As the largest wireless carrier in Japan when the original mobile app store, i-mode, was introduced in 1999, NTT DoCoMo's description of itself as just another player in i-mode's ecosystem is a bit disingenuous. DoCoMo was undeniably the dominant force behind i-mode. It is true, however, that in creating a new mobile content and service platform, DoCoMo deliberately resisted devouring all the revenue in sight. The DoCoMo business model allowed i-mode content and application providers to keep an unprecedented 91 percent share of the application revenue generated by i-mode subscribers. In addition, DoCoMo provided direct billing to its subscribers for all i-mode content, relieving content partners of the need to handle financial transactions. These business decisions made the ecosystem attractive to very small participants as well as to partners with well-established brands. Compared to the terms offered to application developers and content partners by wireless carriers in the USA and Europe at that time, DoCoMo's wireless data strategy was structured to encourage and reward widespread partner participation and innovation.

Attached to almost every house, apartment building, and office complex in the United States is a familiar and rather homely device. Inside its glass cover, a counter ticks off the kilowatts as the meter measures and records the amount of energy consumed by kitchen appliances, air conditioners, computers, light bulbs, and other electrically-powered devices inside the premises. For more than a hundred years the basic electric meter design focused on this mission of accurate measurement, providing utility companies with a reliable record of electricity usage that could be turned into a monthly bill for each customer.

When meters finally entered the digital age during the 1990s, the priority for utilities was automating the time-consuming and costly process of meter reading rather than giving their customers more information about their power usage. The possibility that the humble electric meter could cooperate with the average residential consumer in managing peak energy demand cycles and avoiding brown-outs was, at best, a far-fetched vision for the distant future. To the extent that records of monthly electric power consumption at the residential level were used to forecast future demand and power-generation needs, the utility's strategy was straightforward. The electric utilities simply planned for continued year-over-year increases in energy consumption. The historical data for electric power demand in the United States certainly supported the assumption that consumption and demand would continue to increase; according to the Department of Energy (DoE), the demand for power rose almost 30 percent between 1988 and 1998 (US Department of Energy, 2008a).

In this chapter I bring together my central themes and point to the future. A compelling feature of research on KN is that it stands at the intersection of so many important theoretical and policy issues such as the converging trends surrounding globalization and the “flattening” of our world; the increasing complexity and blurring boundaries represented in new organizational forms; difficult individual challenges selecting career paths and loyalty, or the lack thereof, on everyone's part, cited in Chapter 1. These trends lead to dilemmas, for both individuals and organizations, in the development and sharing of knowledge in KN.

I discussed the fundamental concepts, the building blocks of KN, in Chapters 2 and 3. In Chapter 2 I defined knowledge, distinguishing it from such common terms as information and wisdom. This chapter also discussed the various forms that knowledge can take within organizations, thus making critical distinctions that can be used in defining relationships.

I will use productivity in its broadest sense here, focusing on the generation of wealth in organizations in a variety of forms entailing social as well as economic capital, which also suggests some degree of efficiency and of effectiveness. Since these terms are closely interrelated I will begin with some basic definitional issues. As the colon in the chapter title implies, productivity is a function of both effectiveness and efficiency, with the former term somewhat more difficult to define concretely.

Effectiveness details a desired outcome or result. It therefore implies some degree of rationality, intention, and purpose and could be closely associated in this sense with more functional approaches to organizations. It implies some martialing or matching of organizational outputs to particular goals that here I will discuss primarily in terms of contingency impacts and resource-based views of strategy.

In this and the following chapter on network analysis I will concentrate on building a foundation for what is to follow. I start this work by defining the key concepts associated with knowledge, drawing careful distinctions between them. Needless to say these terms are at times used interchangeably and at times are taken to be quite different things in the burgeoning literature in this area. I then move on to a discussion of various classifications of types of knowledge, starting with the foundational one between tacit and explicit knowledge. These types could serve as the starting point for the definition of relationships in network analysis, the most critical move in any project relating to it. Finally, in part to serve as counterpoint but also to focus on critical dilemmas and questions of balance in organizations, to which managerial judgment must be applied, I discuss ignorance and the positive role it plays in organizations.

What is knowledge?

Knowledge runs the gamut from data, to information, to wisdom, with a variety of distinctions being made between these terms in the literature. While there is a generally recognized ordering among these terms (see Figure 2.1), with wisdom having the least coverage of any of the sets in the figure, they are often used interchangeably and in conflicting ways in the literature, resulting in some confusion (Boahene and Ditsa 2003).