| Title |
Big data and firm marketing performance: Findings from knowledge-based view |
| ID_Doc |
19129 |
| Authors |
Gupta, S; Justy, T; Kamboj, S; Kumar, A; Kristoffersen, E |
| Title |
Big data and firm marketing performance: Findings from knowledge-based view |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.techfore.2021.120986 |
| Abstract |
A universal trend in advanced manufacturing countries is defining Industry 4.0, industrialized internet and future factories as a recent wave, which may transform the production and its related services. Further, big data analytics has emerged as a game changer in the business world due to its uses for increasing accuracy in decisionmaking and enhancing performance of sustainable industry 4.0 applications. This study intends to emphasize on how to support Industry 4.0 with knowledge based view. For the same, a conceptual model is framed and presented with essential components that are required for a real world implementation. The study used qualitative analysis and was guided by a knowledge-based theoretical framework. Thematic analysis resulted in the identification of a number of emergent categories. Key findings highlight significant gaps in conventional decision-making systems and demonstrate how big data enhances firms' strategic and operational decisions as well as facilitates informational access for improved marketing performance. The resulting proposed model can provide managers with a reference point for using big data to line up firms' activities for more effective marketing efforts and presents a conceptual basis for further empirical studies in this area. |
| Author Keywords |
Big data analytics; Artificial intelligence; Marketing performance; Knowledge-based view |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Social Science Citation Index (SSCI) |
| EID |
WOS:000681275900006 |
| WoS Category |
Business; Regional & Urban Planning |
| Research Area |
Business & Economics; Public Administration |
| PDF |
http://manuscript.elsevier.com/S0040162521004182/pdf/S0040162521004182.pdf
|