WEEK 8 ANNOTATED BIBLIOGRAPHY: THE ROLE OF PREDICTIVE ANALYTICS IN ORGANIZATIONAL DECISION MAKING

 

 

 

 

 

WEEK 8 ANNOTATED BIBLIOGRAPHY: THE ROLE OF PREDICTIVE ANALYTICS IN ORGANIZATIONAL DECISION MAKING

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Week 8 annotated bibliography: The role of predictive analytics in organizational decision making

Dubey et al. (2019). Big data and predictive analytics and manufacturing performance: integrating institutional theory, resource‐based view and big data culture. British Journal of Management, 30(2), 341-361.

Predictive analytics has become the pillar of operations in the manufacturing industry. The various literature sources have indicated that predictive analytics, together with big data, has impacted the supply chain besides operational performance in organizations. However, the paper admits to the inefficiencies existing, which borders the pressure of institutions to provide resources which can support big data together with predictive analytics. The importance of predictive analytics and the lack of resource capacity to support it creates a gap in the application of predictive analytics which formed the agenda of this paper. Wit a sample size of 195 surveys, the article carried out a survey which gave the findings for the paper. According to the article findings, the institutions in organizations lack the knowledge to understand how to create value through the provision of necessary resources for predictive analytics. The article further indicates that institutional pressures can help in selecting tangible resources to create the ability to use predictive analytics.

Bose (2009). Advanced analytics: opportunities and challenges. Industrial Management & Data Systems.

The article aimed at understanding how decision making is impacted by advanced analytics. Therefore, the paper investigated the thee mining technologies to understand how they are used and issues related to the effective implementation of predictive analytics in organizations. in the study, the article employed the use of literature on business intelligence from various articles which had been published in the recent years on predictive analytics. From the literature, the author investigated how the current states, challenges and problems learned from the analytics and mining tools impacted decision-making processes in an organization. The findings from the study were reported in two sections. The first section discussed business intelligence technology for advanced analytics with the second discussing the challenges besides opportunities that organizations gained from predictive analytics. The findings showed that though challenges existed such as the experts to be used in data analytics, predictive analytics, and business intelligence had a positive impact on the organizations.

 

 

References

Bose, R. (2009). Advanced analytics: opportunities and challenges. Industrial Management &       Data Systems.

Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and   predictive analytics and manufacturing performance: integrating institutional theory, resource‐based view and big data culture. British Journal of Management, 30(2), 341-361.