Table 1 Example studies that focus on technical or problem-solving aspects of BDA

[3]

(47) content analysis to discover issues

The importance of designing streaming analytics for big data found scalability, privacy, and load-balancing issues of big data technologies

[20]

(84) systematic literature review

Existing BDA mechanisms lead to competitive performance gains for building theory, aligning to resource-based and dynamic capabilities

[21]

(413) content analysis

A framework identifying supply chain functions with BDA models is developed

[22]

(67) systematic review

Organizations may realize Big Data values by analyzing two socio-technical features: portability and interconnectivity influence

[23]

(170) bibliometric analysis and systematic literature review

Created 4 clusters—big data and dynamic capabilities: big data and supply chain management, knowledge management, decision making, business process management, and BDA, determined BDAC and organizational objectives to be aligned so organizations should develop new strategies for dynamic BDAC

[24]

(49) bibliometric and network analysis review

Identified clusters of Big Data to improve business processes in an organization

[25]

(109) descriptive review

Revealed how to establish BDAC for business transformation

[18]

(100) content analysis

Addressed Big Data issues, trends, and views in Supply Chain Management (SCM) to spread Big Data value-adding perspective