Factors Affecting Big Data Adoption: An Empirical Study in Small and Medium Enterprises in Vietnam

Factors Affecting Big Data Adoption: An Empirical Study in Small and Medium Enterprises in Vietnam

Nguyen Xuan Truong
Copyright: © 2022 |Pages: 21
DOI: 10.4018/IJABIM.315825
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Abstract

This study examined factors impacting the big data adoption of small and medium enterprises (SMEs) in Vietnam. The mixed method study was used. The qualitative research was applied by a group discussion with 15 participants and a cross-sectional survey with 372 representatives of SMEs. The results show that perceived benefit, simplicity, compatibility, data quality, security and privacy, vendor support, management support, financial investment, perceived usefulness, and attitudes toward adoption. This research extended the academic framework and examined causal relationships by adopting new characteristics from the integrated perspective of TOE with TAM beyond the existing research models.
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Introduction

The adoption of big data (AB) is often considered a firm’s key asset. It has reflected the interactions among firms and customers and furnishes descriptive, predictive, actionable, and prescriptive outcomes (Baig et al., 2021; Shirdastian et al., 2019). AB helps decision-makers get timely information to make the right decisions and increase revenue (Wahab et al., 2021). The global AB market was valued at USD 66.2 billion in 2020 and is anticipated to develop at an average annual rate of USD 157.2 until 2026 (IDC, 2020). SMEs play an important role in the economy of both developed & developing countries. SMEs in the United States account for 28% of direct exports while accounting for about 41% of the total value added domestically included in U.S. exports (Chong et al., 2019). In Europe, SMEs contribute significantly to the value-added in exported products (Piacentini & Fortanier, 2015). Tang et al. (2016) argue that SMEs in China contribute much higher value added to export products than direct exports. In Vietnam, SMEs comprise 97% of all businesses, contribute 45% of the GDP, and 31% of the country’s overall budget revenue, and draw over 5 million workers (Vietnam MPI, 2021). Many firms view the implementation of AB as being crucial and think it has great potential (Staegemann et al., 2021). However, because of the high volume and velocity and various information assets, valuable knowledge, and information extraction from it remain full of complexity (Volk et al., 2020). Lately, the AB has been reasonably low (Nam et al., 2019). Many firms have not yet incorporated use beyond the initial adoption procedure (Choi et al., 2022).

Firms adopting DB have gained many advantages in improving their operational efficiency (Mikalef et al., 2019; Dubey et al., 2020; Raguseo & Vitari, 2018). Large firms used AB to predict new market trends, evaluate customer behavior and experience to identify new enhancement opportunities and achieve good results. Due to limited resources, SMEs’ AB is still limited (Ghasemaghaei 2019; O’Connor & Kelly, 2017). Studies of SMEs AB are few and limited (Dubey et al., 2020; Al-Sai et al., 2020; Munawar et al., 2020). While some research used Technology-Organization-Environment (TOE) or Technology Acceptance Model (TAM) models, others used an extended TOE model to study technology/innovation adoption (Althunibat et al., 2021; Lutfi et al., 2020). However, the elements of the TOE model are affected and dictated by the technology used, the size of the company, and the study’s setting (Al-Sai et al., 2020; Sun et al., 2020).

The primary factors of the extent of TOE effects include various environmental circumstances and national settings. Given that AB drivers are a new subject with little empirical validation, it is necessary to undertake additional studies and develop a systematized body of information. Therefore, a new paradigm is needed to clarify the motivations for SMEs’ AB (Al-Sai et al., 2020).

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