Capturing Value from Knowledge and Competence

The thesis advanced here is that the competitive advantage of firms in today’s global economy stems not from market position, but from the ownership and/or employment of difficult-to-replicate knowledge assets, and the manner in which they are deployed. It is always useful to distinguish between the creation of new knowl- edge and its commercialization.

The Lucana in Economic Growth Theory

Economic growth theory, be it neoclassical or the so-called “new” growth theory, is limited in its ability to explain differences in growth rates amongst nations. Perhaps the primary reason for this is the low appreciation and understanding amongst economists with respect to the role of institutions, management, and gov- ernance. Few would dispute that

The Role of Management in Economic Growth

Invention does not necessarily lead to innovation. In the first century  AD, Heron of Alexandria invented the aelopile, which was arguably a steam engine, since it converted steam into rotary motion; but it was basically a curiosity, and never put to practical use. The Chinese were the world’s technological leaders from about 500 AD to

Dynamic Capabilities in Economic Growth

Is there any way to distill the basic factors that lie behind busi- ness success? The answer, I believe, is in part context driven. Consider therefore competitive economies open to international trade, investment, and technology flows with  legal  structures that allow credible commitments. In such contexts, what are the foundations of business success? Does

The Dynamically Competitive Enterprise and Talented Individuals

1. General The dynamic capabilities framework outlined above and elaborated in Chapter 1 recognizes certain levels of dependence between the organization and the individual. In particular, if the CEO and the management team fail to perform well with respect to sensing, seizing, and then transforming, performance failure at the enter- prise level is likely.

Market Structure and Innovation in Regimes of Rapid Technological Change

Unfortunately, many economists seem to be stuck in a well- traveled and largely irrelevant debate, now half a century old, as to what form of market structure favors innovation, labeling this as the “Schumpeterian” debate. Regrettably, this is all that many have absorbed from the rich work of Schumpeter, the Austrian School, and extensive

Static and Dynamic Competition in Regimes of Rapid Technological Change

As discussed earlier, there is a third (usually overlooked) but very important proposition embedded in Schumpeter: dynamic competition should be favored over its poorer cousin, static compe- tition. I will describe both static and dynamic competition in turn. In doing so, I recognize that these styles of competition sometimes do not have bright lines

Relevant Aspects of Evolutionary/Behavioral Economics in Regimes of Rapid Technological Change

Evolutionary economics and the behavioral theory of the firm are separate but related frameworks. Both have been in existence for half a century or more. Both embrace firms and markets as we see them. Both recognize a capability to discover new technologies and business models in the economic system. Entrepreneurial activity by individuals and

Implications of Dynamic Competition in Regimes of Rapid Technological Change

1. General Static and dynamic competition have elements in common. Cur- rent law embraces both,6 although in my view when it relies on economic theory to inform it, the law gets a larger injection of static analysis than dynamic analysis. But dynamic analysis has always been embraced to some degree by the law. Traditional

Introduction to Organizational Learning Curves

“Learning curves” have been found in many organizations. As organizations produce more of a product, the unit cost of production typically decreases at a decreasing rate. A learning curve for the production of an advanced military jet built in the 1970s and 1980s is shown in Fig. 1.1. The number of direct labor hours

Historical Overview of Organizational Learning Curves

Psychologists were the first to discover learning curves (see Mazur & Hastie, 1978, for review). These researchers focused on the behavior of individuals. Psychologists found that the time individuals took to perform a task and the number of errors they made decreased at a decreasing rate as experience was gained with the task (Ebbinghaus,

Recent Research Trends on Organizational Learning Curves

1. Expanded Set of Outcomes Several important new trends have occurred in research on organizational learning curves in the 1990s. First, researchers expanded the set of outcome measures used as indicators of organizational learning. Research conducted before the 1980s had shown that outcomes in addition to the number of direct labor hours per unit

Measuring Knowledge and Assessing Learning

The classic form of an organizational learning curve is where y = the number of labor hours per unit a = the number of labor hours required to produce the first unit x = the cumulative number of units produced through time period i b = the learning rate i = a time subscript

Organizations Vary in Their Learning Rates

Organizations vary dramatically in the rate at which they learn. Some organizations show remarkable productivity gains with experience, whereas others evidence little or no learning. A study by Dutton and Thomas (1984) nicely illustrated the tremen- dous variation observed in organizational learning rates. Dutton and Thomas plotted a frequency chart or histogram (see Fig.

Sources of Variation in Learning Rates

What explains the variation in the rate at which organizations learn? Many researchers have speculated about factors explaining organizational learning curves and con- tributing to the variation observed in organizational learning rates. For example, Hayes and Wheelwright (1984) discussed the following factors as facilitators of organizational learning: individual learning, better selecting and training of

Theoretical Models of Organizational Learning

Several theoretical models of organizational learning curves and related phenomena have been proposed (e.g., Dorroh, Gulledge, & Womer, 1994; Levy, 1965; Muth, 1986; Roberts, 1983). Muth (1986) provided an excellent review of these models and developed a new one. Muth (1986) generated the power law relationship between unit cost and experience [see Eq. (1.1)]

Learning Curve Applications

We turn now to a brief overview of applications for improving firm performance that are based on the learning curve. Organizations use learning curves as planning and forecasting tools. Analyses based on organizational learning curves have been used in many applications geared at improving firm performance (Dutton et al., 1984). Learning curves have been

Organizational Learning Defined

Although researchers once debated whether organizational learning should be defined as a change in cognitions or in behavior, that debate has declined (Easterby- Smith, Crossan, & Nicolini, 2000). Most researchers agree with defining organiza- tional learning as a change in the organization’s knowledge that occurs as a function of experience (e.g., Fiol & Lyles,

A Theoretical Framework of Organization Learning

Figure 2.1 depicts a framework for analyzing organizational learning (Argote & Miron-Spektor, 2011). The figure portrays an ongoing cycle through which task performance experience is converted into knowledge through organizational learn- ing processes. Task performance experience interacts with the context to create knowledge. The knowledge flows out of the organization into the environment and

The nature of Organizational Experience

Because various types of experience can affect organizational learning processes and outcomes differently, researchers have characterized experience at a fine-grained level along various dimensions (Argote, McEvily, & Reagans, 2003). The most fundamen- tal dimension of experience is whether it is acquired directly by the focal organiza- tional unit or indirectly from other units (Argote,