Business Model Innovation for Intelligent Automation: Unpacking the Productivity Paradox

Lead Research Organisation: University of Cambridge
Department Name: Engineering


Productivity growth has been slowing down in the last decade in major economies as well as in emerging markets despite the prevalence of digital technologies. This phenomenon is widely known as the productivity paradox. The productivity growth slowdown is particularly acute in the UK compared to other major economies. Moreover, industries that are the most intensive users of Information and Communication Technologies (ICT) appear to have contributed most to the slowdown in productivity. One of the main reasons for this productivity slowdown could be due to the limited redesign of business processes and business models following the adoption of new digital technologies by firms. Through the research programme Dr. Velu will provide a better understanding the relationship between business model innovation and productivity improvements following the adoption of intelligent automation technologies. Dr. Velu will build a digital tool for management information and decision support systems for assessment of productivity of business models in order to enable rapid and sustained improvements in productivity within firms following the adoption of digital technologies. In doing so, the Dr Velu aims to propose a new framework for productivity reporting for national income accounting.

Dr. Velu will conduct historical analysis of firms that have implemented intelligent automation technologies in order to learn and develop the criteria for productivity measurement of business models. This will include analysis from historical publically available data as well as within firm analysis of a number of selected sectors such as manufacturing, distribution and the sharing economy. In addition, the research will conduct longitudinal in depth analysis of firms in similar sectors as the historical analysis in order to build a digital tool that will identify business model innovation opportunities following the adoption of intelligent automation technologies. This will involve working with the senior management team of a selected number of firms in these sectors in order to define the data requirements, draw-up the technology specification, develop the software programme, populate and test the digital tool with data and propose ways to embed the digital productivity tool within existing management reporting systems. The research will benefit firms as it will provide the basis for a systematic evaluation of the need for business model innovation opportunities following the implementation of intelligent automation technologies. The research will also benefit policymakers by defining good quality and appropriate data in addressing the challenges of measuring productivity in the digital economy.

Planned Impact

The research will provide key benefits to the stakeholders across the managerial, policy and academic communities as follows:

(1) Supporting Business Model Transformation
The digital productivity tool will help managers in established and start-up firms to decide how to assess the impact of the intelligent automation technologies that they implement in order to fully leverage the productivity benefits through appropriate business model innovation. Moreover, technology transfer organisations (such as the Digital Catapult, the Advanced Manufacturing Catapults and Innovate UK) will benefit as they would be better able to provide advice to firms testing and implementing the latest intelligent automation systems using the Catapults facilities to gain the full productivity benefits. Moreover, the involvement and support of IBM as well as The Conference Board will ensure that the digital productivity tool is disseminated widely among firms implementing digital technologies.

(2) Influencing National Policy
Policymakers such as the Department for Business, Energy and Industrial Strategy (BEIS) and Office for National Statistics (ONS) will benefit significantly from defining good quality and appropriate data in addressing the challenges of measuring productivity in the digital economy. The research will feed into initiatives related to the recent Industrial Strategy Green Paper. This will result in more effective policymaking on industrial strategy. Policymakers will be involved from the start of the research programme in order to ensure sustained interactions. Policy Fellowships will be organised through our partnership with the Centre for Science and Policy (CSaP) at the University of Cambridge ensuring strong policymaker engagement and dissemination of findings.

(3) Consumers and the Economy
Consumers and the economy will benefit from new digital technologies such as intelligent automaton becoming available sooner as a result of appropriate business model innovation. Moreover, addressing a key element of the productivity paradox by enabling quicker and more effective business model innovations will stimulate faster economic growth.

(4) Further Academic Investigations and Research Capability
Research findings generated during the project will be disseminated at international conferences and through leading academic journals. Dr.Velu will plan a special issue with one of the leading journals on the topic. These will provide valuable routes to publish developments in intelligent automation and its impact on business model innovation and productivity in an accessible manner to academics and practitioners alike. The involvement of the visiting professor who are all experts in digital technologies will support this strand of activity. Moreover, the research programme will complement existing research on productivity such as the Economic Statistics Centre of Excellence (ESCoE) on measuring the modern economy and the Centre for Economic Performance's (CEP) productivity programme and to the forthcoming ESRC Network Plus on Productivity. Finally, the Research Associates will acquire unique skill sets in combining social science with engineering to address productivity issues. These skills will be disseminated widely through research training in order to help train other researchers.


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