Proposal for Research on the Fate of Innovative Public Sector Organizations, Populations and Communities: A Research Synthesis and Prospectus 1
Notice bibliographique
Résumé
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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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,009 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
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