國立虎尾科技大學 |

Transforming Learning & Development : = The Impact of Artificial Intelligence and Automation on Employee Motivation to Learn.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Transforming Learning & Development :/
其他題名:	The Impact of Artificial Intelligence and Automation on Employee Motivation to Learn.
作者:	Reitgruber, Tanja.
面頁冊數:	1 online resource (112 pages)
附註:	Source: Masters Abstracts International, Volume: 86-03.
Contained By:	Masters Abstracts International86-03.
標題:	Educational technology. -
電子資源:	click for full text (PQDT)
ISBN:	9798384276715

Transforming Learning & Development : = The Impact of Artificial Intelligence and Automation on Employee Motivation to Learn.
Reitgruber, Tanja.

Transforming Learning & Development :The Impact of Artificial Intelligence and Automation on Employee Motivation to Learn. - 1 online resource (112 pages)

Source: Masters Abstracts International, Volume: 86-03.

Thesis (M.M.)--Universidade Catolica Portuguesa (Portugal), 2023.

Includes bibliographical references

Technological advancements have transformed employee learning and development (L&D) from one-size-fits-all approaches to personalized initiatives. Given AI's potential to learn and adapt to individuals' demands, researchers and practitioners have started investigating AI applications in L&D. However, whether employees prefer AI-guided learning and whether it actually drives motivation to learn remains an empirical question. Thus, this research aims to investigate the impact of AI-guided L&D compared to simple automation-based L&D on employee motivation to learn, drawing on the Self-determination Theory (SDT) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The proposed model was tested using a PLS-SEM analysis with 144 participants in an experimental survey. The results revealed that AI in L&D increases motivation to learn more than simple automation. However, this effect is fully mediated by the increase in perceived competence due to AI, emphasizing the importance of providing customized trainings tailored to employees' learning styles and skills, along with consistent feedback, to foster perceived competence. Furthermore, the study demonstrates that motivation to learn significantly predicts individuals' behavioural intention to use a L&D system. Specifically, AI-guided L&D, promoting competence, generates higher motivation to learn, leading to increased use intentions. Thus, the study highlights that AI in employee L&D drives autonomous motivation through self-determination surpassing simple automation-based approaches. These findings provide valuable implications for organizations and practitioners seeking to foster employee motivation and technology acceptance through new L&D solutions, suggesting that investing in AI could be beneficial.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798384276715Subjects--Topical Terms:

556755
Educational technology.
Index Terms--Genre/Form:

554714
Electronic books.

Transforming Learning & Development : = The Impact of Artificial Intelligence and Automation on Employee Motivation to Learn.
LDR:04806ntm a22003497 4500 001 1152509
005 20241203090616.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798384276715
035 $a (MiAaPQ)AAI31368459
035 $a (MiAaPQ)Portugal104001442653
035 $a AAI31368459
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Reitgruber, Tanja. $3 1479520
245 1 0 $a Transforming Learning & Development : $b The Impact of Artificial Intelligence and Automation on Employee Motivation to Learn.
264 0 $c 2023
300 $a 1 online resource (112 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 86-03.
500 $a Advisor: de Almeida, Ana Filipa Martinho.
502 $a Thesis (M.M.)--Universidade Catolica Portuguesa (Portugal), 2023.
504 $a Includes bibliographical references
520 $a Technological advancements have transformed employee learning and development (L&D) from one-size-fits-all approaches to personalized initiatives. Given AI's potential to learn and adapt to individuals' demands, researchers and practitioners have started investigating AI applications in L&D. However, whether employees prefer AI-guided learning and whether it actually drives motivation to learn remains an empirical question. Thus, this research aims to investigate the impact of AI-guided L&D compared to simple automation-based L&D on employee motivation to learn, drawing on the Self-determination Theory (SDT) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The proposed model was tested using a PLS-SEM analysis with 144 participants in an experimental survey. The results revealed that AI in L&D increases motivation to learn more than simple automation. However, this effect is fully mediated by the increase in perceived competence due to AI, emphasizing the importance of providing customized trainings tailored to employees' learning styles and skills, along with consistent feedback, to foster perceived competence. Furthermore, the study demonstrates that motivation to learn significantly predicts individuals' behavioural intention to use a L&D system. Specifically, AI-guided L&D, promoting competence, generates higher motivation to learn, leading to increased use intentions. Thus, the study highlights that AI in employee L&D drives autonomous motivation through self-determination surpassing simple automation-based approaches. These findings provide valuable implications for organizations and practitioners seeking to foster employee motivation and technology acceptance through new L&D solutions, suggesting that investing in AI could be beneficial.
520 $a Os avancos tecnologicos revolucionaram a aprendizagem e desenvolvimento dos trabalhadores (L&D), passando de abordagens genericas para personalizadas. Com o potencial da IA em aprender e adaptar-se as necessidades individuais, os investigadores tem-se dedicado a pesquisar potenciais aplicacoes em L&D. No entanto, ainda e uma questao empirica se os funcionarios preferem a aprendizagem orientada por IA e se esta gera maior motivacao para aprender. Esta investigacao visa investigar o impacto da L&D orientada por IA em comparacao com a L&D baseada em automacao simples na motivacao dos funcionarios para aprender, com base na teoria da autodeterminacao (SDT) e na teoria unificada de aceitacao e uso de tecnologia (UTAUT). O modelo proposto foi testado com 144 participantes atraves de uma experiencia utilizando analise PLS-SEM. Os resultados mostraram que a IA em L&D aumenta a motivacao dos trabalhadores mais do que a automacao simples. No entanto, este efeito e mediado pelo aumento da competencia percebida devido a IA, enfatizando a importancia de treino personalizado adaptado aos estilos e habilidades de aprendizagem dos trabalhadores, com feedback consistente, para promover a competencia. A motivacao para aprender demonstrou ser um preditor significativo da intencao comportamental dos individuos de usar um sistema de L&D. Especificamente, a L&D orientada por IA, ao promover a competencia, gera maior motivacao para aprender, resultando em maiores intencoes de uso. Estes resultados tem implicacoes valiosas para organizacoes e profissionais que buscam promover a motivacao dos trabalhadores e a aceitacao de tecnologia em solucoes de L&D, indicando os beneficios do investimento em IA.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Educational technology. $3 556755
650 4 $a Distance learning. $3 1437660
650 4 $a Technology adoption. $3 1466919
650 4 $a Learning management systems. $3 1468432
650 4 $a Software. $2 gtt $3 574116
650 4 $a Online instruction. $3 1413524
655 7 $a Electronic books. $2 local $3 554714
690 $a 0800
690 $a 0710
710 2 $a Universidade Catolica Portuguesa (Portugal). $3 1469780
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Masters Abstracts International $g 86-03.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31368459 $z click for full text (PQDT)