Skip to main navigation Skip to search Skip to main content

Drivers of digital transformation adoption: a weight and meta-analysis

Diego Rodrigues Cavalcanti, Tiago Oliveira, Fernando de Oliveira Santini

Research output: Contribution to journal › Article › peer-review

505 Downloads (Pure)

Abstract

The advent of the global pandemic has accelerated the growing need for product and service transformation, highlighting the emerging importance of technology and creating the opportunity to update the digital transformation (DT) domain through empirical-quantitative research. This weight and meta-analysis enabled the synthesis and integration of previous literature on the scope of individual DT adoption, evaluating the state of the art and filling a void on the subject. Athwart 88 studies and 99 datasets by international sources, our results demonstrate that attitude and satisfaction are relevant predictors of behavioral intentions and promising outcomes, including compatibility and personal innovativeness. Behavioral intentions, satisfaction, and habit are the best predictors for DT use. Usefulness and ease of use are critical for DT adoption intention and use, being moderated by individualism, as a cultural factor, human capital, and knowledge-technology, as innovation indicators. We present a conceptual model of promising and best predictors for future research on DT individual adoption.
Original languageEnglish
Article numbere08911
Pages (from-to)1-17
Number of pages17
JournalHeliyon
Volume8
Issue number2
DOIs
Publication statusPublished - 5 Feb 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth

Keywords

  • Digital transformation
  • Emerging technologies
  • Digital disruption
  • Digital innovation
  • Adoption theories
  • Individual adoption

Fingerprint

Dive into the research topics of 'Drivers of digital transformation adoption: a weight and meta-analysis'. Together they form a unique fingerprint.

Cite this