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Does the Netflix Recommender System Produce Customer Utility? : An Analysis of the Technology Acceptance of the Algorithmic-prediction-based Netflix Recommender System and its Drivers = = O Sistema de Recomendacao Netflix Produz Utilidade Para o Cliente? Uma Analise da Aceitacao da Tecnologia do Sistema Netflix Recommendender Baseado na Previsao Algoritmica e os Seus Drivers.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Does the Netflix Recommender System Produce Customer Utility? : An Analysis of the Technology Acceptance of the Algorithmic-prediction-based Netflix Recommender System and its Drivers =/
Reminder of title:	O Sistema de Recomendacao Netflix Produz Utilidade Para o Cliente? Uma Analise da Aceitacao da Tecnologia do Sistema Netflix Recommendender Baseado na Previsao Algoritmica e os Seus Drivers.
Author:	Lengyel, Daniel.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	80 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
Subject:	Motion pictures. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28992035
ISBN:	9798209805618

Does the Netflix Recommender System Produce Customer Utility? : An Analysis of the Technology Acceptance of the Algorithmic-prediction-based Netflix Recommender System and its Drivers = = O Sistema de Recomendacao Netflix Produz Utilidade Para o Cliente? Uma Analise da Aceitacao da Tecnologia do Sistema Netflix Recommendender Baseado na Previsao Algoritmica e os Seus Drivers.
Lengyel, Daniel.

Does the Netflix Recommender System Produce Customer Utility? : An Analysis of the Technology Acceptance of the Algorithmic-prediction-based Netflix Recommender System and its Drivers =O Sistema de Recomendacao Netflix Produz Utilidade Para o Cliente? Uma Analise da Aceitacao da Tecnologia do Sistema Netflix Recommendender Baseado na Previsao Algoritmica e os Seus Drivers. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 80 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Master's)--Universidade Catolica Portugesa (Portugal), 2021.

This item must not be sold to any third party vendors.

In the last two decades technology companies engaging in surveillance capitalism (gathering data, creating predictive products, implementing behavioural modification) have reaped exorbitant profits. Using big data and machine learning algorithmic recommender systems are able to accurately predict future behaviour of users. However, other aspects than accuracy should be considered for the success of such systems. From a company perspective, Netflix has been successfully engaging in surveillance capitalism in video streaming. Claiming to be a user-centric company, personalised recommendations are the basis of Netflix's success, while it ventures into new strategic directions with original content. Using the technology acceptance model to adopt a user-perspective, this paper examines the utility of users of the Netflix recommender system. The effects of transparency and curation as features of the Netflix platform together with user's level of trust towards the system are examined for their impact on the perceived usefulness and ease of use of the recommender system, in order to determine user's behavioural intent to use the system and actual system usage. Additionally, the potential effects of user interaction as a potential future feature are explored. Using structural equation modelling on data collected from survey respondents, the paper finds that curation and trust in fact impact behavioural intent and usage while transparency fails to impact perceived factors. User interaction does not significantly improve the utility of users. The outcome suggests that Netflix should focus on curation and trust-building features as differentiating characteristics of their platform to sustain competitive advantage.

ISBN: 9798209805618Subjects--Topical Terms:

527819
Motion pictures.

Does the Netflix Recommender System Produce Customer Utility? : An Analysis of the Technology Acceptance of the Algorithmic-prediction-based Netflix Recommender System and its Drivers = = O Sistema de Recomendacao Netflix Produz Utilidade Para o Cliente? Uma Analise da Aceitacao da Tecnologia do Sistema Netflix Recommendender Baseado na Previsao Algoritmica e os Seus Drivers.
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245 1 0 $a Does the Netflix Recommender System Produce Customer Utility? : An Analysis of the Technology Acceptance of the Algorithmic-prediction-based Netflix Recommender System and its Drivers = $b O Sistema de Recomendacao Netflix Produz Utilidade Para o Cliente? Uma Analise da Aceitacao da Tecnologia do Sistema Netflix Recommendender Baseado na Previsao Algoritmica e os Seus Drivers.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 80 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
500 $a Advisor: Durisin, Boris.
502 $a Thesis (Master's)--Universidade Catolica Portugesa (Portugal), 2021.
506 $a This item must not be sold to any third party vendors.
520 $a In the last two decades technology companies engaging in surveillance capitalism (gathering data, creating predictive products, implementing behavioural modification) have reaped exorbitant profits. Using big data and machine learning algorithmic recommender systems are able to accurately predict future behaviour of users. However, other aspects than accuracy should be considered for the success of such systems. From a company perspective, Netflix has been successfully engaging in surveillance capitalism in video streaming. Claiming to be a user-centric company, personalised recommendations are the basis of Netflix's success, while it ventures into new strategic directions with original content. Using the technology acceptance model to adopt a user-perspective, this paper examines the utility of users of the Netflix recommender system. The effects of transparency and curation as features of the Netflix platform together with user's level of trust towards the system are examined for their impact on the perceived usefulness and ease of use of the recommender system, in order to determine user's behavioural intent to use the system and actual system usage. Additionally, the potential effects of user interaction as a potential future feature are explored. Using structural equation modelling on data collected from survey respondents, the paper finds that curation and trust in fact impact behavioural intent and usage while transparency fails to impact perceived factors. User interaction does not significantly improve the utility of users. The outcome suggests that Netflix should focus on curation and trust-building features as differentiating characteristics of their platform to sustain competitive advantage.
520 $a Nas ultimas duas decadas, as empresas tecnologicas envolvidas no capitalismo de vigilancia tem colhido lucros exorbitantes. Utilizando grandes sistemas de recomendacao de dados e algoritmos de aprendizagem de maquinas, sao capazes de prever com precisao o comportamento futuro dos utilizadores. Outros aspectos para alem da precisao devem ser considerados para o sucesso de tais sistemas. Do ponto de vista da empresa, a Netflix tem vindo a envolver-se com sucesso no capitalismo de vigilancia em video streaming. Utilizando o modelo de aceitacao de tecnologia para adoptar um utilizador-perspectivo, este documento examina a utilidade dos utilizadores do sistema de recomendacoes da Netflix. Os efeitos da transparencia e curadoria como caracteristicas da plataforma Netflix, juntamente com o nivel de confianca dos utilizadores no sistema, sao examinados quanto ao seu impacto na percepcao da utilidade e facilidade de utilizacao do sistema de recomendacao, a fim de determinar a intencao comportamental do utilizador de utilizar o sistema e a utilizacao real do sistema. Sao explorados os efeitos potenciais da interaccao do utilizador como uma potencial caracteristica futura. Utilizando modelos de equacoes estruturais sobre dados recolhidos dos inquiridos, o documento conclui que a cura e a confianca tem de facto impacto na intencao comportamental e na utilizacao, enquanto a transparencia nao tem impacto nos factores percebidos. A interaccao dos utilizadores nao melhora significativamente a utilidade dos utilizadores. O resultado sugere que o Netflix deveria concentrar-se nas caracteristicas de cura e de construcao de confianca como caracteristicas diferenciadoras da sua plataforma para sustentar a vantagem competitiva.
590 $a School code: 7020.
650 4 $a Motion pictures. $3 527819
650 4 $a Ratings & rankings. $3 3562221
650 4 $a Recommender systems. $3 3562220
650 4 $a Serendipity. $3 3594030
650 4 $a Fine arts. $3 2122690
650 4 $a Information science. $3 554358
650 4 $a Statistics. $3 517247
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710 2 $a Universidade Catolica Portugesa (Portugal). $3 3437250
773 0 $t Dissertations Abstracts International $g 83-09B.
790 $a 7020
791 $a Master's
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28992035