Competing for Knowledge

Creating, Connecting and Growing

Robert Huggins, Hiro Izushi, Robert A Huggins

• Publisher: Routledge
• Year: 2007
• Print ISBN: 9780415375122
• eBook ISBN: 9781134187911
• Topic: Business
• Subtopic: Business General
• Index: Business

With the buzzwords of knowledge-based economy and knowledge-driven economy, policy-makers, as well as journalists and management consultants, are pushing forward a vision of change that transforms the way advanced economies work. Yet little is understood about how the knowledge-based economy differs from the old, traditional economy. It is generall

1 Introduction

Knowledge is the primary means of wealth creation for a rapidly growing number of individuals, firms, and economies. Key industries in advanced economies in the late twentieth century – microelectronics, telecommunications, and computer software – as well as the twenty-first century’s high hopes – such as biotechnology and nanotechnology – are intensive users of brainpower.¹ A significant proportion of workers in these industries are devoted to the creation and use of state-of-the-art knowledge, earning higher wages than their counterparts in less skill-demanding occupations. An ability to understand, process, and generate advanced knowledge is crucial to the success of these workers. Similarly, firms in high-technology industries rely heavily on the production and use of advanced knowledge as a key source of their value-added.² Endowments of natural resources such as minerals, coal, and oil no longer determine the fortunes of economies. Today, knowledge is the key source of competitive advantage.

Knowledge-based competition is akin to a marathon race – fought over the long-run, but with the possibility of disruptions that radically change the rules of the race. Even in markets where steep learning curves and scale economies help incumbents to fend off competition, new entrants with novel products often dethrone them as market leaders.³ A key to successfully competing in this long yet unpredictable race is through the careful and long-term management of core capabilities and expertise by investing in a range of knowledge-based resources, such as research and development (R&D), education and training, collaborative relationships with other businesses, and information and communication technology infrastructure. To compete in a knowledge-based environment, workers need to continually improve their skills, while firms need to continually upgrade their knowledge base and develop new products and processes. At a macro level, regions and nations compete with one another by raising the standards of their infrastructure in the hope of attracting and retaining skilled workers and blue-chip firms.

As knowledge-based competition becomes evermore global, many firms may find it tempting to try and settle into a market for low quality products, taking advantage of short-run opportunities to satisfy demand. However, in the long-run this will cost them their prosperity, as they fail to adapt to the emergence of new knowledge and innovations. Once a firm is excluded from the knowledge race, it will sooner or later witness a decline in performance and profits, resulting in lower wages and loss of jobs. For those regions and nations accommodating such firms, the inability to compete in the knowledge race creates the probability of increases in social disorder and a growing burden upon fiscal balance. Therefore, however tough it is, stakeholders – individual workers, businesses, and governments – must remain in the race if they are to raise or even keep their current standards of living.⁴

The race has become global due to increasing parts of the developing world becoming integrated with the developed world through international trade and investment. Advances in telecommunications and logistics have made possible the global sourcing of skills, goods, and services. Automotive factory workers in Germany, France, and the United Kingdom now compete with their counterparts in Eastern Europe, based on a combination of available skills and wages. French and Italian workers in apparel and leather workshops are faced with competition from workers in China who are able to do their jobs for significantly lower wages. Call centers in India, which are now drifting to Philippines, deal with customers in the United Kingdom and the United States, replacing local operators in those countries where the customers are located. This cross-border competition is increasingly spreading toward high-technology jobs. Due to the growing cross-national outsourcing of software programming, programmers in North America now have to share the global pool of jobs with programmers in India. Although there are different capitalist models at work across the globe, varying in social responsibility and income distribution across their societies, the basic principle of demand and supply is universal. If a worker can offer the same skills at a lower price, the worker wins the competition against others with the same skill levels. Given this, the choice available for workers in today’s advanced economies is to either upgrade their skill levels or lose out to similarly skilled workers in lower-wage economies.

Global competition is witnessing a power shift in the map of knowledge creation capacity across nations. By 2001, Taiwan and South Korea, two of Asia’s Tiger economies, had already caught up with or almost surpassed the UK in the number of new patents granted by US Patent and Trademark Office (USPTO). These two Tiger economies also recorded a faster growth rate in new patents than the United States.⁵ A growing proportion of USPTO patents are granted to overseas inventors, indicating a growth in innovation capabilities outside the traditionally strong United States. In 1963, foreign inventors outside the United States accounted for only 18.6 percent of the total patents granted by USPTO. This percentage steadily grew from that time and broke the mark of 40 percent in 1981. In 2001, 47.1 percent of the new patents were awarded to overseas inventors.⁶ This mirrors the fading dominance of the United States in human capital development. From 1993 to 1999, the number of researchers in other member countries of the Organisation for Economic Co-operation and Development (OECD) increased from approximately 1.50 million to 2.04 million, an average annual growth rate of 5.3 percent.⁷ During the same period, the United States failed to match this pace, with the number of researchers growing from approximately 965,000 to 1.26 million at an average annual growth rate of 4.6 percent, 0.7 percent lower than the other OECD countries.⁸ The number of researchers in the US labor force might appear large enough to ensure that it keeps its lead in the global race. However, when combined, the total number of workers with university degrees or equivalents in the three largest non-OECD countries – China, India, and Russia – now equals that of the United States.⁹

Coupled with the rise of China and India in high-technology sectors, the failure to keep up with the human capital development taking place outside of its borders creates great concern among US business managers. For instance, Microsoft CEO Steven Ballmer states: ‘The US is No. 3 in the world and falling behind quickly No. 1 [India] and No. 2 [China] in terms of computer-science graduates.’¹⁰

Growing

The focus of this book is the dynamics of what is called the knowledge-based economy. As in the past, economic growth remains a crucial goal in the knowledge-based economy. While the finite amount of natural resources and environmental problems have emerged as global concerns, economic growth, and more precisely productivity growth, continues to be the motor that maintains the fabric of society. As Benjamin Friedman, a Harvard economist, writes, economic growth is essential for greater opportunity, tolerance of diversity, social mobility, commitment to fairness, and dedication to democracy.¹¹ During times of expansion, nations tend to liberalize – increasing rights, reducing restrictions, and expanding benefits for the needy. During times of stagnation, they change direction toward authoritarianism. Not only does economic growth raise living standards and make liberal social policies possible, it also causes people to be optimistic about the future.¹² What has changed from the old, traditional economy is that productivity growth, driven by technological and organizational innovations, has become the key source of economic growth. In many advanced economies, population growth has slowed down significantly or even turned negative. Economic growth propelled by population growth has long faded from the picture in advanced economies. With environmental concerns, we are increasingly aware of the constraints upon the use of natural resources in our economic activities. It is imperative to create a greater amount of economic value under the constraints of these limited natural resources coupled with stagnating population growth. The source that allows us to achieve this is knowledge, and it is the creation and connection of knowledge that underpins the development of new commercial products and services.

Creating

The capacity for knowledge creation and commercialization is being developed at a different rate today than in the past. One example is the growth of investment in individual skills and organizational competencies related to handling and generating knowledge. A growing number of the young in advanced economies now receive higher education or equivalent levels of vocational education, increasing the proportion of the labor force equipped with skills in handling knowledge-intensive tasks. In 1950, only 6.2 percent of those aged 25 years and over in the United States had a bachelor’s or higher degree. By 2005, the proportion had shot up to 27.6 percent.¹³ With the average educational qualification of individual workers becoming higher than ever, firms now assign a growing proportion of their employees to knowledge-creating tasks. For instance, while only 25 out of 10,000 workers in the United States were engaged in R&D in 1950, there were 91 R&D scientists and engineers for every 10,000 workers in 1999.¹⁴ Management has shifted its competitive focus from the control of markets, through entry barriers under relatively stable conditions, to the creation of internal competencies aimed at coping with fast-changing market conditions.¹⁵ In particular, not only is the effective management of the existing knowledge base important, but equally crucial are the organizational capabilities for creating new knowledge in a sustainable manner.¹⁶

In a similar vein, we are witnessing growing efforts to cultivate those knowledge sources that are yet to be fully commercially exploited. An example of this is the growth in industrial research at universities, which is often mediated and facilitated by government. While universities have long supplied industry with the outputs of basic research as a public source of knowledge, industrial links were seen primarily as an essential part of the teaching process, assisting staff in understanding the industry requirements for graduate skills.¹⁷ However, there has been a renewed emphasis by universities upon industrial research and collaboration with industry in advanced economies, particularly since the 1980s in the United States.¹⁸ In the United States, the drive toward commercial and industrial research at higher-education institutions was cemented by the enactment of the Bayh-Dole Act in 1980.¹⁹ The act allowed nonprofit research groups to patent and commercialize technologies developed with federal funds. This has given rise to ‘academic entrepreneurs’ who use their research outputs for commercial products to set up their own businesses.²⁰ Furthermore, many universities have assumed a more proactive role in commercializing their research outputs through licensing and collaborative research agreements with industry. This change in the role of universities is partly the result of government policy that aims to fully integrate a previously unexploited knowledge source into the knowledge-based economy, raising international competitiveness.

Connecting

Most importantly, in this book we explore the growing efforts to connect knowledge within and across organizational units and sectors, aimed at maximizing the sharing and further creation of knowledge. Knowledge is often ‘sticky’ and difficult to transfer, which arises in part from a significant proportion of it being stuck in the heads of individuals. Such difficult-to-transfer knowledge is referred to as tacit and is embodied in individuals as skills they possess. Jeff Hawkins describes three epochs of intelligence, each of which was marked by the use of a new medium for memory. The first epoch is traced back to the use of DNA as a medium. Primitive species passed on their DNA-based memory of the world to their offspring through their genes, but individuals could not yet learn and adapt within their lifetime. The second epoch began when nature invented modifiable nervous systems that could quickly form memories. This allows, for example, an animal – such as a dog – to learn about the structure of its world and adapt its behavior accordingly within its lifetime. The third and final epoch is unique to humans. This began with the invention of language and the expansion of the large neocortex area of our brain. This allows us to learn the structure of the world within our lifetime and effectively communicate this to other humans via language.²¹ However, this unparalleled ability to use languages masks the fact that a significant proportion of our knowledge is still embedded in our brains in a form that is not reliant on language.

As the scientist and philosopher Michael Polanyi stated, ‘we can know more than we can tell’.²² A familiar example to illustrate this fact is our almost infallible ability to recognize a human face.²³ When we see a friend’s face, we automatically recognize the person in less than a second. It does not matter if the friend is close or far away, facing us, turned a little to the side, or in profile. The friend’s visage can appear in countless positions and variations, yet in every case we know instantly that we are looking at the particular friend. We find it hard to describe in words or any other forms of coding what that recognition entails.. Also, robots and computer programs, which are products of our codified knowledge (i.e. knowledge which is relatively easily transferable), are usually poor in handling the variation in visual inputs and recognizing a person’s face.²⁴ As this case illustrates, a significant proportion of knowledge remains tacit and can be gained only through experience. To become an expert wine taster, or to acquire knowledge of innumerable blends of tea, we must go through a long course of experience building, often under the guidance of an expert.²⁵ We also acquire a lot of knowledge with the aid of texts, such as this book, but later forget them. However, even if we forget those texts that conveyed the original knowledge, the knowledge is often retained in a tacit form. Polanyi, a native of Hungary, wrote that although he had forgotten most of the medical terms he learned in that country when training in radiology, and acquired no others in place of them when he subsequently left Hungary, he could still make sense of and comprehend an X-ray if asked to do so.²⁶ In other words, we know a lot in a form that has yet to be articulated for transfer to others.

Ikujiro Nonaka and Hirotaka Takeuchi’s knowledge spiral model, one of the most influential works in the field of knowledge management, is a reflection of the ‘sticky’ nature of knowledge.²⁷ Nonaka and Takeuchi adopt the distinction between tacit and explicit knowledge (i.e. knowledge expressed in languages and codes), further recognizing the following four modes of knowledge conversion:

1. from tacit to tacit
2. from tacit to explicit
3. from explicit to explicit
4. from explicit to tacit.

While most previous knowledge management models focused upon explicit knowledge, Nonaka and Takeuchi stress that the organization has to mobilize tacit knowledge created and accumulated at the individual level and amplify knowledge ‘organizationally’ through the four modes of knowledge conversion. Processes of extracting, combining, and creating tacit knowledge often require particularly intensive interactions among an organization’s members. For instance, sharing experience and learning-by-observing requires opportunities for interaction, such as in the form of apprenticeships and brainstorming sessions. Transferring and combining knowledge, particularly tacit knowledge, within an organization is not a costless and spontaneous process. Therefore, organizations need to devise and manage the process of knowledge transfer and combination effectively if they are to make the best use of their knowledge.

Network capital

Beyond organizational units where every member knows each other, individuals mobilize what we call network capital to share and connect different sources and forms of knowledge. Our contention is that before connection of knowledge happens, individuals and firms have to know the source of the knowledge they require. Knowing the source of knowledge is a resource-consuming activity. When seeking knowledge, individuals and firms may start by scanning their environment and identifying ‘who knows what’. While publications such as Who’s Who and the Yellow Pages and, increasingly, Internet search engines such as Google serve as scanning tools, such information is not always readily available. Sources of useful information, such as industry insiders, do not always publicize themselves to everyone. Instead, they sometimes attempt to increase their value by staying hidden from the public scene and, therefore, hard for outsiders to find. Knowing the source of knowledge does not end here. After identifying the source, knowledge seekers have to establish relationships with it. The creation of such relationships can be instant if the source sells its knowledge as a commercial service. However, this is not always the case, since knowledge seekers often have to prove to the source that they are a ‘worthy’ recipient of the source’s knowledge. Such worthiness may be achieved through, for example, the high likelihood of reciprocity, that is, the seeker providing the source with knowledge in due course. Once relationsh...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Illustrations
• Preface
• 1 Introduction
• 2 The origin and reality of the knowledge-based economy
• 3 Knowledge-based capacity of best-performing regions
• 4 Networks and clusters
• 5 Knowledge clusters and their evolution
• 6 Nurturing and attracting talent
• 7 Managing network capital and innovation
• 8 But what about the money?
• 9 Conclusion
• Appendices
• Notes
• Bibliography