Michael Haenlein is a Professor of Marketing at ESCP Business School and holds the Chair in Responsible Research in Marketing at the University of Liverpool Management School. He holds a Ph.D. from the WHU, Otto Beisheim School of Management (2004), and a Habilitation from the Pantheon-Sorbonne University (2013). Professor Haenlein’s research interest and expertise deal with the impact of new technologies on firms’ customer relationship management activities. This includes work on artificial intelligence, influencer marketing, and, more recently, video games, mobile games, and eSports. Dr. Haenlein is also working in responsible research looking into inequality, well-being, and knowledge diffusion through popular media. He has published in journals such as the Journal of Marketing, JAMS, IJRM, Business Horizons, and the California Management Review. He counts among the top 50 most cited marketing academics worldwide, based on Google Scholar. Professor Haenlein is an Associate Editor at IJRM and JSR, part of the Editorial Review Board of the Journal of Marketing, JAMS, and the Journal of Interactive Marketing, on the International Advisory Board of the California Management Review, and the Advisory Panel of the European Management Journal. Professor Haenlein is also part of the Executive Committee of EMAC, where he serves as Vice President of Corporate Relationships, the Academic Council of AMA, and the Working Board of RRBM.
Panem et Circenses: Some Thoughts on the Video Game Ecosystem
The video game industry has exploded in size over the past years. What started as a niche spare time activity for teenage boys in gaming arcade halls has turned into a multi-billion dollar industry consisting of video game companies, streaming platforms, players, streamers, influencers, and eSports athletes. Video games are a sector worthy of investigation in their own right and are often seen as the precursor of the metaverse. In this presentation, I will talk about a series of projects I am currently working on in the space of video gaming. Specifically, I will look into the use of the video game ecosystem for marketers, how streamers and athletes exert social influence over their followers, and the mental health and well-being streaming can generate for professional streamers. I will also cover aspects of the metaverse and how it relates to video gaming.
Piyushimita (Vonu) Thakuriah
Vonu Thakuriah is a Distinguished Professor in Rutgers University-New Brunswick and Director of the Rutgers Urban and Civic Informatics (RUCI) Lab. Her research interests are in transportation planning and operations; big data, urban informatics, smart cities and social and economic cyberinfrastructure; and the social equity and data justice aspects of information technology, AI and automation. Her work has been supported by research funds from leading sponsors such as the National Science Foundation, European Commission, and UK Research and Innovation. She has also received funding from the U.S. Department of Transportation and a number of state, regional and local agencies. She has delivered keynote addresses and plenaries at prominent international and national venues, such as the such as National Academy of Sciences, European Commission in Brussels and Luxembourg, the Royal Academy of Engineering in London, the Leibnitz Center for Informatics in Germany, and the National Institute of Informatics in Japan. She has a wide range of experience in helping government agencies and private companies find data-intensive and technology-based solutions to complex urban and mobility challenges. She is passionate about addressing such challenges through strategies that focus on social justice, ethics in data and technology, environmental sustainability, and health and well-being.
Exploring the Links Between Travel Behaviour and Physical Distancing in Pre-pandemic Daily Life Using Lifelogging Images and Survey Data
We aim to understand the extent to which transportation mode uses, trip-making rates, and transportation related activities such as picking up or dropping passengers and use of transportation facilities, affect physical distancing in non-pandemic times. We utilize a multi-disciplinary framework to measure physical distancing and model the drivers of physical proximity to others. To measure the proportion of time spent physically distanced both indoors and outdoors, we utilize photographic images automatically taken every 5 seconds by 170 participants of a 2-day wearable camera survey, as part of a larger pre-pandemic (2015) data infrastructure consisting of household surveys and travel and activity diaries, and other heterogeneous sources of text, image, and Internet data. Using deep learning-based machine vision algorithms on the image data, we develop two measures of physical distancing, which are then regressed (using beta regression and multinomial logistic regression) against travel behavior factors, while controlling for covariates suggested by our framework. The findings show that people spend the vast majority of their time physically separated from others, without anyone within 2 meters, and a smaller proportion of their time away from others without anyone within longer distances (13 meters). We found statistically significant relationships between the level of physical distancing and the use of different types of transportation modes, as well as time spent on different activities. We argue for continued investment in operational and messaging strategies that support physical distancing not only for public transit systems, but also for auto-based access and egress points, and bicycle and pedestrian facilities
Edgar Meij is the head of the Search and Discovery group in Bloomberg Engineering’s Artificial Intelligence (AI) group. He holds a Ph.D. in computer science from the University of Amsterdam and has an extensive track record in information retrieval, natural language processing, and machine learning. Edgar leads several teams of researchers and engineers that work on information retrieval, semantic parsing, question answering, and smart contextual suggestions under severe latency constraints. Together, these researchers and engineers build, maintain, and leverage the company’s Search, Autocomplete, and Question Answering systems. They employ all of Bloomberg’s data sources, including companies, people, functions, fields, financial instruments, geographic locations, and the Bloomberg Knowledge Graph to enable Bloomberg Terminal users to ask complex questions and issue complex commands in natural language.
Modeling and Quantifying Uncertainty in Economic Indicators
Economic indicators are pieces of data used by analysts to interpret and predict macroeconomic activity. Governments leverage such analyses to define economic policy and thus manage their economies. Finance professionals keep an eye on economic indicators to help interpret current or future investment possibilities and thus to shape their trading strategies. At Bloomberg, we both provide and produce economic indicators for numerous geopolitical regions in the world, such as the gross domestic product (GDP), i.e., the value of goods and services produced, or the World Interest Rate Probability (WIRP), i.e., an estimate of the expected path of policy rate changes.
Historically, economic indicators are collected by government agencies through surveys and censuses, causing unnecessary lag between measurement and final analysis. In order to arrive at more real-time metrics, central banks are increasingly using sophisticated computer science methods to look at alternative data sources, such as web content and traffic, satellite images, and distributed sensors. In turn, the analysts transforming raw data into economic indicators are leveraging increasingly advanced AI and NLP techniques to obtain richer and more accurate signals for their analyses. In this talk, I will discuss the scale and variety of the sources involved that, coupled with the diverse and idiomatic nature of the financial domain, present unique challenges for the technologies and methodologies we can use.
The Easychair email should be used for enquiries about papers and abstracts.
The glasgow.ac.uk email should be used for enquiries about registration, attendance, and general enquiries.
Location18 Lilybank Gardens
Sir Alwyn Williams Building
University of Glasgow
Glasgow G12 8QN