Track Co-chairs
Shastri L Nimmagadda, CAIR, DSVV, Haridwar, Uttarakhand, India.
Lincoln C Wood, Otago University, Dunedin, New Zealand.
Seema Purohit, Professor Emeritus, Birla College, Mumbai, India.
Shruti Mantri, Indian School of Business, Hyderabad, Telangana, India.
Aarushi Jain, Management Development Institute, Gurgaon, Haryana, India.
Track Description
A digital ecosystem is a community technology comprising interconnected domains and systems powered by Big Data sources. This community technology can be an ecological composite entity with interrelated multidimensional characteristics interpreted within human and environmental ecosystems. The integration of diverse volumes and types of Big Data across various domains brings challenges to effective digital ecosystem management and practices. These challenges are heightened by the heterogeneity, multidimensionality, and complexity involved in ecological evaluations. Implementing a sustainable, integrated methodological framework, like Design Science-guided Information System architecture, can help navigate the complexities of revealing connections between different systems. Significant interactions may occur between ecosystems, influenced by their elemental and process attributes, defined by distinct inherent boundaries that exhibit discontinuities and overlaps due to the placement of multiple system edges across different scales and spatial dimensions (including nested hierarchies). Multi-scaled ecosystems (such as spatial ecologies) can foster sustainability and enhance coexistence through shared features among structural units and domains within Digital Ecosystems and Technology (DEST) contexts.
Researchers in this track can articulate information systems driven by Big Data and related artefacts to unify digital ecosystems and analyse them within a sustainability framework. They demonstrate the connectivity of these ecosystems through application development processes in various industry scenarios. The new insights into ecosystem connectivity can foster sustainable business partnerships, enhancing their ecological viability. Integrating multiple domains can significantly reduce the risk of misinterpretation of knowledge gathered from different ecosystems. Consequently, research objectives can be structured to address issues, clearly defining and framing the aims of the DEST research. The authors can present a cohesive framework to produce and develop new artefacts, interfaces, and agents, facilitating straightforward access to ecosystem information while extracting and analysing data to generate cognitive knowledge from detailed metadata. Strong methodologies can yield new insights into policies aimed at improving sustainability challenges. Evaluating benchmarks and analyzing the effects of various procedures can deepen understanding of sustainability-informed management at micro, meso, and macro levels on a global scale. Researchers can tackle the complexities of ecosystems through community-based IS/IT applications, conducting qualitative and quantitative analyses of Big Data-driven diverse digital ecosystems.
We focus on integrating diverse ecologies, including human, ecological health, and environmental systems. We aim to explore how these interconnected models can be developed using practical analytic tools, particularly when multiple domains and systems emerge within these ecologies. This track welcomes paper submissions that tackle the challenges and opportunities in data modelling, analytics, functional digital ecosystems, and decision support systems. We accept theoretical and practical insights in information systems research related to ecosystem development. The submission is open to various research methods and includes completed works and ongoing research projects. Research interests include, but are not limited to:
- Design of information system constructs and models in ecosystem contexts.
- Integrating and analysing big data in ecosystem contexts.
- Development of IS artefacts within human ecosystem contexts, merging them with ecological health, environmental, and economic systems via attribute modelling.
- Design of data schemas and best practices in ecosystem contexts.
- Development of robust modelling methodologies in ecosystem contexts.
- Multidimensional ecosystem contexts and their data analytics.
- Design and development that support the implementation of IS artefacts in ecosystem contexts.
- Data science tools and techniques to manage ecosystems;
- Articulating visualization tools for ecosystem new knowledge interpretation and managing ecosystem services.
- Digital ecosystems and technologies for decision support systems.
- Implications of IS frameworks and methodological treatment in the ecosystem applications;
- Ecosystem services are impacted by changing human and environmental factors (pre- and post-COVID), including ecosystems.
- Integration of Ecology Data AI models through Information System Articulations.