The document summarizes a study on how big data analytics (BDA) contributes to innovation performance. It presents facts about data growth and BDA adoption. The study examines how BDA use and team sophistication impact sensing agility, and how sensing agility and a data-driven culture impact decision-making agility. A research model and method are described involving a survey of 172 Iranian firms. PLS analysis found support for relationships between BDA use, team sophistication, sensing agility, and decision-making agility. The study contributes to understanding how dynamic capabilities mediate and certain constructs moderate the effect of BDA on innovation performance. Limitations and future research directions are also discussed.

1. An empirical investigation on Big Data
Analytics (BDA) and innovation performance
Ahad Zareravasan, PhD
Masaryk University, Brno, Czech Republic
Email: Ahad.Zareravasan@econ.muni.cz
Amir Ashrafi
Allameh Tabataba’i University, Tehran, Iran
3rd International Conference on Business and Information Management (ICBIM 2019), Paris, France, 12-14 Sep 2019

2. Some facts
 The data volumes are exploding, more data has been
created in the past two years than in the entire previous
history of the human race.
 By the year 2020, about 1.7 megabytes of new information
will be created every second for every human being on the
planet.
 Internet users generate about 2.5 quintillion (10^30) bytes of
data each day.
3rd International Conference on Business and Information Management (ICBIM 2019), Paris, France2

3. Some facts
 97.2% of organizations are investing in big data and AI.
 Using big data, Netflix saves $1 billion per year on customer
retention.
 Less than 0.5% of all data is ever analyzed and used.
 Today it would take a person approximately 181 million
years to download all the data from the internet (download
speed of 46Mbps)
3rd International Conference on Business and Information Management (ICBIM 2019), Paris, France3

4. Define footer – presentation title / department4

5. Big Data Analytics (BDA)
“a holistic approach to manage, process and analyze the ‘5 Vs’ data-related
dimensions to create actionable insights for sustained value delivery,
measuring performance and creating competitive advantages”
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6. Research main question
How does BDA applications contribute to create business value?
The topic of BDA and innovation performance needs further investigation
(Akter et al., 2019).
RQ. How does BDA contribute to innovation performance?
3rd International Conference on Business and Information Management (ICBIM 2019), Paris, France6

7. Research Model
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DCs refers to “the ability to integrate, build, and
reconfigure internal and external competencies
to address rapidly-changing environments”

8. Research Method
• We used a seven-point Likert scale for all survey items (except for
control variables).
• We utilized a categorical description (dummy variable) of the firm’s
size (<50 employees as ‘small, 1’, >51 and <250 employees as
‘medium, 2’, and >251 employees as ‘large, 3’.
• Industry type was also coded as a dummy variable (1:
Manufacturing; 2: Services).
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9. Research Method
• The proposed research model was tested using a field survey of key
informants (CIOs, or product/marketing managers in Iranian firms).
• A modified snowball sampling technique was applied for data collection.
• A total of 172 valid responses were collected.
• Partial least squares (PLS) technique has been used for data analysis
employing SmartPLS (v. 3.2.7) tools (Ringle et al., 2015).
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10. PLS Analysis Results
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11. M1: BDA Use × BDA Team Sophistication →
Sensing Agility (H5a)
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12. M4: Sensing Agility × Data-Driven Culture →
Decision-making Agility (H6b)
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13. Major Implications for research and practice
• Among the first who studied the effect of BDA use on innovation
performance,
• Explored mediating role of dynamic capabilities,
• Validated moderating role of the suggested constructs
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14. Limitations and future research directions
• cross-sectional data
• single country survey
• possible moderators such as absorptive capacity or
organizational learning
• using other overarching themes (e.g., knowledge-based view
theory or institutional theory)
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15. Thank you for your
attention!
Questions, comments or remarks?
Email: Ahad.Zareravasan@econ.muni.cz