Do Innovative Organisations Survive Longer Than Non-Innovative Organisations? Initial Evidence from an Empirical Study of Normal Organizations
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACTThe literature focuses on innovation as an adaptive mechanism. From functionalist and evolutionary adaptation perspectives, innovative organizational populations should be expected to survive longer than normal ones, because innovative organizations and populations should be more adaptive than normal ones. The mortality rates of innovative organizational populations have not been identified, however, and the proposition of lower levels of mortality has not been tested. While the mortality rates of a number of organizational populations are known from the literature, the normal mortality rate for organizational populations has not been established either. This paper sets the stage for comparison of innovative organizations and populations to normal organizational population by identifying normal organizational population mortality rates. It concludes by discussing the basics of determining the mortality rate of innovative organizational populations.The approach to identifying normal organizational population mortality is demographic and the methodology a research synthesis of organizational population mortality studies described in the academic literature. It identifies the range of normal (mean) population mortality rates for all organizational populations assessed, and for the private (PS), non-profit (NPS) and public sectors (PSE) separately. A search of the literature for organizational population mortality studies found 33 published studies including one usable database (on the Internet). To assure only appropriate studies were included in the analysis, two criteria (screenings) were first applied to them: (1) unbiased study, covering a full population, and (2) not an outlier population. Twenty-eight studies met the standards set for the first screening and of these 21 survived the second screening. The expectation was that mortality rates would be highest in the PS; surprisingly, the highest mean mortality rates were discovered in the PSE (in the American federal government) followed by the PS. Should researchers be interested in studying innovative organizational populations using an organizational demography approach, recommendations are made as to how this could be done.Key words: Organizational demography, organizational ecology, organizational mortality, organizational population mortality, public sector innovation, innovative organization, mortality of innovative organizational populationsIntroductionInnovation has been promoted for all organizational sectors for two generations. We know little, however, about the impact of innovativeness on the survival of organizations or innovative organizational populations in any of the three societal sectors-the private sector (PS), non-profit (NPS) and public sector (PSE). To determine whether innovative organizational populations have different mortality rates than normal populations requires creation of a theory linking the effect of innovation on the mortality of its organization and its population, a methodology for tracking this link and the identification of a normal organizational population mortality rate against which to compare the results for innovative populations. This paper addresses the third element. First, normal organizational population rates are established. Then, suggestions are made for a methodology for researching innovative organizational populations. To discuss these issues effectively requires clarity of concepts, so the paper begins with a discussion of key concepts in organizational demography.DefinitionsAn organizational population is all or almost all of the organizations in a population, a population being, for example, an industry or all of the newspapers in a country or all of the trade associations in a country or all of the departments and agencies in a government. It is proposed that in the public sector (the publicly-owned sector) a population is a government responsible for a wide territory and a wide range of programs and services, such as a provincial or federal or a large local government but likely not a small local government. …
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it