Satisfação do cliente na prestação de serviços com inteligência artificial: Um estudo meta-analítico

Aguiar-Costa, Laura M.; Cunha, Carlos A. X. C.; Silva, Wallysson K. M.; Abreu, Nelsio R.

doi:10.1590/1678-6971/eRAMD220003.en

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Sumário

Special Issue • RAM, Rev. Adm. Mackenzie 23 (06) • 2022 • https://doi.org/10.1590/1678-6971/eRAMD220003.en linkcopiar

Satisfação do cliente na prestação de serviços com inteligência artificial: Um estudo meta-analítico

Autoria SCIMAGO INSTITUTIONS RANKINGS

Objetivo: O presente estudo tem como objetivo identificar os principais construtos antecedentes e consequentes que compõem a satisfação do consumidor na prestação de serviços que utilizam inteligência artificial (IA) e suas magnitudes.

Originalidade/valor: Este estudo busca preencher uma lacuna advinda da escassez de pesquisas meta-analíticas sobre a prestação de serviços com IA e sua relação com a satisfação do consumidor. Design/metodologia/abordagem: O presente trabalho adotou o método meta-analítico e o seu desenvolvimento seguiu três fases: 1. pesquisa; 2. coleta; e 3. codificação e análise de dados. Foram analisados 19 artigos publicados em periódicos de relevância internacional no período de janeiro de 2000 e dezembro de 2020, presentes nas plataformas Web of Science e Science Direct, que totalizaram 128 observações e 28 relações.

Resultados: Foram identificados cinco construtos antecedentes e um construto consequente. A partir disso, construiu-se um modelo integrado para ilustrar as relações da satisfação do consumidor em serviços inteligentes. Os resultados alcançados demonstram que a satisfação do consumidor na prestação de serviços está significantemente correlacionada a adoção de inteligência artificial. Em suma, a avaliação quantitativa integrada que foi realizada neste estudo, visa contribuir com futuras evidências empíricas de tal maneira que ocorra um aumento do alcance dos estudos em inteligência artificial e satisfação do consumidor, a partir da análise dos seguintes construtos: valor percebido, recursos percebidos, percepção da qualidade, orientação mercadológica, identificação com o serviço e comportamento de uso de AI em serviços.

Palavras-chave
inteligência artificial; serviços; satisfação do consumidor; meta-análise; modelo integrado

Construct	Definition
Perceived value	Set of values that impact consumers’ perceptions of the use of AI in the services provided
Components	Definitions	References
Trust	Intentions and behaviors that indicate in the security that the service has added value from the use of AI.	Ameen et al. (2021), Purwanto et al. (2020), Chen et al. (2021), Chiang and Trimi (2020), Payne et al. (2018), Pillai et al. (2020), Zeinalizadeh et al. (2015), Kervenoael et al. (2020).
Control	Perception of control that the consumer has that he can influence the service that relies on an AI.	Purwanto et al. (2020), Kervenoael et al. (2020), Pillai et al. (2020), Söderlund (2020).
Empathy	Perception of care from a service delivery that uses AI.	Chiang and Trimi (2020), Gursoy et al. (2019), Lin et al. (2020), Pillai et al. (2020), Prentice et al. (2020), Qiu et al. (2020), Söderlund (2020), Zhu and Chang (2020), Kervenoael et al. (2020).
Hedonism	Perception of pleasure associated with a service that uses AI.	Gursoy et al. (2019), Lin et al. (2020).
Negative effects	Perception that using AI in service may not generate value.	Ameen et al. (2021), Pillai et al. (2020), Pillai and Sivathanu (2020), Sorderlund and Trivedi (2019).
Construct	Definition
Perceived value	Set of values that impact consumers’ perceptions of the use of AI in the services provided
Components	Definitions	References
Utility	Perception that there is useful value in services that make use of AI.	Payne et al. (2018), Prentice et al. (2020), Zeinalizadeh et al. (2015), Kervenoael et al. (2020).
Construct	Definition
Perceived resources	Tangible and intangible resources perceived in customer service from the use of AI
Components	Definitions	References
Access	Perception of tangibility in a service using AI.	Chiang and Trimi (2020), Zhu and Chang (2020), Kervenoael et al. (2020).
Customization	Perception that it is possible to adapt a service with AI to the consumer’s needs.	Ameen et al. (2021), Chen et al. (2021), Pillai et al. (2020), Söderlund (2020).
Usability	Perception that there are facilities in the effort employed to use the service with AI.	Chen et al. (2021), Qiu et al. (2020), Zeinalizadeh et al. (2015).
Performance	Expectation that the service using AI will meet expected performance.	Purwanto et al. (2020), Gursoy et al. (2019), Lin et al. (2019).
Service	Expectation that employees are trained to provide a service that uses AI.	Prentice and Nguyen (2020), Prentice et al. (2020), Söderlund (2020), Zeinalizadeh et al. (2015).
Construct	Definition
Quality perception	Perceptions of the quality involved in the services provided with the adoption of AI
Components	Definitions	References
Quality in the use of AI	Evaluation of the quality perceived by the adoption of AI in the execution of the service.	Ameen et al. (2021), Payne et al. (2018), Prentice and Nguyen (2020), Moriuchi et al. (2021), Pillai and Sivathanu (2020).
Quality in service	Perception of quality of the support given in the service.	Payne et al. (2018), Moriuchi et al. (2021), Trivedi (2019).
Quality in the systems	Perception of the quality of service technology systems using AI.	Purwanto et al. (2020), Trivedi (2019), Zeinalizadeh et al. (2015).
Construct	Definition
Quality perception	Perceptions of the quality involved in the services provided with the adoption of AI
Components	Definitions	References
Quality in the results	Perception of the quality of the results generated after contact with the service you use AI.	Purwanto et al. (2020), Choi et al. (2020), Moriuchi et al. (2021), Zeinalizadeh et al. (2015).
Construct	Definition
Marketing orientation	Strategies used by service providers that adopt AI to meet market demands
Components	Definitions	References
Performance	Use of process improvement-oriented systems.	Ameen et al. (2021), Chen et al. (2021), Gursoy et al. (2019), Lin et al. (2020), Zeinalizadeh et al. (2015).
Competitiveness	Service management with the goal of gaining a competitive advantage.	Payne et al. (2018), Pillai et al. (2020), Kervenoael et al. (2020), Moriuchi et al. (2021).
Financial advantage	Consideration of the financial impact of adopting AI on service execution.	Zeinalizadeh et al. (2015).
Construct	Definition
Identification with the service	Factors that demonstrate the consumer’s identification with the service they use AI
Components	Definitions	References
Relationship	Perception that the service connects with the consumer from the use of AI.	Qiu et al. (2020), Trivedi (2019).
Emotions	Evaluations of the feelings generated in the service experience with AI.	Gursoy et al. (2019), Lin et al. (2020), Pillai et al. (2020), Prentice and Nguyen (2020), Qiu et al. (2020).
Environment	Perception of the service atmosphere created from the use of AI.	Pillai and Sivathanu (2020), Qiu et al. (2020), Zhu and Chang (2020), Payne et al. (2018).
Construct	Definition
Usage behavior	Intentions and behaviors that result from consumers’ satisfaction with the use of AI in services
Components	Definitions	References
Consumer experience	Intentions and behaviors that indicate favorable or unfavorable perceptions in the service user experience.	Ameen et al. (2021), Purwanto et al. (2020), Chen et al. (2021), Chiang and Trimi (2020), Choi et al. (2020), Qiu et al. (2020), Zeinalizadeh et al. (2015), Zhu and Chang (2020).
Intention to use	Behaviors that indicate that there is a propensity for a consumer to use a service that contains AI.	Gursoy et al. (2019), Lin et al. (2020), Pillai et al. (2020), Kervenoael et al. (2020).
Use objection	Behaviors that indicate that there is no propensity for a consumer to use a service that contains AI.	Gursoy et al. (2019), Lin et al. (2020).
Attitude towards AI	Perceptions of a consumer’s action when experiencing a service encounter that used AI.	Payne et al. (2018).
Loyalty	A consumer’s intention to maintain long-term contact with a service company using AI.	Moriuchi et al. (2021), Prentice and Nguyen (2020), Prentice et al. (2020), Trivedi (2019).
Purchase intention	Perception of the influence of AI on the propensity of a consumer to contract a service.	Moriuchi et al. (2021), Pillai et al. (2020).

Components	k	o	N	ES	d of Cohen	IClower	ICupper	Q	FSN
Trust	9	10	3705	0.63	2.06	0.60	0.66	356.33	114
Control	6	8	3661	0.75	2.16	0.71	0.78	961.33	89
Empathy	9	9	4440	0.93	2.30	0.90	0.96	1683.99	167
Hedonism	3	3	2294	0.52	2.25	0.47	0.56	1466.94	31
Negative effects	5	6	3760	0.80	2.19	0.76	0.83	1144.62	80
Interaction	7	7	3752	0.86	2.27	0.82	0.89	1559.16	120
Utility	4	6	1558	0.34	2.23	0.29	0.39	1364.03	27

Components	k	o	N	ES	d of Cohen	IClower	Icupper	Q	FSN
Access	3	4	865	0.16	2.10	0.09	0.23	64.23	10
Customization	4	4	2401	0.57	2.41	0.53	0.62	541.73	46
Usability	3	3	995	0.23	2.32	0.17	0.29	189.15	14
Performance	3	3	1194	0.29	2.44	0.23	0.35	282.02	17
Service	4	4	1615	0.36	2.27	0.31	0.41	264.90	29

Components	k	o	N	ES	d of Cohen	IClower	ICupper	Q	FSN
Quality in the use of AI	5	5	2580	0.63	2.44	0.59	0.66	609.39	63
Quality in the service	3	3	544	0.13	2.41	0.05	0.21	121.71	8
Quality in the systems	3	4	808	0.20	2.55	0.13	0.27	218.48	12
Quality in the results	4	4	957	0.24	2.55	0.18	0.30	258.77	19

Components	k	o	N	ES	d of Cohen	IClower	ICupper	Q	FSN
Performance	5	5	2257	0.57	2.55	0.53	0.61	610.29	57
Competitiveness	4	4	1979	0.49	2.51	0.45	0.54	514.74	39
Financial advantage	1	2	400	0.07	2.08	-0.03	0.17	24.01	1

Components	k	o	N	ES	d of Cohen	IClower	ICupper	Q	FSN
Relationship	2	3	474	0.13	2.76	0.04	0.22	154.00	5
Emotions	5	5	2890	0.76	2.67	0.72	0.79	880.59	76
Environment	4	4	2135	0.65	4.10	0.61	0.69	1016.47	52

Components	k	O	N	ES	d of Cohen	IClower	ICupper	Q	FSN
Consumer experience	9	9	2558	0.67	2.66	0.63	0.71	773.80	120
Intention to use	4	4	2967	0.82	2.94	0.78	0.85	1068.12	65
Use objection	3	2	1044	0.30	3.40	0.24	0.37	441.09	18
Attitude towards AI	1	1	218	0.04	2.06	-0.10	0.17	9.61	1
Loyalty	4	4	1203	0.35	3.25	0.29	0.40	488.14	28
Purchase intention	3	2	1318	0.38	3.28	0.33	0.43	539.85	23

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