I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT
This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (infor...
Ausführliche Beschreibung
Autor*in: |
Niu, Ben [verfasserIn] Mvondo, Gustave Florentin Nkoulou [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2023 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
Enthalten in: Journal of retailing and consumer services - Amsterdam : Elsevier Science, 1994, 76 |
---|---|
Übergeordnetes Werk: |
volume:76 |
DOI / URN: |
10.1016/j.jretconser.2023.103562 |
---|
Katalog-ID: |
ELV065615808 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV065615808 | ||
003 | DE-627 | ||
005 | 20231217093119.0 | ||
007 | cr uuu---uuuuu | ||
008 | 231117s2023 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.jretconser.2023.103562 |2 doi | |
035 | |a (DE-627)ELV065615808 | ||
035 | |a (ELSEVIER)S0969-6989(23)00313-2 | ||
040 | |a DE-627 |b ger |c DE-627 |e rda | ||
041 | |a eng | ||
082 | 0 | 4 | |a 380 |q VZ |
100 | 1 | |a Niu, Ben |e verfasserin |4 aut | |
245 | 1 | 0 | |a I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT |
264 | 1 | |c 2023 | |
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services. | ||
650 | 4 | |a Information quality | |
650 | 4 | |a Perceived coolness | |
650 | 4 | |a Technology affinity | |
650 | 4 | |a Posthuman ability | |
650 | 4 | |a Loyalty | |
650 | 4 | |a Ethical usage concerns | |
700 | 1 | |a Mvondo, Gustave Florentin Nkoulou |e verfasserin |0 (orcid)0000-0001-5871-8953 |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Journal of retailing and consumer services |d Amsterdam : Elsevier Science, 1994 |g 76 |h Online-Ressource |w (DE-627)320606244 |w (DE-600)2020784-0 |w (DE-576)094058547 |x 0969-6989 |7 nnns |
773 | 1 | 8 | |g volume:76 |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a SYSFLAG_U | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_32 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_90 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_100 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_165 | ||
912 | |a GBV_ILN_187 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_224 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_702 | ||
912 | |a GBV_ILN_2001 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2004 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2007 | ||
912 | |a GBV_ILN_2008 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2010 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2015 | ||
912 | |a GBV_ILN_2020 | ||
912 | |a GBV_ILN_2021 | ||
912 | |a GBV_ILN_2025 | ||
912 | |a GBV_ILN_2026 | ||
912 | |a GBV_ILN_2027 | ||
912 | |a GBV_ILN_2034 | ||
912 | |a GBV_ILN_2044 | ||
912 | |a GBV_ILN_2048 | ||
912 | |a GBV_ILN_2049 | ||
912 | |a GBV_ILN_2050 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2056 | ||
912 | |a GBV_ILN_2059 | ||
912 | |a GBV_ILN_2061 | ||
912 | |a GBV_ILN_2064 | ||
912 | |a GBV_ILN_2088 | ||
912 | |a GBV_ILN_2106 | ||
912 | |a GBV_ILN_2110 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2112 | ||
912 | |a GBV_ILN_2122 | ||
912 | |a GBV_ILN_2129 | ||
912 | |a GBV_ILN_2143 | ||
912 | |a GBV_ILN_2152 | ||
912 | |a GBV_ILN_2153 | ||
912 | |a GBV_ILN_2190 | ||
912 | |a GBV_ILN_2232 | ||
912 | |a GBV_ILN_2336 | ||
912 | |a GBV_ILN_2470 | ||
912 | |a GBV_ILN_2507 | ||
912 | |a GBV_ILN_4035 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4242 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4251 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4326 | ||
912 | |a GBV_ILN_4333 | ||
912 | |a GBV_ILN_4334 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4393 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 76 |
author_variant |
b n bn g f n m gfn gfnm |
---|---|
matchkey_str |
article:09696989:2023----::acagthutmtacabtnetgtnteeemnnsfsrlylyneh |
hierarchy_sort_str |
2023 |
publishDate |
2023 |
allfields |
10.1016/j.jretconser.2023.103562 doi (DE-627)ELV065615808 (ELSEVIER)S0969-6989(23)00313-2 DE-627 ger DE-627 rda eng 380 VZ Niu, Ben verfasserin aut I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services. Information quality Perceived coolness Technology affinity Posthuman ability Loyalty Ethical usage concerns Mvondo, Gustave Florentin Nkoulou verfasserin (orcid)0000-0001-5871-8953 aut Enthalten in Journal of retailing and consumer services Amsterdam : Elsevier Science, 1994 76 Online-Ressource (DE-627)320606244 (DE-600)2020784-0 (DE-576)094058547 0969-6989 nnns volume:76 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 76 |
spelling |
10.1016/j.jretconser.2023.103562 doi (DE-627)ELV065615808 (ELSEVIER)S0969-6989(23)00313-2 DE-627 ger DE-627 rda eng 380 VZ Niu, Ben verfasserin aut I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services. Information quality Perceived coolness Technology affinity Posthuman ability Loyalty Ethical usage concerns Mvondo, Gustave Florentin Nkoulou verfasserin (orcid)0000-0001-5871-8953 aut Enthalten in Journal of retailing and consumer services Amsterdam : Elsevier Science, 1994 76 Online-Ressource (DE-627)320606244 (DE-600)2020784-0 (DE-576)094058547 0969-6989 nnns volume:76 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 76 |
allfields_unstemmed |
10.1016/j.jretconser.2023.103562 doi (DE-627)ELV065615808 (ELSEVIER)S0969-6989(23)00313-2 DE-627 ger DE-627 rda eng 380 VZ Niu, Ben verfasserin aut I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services. Information quality Perceived coolness Technology affinity Posthuman ability Loyalty Ethical usage concerns Mvondo, Gustave Florentin Nkoulou verfasserin (orcid)0000-0001-5871-8953 aut Enthalten in Journal of retailing and consumer services Amsterdam : Elsevier Science, 1994 76 Online-Ressource (DE-627)320606244 (DE-600)2020784-0 (DE-576)094058547 0969-6989 nnns volume:76 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 76 |
allfieldsGer |
10.1016/j.jretconser.2023.103562 doi (DE-627)ELV065615808 (ELSEVIER)S0969-6989(23)00313-2 DE-627 ger DE-627 rda eng 380 VZ Niu, Ben verfasserin aut I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services. Information quality Perceived coolness Technology affinity Posthuman ability Loyalty Ethical usage concerns Mvondo, Gustave Florentin Nkoulou verfasserin (orcid)0000-0001-5871-8953 aut Enthalten in Journal of retailing and consumer services Amsterdam : Elsevier Science, 1994 76 Online-Ressource (DE-627)320606244 (DE-600)2020784-0 (DE-576)094058547 0969-6989 nnns volume:76 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 76 |
allfieldsSound |
10.1016/j.jretconser.2023.103562 doi (DE-627)ELV065615808 (ELSEVIER)S0969-6989(23)00313-2 DE-627 ger DE-627 rda eng 380 VZ Niu, Ben verfasserin aut I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services. Information quality Perceived coolness Technology affinity Posthuman ability Loyalty Ethical usage concerns Mvondo, Gustave Florentin Nkoulou verfasserin (orcid)0000-0001-5871-8953 aut Enthalten in Journal of retailing and consumer services Amsterdam : Elsevier Science, 1994 76 Online-Ressource (DE-627)320606244 (DE-600)2020784-0 (DE-576)094058547 0969-6989 nnns volume:76 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 76 |
language |
English |
source |
Enthalten in Journal of retailing and consumer services 76 volume:76 |
sourceStr |
Enthalten in Journal of retailing and consumer services 76 volume:76 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Information quality Perceived coolness Technology affinity Posthuman ability Loyalty Ethical usage concerns |
dewey-raw |
380 |
isfreeaccess_bool |
false |
container_title |
Journal of retailing and consumer services |
authorswithroles_txt_mv |
Niu, Ben @@aut@@ Mvondo, Gustave Florentin Nkoulou @@aut@@ |
publishDateDaySort_date |
2023-01-01T00:00:00Z |
hierarchy_top_id |
320606244 |
dewey-sort |
3380 |
id |
ELV065615808 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV065615808</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231217093119.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231117s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jretconser.2023.103562</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV065615808</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0969-6989(23)00313-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">380</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Niu, Ben</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Perceived coolness</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Technology affinity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Posthuman ability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Loyalty</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ethical usage concerns</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mvondo, Gustave Florentin Nkoulou</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-5871-8953</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of retailing and consumer services</subfield><subfield code="d">Amsterdam : Elsevier Science, 1994</subfield><subfield code="g">76</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320606244</subfield><subfield code="w">(DE-600)2020784-0</subfield><subfield code="w">(DE-576)094058547</subfield><subfield code="x">0969-6989</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:76</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_165</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield 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_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">76</subfield></datafield></record></collection>
|
author |
Niu, Ben |
spellingShingle |
Niu, Ben ddc 380 misc Information quality misc Perceived coolness misc Technology affinity misc Posthuman ability misc Loyalty misc Ethical usage concerns I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT |
authorStr |
Niu, Ben |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)320606244 |
format |
electronic Article |
dewey-ones |
380 - Commerce, communications & transportation |
delete_txt_mv |
keep |
author_role |
aut aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
issn |
0969-6989 |
topic_title |
380 VZ I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT Information quality Perceived coolness Technology affinity Posthuman ability Loyalty Ethical usage concerns |
topic |
ddc 380 misc Information quality misc Perceived coolness misc Technology affinity misc Posthuman ability misc Loyalty misc Ethical usage concerns |
topic_unstemmed |
ddc 380 misc Information quality misc Perceived coolness misc Technology affinity misc Posthuman ability misc Loyalty misc Ethical usage concerns |
topic_browse |
ddc 380 misc Information quality misc Perceived coolness misc Technology affinity misc Posthuman ability misc Loyalty misc Ethical usage concerns |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Journal of retailing and consumer services |
hierarchy_parent_id |
320606244 |
dewey-tens |
380 - Commerce, communications & transportation |
hierarchy_top_title |
Journal of retailing and consumer services |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)320606244 (DE-600)2020784-0 (DE-576)094058547 |
title |
I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT |
ctrlnum |
(DE-627)ELV065615808 (ELSEVIER)S0969-6989(23)00313-2 |
title_full |
I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT |
author_sort |
Niu, Ben |
journal |
Journal of retailing and consumer services |
journalStr |
Journal of retailing and consumer services |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
300 - Social sciences |
recordtype |
marc |
publishDateSort |
2023 |
contenttype_str_mv |
zzz |
author_browse |
Niu, Ben Mvondo, Gustave Florentin Nkoulou |
container_volume |
76 |
class |
380 VZ |
format_se |
Elektronische Aufsätze |
author-letter |
Niu, Ben |
doi_str_mv |
10.1016/j.jretconser.2023.103562 |
normlink |
(ORCID)0000-0001-5871-8953 |
normlink_prefix_str_mv |
(orcid)0000-0001-5871-8953 |
dewey-full |
380 |
author2-role |
verfasserin |
title_sort |
i am chatgpt, the ultimate ai chatbot! investigating the determinants of users' loyalty and ethical usage concerns of chatgpt |
title_auth |
I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT |
abstract |
This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services. |
abstractGer |
This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services. |
abstract_unstemmed |
This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 |
title_short |
I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT |
remote_bool |
true |
author2 |
Mvondo, Gustave Florentin Nkoulou |
author2Str |
Mvondo, Gustave Florentin Nkoulou |
ppnlink |
320606244 |
mediatype_str_mv |
c |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1016/j.jretconser.2023.103562 |
up_date |
2024-07-06T23:39:05.007Z |
_version_ |
1803874882036957184 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV065615808</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231217093119.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231117s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jretconser.2023.103562</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV065615808</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0969-6989(23)00313-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">380</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Niu, Ben</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Perceived coolness</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Technology affinity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Posthuman ability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Loyalty</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ethical usage concerns</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mvondo, Gustave Florentin Nkoulou</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-5871-8953</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of retailing and consumer services</subfield><subfield code="d">Amsterdam : Elsevier Science, 1994</subfield><subfield code="g">76</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320606244</subfield><subfield code="w">(DE-600)2020784-0</subfield><subfield code="w">(DE-576)094058547</subfield><subfield code="x">0969-6989</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:76</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_165</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield 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_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">76</subfield></datafield></record></collection>
|
score |
7.3975964 |