Conflicting Institutional Logics: How Academic Spinoffs Handle

Academic Affiliation

Daniela Bolzani, Riccardo Fini, Joel Gehman & Antonio Giuliani

2015 Alberta Institutions Conference

“Storytelling is an essential component of an entrepreneurs’ toolkit”

(Martens, Jennings & Jennings, 2007: 1107)

Why do narratives matter?

Recently established organizations suffer from the liability of newness. Therefore they need to demonstrate legitimacy and acquire resources.

Entrepreneurs use narratives to: Generate meaning in terms of distinctiveness

and legitimization Attract resources and support from external

stakeholders

But what about a larger n study?

Entrepreneurs choose how and to what extent to narrate their ventures’ characteristics Importance of vocabularies, i.e. systems of

words and the meanings of these words (Loewenstein et al., 2012)

Narratives contain vocabularies which refer to categories with a shared meaning: rooted in institutional logics in circulation

Theoretical lacunae

The importance of entrepreneurial narratives has been stressed by previous literature (e.g., Lounsbury & Glynn, 2001; Martens, Jennings & Jennings, 2007; Garud, Gehman & Giuliani, 2014)

But how are entrepreneurial narratives at a given time shaped by an organization’s founders, trajectory and institutional context?

Working model

NarrativeLogic(s)

Entrepreneurial team

Organizational trajectory

Institutional environment

Research context

Panel of Italian Academic Spinoff Organizations (ASOs), 2000-2012 (Project TASTE; n = 922)

Competing logics (Sauermann & Stephan, 2013): academic logic: search for fundamental

knowledge, research freedom, peer recognition, public disclosure of research results

commercial logic: applied research, bureaucratic control, limited disclosure, and private appropriation of financial returns

Dependent variable

Text analysis of website logics using dictionaries developed for this project

Companies’ websites as “minimal narratives” (Czarniawska, 1998: 17)

• 137 English language only• 267 Italian and English language• 236 Italian language only

English = 404

Italian = 503

Step 1. Deductive approach

1. Define constructs2. Develop keyword lists based on existing literature

and suggestions from scholars 3. Supplement keyword lists with synonyms 4. Validate with external judges (IRR=88%)5. Complete keyword lists through word stemming6. Translate to Italian

Academic logic

An academic logic is rooted in a set of norms comprising the ethos of science (i.e., universality, communalism, disinterestedness, and organized skepticism) aimed at adding to the stock of public knowledge. An academic logic operates through organizational practices intended to support all sets of legitimated activities, which are based on experimentation, research freedom, individualistic/small group management, long-term orientation and a collegiate reputational discipline system. The outputs of this logic are open, public disclosure of fundamental research results through publishing, and rewards in the form of peer recognition and status.

Commercial logic

A commercial logic is rooted in a set of norms comprising uniqueness (i.e., preventing others from replicating the organization’s work), private property (i.e., restricted and limited disclosure of research results and benefits) and obtainment of competitive advantage and market success, aimed at adding to the stream of private rents that might be derived from the possession of knowledge. A commercial logic operates through organizational practices intended to support all sets of legitimated activities, which are based on solving applied problems, bureaucratic control, team management towards efficiency, short-term orientation and a market-based discipline system. The outputs of this logic are the production of products/services, private property of ideas and economic/financial rewards.

Step 2. Inductive approach

1. Identify significant texts Merton (1973); Italian University Statutes (n=64); Il Sole 24 Ore (n=180)

2. Word frequency analysis (ATLAS.TI)3. Define the constructs4. Coding by two authors (IRR=79.8%)5. Translation to Italian6. Completion of word list through word

stemming

Step 3. Combining approaches

EXAMPLE ACADEMIC-LOGIC VOCABULARY (ENGLISH)

AccessibilityAccessibleAccessingAdvanceAdvancedAdvancementAdvancementsAdvancesAdvancingAltruismAltruisticAltruistically

Area of studyBasic researchBlue sky researchBlue-sky researchBody of factsBody of knowledgeBody of lawsBody of principlesBookBooksBreakthroughBreakthroughs

EXAMPLE COMMERCIAL-LOGIC VOCABULARY (ENGLISH)

ACCOUNTACCOUNTANTACCOUNTINGACCOUNTSACQUISITIONACQUISITIONSADVANTAGEADVANTAGESAGENTAGENTSAMORTIEAMORTISED

AMORTIZATIONSAMORTIZEAMORTIZEDAMORTIZEMENTAMORTIZEMENTSAMOUNTAPPLIEDApplied researchAppropriateAppropriatedAppropriatesAppropriating

Gehman & Grimes, 2014

Preliminary sampleItalian language

scraped websitesEnglish language scraped websites

Organizational attributes Mean S.D. Mean S.D.Year founded 2007 3.36 2007 3.32Limited liability legal form .98 .12 .99 .08Equity (in 000s of Euros) 49.25 192.95 75.05 361.96Industry sector Freq. % Freq. %Professional, scientific and technical 256 58.31% 187 53.43%Information and communication 98 22.32% 85 24.29%Manufacturing 58 13.21% 69 19.71%Other services 14 3.19% 3 .86%Wholesale and retail trade 8 1.82% 3 .86%Construction 2 .46% 2 .56%Agriculture, forestry and fishing 3 .69% 1 .29%Geographic area Freq. % Freq. %ITC - Northwestern Italy 106 24.15% 111 31.71%ITD - Northeastern Italy 124 28.25% 108 30.86%ITE - Central Italy 112 25.50% 73 20.86%ITF ITG - South&Insular Italy 97 22.10% 58 16.57%

Scraping results

English websites (n = 350) Mean SD Min MaxAcademic logic dictionary matches 739.0 3,793.4 0 57,930Commercial logic dictionary matches 1,072.0 3458.9 0 41,155Total words on website 54,268.4 247,506.3 141 2,987,633Italian websites (n = 439) Mean SD Min MaxAcademic logic dictionary matches 980.2 4,985.6 0 78,524Commercial logic dictionary matches 1,751.8 9,498.8 0 1,289,60Total words on website 68,427.8 443,921.7 16 8,008,865

Next steps

Validate and operationalized scraping resultsUse longitudinal, multilevel, secondary data to explain 2015 narratives. As some examples:• Entrepreneurial team: entrepreneurs’ length of academic

vs. commercial career experience• Organizational trajectory: company’s affiliation with

parent university (e.g., board membership; equity), past successes and failures

• Institutional context: University’s scientific prestige; experience with commercial activities (e.g., length of TTO establishment; patent/spinoff regulations)

Possible contributions

Organization studies: theorize and empirically test, in a systematic, large-scale way, the effect of multilevel historical factors on how entrepreneurial ventures transform cultural registers into narratives

Entrepreneurship: embracing the “linguistic turn” (Martens et al., 2007) and analyze how vocabularies are adopted and used to shape entrepreneurial narratives

Thank You!