Author: Elizabeth G. Pontikes (firstname.lastname@example.org)
Interviewers: Stefan Beljean (email@example.com)
Jensen Sass (firstname.lastname@example.org)
Article link: http://asq.sagepub.com/content/57/1/81
Question 1. From your faculty profile on the University of Chicago website we learn that you used to be an insider of the software industry. You were in charge of the “sales strategies and competitive positioning” of the company you worked for. To what extent has your study been inspired (and guided) by your first-hand experience as a market-participant?
My work in the software industry has had a large influence on this study, and my work on categorization in general. In developing competitive positioning for both start-ups and mid-sized companies, issues of categorization were critical. We thought a lot about how to use market categories to convey what our organization did and why it would be useful. This is especially important in the sales process – just to get an initial meeting in order to be able to make a more detailed pitch.
Question 2. In your paper, you focus on four hypotheses regarding the relationship of classifications and audience evaluations. When you began your research, did you have other hypotheses in mind that are now not part of the paper?
I did not have other hypotheses when I started this paper. But, I did discover some surprising results. The effects of category spanning were not what I predicted. The overall effect of category spanning was positive for the customer audience (the opposite of what I predicted), and there was no effect for the venture capital audience. In thinking about this result, I realized that effects of category spanning depend on categories having clear boundaries. If we think about the theory behind what drives penalties to category spanning, it results from the fact that the categories spanned are well-bounded entities. Ruef and Patterson (2009) illustrate this point. When I separated organizations that span unambiguous categories from those that span ambiguous categories, the surprising effects resulted from those spanning ambiguous categories.
Similarly, I had not initially hypothesized about differences between Corporate Venture Capitalists (CVC) and Private Equity Venture Capitalists in terms of how they evaluated ambiguous categories. This idea came from a reviewer, who asked whether all Venture capitalists would have the same reaction. I dug into the literature on corporate venture capitalists, and realized that CVCs are very different in terms of how they approach their investments. Thinking about the theoretical mechanisms that underlie the hypotheses, I predicted that they would act more like consumers, and would not have a preference for ambiguity. – Both of these examples illustrate how sometimes it is the results that you don’t expect that lead to some of the most interesting parts of a study.
Question 3. Your key contribution to the literature on categories and valuation is that you introduce a much needed distinction between different types of audiences. In your analysis you then focus on two broad groups of actors: market-makers and market-takers. Two related questions on that: A) How did you decide to focus on these particular two groups and not on others (e.g. shareholders, market analysts etc.)? B) You acknowledge that within the category of market-makers, we have to distinguish between independent and corporate venture capitalists. What other within-category variations do you think might potentially matter, e.g. within the category of consumers?
A) Venture capitalists and customers were good audiences to test the theoretical ideas presented in the paper. Venture capitalists are a key audience for software organizations, and they are traditional “market makers” who are interested in developing novel types of classification. Customers are at the other end of the spectrum, “market takers” who use classification to understand organizations and their products. In addition, looking at shareholders or stock market analysts would have excluded young, private firms that drive much of the innovation in this industry.
B) There are a number of distinctions within the customer audience that could affect preferences for ambiguity. Drawing on the theoretical reasoning, customers who are very engaged with the structure of the industry, and who enjoy trying out new types of things that cross market boundaries, will act like market makers and have positive reactions to ambiguity. I would expect that this would be the case for lead users or other early adopters of technology. Unfortunately, data on customer evaluations was too coarse to be able to test reactions of different customer segments.
Question 4. What (if anything) do you think might condition the scope of your argument? Could there be variation across different kinds of markets? For example, might venture capitalists evaluate software start-ups differently to, say, those in pharmaceuticals, bulk chemicals, or luxury goods? Further, could there be historical variation in their evaluations due to changes in the prevalence of different business models? Between the 1960s and 1980s, for example, we saw a profound shift from the model of the ‘diversified’ firm to that of the ‘focused’ firm. Perhaps now – as with Google and Microsoft – we are seeing the return of more highly diversified enterprises…
The argument is predicated on the orientation a person has to market classification. Audiences who are “market makers” seek novelty, and as a result are interested in engaging with, and changing, existing classification. Those that are “market takers” use classification to understand a complex environment. This means that if, in a different context, venture capitalists are not “market makers,” they will be less receptive to ambiguity. In industries that are less fast-paced, and where innovation is less important, venture capitalists may not be as interested in creating new markets and so would evaluate start-ups differently. It is also possible that venture capitalists of a different era were less focused on finding the next “new, new thing” and may have looked for safer investments.
It is possible that venture capitalists’ preference for ambiguity could lead to a greater shift in business thinking, where people see more value in broader, less focused firms. This is an interesting question for future research.
Question 5. To collect data, you made use of a cutting-edge method which involved “scraping” information from websites. What was the reaction of your reviewers, and how might this be combined with other research methods to build, extend, or refine your study?
Online data sources, whether scraped or from digital archives, are a critical source of data for social science research. When I first started this study it was an innovative way to collect data, but now is mainstream, and a critical skill to develop. The reviewers were not concerned about the method by which I gathered data. But they did have questions about using statements in press releases as a historical record of category affiliation, and asked me to validate these data. I did this by comparing claims made in press releases to other public documents using the archived Web and annual reports. In general, when using a new data source, it is important to compare it to familiar data sources as a validity check.