Crowd-sourced idea filtering with Bag of Lemons : the impact of the token budget size

Gespeichert in:

Autor*in:	Lukumon, Gafari [verfasserIn] Klein, Mark - 1960- [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Bag of Lemon Bag of Stars Collective intelligence Filtering Idea filtering Likert Token budget size

Übergeordnetes Werk:	Enthalten in: Decision - [Erscheinungsort nicht ermittelbar] : Springer India, 2004, 50(2023), 2 vom: Juni, Seite 205-219
Übergeordnetes Werk:	volume:50 ; year:2023 ; number:2 ; month:06 ; pages:205-219

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1007/s40622-023-00349-w

Katalog-ID:	1865957534

Internformat


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982			\|2 26 \|1 00 \|x DE-206 \|b Identifying the best ideas from the vast volumes generated by open innovation engagements is costly and often time-consuming. One approach is to engage crowds in filtering the ideas, not just generating them. Klein and Garcia, 2015 proposed a “BOL” approach that is better (in terms of accuracy and speed) at idea filtering than other filtering methods such as a conventional Likert approach. The idea behind this approach (BOL) is that it asks the crowd to distribute a fixed budget of tokens that eliminate bad ideas rather than select good ones. In this paper, we explain why BOL works better than other filtering methods using empirical experiments (with n = 850 subjects). Also, we present the effect of the token budget size on idea-filtering engagement and found, among others, that the accuracy of a filter depends on the token budget size.

Indexfelder

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allfields	10.1007/s40622-023-00349-w doi (DE-627)1865957534 (DE-599)KXP1865957534 DE-627 ger DE-627 rda eng Lukumon, Gafari verfasserin aut Crowd-sourced idea filtering with Bag of Lemons the impact of the token budget size Gafari Lukumon, Mark Klein 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bag of Lemon (dpeaa)DE-206 Bag of Stars (dpeaa)DE-206 Collective intelligence (dpeaa)DE-206 Filtering (dpeaa)DE-206 Idea filtering (dpeaa)DE-206 Likert (dpeaa)DE-206 Token budget size (dpeaa)DE-206 Klein, Mark 1960- verfasserin (DE-588)143509535 (DE-627)704512629 (DE-576)337445133 aut Enthalten in Decision [Erscheinungsort nicht ermittelbar] : Springer India, 2004 50(2023), 2 vom: Juni, Seite 205-219 Online-Ressource (DE-627)491442440 (DE-600)2194154-3 (DE-576)273957856 2197-1722 nnns volume:50 year:2023 number:2 month:06 pages:205-219 https://link.springer.com/content/pdf/10.1007/s40622-023-00349-w.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s40622-023-00349-w Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 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_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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 50 2023 2 6 205-219 26 01 0206 4391074168 x1z 17-10-23 26 00 DE-206 Identifying the best ideas from the vast volumes generated by open innovation engagements is costly and often time-consuming. One approach is to engage crowds in filtering the ideas, not just generating them. Klein and Garcia, 2015 proposed a “BOL” approach that is better (in terms of accuracy and speed) at idea filtering than other filtering methods such as a conventional Likert approach. The idea behind this approach (BOL) is that it asks the crowd to distribute a fixed budget of tokens that eliminate bad ideas rather than select good ones. In this paper, we explain why BOL works better than other filtering methods using empirical experiments (with n = 850 subjects). Also, we present the effect of the token budget size on idea-filtering engagement and found, among others, that the accuracy of a filter depends on the token budget size.
spelling	10.1007/s40622-023-00349-w doi (DE-627)1865957534 (DE-599)KXP1865957534 DE-627 ger DE-627 rda eng Lukumon, Gafari verfasserin aut Crowd-sourced idea filtering with Bag of Lemons the impact of the token budget size Gafari Lukumon, Mark Klein 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bag of Lemon (dpeaa)DE-206 Bag of Stars (dpeaa)DE-206 Collective intelligence (dpeaa)DE-206 Filtering (dpeaa)DE-206 Idea filtering (dpeaa)DE-206 Likert (dpeaa)DE-206 Token budget size (dpeaa)DE-206 Klein, Mark 1960- verfasserin (DE-588)143509535 (DE-627)704512629 (DE-576)337445133 aut Enthalten in Decision [Erscheinungsort nicht ermittelbar] : Springer India, 2004 50(2023), 2 vom: Juni, Seite 205-219 Online-Ressource (DE-627)491442440 (DE-600)2194154-3 (DE-576)273957856 2197-1722 nnns volume:50 year:2023 number:2 month:06 pages:205-219 https://link.springer.com/content/pdf/10.1007/s40622-023-00349-w.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s40622-023-00349-w Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 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_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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 50 2023 2 6 205-219 26 01 0206 4391074168 x1z 17-10-23 26 00 DE-206 Identifying the best ideas from the vast volumes generated by open innovation engagements is costly and often time-consuming. One approach is to engage crowds in filtering the ideas, not just generating them. Klein and Garcia, 2015 proposed a “BOL” approach that is better (in terms of accuracy and speed) at idea filtering than other filtering methods such as a conventional Likert approach. The idea behind this approach (BOL) is that it asks the crowd to distribute a fixed budget of tokens that eliminate bad ideas rather than select good ones. In this paper, we explain why BOL works better than other filtering methods using empirical experiments (with n = 850 subjects). Also, we present the effect of the token budget size on idea-filtering engagement and found, among others, that the accuracy of a filter depends on the token budget size.
allfields_unstemmed	10.1007/s40622-023-00349-w doi (DE-627)1865957534 (DE-599)KXP1865957534 DE-627 ger DE-627 rda eng Lukumon, Gafari verfasserin aut Crowd-sourced idea filtering with Bag of Lemons the impact of the token budget size Gafari Lukumon, Mark Klein 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bag of Lemon (dpeaa)DE-206 Bag of Stars (dpeaa)DE-206 Collective intelligence (dpeaa)DE-206 Filtering (dpeaa)DE-206 Idea filtering (dpeaa)DE-206 Likert (dpeaa)DE-206 Token budget size (dpeaa)DE-206 Klein, Mark 1960- verfasserin (DE-588)143509535 (DE-627)704512629 (DE-576)337445133 aut Enthalten in Decision [Erscheinungsort nicht ermittelbar] : Springer India, 2004 50(2023), 2 vom: Juni, Seite 205-219 Online-Ressource (DE-627)491442440 (DE-600)2194154-3 (DE-576)273957856 2197-1722 nnns volume:50 year:2023 number:2 month:06 pages:205-219 https://link.springer.com/content/pdf/10.1007/s40622-023-00349-w.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s40622-023-00349-w Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 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_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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 50 2023 2 6 205-219 26 01 0206 4391074168 x1z 17-10-23 26 00 DE-206 Identifying the best ideas from the vast volumes generated by open innovation engagements is costly and often time-consuming. One approach is to engage crowds in filtering the ideas, not just generating them. Klein and Garcia, 2015 proposed a “BOL” approach that is better (in terms of accuracy and speed) at idea filtering than other filtering methods such as a conventional Likert approach. The idea behind this approach (BOL) is that it asks the crowd to distribute a fixed budget of tokens that eliminate bad ideas rather than select good ones. In this paper, we explain why BOL works better than other filtering methods using empirical experiments (with n = 850 subjects). Also, we present the effect of the token budget size on idea-filtering engagement and found, among others, that the accuracy of a filter depends on the token budget size.
allfieldsGer	10.1007/s40622-023-00349-w doi (DE-627)1865957534 (DE-599)KXP1865957534 DE-627 ger DE-627 rda eng Lukumon, Gafari verfasserin aut Crowd-sourced idea filtering with Bag of Lemons the impact of the token budget size Gafari Lukumon, Mark Klein 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bag of Lemon (dpeaa)DE-206 Bag of Stars (dpeaa)DE-206 Collective intelligence (dpeaa)DE-206 Filtering (dpeaa)DE-206 Idea filtering (dpeaa)DE-206 Likert (dpeaa)DE-206 Token budget size (dpeaa)DE-206 Klein, Mark 1960- verfasserin (DE-588)143509535 (DE-627)704512629 (DE-576)337445133 aut Enthalten in Decision [Erscheinungsort nicht ermittelbar] : Springer India, 2004 50(2023), 2 vom: Juni, Seite 205-219 Online-Ressource (DE-627)491442440 (DE-600)2194154-3 (DE-576)273957856 2197-1722 nnns volume:50 year:2023 number:2 month:06 pages:205-219 https://link.springer.com/content/pdf/10.1007/s40622-023-00349-w.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s40622-023-00349-w Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 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_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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 50 2023 2 6 205-219 26 01 0206 4391074168 x1z 17-10-23 26 00 DE-206 Identifying the best ideas from the vast volumes generated by open innovation engagements is costly and often time-consuming. One approach is to engage crowds in filtering the ideas, not just generating them. Klein and Garcia, 2015 proposed a “BOL” approach that is better (in terms of accuracy and speed) at idea filtering than other filtering methods such as a conventional Likert approach. The idea behind this approach (BOL) is that it asks the crowd to distribute a fixed budget of tokens that eliminate bad ideas rather than select good ones. In this paper, we explain why BOL works better than other filtering methods using empirical experiments (with n = 850 subjects). Also, we present the effect of the token budget size on idea-filtering engagement and found, among others, that the accuracy of a filter depends on the token budget size.
allfieldsSound	10.1007/s40622-023-00349-w doi (DE-627)1865957534 (DE-599)KXP1865957534 DE-627 ger DE-627 rda eng Lukumon, Gafari verfasserin aut Crowd-sourced idea filtering with Bag of Lemons the impact of the token budget size Gafari Lukumon, Mark Klein 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bag of Lemon (dpeaa)DE-206 Bag of Stars (dpeaa)DE-206 Collective intelligence (dpeaa)DE-206 Filtering (dpeaa)DE-206 Idea filtering (dpeaa)DE-206 Likert (dpeaa)DE-206 Token budget size (dpeaa)DE-206 Klein, Mark 1960- verfasserin (DE-588)143509535 (DE-627)704512629 (DE-576)337445133 aut Enthalten in Decision [Erscheinungsort nicht ermittelbar] : Springer India, 2004 50(2023), 2 vom: Juni, Seite 205-219 Online-Ressource (DE-627)491442440 (DE-600)2194154-3 (DE-576)273957856 2197-1722 nnns volume:50 year:2023 number:2 month:06 pages:205-219 https://link.springer.com/content/pdf/10.1007/s40622-023-00349-w.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s40622-023-00349-w Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 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_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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 50 2023 2 6 205-219 26 01 0206 4391074168 x1z 17-10-23 26 00 DE-206 Identifying the best ideas from the vast volumes generated by open innovation engagements is costly and often time-consuming. One approach is to engage crowds in filtering the ideas, not just generating them. Klein and Garcia, 2015 proposed a “BOL” approach that is better (in terms of accuracy and speed) at idea filtering than other filtering methods such as a conventional Likert approach. The idea behind this approach (BOL) is that it asks the crowd to distribute a fixed budget of tokens that eliminate bad ideas rather than select good ones. In this paper, we explain why BOL works better than other filtering methods using empirical experiments (with n = 850 subjects). Also, we present the effect of the token budget size on idea-filtering engagement and found, among others, that the accuracy of a filter depends on the token budget size.
language	English
source	Enthalten in Decision 50(2023), 2 vom: Juni, Seite 205-219 volume:50 year:2023 number:2 month:06 pages:205-219
sourceStr	Enthalten in Decision 50(2023), 2 vom: Juni, Seite 205-219 volume:50 year:2023 number:2 month:06 pages:205-219
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topic_facet	Bag of Lemon Bag of Stars Collective intelligence Filtering Idea filtering Likert Token budget size
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container_title	Decision
authorswithroles_txt_mv	Lukumon, Gafari @@aut@@ Klein, Mark @@aut@@
publishDateDaySort_date	2023-06-01T00:00:00Z
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author	Lukumon, Gafari
spellingShingle	Lukumon, Gafari misc Bag of Lemon misc Bag of Stars misc Collective intelligence misc Filtering misc Idea filtering misc Likert misc Token budget size Crowd-sourced idea filtering with Bag of Lemons the impact of the token budget size
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topic_title	26 00 DE-206 Identifying the best ideas from the vast volumes generated by open innovation engagements is costly and often time-consuming. One approach is to engage crowds in filtering the ideas, not just generating them. Klein and Garcia, 2015 proposed a “BOL” approach that is better (in terms of accuracy and speed) at idea filtering than other filtering methods such as a conventional Likert approach. The idea behind this approach (BOL) is that it asks the crowd to distribute a fixed budget of tokens that eliminate bad ideas rather than select good ones. In this paper, we explain why BOL works better than other filtering methods using empirical experiments (with n = 850 subjects). Also, we present the effect of the token budget size on idea-filtering engagement and found, among others, that the accuracy of a filter depends on the token budget size Crowd-sourced idea filtering with Bag of Lemons the impact of the token budget size Gafari Lukumon, Mark Klein Bag of Lemon (dpeaa)DE-206 Bag of Stars (dpeaa)DE-206 Collective intelligence (dpeaa)DE-206 Filtering (dpeaa)DE-206 Idea filtering (dpeaa)DE-206 Likert (dpeaa)DE-206 Token budget size (dpeaa)DE-206
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title	Crowd-sourced idea filtering with Bag of Lemons the impact of the token budget size
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title_full	Crowd-sourced idea filtering with Bag of Lemons the impact of the token budget size Gafari Lukumon, Mark Klein
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title_auth	Crowd-sourced idea filtering with Bag of Lemons the impact of the token budget size
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title_short	Crowd-sourced idea filtering with Bag of Lemons
url	https://link.springer.com/content/pdf/10.1007/s40622-023-00349-w.pdf https://doi.org/10.1007/s40622-023-00349-w
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up_date	2024-07-04T11:17:56.064Z
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code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">50</subfield><subfield code="j">2023</subfield><subfield code="e">2</subfield><subfield code="c">6</subfield><subfield code="h">205-219</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4391074168</subfield><subfield code="y">x1z</subfield><subfield code="z">17-10-23</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">Identifying the best ideas from the vast volumes generated by open innovation engagements is costly and often time-consuming. One approach is to engage crowds in filtering the ideas, not just generating them. Klein and Garcia, 2015 proposed a “BOL” approach that is better (in terms of accuracy and speed) at idea filtering than other filtering methods such as a conventional Likert approach. The idea behind this approach (BOL) is that it asks the crowd to distribute a fixed budget of tokens that eliminate bad ideas rather than select good ones. In this paper, we explain why BOL works better than other filtering methods using empirical experiments (with n = 850 subjects). Also, we present the effect of the token budget size on idea-filtering engagement and found, among others, that the accuracy of a filter depends on the token budget size.</subfield></datafield></record></collection>
score	7.401991

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Crowd-sourced idea filtering with Bag of Lemons : the impact of the token budget size

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