The relationship between big data usage, product and process innovation, and the moderating role of data diagnosticity

Recent development and advances in technological infrastructure enable firms to capture, store and process rich sets markets, customers and competitor's data popularly known as "Big data". Characterized as data high in volume, velocity, variety, variability, and value (5V'). Big data possession and usage has become a vital lifeline for firm success as it enables firms to sense market opportunities and threats. Anecdotal evidence suggests that big data usage can have both positive and negative results largely due to quality data and diagnosticity. This thesis aim is to provide empirical evidence on the effect of big data usage on product and process innovation. And, the moderating role of data diagnosticity on the relationship between big data usage and product innovation, and big data usage and process innovation using knowledge-based theory. Survey data was obtained from employees working in a Adana Çukurova techno-city (n=97) and the obtained data is analyzed using partial least squares structural equation modeling (PLS-SEM). Results from PLS-SEM analysis found that big data usage has a positive and significant effect on product innovation. Big data usage has a positive and significant effect on process innovation. Data diagnosticity did not moderate the relationship between big data usage and process innovation. Finally, data diagnosticity did not moderate the relationship between big data usage and process innovation. The findings confirm the critical role of big data usage on product and process innovation; suggesting that big data usage by Turkish firms is a source of product and process innovation based on knowledge-based theory. These findings provide actionable guidelines for firms regarding the role of big data usage and data diagnosticity. In addition, product and process innovation are generated in this study.