Understanding the Determinants of Big Data Adoption in India: An Analysis of the Manufacturing and Services Sectors

Understanding the Determinants of Big Data Adoption in India: An Analysis of the Manufacturing and Services Sectors

Hemlata Gangwar (Pune Institute of Business Management, Pune, India)
Copyright: © 2018 |Pages: 22
DOI: 10.4018/IRMJ.2018100101
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This article sought to identify the drivers of Big Data adoption within the manufacturing and services sectors in India. A questionnaire-based survey was used to collect data from manufacturing and service sector organizations in India. The data was analyzed using exploratory and confirmatory factor analyses. Relevant hypotheses were then derived and tested by SEM analysis. The findings revealed that the following factors are important for both sectors: relative advantage, compatibility, complexity, organizational size, top management support, competitive pressure, vendor support, data management and data privacy. Statistically significant differences between the service and the manufacturing sectors were found. In other words, the relative importance of the factors for Big Data adoption differs between the sectors. The only exception was complexity, which was found to be insignificant in regard to the manufacturing sector. The factors identified can be used to facilitate Big Data adoption outcomes in organizations.
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1. Introduction

The emergence of Big Data has changed the way organizations operate and compete. The advent of Big Data has already and will further modernize many fields, including businesses, scientific research, public administration, genomics, healthcare, engineering, operations management, the industrial internet, finance, and so on. Big Data may be defined as a collection of massive and diverse data sets requiring advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information (Gandomi and Haider 2015). In other words, it is a collection of huge and complex amalgamation of data sets which make it difficult to process using traditional data processing platforms. Big Data Analytics (BDA) refers to the process and techniques used to analyze massive data in order to obtain value from that data.

It is pragmatic today to recognize Big Data brings in many attractive opportunities, such as increasing operational efficiency, enhancing strategic directions, developing better customer service, and identifying and developing new products, services, customers, and markets. However, despite these advantages of Big Data, evidence suggests that not all companies are rushing to adopt Big Data, or for that matter, BDA (Kwon et al 2014). As a disruptive technology still at the nascent stage of its adoption, it shows a gap in terms of its conformity to various industry-specific standards, and brings along a high level of risk and costs due to a lack of a holistic understanding and extensive experience with the technology itself (Kwon et al. 2014; Oliveira et al. 2014). The purpose of this study is to understand the determinants of the adoption of Big Data within organizations.

Most of the earlier studies on Big Data have focused solely on technical and operational issues (Chen and Zhang 2014; Lee et al., 2014). Only a few studies have addressed the adopting Big Data from an organizational perspective. As a matter of fact, no study has conducted a satisfactorily comprehensive evaluation of the determinants on Big Data adoption. As shown in Figure 1, his study therefore seeks to lend greater clarity by applying the Technological, Organizational and Environmental (TOE) framework of Tornatzky and Fleischer (1990), but in combination with two additional constructs specific to Big Data security: data management and privacy concerns. In addition, both the manufacturing and the service sectors were considered so as to offer a more holistic assessment of the determinants of Big Data adoption.

Our study believes in the importance of systematically evaluating the determinants of Big Data adoption. First, we provide a state-of-the-art overview of Big Data adoption research. We then describe the theoretical foundations for our research model, and propose testable hypotheses before presenting the empirical research methodology and the results. We close with a discussion of the major findings and a brief description of some opportunities for future study.

Figure 1.

The extended TOE framework for Big Data adoption


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