There is a growing interest in ecosystems as an approach for understanding the context of entrepreneurship at the macro level of an organizational community. It consists of all the interdependent actors and factors that enable and constrain entrepreneurship within a particular territory. Although growing in popularity, the entrepreneurial ecosystem concept remains loosely defined and measured. This paper shows the value of taking a systems view of the context of entrepreneurship: understanding entrepreneurial economies from a systems perspective. We use a systems framework for studying entrepreneurial ecosystems, develop a measurement instrument of its elements, and use this to compose an entrepreneurial ecosystem index to examine the quality of entrepreneurial ecosystems in the Netherlands. We find that the prevalence of high-growth firms in a region is strongly related to the quality of its entrepreneurial ecosystem. Strong interrelationships among the ecosystem elements reveal their interdependence and need for a systems perspective.

This article describes how innovations develop over time based on findings emerging from seven innovations included in the Minnesota Innovation Research Program. These observations are very different from typical models in the literature of the innovation process. The actual process is fluid, and includes an initial shock to propel the innovation into being, proliferation of the original idea, setbacks and surprises along the way which provide numerous opportunities for learning and failure, and a blending of the old and the new as the innovation is implemented and diffused. This article is one small step in developing descriptively more accurate and useful models of the innovation process based on longitudinal research studies.

to develop and commercialize an innovation. Entrepreneurship is a collective achievement that resides not only within the parent organization of the innovation but also in the construction of an industrial infrastructure that facilitates and constrains innovation. This infrastructure includes (1) institutional arrangements to legitimize, regulate, and standardize a new technology; (2) publicresource endowments of basic scientific knowledge, financing mechanisms, and a pool of competent labor; (3) development of markets, consumer education, and demand; and (4) proprietary research and development, manufacturing, production, and distribution functions by private entrepreneurial firms to commercialize the innovation for profit. This chapter takes a macroperspective of the innovation journey and focuses on the issues and events involved in developing an industry infrastructure for innovation. In doing so, we make three contributions to managing the innovation journey:

PURPOSE Scale-up and spread of evidence-based practices is one of the most important challenges facing health care. We tested whether a statewide initiative, Depression Improvement Across Minnesota-Offering a New Direction (DIA-MOND), to implement the collaborative care model for depression in 75 primary care clinics resulted in patient outcome improvements corresponding to those reported in randomized controlled trials.METHODS Health plans provided a new monthly payment to participating clinics after a 6-month intensive training program with ongoing data submission, networking, and consultation. Implementation was staggered, with 5 sequences of 10 to 40 clinics every 6 months. Payers provided weekly contact information for members from participating clinics who were filling antidepressant prescriptions, and we conducted baseline and 6-month surveys of 1,578 patients about their care and outcomes.RESULTS There were 466 patients in DIAMOND clinics who received usual care before implementation (UCB), 559 who received usual care in DIAMOND clinics after implementation (UCA), 245 who received DIAMOND care after implementation (DCA), and 308 who received usual care in comparison clinics (UC). Patients who received DIAMOND care after implementation reported more collaborative care depression services than the 3 comparison groups (10.9 vs 6.4-6.7, on a scale of 0 of 14, where higher numbers indicate more services; P <.001) and more satisfaction with their care (4.0 vs 3.4 on a scale 1 to 5, in which higher scores indicate higher satisfaction; P ≤.001). Depression remission rates, however, were not significantly different among the 4 groups (36.4% DCA vs 35.8% UCB, 35.0% UCA, 33.9% UC; P = .94).CONCLUSIONS Despite the incentive of a supporting payment change and intensive training and support for clinics volunteering to participate, no difference in depression outcomes was documented. Specific unmeasured actions present in trials but not present in these clinics may be critical for successful outcome improvement. Ann Fam Med 2015;13:412-420. doi: 10.1370/afm.1842. INTRODUCTIOND espite 25 years of extensive efforts to improve care quality and extensive research efforts to identify the best methods for widespread implementation of evidence-based practices, we still have limited knowledge about how to facilitate such spread. One of the most striking examples of a gap between evidence and practice in need of such spread is in primary care for depression. At least 79 randomized controlled trials of the collaborative care model for depression have found improved patient outcomes, and there is evidence that the model is cost-effective and even cost-saving.1-8 As a result, one might expect widespread implementation of collaborative care, but even care systems and clinics that participated in randomized trials have not usually continued the approach. 9,10 A key barrier to implementation of collaborative care is lack of reimbursement for the model's components. The model also requires major changes in traditional primary ...

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