An SEM-ANN analysis of the impacts of Blockchain on competitive advantage

Abstract

Purpose

Though Blockchain has been studied in numerous contexts, the understanding of the impacts of Blockchain in achieving competitive advantages remains unexplored. Many industries, organizations and firms are still in a “wait and see” mode. This study aims at examining the effects of the technological, organizational and environmental factors drawn from the TOE framework in generating competitive advantage.

Design/methodology/approach

A dual-staged deep learning structural equation modeling artificial neural network analysis was conducted on 211 samples of small and medium enterprises. Four neural network models were engaged to rank the normalized importance of each of the predictor variables.

Findings

The research model can expound 57.99 and 47.33% of the variance in Blockchain adoption and competitive advantage correspondingly. The study successfully identified nonlinear relationships. The theoretical and managerial contributions are useful to scholars and practitioners such as industrial players, investors, chief executive officers (CEOs), managers, decision-makers and other stakeholders that intend to use Blockchain technology.

Originality/value

Unlike the existing technological–organizational–environmental (TOE) framework that uses a linear model and theoretically assumes that all relationships are linear, this has been the first study, which has successfully validated that there exist nonlinear relationships in the TOE framework. Further, very little has been theorized on the impacts of Blockchain adoption on competitive advantage, especially in the context of SMEs. Therefore, this study is the first one to provide the necessary theoretical foundation that may further extend the current knowledge of Blockchain technology adoption and its impacts.

Citation

Leong, L.-Y., Hew, J.-J., Lee, V.-H., Tan, G.W.-H., Ooi, K.-B. and Rana, N.P. (2023), "An SEM-ANN analysis of the impacts of Blockchain on competitive advantage", Industrial Management & Data Systems, Vol. 123 No. 3, pp. 967-1004. https://doi.org/10.1108/IMDS-11-2021-0671