My research interests have been in the fields of Operations Management and Data Analytics, in particular the area of risk and resilience in both supply chain and big data environments. I have gained wide recognition in this field, both nationally and internationally. Much of my research focuses on the investigation of risk dimensions and risk remedy practices that organisations adopt in a changing and complex environment. Faced with uncertainties and environmental dynamism, organisations are having to find alternative ways to react and survive in company and/or supply chain crises. My interest is in how companies and their decision-makers respond to a devastating crisis (such as a product recall incident), an event that demands a quick and effective risk management strategy and changes in the nature of company operations. In order to ensure organisational resilience, such changes will often involve new activities, skill-sets and technologies.My other chief research interest is the application of Social Media Analytics in company crises. This is a spin-off from two of my previous projects, funded by the British Academy and Chartered Institute of Logistics and Transport. Here, I use a cross-disciplinary approach to study practical research problems. For example, I have worked with colleagues in the fields of Computer Science and Sociology to develop an analytical approach to integrating secondary data, empirical data and social media data to study the stakeholder perceptions towards supply chain scandals, such as the Horsemeat Scandal and Volkswagen Emissions Scandal. More recently I have explored how to conduct risk and resilience analysis by integrating different types of data sources, such as financial, panel data and social media data. This work, supported by grants funded by CESFS and N8 AgriFood, aims to trace the UK food chain(s) by using secondary data, and explores how to integrate various secondary and open data sources to understand the adoption and diffusion of resilient practices in UK food supply chains.