Deadline: March
1, 2021
The minitrack aims to explore the business and societal
transformations big data entail, and how they enable innovative
ways of conducting business supporting rapid decision making
with external stakeholders such as business partners, customers,
public authorities, and citizens. To understand how big data can
be of value requires an examination of the interplay between
various factors (e.g., social, technical, economical,
environmental), as well as the interrelation between different
actors in a big data ecosystem (e.g., academia, private and
public organisations, civil society, and individuals).
Emphasis will be placed on interdisciplinary papers that bridge
the domains of organizational science, information systems
strategic management, information science, marketing, and
computer science. Despite the hype surrounding big data, the
aforementioned predicaments still remain largely unexplored,
severely hampering the business and societal benefits of big
data analytics. This mini track aims to add in this direction
and therefore welcomes quantitative, qualitative, and mixed
methods papers, as well as reviews, conceptual papers, and
theory development papers. Topics of interest include but are
not limited to the following:
Big data and management
• Data-driven
competitive advantage
• Big
data enabled organizational capabilities
• Big
data strategic alignment
• Organizational
learning and innovation from big data analytics
• Big
data and its impact on business strategy-formulation
• Leveraging
big data for social innovation and entrepreneurship
• Human
resource management in the data-driven enterprise
• How
big data shapes strategy and decision making
• Big
data digital business models
• Big
data and the dynamics of societal change
• Big
data for social good
• The
role of big data in social innovation
• Proactive
strategy formulation from big data analytics
• Data
and text mining for business analytics
• Behavioural
and Recommender Systems Analytics
• Big
data analytics for strategic value
• Data
quality improvement for business analytics
• Application
of big data to address societal challenges