BIG DATA WITHIN INNOVATION PROCESSES: A Quantitative Study of Fortune 200 Global Companies

University essay from Göteborgs universitet/Graduate School

Abstract: Background: The importance of innovation simply continues to stagger. Albeit, many firms continue to struggle in the launch of new technologies that meet the objectives of successful innovation. As a result, the life cycles of innovations are shortened and inadequate returns endure. Current research suggests modern technologies of big data can facilitate businesses as they strive to generate value-generating innovations. However, few explain what firms actually aim to find and how the extracted information supports activities within innovation. Purpose: The purpose of this study is to investigate firms’ usage of big data to facilitate market orientation related to the innovative process, while assess the impact such efforts have on firms’ innovative output. Theory: Shaping the research are the fields of: proactive- and responsive market orientation (Narver & Slater, 1990), pre-development theory inspired by the stage-gate framework and innovative productivity, as described by Kim and Mauborgne (2004). The latter enacts the comparative measure of innovative performance within this report, since it represents both the speed of innovative processes whilst simultaneously represents value-generating output of innovation. Method: This study is conducted through a deductive approach to research. Four hypotheses were constructed for testing against the gathered findings. With a cross-sectional design, an online questionnaire was distributed via LinkedIn to the Fortune 200 Global Companies of 2017 to collect quantifiable data. To test the hypotheses five main concepts were constructed on the basis of previous theory and the operationalization within adjacent research. Result: Given the four originally stated hypotheses due for assessment, three were accepted. Consequently, the findings suggest: (1) big data adoption; big data analysis within proactive market-oriented activities; and big data derived customer insight used within pre-development activities all demonstrate positive and significant correlations with innovative productivity. (2) The studied population preferably pursues market-oriented activities to extract and assess the latent needs of customers vis-à-vis expressed needs.

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