Proposal for Research on the Fate of Innovative Public Sector Organizations, Populations and Communities: A Research Synthesis and Prospectus 1
Bibliographic record
Abstract
ABSTRACTThe impact of innovation on organizational survival is not known, despite organizational survival being the basic measure of organizational success (Wischnevsky and Damanpour (2008: 62). Working from Glor's (2014a, b) framework for identifying the impacts of innovation on public sector organizations (PSO), this paper proposes an approach to studying the effect of innovations on their PSO that could be used for comparative research. A structural approach is proposed, focused on the impact of innovation on organizational mortality rates and the demographics of innovative organizations, organizational populations and organizational communities. The paper identifies the elements necessary to both the research proposal and the proposed demographic research approach.Key words: Impact of public sector innovation, innovative organization, innovative public sector population, organizational demography.Natural selection works at the individual level and is not necessarily good for the survival of the Things can and do get selected that are bad for the population. Quirks and Quarks, CBC Radio (Canada), October 6, 2012IntroductionDespite the promotion of innovation in the public sector (PSE) for two generations, the impacts of innovation have still rarely been addressed. Impacts are defined as downstream results or outcomes of innovations. In the few studies of impact, the focus has tended to be impacts on organizational functions (Damanpour is prominent in this work) and successful case studies,2 rather than impacts on organizational objectives, people, structures and survival. Because organizational survival is the basic measure of organizational success (Wischnevsky and Damanpour, 2008: 62), the effect of innovation (defined on page 9) on organizational survival needs to be assessed. Whether innovation is good or bad for the survival of organizations has not been determined, especially in the longer term. An exception is a pilot study done on five innovations, to determine if survival data could be collected after a long time (32 to 43 years) had elapsed (Glor and Ewart, 2015). The pilot authors were able to collect the needed information.This paper proposes study of the mortality rate of innovative organizations, populations and communities and their comparison to each other and to normal or non-innovative ones.3 The mortality rates of normal organizations have been studied (integrated by Baum, 1996; Glor, 2011, 2013). The logic is that innovation is an adaptation mechanism that reduces organizational mortality (March, 1991; Nohria and Gulati, 1996; Damanpour and Gopalakrishnan, 1999) and thus affects innovative population demographics. This structural perspective considers innovations as an important source of adaptation: organizations must adapt to survive and organizations that do not adapt do not survive.The key survival factor is not likely to be whether the organization invented the innovation (Damanpour and Wischnevsky, 2006) unless the organization is in the business of inventing innovation. Being in the business of inventing innovations is rare in the PSE except in research councils and innovative governments. Some key survival factors are more likely to be whether the objective has popular support, who promoted and initiated the innovation, organizational innovativeness, whether an organization fully implemented the innovation(s), whether the implemented innovation achieved the intended results, how much impact it had on the issue, and whether the objective and the impact could be effectively communicated to the public or some portion thereof (e.g. political party membership or a pressure group). Implementation is likely more difficult for innovators and early adopters (Rogers, 2010) but only full implementation can plausibly link the innovation and the organization's structural survival. Only if they achieve the intended results can whether innovations helped organizations survive be tested. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".