Industrial Internet of Things and Emerging Digital Technologies–Modeling Professionals’ Learning Behavior

Industrial Internet of Things and Emerging Digital Technologies–Modeling Professionals’ Learning Behavior

Industrial internet of things (IIoT) and digital technologies have been evolving fast, leading to a challenge in the availability of skills and commotion in job profiles. While existing job profiles are changing, new job profiles are getting created. Professionals face the challenge of obsolescence...

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Autores principales: Sudatta Kar, Arpan K. Kar, M. P. Gupta
Formato: article
Lenguaje:EN
Publicado: IEEE 2021
Materias:
Industrial IoT (IIoT)
emerging digital technology
ambidextrous learning behavior
learning of emerging digital skills (LEDS)
future of work
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Acceso en línea:https://doaj.org/article/8cab530e59204023a0f457f5a03f3f6b
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spelling oai:doaj.org-article:8cab530e59204023a0f457f5a03f3f6b2021-11-09T00:00:28ZIndustrial Internet of Things and Emerging Digital Technologies–Modeling Professionals’ Learning Behavior2169-353610.1109/ACCESS.2021.3059407https://doaj.org/article/8cab530e59204023a0f457f5a03f3f6b2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9354166/https://doaj.org/toc/2169-3536Industrial internet of things (IIoT) and digital technologies have been evolving fast, leading to a challenge in the availability of skills and commotion in job profiles. While existing job profiles are changing, new job profiles are getting created. Professionals face the challenge of obsolescence and pressure for continuous reskilling and prepare for the future of work. The fast-changing innovations in digital technologies of IIoT like the internet of things, robotics, augmented reality, artificial intelligence, and big data analytics trigger in-depth analysis of professionals' learning behavior. This study extends the individual's ambidextrous learning theory and unified theory of acceptance and use of technology (UTAUT) to develop a quantitative behavioral model Learning Emerging Digital Skills (LEDS). LEDS model describes the antecedents of professionals' learning behavior towards fast-changing emerging digital technologies involved in IIoT. A nation-wide structured survey of 685 professionals across 95 firms in India across industry sectors engaged in IIoT product and solution development in sectors like automotive, aerospace, healthcare, and energy were undertaken. Findings from structural equation modeling are validated via a qualitative study. Social influence and personal innovativeness, anxiety, long-term consequence, and job relevance affect behavioral intention to learn. Professionals' performance level and technology preference moderate the relationship between antecedents and the intention to learn. For exceptional performers, personal innovativeness is the key driver in the intention to learn. For average performers, social influence and anxiety are additional significant factors towards intention to learn. Technology itself moderates the learning behavior, which indicates professionals' preference to learn a technology over the other based on technology maturity and use potential. This study can help practitioners design ramp-up strategies to meet the current and future demand of emerging digital skills to meet their IIoT strategy. Policymakers can use antecedents of employees' ambidextrous learning behavior to formulate policies to achieve ambidextrous organization's goals.Sudatta KarArpan K. KarM. P. GuptaIEEEarticleIndustrial IoT (IIoT)emerging digital technologyambidextrous learning behaviorlearning of emerging digital skills (LEDS)future of workElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 30017-30034 (2021)
institution DOAJ
collection DOAJ
language EN
topic Industrial IoT (IIoT)
emerging digital technology
ambidextrous learning behavior
learning of emerging digital skills (LEDS)
future of work
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
spellingShingle Industrial IoT (IIoT)
emerging digital technology
ambidextrous learning behavior
learning of emerging digital skills (LEDS)
future of work
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Sudatta Kar
Arpan K. Kar
M. P. Gupta
Industrial Internet of Things and Emerging Digital Technologies–Modeling Professionals’ Learning Behavior
description Industrial internet of things (IIoT) and digital technologies have been evolving fast, leading to a challenge in the availability of skills and commotion in job profiles. While existing job profiles are changing, new job profiles are getting created. Professionals face the challenge of obsolescence and pressure for continuous reskilling and prepare for the future of work. The fast-changing innovations in digital technologies of IIoT like the internet of things, robotics, augmented reality, artificial intelligence, and big data analytics trigger in-depth analysis of professionals' learning behavior. This study extends the individual's ambidextrous learning theory and unified theory of acceptance and use of technology (UTAUT) to develop a quantitative behavioral model Learning Emerging Digital Skills (LEDS). LEDS model describes the antecedents of professionals' learning behavior towards fast-changing emerging digital technologies involved in IIoT. A nation-wide structured survey of 685 professionals across 95 firms in India across industry sectors engaged in IIoT product and solution development in sectors like automotive, aerospace, healthcare, and energy were undertaken. Findings from structural equation modeling are validated via a qualitative study. Social influence and personal innovativeness, anxiety, long-term consequence, and job relevance affect behavioral intention to learn. Professionals' performance level and technology preference moderate the relationship between antecedents and the intention to learn. For exceptional performers, personal innovativeness is the key driver in the intention to learn. For average performers, social influence and anxiety are additional significant factors towards intention to learn. Technology itself moderates the learning behavior, which indicates professionals' preference to learn a technology over the other based on technology maturity and use potential. This study can help practitioners design ramp-up strategies to meet the current and future demand of emerging digital skills to meet their IIoT strategy. Policymakers can use antecedents of employees' ambidextrous learning behavior to formulate policies to achieve ambidextrous organization's goals.
format article
author Sudatta Kar
Arpan K. Kar
M. P. Gupta
author_facet Sudatta Kar
Arpan K. Kar
M. P. Gupta
author_sort Sudatta Kar
title Industrial Internet of Things and Emerging Digital Technologies–Modeling Professionals’ Learning Behavior
title_short Industrial Internet of Things and Emerging Digital Technologies–Modeling Professionals’ Learning Behavior
title_full Industrial Internet of Things and Emerging Digital Technologies–Modeling Professionals’ Learning Behavior
title_fullStr Industrial Internet of Things and Emerging Digital Technologies–Modeling Professionals’ Learning Behavior
title_full_unstemmed Industrial Internet of Things and Emerging Digital Technologies–Modeling Professionals’ Learning Behavior
title_sort industrial internet of things and emerging digital technologies–modeling professionals’ learning behavior
publisher IEEE
publishDate 2021
url https://doaj.org/article/8cab530e59204023a0f457f5a03f3f6b
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AT arpankkar industrialinternetofthingsandemergingdigitaltechnologiesx2013modelingprofessionalsx2019learningbehavior
AT mpgupta industrialinternetofthingsandemergingdigitaltechnologiesx2013modelingprofessionalsx2019learningbehavior
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