In a dynamic environment where underlying competition is “for the market,” this chapter examines what happens when entrants and incumbents can instead negotiate for the market…
Abstract
In a dynamic environment where underlying competition is “for the market,” this chapter examines what happens when entrants and incumbents can instead negotiate for the market. For instance, this might arise when an entrant innovator can choose to license to or be acquired by an incumbent firm (i.e., engage in cooperative commercialization). It is demonstrated that, depending upon the level of firms’ potential dynamic capabilities, there may or may not be gains to trade between incumbents and entrants in a cumulative innovation environment; that is, entrants may not be adequately compensated for losses in future innovative potential. This stands in contrast to static analyses that overwhelmingly identify positive gains to trade from such cooperation.
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The contingent valuation method (CVM) for assessing non‐use values has undergone significant criticism recently on various fronts. In this paper, the author analyses the notion…
Abstract
The contingent valuation method (CVM) for assessing non‐use values has undergone significant criticism recently on various fronts. In this paper, the author analyses the notion that imposing reasonable bounds on the rationality of agents might undermine the basis for any method that attempts to elicit non‐use values on environmental goods from individuals, including CVM. The model of bounded rationality applied is that of Gans (1996). On the basis of that model, it is argued that in complex and unfamiliar situations one would not expect individuals to be able to express their true preferences. Following this line, the author discusses the possibilities for using experts for valuation as well as for providing information for decision making on the preservation of public resources.
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Despite the hype about transformative technology, the authors of “Power and Prediction: The Disruptive Economics of Artificial Intelligence” see the recent AI advances as all…
Abstract
Purpose
Despite the hype about transformative technology, the authors of “Power and Prediction: The Disruptive Economics of Artificial Intelligence” see the recent AI advances as all basically ‘better statistical techniques’ that allow us to take really big data sets and come up with more refined predictions.
Design/Methodology/Approach
University of Toronto experts, Ajay Agrawal, Joshua Gans and Avi Goldfarb explain why transformation of business model by AI will be some time in the future when we move beyond simply substituting the new technology into existing systems and start to leverage its potential to enable the reimagining of old system solutions and innovate radically new value propositions.
Findings
What economic history tells us is that technology-driven transformation does not come easy and real adoption only occurs when new systems are created.
Practical Implications
As there are likely many decisions in your organization that have been codified into rules, AI offers the potential to turn them to dynamic decisions.
Originality/Value
To realize the full potential of AI, companies need to adopt a “system mind-set,” in contrast to the “task-level thinking” that still predominates in most companies.
Under the co-direction of John Hagel, Deloitte’s Center for the Edge has been publishing important new studies of disruption with an ‘outcome-based approach to disruption.’ This…
Abstract
Purpose
Under the co-direction of John Hagel, Deloitte’s Center for the Edge has been publishing important new studies of disruption with an ‘outcome-based approach to disruption.’ This research is discovering patterns that may help leaders institute defenses against threats and identify opportunities for innovators
Design/methodology/approach
Deloitte research is focusing on patterns of disruption that hit more than one market, but not all markets. It is examining: what are the characteristics of markets that would make them vulnerable to a particular pattern?
Findings
After six months of research, Deloitte has identified nine patterns that meet its outcome-based criteria. A number of the patterns are based on creating network effects that grow so quickly they become hard to compete with if the rival firm does not already have an established market position. Another set of the patterns identifies ways to fundamentally transform the value-cost equation, but without network effects.
Research limitations/implications
More patterns may be discerned as the research proceeds.
Practical implications
For example, if incumbents and innovators just think about driverless cars as the auto industry, they are never going to fully see the disruption that is coming. By contrast, by thinking about it as a mobility ecosystem, then many other key players, risks and opportunities become apparent
Originality/value
The patterns identified by Deloitte research may provide leaders with insights into how to defend against specific disruptions and also offer innovators inspiration for new opportunities in established markets and Blue Ocean ventures.
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The American culture is at risk because digital communication technologies insidiously undermine the concept of American individualism. As explained by Herbert J. Gans (1988),At…
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The American culture is at risk because digital communication technologies insidiously undermine the concept of American individualism. As explained by Herbert J. Gans (1988),At its most basic, individualism is the pursuit of personal freedom and of personal control over the social and natural environment. It is also an ideology – a set of beliefs, values, and goals – and probably the most widely shared ideology in the U.S. (p. 1).