Kristina holds a joint position between the department of Engineering Science and the Saïd Business School. She co-manages the “Engineering Entrepreneurship and Management” pathway for third and fourth year engineering students. She is a planning to teach a course in High tech entrepreneurship in the MBA program. Kristina is an organisational scholar with an interest in the intersection of technology, learning and organisations.
MSc and a PhD (2001) in Organisational Behaviour and Theory from Carnegie Mellon University’s Tepper School (PhD thesis: Selection, adaptation or chance? Determinants of firm performance in the U.S. tennis racket industry 1960-1991).
MSc in mechanical engineering at Chalmers Institute of Technology in Goteborg, Sweden (Master’s thesis: Market analysis and development for polyethylene paper).
Kristina is a member of the Academy of Management and the Strategic Management Society.
Kristina has two main research streams. The first is about learning: how organisations learn to manage unwanted events such as competitors introducing radical new technologies that undermine your product technology, or operational failures, such as train accidents. She has an interest in failure learning and has a review paper on the failure and error learning literature, and a series of papers on train accidents and how firms learn from their own vs their competitors’ accidents and how third parties such as government agencies facilitate this process.
The second stream is focused on technology and technical change as well as on the interface between innovation and business. Kristina's early work was on the technical content of independent inventors’ vs firm-based inventors’ patents (surprisingly similar, but independent inventors produce both more unique and more low-quality patents); and the problem that many scholars study radical/breakthrough/breakthrough innovations but that definitions and measurements of these critical events are inconsistent.
Kristina continues the work on how to best think about technology in the “Neophilia” project which contrasts the claim that the more disruptive an innovation is, the greater will the innovator’s success be, with empirical studies that find that novelty is risky and the benefits that accrue more often go to second or third movers in a market. In this project she wants to find out who and under what circumstances novelty is beneficial to the originator (firm or individual) of the novel invention.
Addressing firm strategies in high tech industries she has studied how firms use strategic alliances to manage competition. The alliance studies find that firms use alliances to encourage diffusion of their standard when there are competing standards (we studied this in the US cellular phone industry during the 2G era). Firms also use alliances to lower competition further in multimarket competition situations (this we studied using the world semiconductor industry). That is, we find that alliances are, indeed, used strategically by firms to improve their competitive position.
Kristina works on three levels of analysis: firm, technology and group. She uses longitudinal cross sectional data in most cases to capture how the same unit changes over time. Kristina's data sources include archival government data sets (the Federal Railroad Administrations’ rail safety data; US Patent Office data) to magazine text (advertisements and product tests) and student reports, quantifying different textual sources to allow formal hypotheses tests. She has studied tennis rackets, US freight rail, world semiconductors, wireless telephony and tennis racket manufacturers.
Kristina has worked with the OECD’s Directorate for Science, Technology and Industry to scale up the measure. The 'Neophilia' project is continuing this work.
The article 'Today’s Edisons or weekend hobbyists: Technical merit and success of inventions by independent inventors' was cited in US Supreme Court case 05-130
Strategic management and technology strategy courses are based on fuzzy models that link important but often unwieldy concepts held together in a strong logic but without mathematized relationships. These models require a large amount of data and even though each element might be straightforward, combining concepts into a whole becomes complex. To help students develop skills in managing this complexity, Kristina applies the Confucian logic of: 'I hear and I forget. I see and I remember. I do and I understand' and organise course work around projects: Using business, engineering and patent databases, students collect the information they need to measure and describe model constructs (rather than being presented the data in the form of a case study). After having collected data, students link concepts and provide evaluations and a quantitative forecast. In the ideal case, students learn model concepts deeply, see strengths and weaknesses in the models so that they can improve existing models and develop their own. Students should also be well versed in pursuing independent projects and have developed a critical attitude to how much one can rely on different data sources.
Said Business School
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