One of the outcomes of this research was to identify those Factors and even attempt to quantify those identified. Further, an attempt was made to arrange the Factors according to the most fitting types of technology-based economic development. Whether a region was going to
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seek mass job growth or knowledge growth were the two massively different ways to approach technology-based economic development.
Numerous pieces of data have been identified in this dissertation. Further, much of the data has been characterized and utilized, sometimes with great result and sometimes not. Too often we have seen data misused. So this final decision analysis begins with that which is known and presents a logical decision approach.
As discussed in Section 6, there are many factors that must be considered in developing a regional technology-based economic development program. There are also many qualitative factors that, as shown previously, can be quantified even in the case of qualitative, affective variables that comprise the factors that impact technology-based economic development, the simple decision analysis structure will be utilized to represent the impact of both the quantitative and qualitative variable within each factor.
The first decision to be represented is whether or not a region has or can develop the leadership, vision, and governance to pursue technology-based economic development.
Although it may seem that the question is simply pursue/don’t pursue regional technology-based economic development, the impact of failing to have leadership, vision, and governance is the same as a “no” decision, so this can be easily represented as a single decision tool (Figure 9.6.1).
141 Governance Leadership Vision No Yes No Yes Yes Baseline Do not pursue technology-based economic development Baseline for pursuing technology based economic development
Initial Decision Whether or Not to Purse Regional Technology-Based Economic Development
No
No
Figure 9.6.1: Model for Pursuing Technology-Based Economic Development If the leadership, vision, and governance factor is credible, the next decision factor is whether to pursue a knowledge-based economy or one which seeks mass-job creation. This is critical to the determination of which factors you want to focus, as described in Section 9.6. Figure 9.6.2 provides a simple representation of this next decision phase.
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Figure 9.6.2: Model for Pursuing Knowledge-Based Economic Development Based on these simple decision trees/decision analysis diagrams, and the analysis of factors and variables presented in Section 9.5 and Appendix D, it is possible to generate formulas for ranking the relative performance of a region’s success in technology-based economic
development. It should be noted that there is no right or wrong formula, and that these ratings are not intended to be used for ranking one region against another. Rather, it is designed to help a region to honestly evaluate itself against these critical factors and, hopefully, identify areas where improving performance might have a positive impact on the regional technology-based economy. It is also hoped that these formulas can serve as a starting point for continuous
Regional technology based economic development program Mass job creation focus Knowledge-based regional economy Primary Factors Policy Factors Inflow Factors Environmental Factors Secondary Factors Knowledge Factors Attitudinal Factors Social Factors Primary Factors Knowledge Factors Environmental Factors Social Factors Secondary Factors Policy Factors Inflow Factors
Choosing Which Type of Regional Technology-Based
Economic Development to Pursue
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improvement that will one day produce much more effective formulas. On this basis, the formulas proposed are as follows:
Rate mass job creation = 2P + 2* n/3(I) + 2E + K + A + S Rate knowledge economy = 2K + 2E + 2A + 2F + P + I
Where each factor constitutes the average of each variable that comprises that factor where each variable is rated from 1 (lowest) to 5 (highest) for the region in question. There is one critical caveat to the ratings, is a regional economy is too dependant upon any single factor it cannot reach the mature category. For example, a region that is highly dependant upon a single source of revenue inflow, such as Huntsville, Alabama is with federal spending, that region cannot become truly mature unless sufficient diversity of revenue can be achieved. Therefore, the multiplier for the inflow variable is handled differently than the multipliers for other variables. It is critical that inflow variables originate with a minimum of three sources (per expert interviews) and that the origins of these sources be sufficiently different as to not be impacted by a single action. Therefore, the multipliers utilized for Inflow Factors is the number of primary sources (n) divided by the minimum required sources (3). As our example, to determine the value of P, the Policy Factor, assuming the values shown, would be calculated as follows:
Value of Business Friendly Tax Structure = 4 (1-5) + Value of Tax Breaks for R&D and AI = 3 (1-5)
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+ State and Local Support for Workforce Training = 3 (1-5) + Simplified Application Process for New Businesses = 3 (1-5) + Establishment of Business Incubators and Tech Parks = 2 (1-5)
Sum 15
Divided by Number of Variables 5 Value of P = 3
If a hypothetical region seeking a knowledge-based economy is rated as follows: K = 4; E = 4; A = 5; S = 3; P = 3; I = 2/3 (3)
Then that region’s rate would be:
R = 2 (4) + 2 (4) + 2 (5) + 2 (3) + 3 + 1 = 36
As discussed previously, regions can be rated as Mature, Adolescent, or Neophyte, based primarily on the regions studied and the anecdotal evidence from the expert interviews, the recommended rating ranges for each category in each type of regional economic development type are as follows:
Mass Job Creation Knowledge Economy
Mature 36 + 40 +
Adolescent 25 – 36 30 - 40
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