What Happens to Innovations and Their Organizations? Piloting an Approach to Research
Bibliographic record
Abstract
Introduction2Managers and personnel in all organizations have been strongly encouraged to innovate since the 1980s (e.g. Peters and Waterman, 1982; Drucker, 1985), but what happens to innovations and their organizations that innovate and why? Is innovation adaptive? Does it enhance survival? In order for innovations to fulfill their program or process objectives, they must first be fully implemented. Are they? They must identify and use efficacious models. Do they? Then they must survive. Do they? How does their survival compare to that of normal3 programs and organizations? Normal survival for organizations was established by Glor (2013).Is developing or implementing innovations4 good for the survival of organizations or is it a detriment? The answers to these questions are relevant for both researchers and practitioners.The next subsections identify what we know about factors contributing to and survival of innovations and organizations. They draw on the published literature for help understanding: (1) the factors correlating with organizational fates for normal and changed organizational populations, (2) the demography of normal and changed organizational populations, and (3) the demography of innovations and their organizations.Factors influencing fate of programs and organizations. Only one study was found on a population of programs-Corder (2004) examined USA programs run by Cabinet departments and independent agencies listed in the Catalogue of Federal Domestic Assistance (CFDA). Including programs in existence both in the starting year (1974) and ones created after that date, he found a 56 per cent program mortality rate in 26 years, a mean mortality rate of 2.2 per cent per year. Studies of normal and changed organizational populations identified independent (not dependent on the organizations) factors correlating significantly with reduced survival included: young organizational age (Freeman, Carroll and Hannan, 1983), low endowment (Carroll and Hannan, 2000), small size (Bruderl and Schussler, 1990; Carroll and Huo, 1988; Fichman and Levinthal, 1991), fewer resources (Bruderl and Schussler, 1990; Singh, House and Tucker, 1986), high competition (Lewis, 2002), Republican politics (Lewis, 2002), narrow niche width and high population density (Carroll and Huo, 1988). In governments, factors positively correlated with innovation survival included environmental health (deprivation negatively) (de Lancer Jules and Holzer, 2001), higher urbanization, more resources, and large size of full-time employee group. Being rural or small had negative associations. Damanpour (1987) nuanced the factors in 75 non-profit libraries in the USA. Survival analysis (e.g. time series, survivor function, hazard rate) was often used to identify differences in the fate of organizations. These same factors are potentially also affecting the fate of innovations and organizations.The demography of normal and some abnormal organizational populations and have been published (summarized Baum, 1996; Glor, 2013).5 Abnormal is defined as biased or outlier studies. Once biased and outlier studies were removed, Glor calculated a baseline mortality rate for the 21 normal organizational populations6: the mortality rates for all 21 organizational population studies were less than 1.3 per cent per year. The baseline mortality rate in non-profit sector and private sector populations was lower than for the public (government) sector (Glor, 2015: Figure 9.1). The mortality rate for the ten public sector populations was under 1.3 per cent. These rates could be compared to the mortality rates of innovative public sector populations, should such research be done.The mortality rates of two changed organizational populations have also been studied. In a first study, Singh, House, and Tucker (1986) studied all 389 voluntary (non-profit) sector day care centres coming into existence from 1970 to 1980 in Toronto, Canada. They studied six types of changes: in goals, sponsorship, chief executive, service areas, location, and structure (e. …
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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 | 0.084 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.018 | 0.036 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".