1. This study proposes a big data analytics capability (BDAC) model, which consists of three primary dimensions (i.e., management, technology, and talent capability) and 11 subdimensions.
2. The findings from two Delphi studies and 152 online surveys of business analysts in the U.S. confirm the value of the entanglement conceptualization of the higher-order BDAC model and its impact on firm performance (FPER).
3. The results also illuminate the significant moderating impact of analytics capability–business strategy alignment on the BDAC–FPER relationship.
The article “How to improve firm performance using big data analytics capability and business strategy alignment?” is an informative piece that provides a comprehensive overview of how firms can leverage their big data analytics capabilities to improve their performance. The article is well-researched and provides evidence from two Delphi studies and 152 online surveys conducted with business analysts in the U.S., which adds credibility to its claims. Furthermore, it draws upon theoretical lenses such as resource-based theory (RBT), IT capability, and sociomaterialism perspective to provide a comprehensive understanding of how firms can use their BDACs to achieve superior FPERs.
However, there are some potential biases in the article that should be noted. Firstly, it does not explore any counterarguments or alternative perspectives on how firms can use their BDACs to improve their performance; instead it focuses solely on RBT as its theoretical lens for understanding this relationship. Secondly, while it does provide evidence from two Delphi studies and 152 online surveys conducted with business analysts in the U.S., it does not provide any evidence from other countries or regions which could add further credibility to its claims. Finally, while it does mention possible risks associated with leveraging BDACs for improved FPERs, it does not go into detail about what these risks are or how they can be mitigated; this could have been explored further in order to provide a more comprehensive understanding of this topic.
In conclusion, while this article provides an informative overview of how firms can leverage their big data analytics capabilities to improve their performance, there are some potential biases that should be noted when considering its trustworthiness and reliability such as lack of exploration into counterarguments or alternative perspectives on this topic as well as lack of evidence from other countries or regions outside the U.S..