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As we have entered into the twenty-first century, the economy has undergone a great transformation. The economy has literally become weightless. In the weightless economy, the emphasis has shifted from machines, materials, and other physical resources to information and knowledge. Information and knowledge are the thermonuclear competitive weapons nowadays. More and more economic activity has become invisible and intangible. The focal point in the new economy has shifted from exploration of physical objects to exploration of knowledge-based resources and their efficient and effective management. In the last decade of the twentieth century, almost unnoticed revolution in the corporate world took place: the transition from industrial capitalism, where business was based on tangible physical assets, to a new economy, where the production of goods and services and value creation in general depends and relies on invisible intangible assets. The primary objective of the present study is to build a theoretical construct in the field of evolution of knowledge asset with a view to exploration of the concept of knowledge asset and the need for its management in modern-day life. It further aims to investigate through an empirical study the qualitative disclosure of knowledge assets in terms of selected attributes for the Indian Pharmaceutical and IT Industries based on their annual reports. Content Analysis technique has been used to analyze the degree of disclosure of knowledge assets in terms of attributes.

This chapter fills a significant gap in the intelligence literature and the knowledge sciences literature by aligning definitions and characterizations of knowledge capital as an essential intelligence source. The chapter also explains how knowledge capital might be leveraged in each of the four capabilities. The chapter presents a well-researched characterization of knowledge capital drawing from international scholars and practitioners’ work. The value of human capital, structural capital, and relational capital in intelligence work is explored.

Resource reconfiguration enables firms to adapt in dynamic environments by supplementing, removing, recombining, or redeploying resources. Whereas prior research has underscored the merits of resource reconfiguration and the modes for implementing it, little is known about the antecedents of this practice. According to prior research, under given industry conditions, resource reconfiguration is prompted by a firm’s corporate strategy and by characteristics of its knowledge assets. We complement this research by identifying learning from performance feedback as a fundamental driver of resource reconfiguration. We claim that performance decline relative to aspiration motivates the firm’s investment in knowledge reconfiguration, and that this investment is reinforced by the munificence of complementary resources in its industry, although uncertainty about the availability of such resources limits that investment. Testing our conjectures with a sample of 248 electronics firms during the period 1993–2001, we reveal a clear distinction between exploitative reconfiguration, which combines existing knowledge elements, and exploratory reconfiguration, which incorporates new knowledge elements. We demonstrate that performance decline relative to aspiration motivates a shift from exploitative reconfiguration to exploratory reconfiguration. Moreover, munificence of complementary resources mitigates the tradeoff between exploratory and exploitative reconfigurations, whereas uncertainty weakens the motivation to engage in both types of reconfiguration, despite the performance gap. Nevertheless, codeployment, which extends the deployment of knowledge assets to additional domains, is more susceptible to uncertainty than redeployment, which withdraws those assets from their original domain and reallocates them to new domains. Our study contributes to emerging research on resource reconfiguration, extends the literature on learning from performance feedback, and advances research on balancing exploration and exploitation.

In this chapter, the authors make the case that preserving and curating knowledge for the future involves more than changing methods and tactics or extending our current applications and technology to support knowledge capital. It means changing the way we think about the future. It means envisioning multiple futures where various elements may be known or unknown – a four-future quadrant. First, the authors explain what it means to think strategically in multiple known and unknown futures. Next, the chapter presents ideas for strategic thinking about future knowledge preservation and curation. Finally, the authors consider using the four futures to develop a flexible and relevant knowledge preservation and curation strategy.