Impact of Innovation Initiatives in a Federal Government Agency: Measuring and Understanding the Influence of Culture and Employee Attitudes
Bibliographic record
Abstract
IntroductionThe design and implementation of institutional programs that promote innovation represent one manifestation of a heightened interest in the way management practices can be leveraged to stimulate global competitiveness, enhance human capital, develop a pipeline for new and more efficient products and services, and improve overall organizational productivity (Schrage, 2016). A great deal of discussion among leaders in the innovation community and authors of management literature, for instance, has focused on the role of open innovation practices, new business partnership models, and rapid scale development and dissemination of innovations in catalyzing organizational change (Chesbrough & Di Mini, 2014; pp. 169-188) (Sullivan, 2016). The rising degree of interest in innovation-related topics as potential drivers of such change underscores the importance of developing corresponding organizational capacity to measure the impact of innovation-related efforts and initiatives, particularly as such efforts relate to outcomes such as developing solutions to common organizational problems, achieving program-specific goals, and increasing the probability of attaining longer-term organizational objectives (e.g., the five-year goals documented in an organization's strategic plan). It is commonplace for leaders of large established organizations to ponder what can be done to change their culture to become more innovative and adapt to changing times and needs.The trends in current literature on the topic of measuring innovation-related impacts and outcomes typically focus on private sector strategic initiatives such as international development, small business creation, or employment training and workforce competencies that enhance competitiveness (Crespi, Maffioli, Mohnen, & Vasquez, 2011). In addition, the traditional outcome measures commonly used to assess the impact of innovation in these private sector areas (e.g., revenue, market share, shareholder value, and other aspects of business performance), though sound, are by their very nature generally inapplicable to the federal government or its respective entities, thus necessitating the exploration and creation of alternative measures by which to assess and demonstrate the value of innovation to publicly-funded programs. Generation of such measures is particularly important to demonstrate value in instances where delivery science methodology is used or employed. This methodology entails establishment of a specific functional unit within an organization, independent of that organization's overall operational infrastructure, but with an intense focus on solving problems and achieving or 'delivering' results. The degree of independence afforded such a functional unit provides for both added objectivity and the ability to independently test, scale, evolve, and apply new and non-traditional (i.e., more innovative) methodologies to existing problems without fear of penalization or reprisal (Barber, 2015). The ultimate impact of investments in such a methodology, however, can only be fully realized when accompanied by the ability to measure and evaluate it.This report describes the evaluation tools, methodologies, metrics, and measures used to assess the design and impact of specific innovation programs sponsored by a delivery science entity within an agency of the United States federal government. Further, to demonstrate the impact of this unit and its associated programs on workforce culture, several of these evaluation methods were applied to the participants of such programs in order to measure perceptions about their individual and collective experiences. The findings presented herein represent one example of the manner in which evaluation methodologies, metrics, and measures can be successfully evolved, applied, and adapted to federal or public sector environments to assess the impact of innovation related efforts on program design, program implementation, program effectiveness, employee perceptions, and workforce culture. …
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".