Term Paper: Innovation and Entrepreneurship

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Innovation and Entrepreneurship

The word entrepreneurship has its origins in the French verb entreprendre, meaning "to undertake." In one sense, every business idea starts with a core philosophy of entrepreneurialism, usually expressing itself through innovation and a willingness to risk in order to turn these ideas into economic gain. This is usually expressed either in ideas, small businesses, or in a mind-set. Entrepreneurship is an attitude more than anything else; it is the difference between leading and managing, between quitting if there are barriers and persevering. Entrepreneurial culture is part of the core of Western expansion into the new world, and requires distinguishing between motivation and opportunity. The very nature of economic growth opportunities depend on national institutions in conjunction with economic conditions, while motivations are determined by preferences -- and the entire risk-reward operation (Foreman-Peck & Zhou, 2010).

Management scholar Peter Drucker believes that appropriate decision making is perhaps the fundamental key to success or failure within a modern organization. In fact, he notes that "the skill we need is not long-range planning. It is strategic decision making or perhaps strategic planning" (Drucker, 2001, p.116). If we combine this idea of decision making at the nth level with innovation, we have the crux of the modern organization. For a modern organization to even hope to survive, however, strategic and innovation decisions must be made in almost every aspect of the business. Decision making success is now measures in minutes rather than days, and requires managers to be more informed and more willing to act. In terms of innovation, Drucker names seven classes of the source of innovation in the entrepreneurial spirit. These include: 1) the unexpected (developments or something new); 2) Incongruities (conflicts between opposing functions); 3) Process Needs, changes in culture or society that require innovation; and 4) Industry and Market Structure -- merging of industries, blending, accelerating new product growth as spin offs, etc. (Drucker, Innovation and Entrepreneurship, 2007).

Taking those four sources and extrapolating them into the modern world, we then understand that entrepreneurship is moving through job creation and integration that allows a forward purpose or progression. The activity may result in new organizations, revitalize older organizations, or even exist as a perceived response to opportunities that did not exist due to technology or location. Thus, entrepreneurship is not simply a business activity, but a multidimensional source of inspiration that moves from the various structures of business to psychological characteristics, engineering or re-engineering, through the social sciences and education (Audretsch, 2007). But one example of this is the move into predictive technology. This idea focuses on the fact that computer chips have become more powerful, resulting in greater computing power for less money (meaning more access from consumers). Predictive technology can be used in so many innovative ways: to develop a navigation system that predicts routes based on traffic or time of day, how much paper or ink your printer might use, regulate home heating and cooling based on weather data, or even a refrigerator that predicts a listing of groceries needed based on consumption trends (Brandon, 2012).

There are certainly companies and products already on the market that use predictive technology in an entrepreneurial fashion. For instance, Applied Predictive Technologies produces business analytics software that "reduces the risk of any new initiative by systematically testing the idea with a subset of stores, customers, or employees," by using complex algorithms that test multiple processes and ideas (scenarios) for viability (APT Technologies, 2013). This approach is basically a collection of "what if" scenarios that takes consumer and business data and "predicts" what is likely to happen based on demographics, psychographics, fiscal analysis, and operations. The power of this technology is that it is far less expensive than market research, and clients can, in theory, run infinite "games" and scenarios.

Using Drucker's Paradigm, we can analyze predictive technology in a way that shows how it is innovative, timely, and entrepreneurial:

Drucker Model

Predictive Technology

The Unexpected

Computer technology has improved over time. When we consider that the average SmartPhone has more computing power than all of NASA's first Apollo missions, we get the idea of access. If we look at Moore's law, every 18 months, the industry changes drastically (unexpected and planned changes). This allows for the development of predictive technologies to roll out in all sorts of products: coffee makers, the home (lighting, music, heating predicted when you are almost home), transportation, etc.


Drucker's idea is to see what is and "what ought to be." Certainly, predictions of life in 2012 are far more superior technologically in some ways, but in others, not as advanced. The incongruity though, for predictive technology might also be driven by the economics of using up current technology before introducing new technology. For instance, if Intel makes 1 million chips, they are likely not going to introduce too many new models until those 1 million are sold. So, while predictive technologies are becoming slowly available, there remains an incongruity in terms of availability to market. Manufacturers alike want to sell their current models prior to discounting them to introduce new models with predictive technology.

Process Needs

Innovation based on process needs implies there is something missing in the current process or technology. Process needs, from the manufacturing point-of-view, surround the ROI. If the cost to add predictive technology in 2010 is X, but 30% less in 2012 with a higher quality rate, then many manufacturers will wait until there is a true benefit (both fiscal and competitive) to adapting their systems. With predictive technologies added to products, there would need to be additional training, customer service issues, as well as warranty issues.

Industry and Market Structure

Changes in industry are often the result of consumer pressure; thus, with the half-life of technology, consumers are expecting more and more from the electronic appliances, and if adding a "smart-chip" makes the product more user friendly, the bar for the expectation has then risen to: "Why are more appliances and items not predictive." Also, while a consumer might pay 10-20% more for a predictive GPS, they might not pay hundreds of dollars more for a predictive heating or cooling system in their home. The cost-benefit ratio is both industry and market dependent -- advertising and market familiarity can change the consumer's view -- up to a point.

(Swaim, 2011)

Using the above model, it is easy to understand that much of the economic growth of both developed and developing countries can be firmly attributed to new businesses that are created and managed by nascent entrepreneurs. However, the concept of nascent entrepreneurship varies considerably according to context. These individuals may form the backbone of much of the new global growth patterns, and models the entrepreneur as an individual who is competent in many things but really does not excel in any one skill. Individuals with more balanced skills are more likely to embrace this model, and research shows that the most important determinant of entrepreneurship is having a broad-based background. Predictive technology is the type of technology that spans several business models and/or genres -- it can be adapted to the transportation industry, food industry, entertainment industry, and more -- nor is it culturally based (Soriano, D., et al., 2010). While the actual "product" that is being predicted might change, the idea that something is being counted, scenarios are being run, or more efficient ways of planning are taken place fits quite well into the world of global economic development.

Overall, the time has come for predictive technologies. Just as scanners and OCR software were prohibitively expensive for all but the most complex of applications two decades ago, the cost of adding predictive technologies to numerous innovations has dropped considerably. There are thousands of examples of using predictive technology in our daily lives, but five have received the most media attention:

Vehicles -- Based on the individual's route, time of day, traffic and weather data automatically uploaded, the vehicle can suggest a route home, or an alternative route, or a way to predict that you might be thirsty, and in need of an espresso. Ford and Google are working to predict the perfect route, using the right amount of gas for the types of driving conditions and geography.

Travel -- Everyone has had the experience of searching for just the right airfare to a certain location. Using data mining and predictive technology, predictive technology can tell the consumer when to purchase airfare to certain cities based on time of year, route, number of people travelling, etc.

Computing -- Whether a laptop, tablet, desktop or Smartphone, the idea of predictive technologies within the computer would base power, storage and memory usage on your personal surfing, work, or entertainment habits. The computer could predict when the backup was necessary; the Reg Entry was out of whack, or how to automate certain repetitive tasks (e.g. checking email or Facebook for a specific message, etc.).

Genetics -- While controversial, predictive technology in genetics would look into personal genomics [END OF PREVIEW]

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