The recent exponential growth in data production has provided organizations with the challenge of not only managing their database efficiently, but also using them adequately in order to improve their decision-making process giving more value to their businesses and operations. Data analytics should be able to support decision-makers in developing correct and best decisions in a variety of of contexts, whatever their complexities. Therefore, it is crucial for organizations to be fully aware of the impact and importance of data analytics tools, as well as of the required abilities and capabilities. Such awareness has significantly increased in the recent years. It is clear that a better understanding of an organization’s data analytics capabilities requires a certain degree of competency through a highly influential approach. This approach has to be the focus of attention of scholars exploring data analytics-based resources. Many of them have dedicated significant efforts to developing this subject and the related themes, but the relevant literature is limited and needs in-depth discussions on the effects of data analytics from both the intra-organizational and inter-organizational perspectives. Therefore, this track aims to bring more elements, insights, models, and robust empirical findings on the latest advances concerning data analytics and its contribution to the improvement and performance of business processes, the interplay with competitive advantage, and the managerial and social implications.
The track welcomes papers that address topics on data analytics and its interplay with other cutting-edge technologies employing a wide array of empirical methods (e.g., surveys, in-depth cases, mixed methods, etc.). Topics should include the follwing:
· Data analytics competency
· Critical factors of data analytics competency
· Organization’s analytics capabilities
· Human analytics skills and capabilities
· What is the role of data analytics to improve an organization’s decision process?
· Stages of data analytics adoption (adoption intention, routinization, and continuance usage)
· Barriers to and facilitators of data analytics adoption and use
· Relationship between data analytics and the organization’s knowledge improvement
· Data analytics impacts in intra-organizational and inter-organizational knowledge sharing
· Critical success factors of data analytics in different fields (supply chain management, food, manufacturing, electronic markets, oil and gas, education, etc.)
· The interplay between data analytics and other cutting-edge technologies (e.g., artificial intelligence, blockchain, the internet of things, among others)
· Frameworks for gaining insights into data analytics business value
· Complexities of the social aspects of data analytics exploration and management (e.g., trust, collaboration, power, commitment, etc.)
· Managers' awareness of data analytics about business performance and value
· Ethics about using public and private data for data analytics purposes
· Data analytics readiness capabilities
· Data analytics to minimize organization’s risks.