc Latent class model Heterogeneity Measures in Customer Satisfaction Analysis |
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Heterogeneity Measures in Customer Satisfaction Analysis Response pattern (dpeaa)DE-He213 Heterogeneity (dpeaa)DE-He213 Heterogeneity profile (dpeaa)DE-He213 Latent class model (dpeaa)DE-He213 |
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Heterogeneity Measures in Customer Satisfaction Analysis |
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Valentini, Pasquale Di Battista, Tonio Antonio Gattone, Stefano |
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heterogeneity measures in customer satisfaction analysis |
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Heterogeneity Measures in Customer Satisfaction Analysis |
| abstract |
Abstract In this paper we deal with the problem of identifying a valid way to characterize heterogeneity in the analysis of customer satisfaction observing the phenomenon through a new perspective. In the literature, the variability of a Customer Satisfaction index is measured by the standard deviation or the coefficient of variation. In this way, heterogeneity among customers may be masked. To overcome this drawback, we provide a new approach to the construction of a multi-dimensional measure of heterogeneity of the Customer Satisfaction index not depending on the choice of a particular heterogeneity index. The approach is based on heterogeneity profiles which lead to a more detailed description of heterogeneity than alternative measures. Moreover, a latent class model is used for classifying individuals into distinct groups based on responses to a set of items. Once groups are formed, Customer Satisfaction researchers can make conclusions about the level of satisfaction and the characteristics of groups in terms of heterogeneity. © Springer Science+Business Media, LLC 2011 |
| abstractGer |
Abstract In this paper we deal with the problem of identifying a valid way to characterize heterogeneity in the analysis of customer satisfaction observing the phenomenon through a new perspective. In the literature, the variability of a Customer Satisfaction index is measured by the standard deviation or the coefficient of variation. In this way, heterogeneity among customers may be masked. To overcome this drawback, we provide a new approach to the construction of a multi-dimensional measure of heterogeneity of the Customer Satisfaction index not depending on the choice of a particular heterogeneity index. The approach is based on heterogeneity profiles which lead to a more detailed description of heterogeneity than alternative measures. Moreover, a latent class model is used for classifying individuals into distinct groups based on responses to a set of items. Once groups are formed, Customer Satisfaction researchers can make conclusions about the level of satisfaction and the characteristics of groups in terms of heterogeneity. © Springer Science+Business Media, LLC 2011 |
| abstract_unstemmed |
Abstract In this paper we deal with the problem of identifying a valid way to characterize heterogeneity in the analysis of customer satisfaction observing the phenomenon through a new perspective. In the literature, the variability of a Customer Satisfaction index is measured by the standard deviation or the coefficient of variation. In this way, heterogeneity among customers may be masked. To overcome this drawback, we provide a new approach to the construction of a multi-dimensional measure of heterogeneity of the Customer Satisfaction index not depending on the choice of a particular heterogeneity index. The approach is based on heterogeneity profiles which lead to a more detailed description of heterogeneity than alternative measures. Moreover, a latent class model is used for classifying individuals into distinct groups based on responses to a set of items. Once groups are formed, Customer Satisfaction researchers can make conclusions about the level of satisfaction and the characteristics of groups in terms of heterogeneity. © Springer Science+Business Media, LLC 2011 |
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Heterogeneity Measures in Customer Satisfaction Analysis |
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https://dx.doi.org/10.1007/s00357-011-9075-y |
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Di Battista, Tonio Antonio Gattone, Stefano |
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| doi_str |
10.1007/s00357-011-9075-y |
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2024-07-04T01:09:22.647Z |
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| score |
7.3985367 |