Digital Talent Capability Model for Transforming Technology-Based Holding Companies
Abstract. Digital transformation is inevitable for organizations to remain competitive in the current business landscape. In reality, organizations rely heavily on their human capabilities to achieve digital transformation goals. However, most studies have neglected this dynamic component and have f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Institut Teknologi Bandung
2020-12-01
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| Series: | Asian Journal of Technology Management |
| Subjects: | |
| Online Access: | https://journal.sbm.itb.ac.id/index.php/ajtm/article/view/3761 |
| Summary: | Abstract. Digital transformation is inevitable for organizations to remain competitive in the current business landscape. In reality, organizations rely heavily on their human capabilities to achieve digital transformation goals. However, most studies have neglected this dynamic component and have focused instead on organizational readiness. This study strives to fill such a research gap by uncovering digital talent capabilities that could accelerate digital transformation. We propose a conceptual model based on a literature review and enriched by qualitative research in a representative Telecommunication Company. The conceptual model highlights attitudes to change as a critical factor in transforming individual capability towards digital transformation. The model also explains how culture and digital literacy can positively relate to digital talents’ attitudes towards change and performance. Besides giving theoretical contributions to support the literature, the study’s outcomes can be utilized by organizations intending to transform their human capitals’ digital capabilities. Although there is still a possibility of real behavior arising from individual problem-solving habits, the paper ends with some recommendations, stressing the need for future empirical work.
Keywords: Attitudes to Change, Culture, Digital Literacy, Digital Talent Capability, Performance
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| ISSN: | 1978-6956 2089-791X |