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Exploring the role of knowledge management capacity on SMEs’ digital transformation intentions: Integrating signaling theory and the technology acceptance mode

知识管理 数字化转型 模式(计算机接口) 转化(遗传学) 业务 心理学 计算机科学 人机交互 万维网 生物化学 基因 化学
作者
Hoang Canh,Khoa Bùi Thành
出处
期刊: 卷期号:: 48-62 被引量:1
标识
DOI:10.33301/jed-05-2024-0048
摘要

Purpose - Digital transformation through adopting emerging technologies is critical for Small and medium-sized enterprises (SMEs) to remain competitive advantages. However, research on drivers of SMEs' digital transformation intention is limited. Knowledge management capacity (KMC) can potentially influence technology adoption by signaling the value of digital tools, but empirical evidence is lacking. This study examines the impact of KMC on SMEs' digital transformation intention and the mechanisms by integrating the Technology Acceptance Model (TAM) and signaling theory. Design/methodology/approach - A survey of 214 SME managers in Vietnam was conducted. Partial Least Squares Structural Equation Modeling was employed to test the research model. Findings - KMC positively influenced perceived usefulness and ease of use, subsequently impacting attitude and digital transformation intention. Implication - KMC plays an important role in the digital transformation of SMEs by signaling the benefits and simplicity of new digital technologies. This result shaped owner-managers’ beliefs and attitudes, highlighting the need to develop KMC capabilities. The study provides the first empirical evidence of the mechanisms linking KMC and digitalization in SMEs. Originality/value - This research introduced an innovative approach by integrating KMC as an external, organization-level antecedent that influences individual perceptions, thus serving as a signaling mechanism. By doing so, the study substantially enhanced the empirical foundation for signaling theory within the domain of technology acceptance. Furthermore, the incorporation of KMC into the TAM framework had markedly augmented the model's predictive capabilities, offering a more robust and comprehensive understanding of the determinants that drive technology adoption behavior.

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