E. Wetter

Patterns of Performance in New Firms: Estimating the Effects of Absorptive Capacity

... Leif L., Mikael S., Johan W., Alex McK., Karin H., and the people at EM Lyon have been very h... more

Improving Business Failure Prediction for New Firms: Benchmarking Financial Models with Human and Social Capital

The Journal of Private Equity, 2009

Business failure prediction is a prominent issue in research and practice. However, the relation ... more

Predicting New Firm Performance: A Novel Approach to Absorptive Capacity Measurement

... based resources (applicable to discovery and exploitation of opportunities) are positively re... more

Patterns of Performance in New Firms: The Relative Effects of Potential and Realized Absorptive Capacity

Scholars agree on the importance of new firms in knowledge-intensive fields for creating economic... more Scholars agree on the importance of new firms in knowledge-intensive fields for creating economic development and growth, yet there is still not a refined framework for assessing and predicting new firm performance. Absorptive Capacity (ACAP) is the capability of a firm to discover, recombine, and exploit technological knowledge internal and external to the firm, and it has been shown to be a relevant predictor of performance in knowledge-intensive fields such as biotechnology. Common operationalizations of ACAP are less useful when assessing the performance of new firms. This paper proposes the proportion of employees with technology or science education as an alternative measure for potential ACAP and patent data as a proxy for realized ACAP. Applying these measures on a full population of knowledgeintensive start-ups we find that ACAP is a relevant construct when predicting survival and multiple aspects of new firm performance, and that proportion of employees with university-level technology/science education seems to be a working and reliable operationalization.

Download

Education and New Firm Creation

Remotely measuring populations during a crisis by overlaying two data sources

by Nita Bharti and E. Wetter

International health, 2015

Societal instability and crises can cause rapid, large-scale movements. These movements are poorl... more Societal instability and crises can cause rapid, large-scale movements. These movements are poorly understood and difficult to measure but strongly impact health. Data on these movements are important for planning response efforts. We retrospectively analyzed movement patterns surrounding a 2010 humanitarian crisis caused by internal political conflict in Côte d'Ivoire using two different methods. We used two remote measures, nighttime lights satellite imagery and anonymized mobile phone call detail records, to assess average population sizes as well as dynamic population changes. These data sources detect movements across different spatial and temporal scales. The two data sources showed strong agreement in average measures of population sizes. Because the spatiotemporal resolution of the data sources differed, we were able to obtain measurements on long- and short-term dynamic elements of populations at different points throughout the crisis. Using complementary, remote data s...

Download

Approaching the Limit of Predictability in Human Mobility

by Nita Bharti, E. Wetter, and L. Bengtsson

Scientific Reports, 2013

In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile... more In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy. S tudies of mobility patterns and predictions of individual mobility trajectories are important in many research fields, such as mobile computing, epidemic modeling, traffic planning and disaster response 1-3 . Real-time locations visited by individuals are typically collected through mobile devices equipped with global-positioning system (GPS) capability, mobile phone cell towers, or wireless local area network (WLAN) access points.

Download

Enhanced action on livelihood resilience to climate change with Big Data in Bangladesh: Mobile Data, Environmental Extremes and Population (MDEEP)

by David Wrathall and E. Wetter

Download

Flowminder Foundation (www. flowminder. org) West Africa human mobility models: Version 1.0, August 21st 2014

Commentary: Containing the Ebola outbreak–the potential and challenge of mobile network data

by E. Wetter and Caroline Buckee

Uploads

Papers by E. Wetter

Log In