Integration and Specialization in Academic Research

Birnbaum, Philip H.

doi:10.5465/255570

Cited by 12 publications

(18 citation statements)

References 17 publications

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“…Research fronts provide a way to study areas of science that scientists find interesting and useful, and also reveal important insights into how new scientific knowledge is incorporated into existing research (Birnbaum 1981c;Kuhn 1970;Small 1999Small , 2003. For qualitative richness and for expert external validation, we interviewed researchers in industry and academia to obtain their feedback regarding our method for identifying ''hot'' areas of emerging research.…”

Section: Resultsmentioning

confidence: 99%

Emerging research fronts in science and technology: patterns of new knowledge development

2009

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Research fronts represent the most dynamic areas of science and technology and the areas that attract the most scientific interest. We construct a methodology to identify these fronts, and we use quantitative and qualitative methodology to analyze and describe them. Our methodology is able to identify these fronts as they form-with potential use by firms, venture capitalists, researchers, and governments looking to identify emerging high-impact technologies. We also examine how science and technology absorbs the knowledge developed in these fronts and find that fronts which maximize impact have very different characteristics than fronts which maximize growth, with consequences for the way science develops over time.

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Section: Resultsmentioning

confidence: 99%

Emerging research fronts in science and technology: patterns of new knowledge development

2009

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“…Academic job markets reward scholars who have developed simple, easily recognizable identities by specializing in narrow subfields (Leahey, 2007); specialization in non-academic job markets brings similar benefits (Zuckerman et al, 2003;Ferguson and Hasan, 2013). Researchers who consistently collaborate within academic disciplines outperform those colleagues who dilute their identity by collaborating across specializations (Birnbaum, 1981). Knowledge-intensive organizations face similar consistency pressures-actions that are misaligned with established organizational identities cause the stakeholders to devalue organizations (Hsu and Hannan, 2005;Hannan, 2010).…”

Section: The Rewards Of Consistencymentioning

confidence: 97%

How novelty in knowledge earns recognition: The role of consistent identities

Trapido

2015

Research Policy

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a b s t r a c tThe novelty of scientific or technological knowledge has a paradoxical dual implication. Highly novel ideas are subject to a higher risk of rejection by their evaluating audiences than incremental, "normal science" contributions. Yet the same audiences may deem a contribution to knowledge valuable because it is highly novel. This study develops and tests an explanation of this dual effect. It is argued that the recognition premium that highly acclaimed authors' work enjoys disproportionately accrues to work that is consistent with the authors' previously developed identity. Because high novelty is a salient identity marker, authors' past recognition for highly novel work helps same authors' new highly novel work earn positive audience valuation. It is further argued that, because recognition for novelty is partly inherited from mentors, disciples of highly acclaimed producers of novel work are more likely to have their work prized for its novelty. In contrast, the authors' or their mentors' recognition earned for relatively less novel work does not trigger similar spillover effects and leaves the authors vulnerable to the novelty discount. Unique data on the productivity, career histories, and mentoring relations of academic electrical engineers support these arguments.

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“…With the exception of Birnbaum (1979) all studies under review operationalized group age (group longevity) as the average length of time group members had worked together in the respective group (Shepard, 1956;Wells, 1962;Smith, 1970 andWells/ Pelz, 1976;StahVSteger, 1977;Manners/ Stahl, 1978;Katz, 1982a and1982b;Katz/ Allen, 1982). We adopt this definition in the rest of our paper unless we explicitly state otherwise.…”

Section: Performance -A Short Reviewmentioning

confidence: 99%

“…Two types of measures of group peifomzance have to be distinguished clearly. One approach is to measure performance of groups by averaging individual level criterion scores (i.e., qualitative evaluations of performance or quantitative output counts) across all group members (Wells, 1962;Smith, 1970Smith, -and 1971Wells/Pelz, 1976;StahVSteger, 1977;MannerdStahl, 1978;Birnbaum, 1979). The other measurement approach is to assess group performance as such by employing qualitative evaluations of performance of the whole group (Shepard, 1956;Katz, 1982a and1982b;Katz/Allen, 1982).…”

Section: Performance -A Short Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Age structure shifts in Research & Development (R&D): Some statistical evidence and an integrative approach to link behavioural aspects of individual and group age in collaborative R&D

Gerpott

Domsch

1984

R & D Management

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Since empirical material concerning the age structure of industrial R&D organizations is scarce an attempt is made to present some statistical evidence in support of the wide‐spread notion of a shift toward a higher median age in R&D laboratories during the past years. Then, on an individual level of analysis the results of an extensive review of existing empirical research on the covariation of individual age and scientific performance are summarized in brief. On a group level of analysis research relating age of collaborative R&D groups (i.e., average length of time group members had worked together in a particular group) to group performance is discussed. To integrate the contradictory findings of prior research two analytic concepts are introduced, namely the discrimination of different situations of collaboration and the identification of two basic functions of interactions in collaborative R&D. Based upon these concepts an integrative approach to account for time‐related performance patterns on an individual and group level of analysis is laid out. Finally, possibilities of a diagnostic application of the suggested concepts are indicated.

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Integration and Specialization in Academic Research

Cited by 12 publications

References 17 publications

Emerging research fronts in science and technology: patterns of new knowledge development

Emerging research fronts in science and technology: patterns of new knowledge development

How novelty in knowledge earns recognition: The role of consistent identities

Age structure shifts in Research & Development (R&D): Some statistical evidence and an integrative approach to link behavioural aspects of individual and group age in collaborative R&D

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