Research.

Current Academic Research

The scope of this work involves areas of potential research and impact relating to Artificial Intelligence and Business Models/strategies.    Some examples are illustrated here. 

For more information  on collaboration with AIIN go here

or contact Professor Mark Skilton, Industry Director of AIIN 

THEME: Implementing Human Machine Tasks - an adoption framework

RESEARCH:  Development of an empirically-derived Overarching ambition to improve the productivity of organizations through the application of an empirically-derived framework/Heuristic Tool Set for AI Implementation in Incumbent Firms. 

The implementation of Artificial Intelligence (AI) brings at least two challenges for the incumbent firm. It involves a) determining which tasks in an already established business to target, and b) identifying AI technologies that correspond to these value creation opportunities. However, there is limited research that can guide incumbent firms through the implementation of AI.  Extant research tends to be technical, or uncritically presenting an idealized future, largely detached from the business reality of incumbent firms.  To address these challenges, we develop a “heuristic tool” set for AI implementation in incumbent firms by embarking on an Action Design Research (ADR) project, designed to contain two ADR cycles, in collaboration with a long-established, multinational food processing and retailing company, AIXCo. In this short paper, we document our research-in-progress and its ambition to contribute to the emergent literature knowledge in information systems on AI in general and AI implementation in particular. The proposed heuristic tool views AI implementation as recombination of humans and machines capacities, helping firms to break down their current business tasks, to identify where it generates value, and to plan a feasible change.   

THEME: Machine Learning of Legal text policy verification and validation

RESEARCH: Applying AI-based solutions into high-value litigation

Litigation analytics is now increasingly seen as critical to the evolution of the litigation sector in England and Wales. In partnership with  Solomonic and funded by Innovate UK, this research aims to develop machine learning capabilities that will drive world-leading litigation data and analytics.

 

​THEME: Representations of physical-virtual environments across Human and Machine boundaries - a framework for classifying knowledge, learning, and intelligence decision 
 

RESEARCH: New Information theory for human and machine automation boundaries of knowledge and intelligence interaction.
 

Development of a new data—knowledge—intelligence-learning taxonomy for human-machine interaction. How to bridge the current trilemma divide between social science/IS, Computer science and behavioral cognitive fields.   This addressed several gaps in practice and academic theory that might be summarized as the way human knowledge- machine knowledge and human learning and machine learning are defined in a physical and virtual environment.  This is central to the problems of define skills, task intelligence and higher forms of reflective intelligence which currently lacks frameworks to successful define how physical and virtual spatial, temporal representations of knowledge, learning and intelligence are and will evolve.  For example, how virtual experience in VR/AR captures knowledge and learning.  For example, how automation of tasks and reinforcement learning is defined in a state-space (computer science)/ value space (social science) perspective.

A supporting rationale for this work in recent research and pilot work into Digital twinning and the use of VR/AR models and how to integrate AI into these physical-virtual environments.  How do information models need to evolve to represent both 3D representations of environments, humans and machine skills and intelligence? Current classical semantic ladders and 3D modeling lacks a new nomenclature that described the data and knowledge assets and learning intelligence structures. This is a current impediment for companies seeking where to apply AI impact for productivity and performance improvement.  Practical research is ongoing on this topic and seeking other potential industry partners and academic research collaborators. 

THEME: Decentralized, distributed knowledge, and decision models for Business - a Multi-agent systems case approach

RESEARCH: New domain model of interactive intelligence for ecosystem representations

This is based on the research into multi-agent systems (MAS) and Blockchain/ledger technology with Fetch.ai and specifically how business models will need to evolve to support a decentralized intelligence model of interaction and trading. 

Selected Published
Academic  Research

THEME: Digital Platform Strategy and AI 

Rai, A., Constantinides, P., and Sarker, S. (2019) Editorial - Next Generation Digital Platforms: Toward Human-AI Hybrids. MIS Quarterly, 43(1), pp. iii-ix 

 

THEME: AI and Healthcare
 
Constantinides, P., and Fitzmaurice, D. (2018). Artificial Intelligence in Cardiology: Applications, Benefits and Challenges. British Journal of Cardiology 25(3), pp. 1-3.

 

THEME: Digital Platforms
 
Constantinides, P., Henfridsson, O., Parker, G.G. (2018) Platforms and Infrastructures in the Digital Age. Information Systems Research, Articles in Advance, pp. 1–20

 

THEME: AI Governance
 

Möhlmann, M. and Zalmanson, L. (2017): Hands on the wheel: Navigating algorithmic management and Uber drivers' autonomy. Proceedings of the International Conference on Information Systems (ICIS 2017), December 10-13, Seoul, South Korea

 

THEME: Social Media Behavior Analytics


Guo, W., Gupta, N., Pogrebna, G., and Jarvis, S. A. (2016) Understanding Happiness in Cities using Twitter: Jobs, Children, and Transport, Proceedings of the IEEE International Smart Cities Conference, London, UK, 29-30 November, 2016 
 
 

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