Value Creation and Games of Innovation: The Competencies That Lead Firms to Business Success Depend on the Demands of the Particular "Game" in Which They Compete, a Study Reveals
Bibliographic record
Abstract
Many people believe that is unmanageable, given the uncertainties involved, the tendency toward unduly optimistic forecasts and the difficulty of predicting consumers' responses. The solution they often propose, however, is to foster many entrepreneurial flowers and let markets select the best products. This romantic view of is flawed because it allows too many resources to be allocated to hot sectors, thereby creating bubbles. The research we conducted, and which is explained in this article, has led us to a contrary view; namely, that is manageable and is not totally a gamble. Our study was carried out with senior R&D leaders from around the world, as part of an Industrial Research Institute subcommittee investigation on Managing R&D for Growth. Its original purpose was to identify: 1) the effective R&D strategies to manage for growth and (2) the best practices to achieve superior performance. Our central finding is that firms achieve high levels of performance in terms of profitability and growth not so much by adopting best practices but by adapting their capabilities and practices to the requirements of value creation and capture in the particular game, or games, in which they have elected to compete. The competitive and technological contexts orient and structure each game differently. In the first section of this paper, we outline the research methods, data sources and data analysis. Next, we describe the eight games of innovation that were identified by understanding value creation. Then, in the third section, the generic practices for managing are shown to vary with value creation activities in each game of innovation. In the fourth section, the specific capabilities and practices that are significantly associated with high levels of sales growth are presented for four games. We conclude by summarizing our key findings and their strategic implications. How the Study Was Conducted As this research began, the context created by the New Economy challenged many received practices and theories related to the management of innovation. Novel ways of managing R&D were identified that included cross-functional teamwork, speed in bringing products to markets, technology outsourcing, and so forth (1,2). For our part, instead of relying on the management literature to identify best practices, we decided to build from reality and ask CTOs and R&D vice presidents in the United States, Canada and Europe which strategies and practices they had developed to face the new situation. From our extensive discussions, a theoretical model was elaborated and a survey instrument was designed to quantify value creation and capture best practices for managing innovation. Then, the same executives as well as a broader group were invited to respond to the instrument in personal or virtual meetings. Seventy-three CTOs and R&D VPs in Europe (25), Canada (20) and the United States (28) agreed. From the data they provided, we identified 125 best practices for managing in such areas as exploration, portfolio and project management, transfer to business units, and market shaping. We then asked our respondents to rate the extent to which these practices were actually used within their firms. Ratings were obtained on each of the 125 dimensions. Our sample was not random but designed for learning by including a substantial number of firms in fast-moving, R&D-intensive, sectors. The industries covered are both capital- and knowledge-intensive, but only 20 percent of our sample is from the IRI membership. Data on the financial performance of firms were gathered independently from public databanks and corporate websites. The wealth of information we gathered made it possible to undertake statistical analyses. First, factor analyses were made to identify the underlying vectors that characterize the responses of the CTOs and VPs to their use of management practices. …
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".