Understanding machine learning adoption: The moderating effects of process sophistication and mimetic pressures

Vasyl Tsykolanov; Pedro Ruivo; Tiago Oliveira

doi:10.1016/j.procs.2023.01.385

Understanding machine learning adoption: The moderating effects of process sophistication and mimetic pressures

Vasyl Tsykolanov, Pedro Ruivo, Tiago Oliveira

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

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Abstract

Machine learning (ML) gives organizations the power to predict the future, and this has become a core element of modern enterprises. Three contexts, grounded in the technology-organizational-environmental (TOE) framework, were scrutinized to explain ML adoption. Data collected from 319 firms are used to test conceptual model. Additionally, this study investigates the use of process sophistication and mimetic pressures as moderators. The significance of the technological, organizational, and environmental contexts for ML adoption is confirmed. Furthermore, the moderator influence of mimetic pressures and process sophistication between the technological and organizational context and ML adoption was confirmed.

Original language	English
Pages (from-to)	1067-1074
Number of pages	8
Journal	Procedia Computer Science
Volume	219
DOIs	https://doi.org/10.1016/j.procs.2023.01.385
Publication status	Published - 1 Jan 2023
Event	2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022 - Lisbon, Portugal Duration: 9 Nov 2022 → 11 Nov 2022

Keywords

big data and analytics
Machine learning
process sophistication
technology adoption
technology-organization-environment

UN SDGs

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

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machine_learning_adoption_Final_version_2023Final published version, 708 KBLicence: CC BY-NC-ND

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abstract = "Machine learning (ML) gives organizations the power to predict the future, and this has become a core element of modern enterprises. Three contexts, grounded in the technology-organizational-environmental (TOE) framework, were scrutinized to explain ML adoption. Data collected from 319 firms are used to test conceptual model. Additionally, this study investigates the use of process sophistication and mimetic pressures as moderators. The significance of the technological, organizational, and environmental contexts for ML adoption is confirmed. Furthermore, the moderator influence of mimetic pressures and process sophistication between the technological and organizational context and ML adoption was confirmed.",

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AU - Tsykolanov, Vasyl

AU - Ruivo, Pedro

AU - Oliveira, Tiago

N1 - Tsykolanov, V., Ruivo, P., & Oliveira, T. (2023). Understanding machine learning adoption: The moderating effects of process sophistication and mimetic pressures. Procedia Computer Science, 219, 1067-1074. https://doi.org/10.1016/j.procs.2023.01.385

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N2 - Machine learning (ML) gives organizations the power to predict the future, and this has become a core element of modern enterprises. Three contexts, grounded in the technology-organizational-environmental (TOE) framework, were scrutinized to explain ML adoption. Data collected from 319 firms are used to test conceptual model. Additionally, this study investigates the use of process sophistication and mimetic pressures as moderators. The significance of the technological, organizational, and environmental contexts for ML adoption is confirmed. Furthermore, the moderator influence of mimetic pressures and process sophistication between the technological and organizational context and ML adoption was confirmed.

AB - Machine learning (ML) gives organizations the power to predict the future, and this has become a core element of modern enterprises. Three contexts, grounded in the technology-organizational-environmental (TOE) framework, were scrutinized to explain ML adoption. Data collected from 319 firms are used to test conceptual model. Additionally, this study investigates the use of process sophistication and mimetic pressures as moderators. The significance of the technological, organizational, and environmental contexts for ML adoption is confirmed. Furthermore, the moderator influence of mimetic pressures and process sophistication between the technological and organizational context and ML adoption was confirmed.

KW - big data and analytics

KW - Machine learning

KW - process sophistication

KW - technology adoption

KW - technology-organization-environment

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DO - 10.1016/j.procs.2023.01.385

M3 - Conference article

SN - 1877-0509

VL - 219

SP - 1067

EP - 1074

JO - Procedia Computer Science

JF - Procedia Computer Science

Y2 - 9 November 2022 through 11 November 2022

ER -

Understanding machine learning adoption: The moderating effects of process sophistication and mimetic pressures

Abstract

Keywords

UN SDGs

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