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Small- and medium-sized enterprises (SMEs) often do not have slack resources with which to develop internally a broad spectrum of capabilities and to observe in depth the firm's environment. Therefore, they need to carefully develop abilities to absorb knowledge from outside the firm's boundaries so as to have access to cutting-edge knowledge in spite of limited resources. One strategy is to establish knowledge management (KM) projects for this purpose. In this paper, we describe how KM projects and subsequently emerging KM routines in SMEs facilitate the enhancement of the firm's absorptive capacity (AC; i.e., the ability to recognize, capture, and assimilate external knowledge). Our results indicate the importance of recognizing potential knowledge providers prior to any absorption of knowledge from external sources. Furthermore, we emphasize the relevance of routines for absorbing knowledge and we distinguish between KM routines that are deliberately developed for absorbing knowledge and KM projects where knowledge absorption happens unconsciously. Finally, we point out that different stages of an AC process follow different logics (exploration vs. exploitation) and, thus, a skilful management of the AC cycle is necessary to leverage externally absorbed knowledge.

Bearing in mind reviews of the existing corporate management leadership theory, this chapter presents a theoretical framework of holistic leadership for top and middle management as well as the staff for strategically promoting knowledge creation activities in companies in industries with rapidly changing competitive environments. “Holistic leadership” here refers to leadership with characteristics that allow for the coexistence of centralized leadership, distributed leadership, and dialectical leadership and their dynamic application according to circumstances by practitioners at each management level (top management, middle management, and staff) of the three practice layers, that is, the formal organizational layer, the psychological boundary layer, and the informal organizational layer. This new theoretical concept of leadership has been derived a posteriori from existing theory and cumulative fieldwork by the author to date.

Much forecasting is done by experts, who either make the forecasts themselves or who do opinion research to gather such forecasts. This is consistent with previous knowledge management research that typically has focused on directly soliciting knowledge from those with greater recognized expertise.

However, recent research has found that in some cases, electronic markets, whose participants are not necessarily individual experts, often have been found to be more effective aggregated forecasters. This suggests that knowledge management take a similar tact and expand the perspective to include internal markets. As a result, this chapter extends the use of internal markets to be included in knowledge management, thus expanding the base of knowledge to gathering from nonexperts.

In particular, in this paper I examine the use of human expertise and opinion as a basis to forecast a range of different events. This chapter uses a “knowledge distribution grid” as a basis for understanding which kind of forecasting tool is appropriate for particular forecasting situations. We examine a number of potential sources of forecast information, including knowledge acquisition, Delphi techniques, and internal markets. Each is seen as providing forecasting information for unique settings.

Networks and learning matter to small- and medium-sized enterprises (SMEs). Networks and learning are also further elaborations on the exploration–exploitation (EE) dilemma. Ambidexterity, that is, managing this apparent dilemma, can be difficult as a result of many constraints. One of these constraints is that of mutually exclusive network structures. Consequently, ambidexterity is the ability to change networks, depending on need using mixed data on four small companies formed as part of an undergraduate management class, I hypothesize how specific network properties of the advice-seeking relationship, including density, cohesion, centralization, and embeddedness, affect two outcomes. Specifically, early exploratory learning is proposed to be positively affected by less-dense networks that maintain cohesion without centralization and do not have relations embedded in other relations. In contrast, later exploitative learning should be associated with denser networks that also have higher cohesion, higher centralization, and greater embeddedness. The results provide some support for these hypotheses and suggest further research in two areas that will benefit SMEs. One, how do early networks affect learning mode? Two, how does the ability to rewire networks provide the relational infrastructure to shift from exploration to exploitation – that is, to be ambidextrous in the face of the exploration–exploitation tradeoff?