ارزیابی تاثیر کلان داده ها بر عملکرد نوآوری شرکت
Abstract
1- Introduction
2- Relevant literature
3- Hypotheses development
4- Methodology
5- Results
6- Post hoc analyses
7- Discussion
8- Conclusion
Acknowledgment
Appendix A.
Appendix B.
References
Abstract
In this study, we explore the impacts of big data’s main characteristics (i.e., volume, variety, and velocity) on innovation performance (i.e., innovation efficacy and efficiency), which eventually impacts firm performance (i.e., customer perspective, financial returns, and operational excellence). To address this objective, we collected data from 239 managers and empirically examined the relationships in the proposed model. The results reveal that, while data variety and velocity positively enhance firm innovation performance, data volume has no significant impact. The finding that data volume does not play a critical role in enhancing firm innovation performance contributes novel insights to the literature by contradicting the prevalent belief that big data is better data. Moreover, the findings reveal that data velocity plays a more important role in improving firm innovation performance than other big data characteristics.
Introduction
In today’s complex business world, many firms are investing in big data to find innovative ways to differentiate themselves from their competitors (Côrte-Real, Oliveira, & Ruivo, 2017). Indeed, 87 percent of firms believe big data will change the competitive landscape, and 89 percent believe they will lose considerable market share if they do not adopt big data within the next few years (Akter, Wamba, Gunasekaran, Dubey, & Childe, 2016). The extant literature has identified big data as “the next frontier for innovation, competition, and productivity” (Manyika & Roxburgh, 2011, p. 1) and the “next big thing in innovation” (Gobble, 2013). Big data, which is characterized by data variety, velocity, and volume (Ohlhorst, 2012), is capable of changing the innovation landscape by increasing the fit between consumers’ preferences and product features (Günther, Mehrizi, Huysman, & Feldberg, 2017; Johnson, Friend, & Lee, 2017). In turn, through innovation, big data may improve firm performance. However, there is still a lack of understanding about the relationships among big data, firm innovation performance, and overall firm performance. The present study investigates this “unknown.”
Recent studies have called for a better understanding of the claimed positive relation between big data and innovation success (Gunasekaran et al., 2017; Johnson et al., 2017). Innovation refers to the exploitation of new information to create, accept, and implement new ideas (Calantone, Cavusgil, & Zhao, 2002). According to Alegre, Lapiedra, and Chiva (2006), innovation performance can be decomposed into innovation efficacy and innovation efficiency. Innovation efficacy refers to the extent to which innovation is beneficial to the firm, while innovation efficiency reflects the time and effort required to achieve that degree of benefit (Alegre & Chiva, 2008). Utilizing big data may allow firms to demonstrate efficient and effective firm innovation. Specifically, big data can help firms collect and process market information to better understand consumers’ preferences, which can play a critical role in innovation performance. Firms that use big data in their business processes may have a better chance of enhancing their operating efficiency and revenue growth compared to their competitors (Marshall, Mueck, & Shockley, 2015). However, despite these potential benefits, many firms have failed to enhance their innovation performance through the use of big data (Johnson et al., 2017), and others are still unsure whether processing big data is positively associated with their outcomes (Ghasemaghaei, Hassanein, & Turel, 2017; Ghasemaghaei, Ebrahimi, & Hassanein, 2017; Kwon, Lee, & Shin, 2014).