مقاله انگلیسی رایگان در مورد مدیریت دانش، سرمایه فکری، پیچیدگی اقتصادی و رفاه ملی – امرالد 2017

Prosperity is a function of the value that an economy can both create and retain. Key drivers of national prosperity have been argued to be well-functioning institutions (North, 1990), good institutional infrastructure, capital accumulation, free trade, efficient markets, personal initiative, and appropriate role for government (Smith, 1776). Today’s economists generally point to three important characteristics influencing growth: the extent of a country’s openness to trade and its integration with the rest of the world; the quality of a country’s institutional infrastructure; and the success of its policy makers in implementing the measures necessary for macroeconomic stability (Greenspan, 2002, p. 4). Additional drivers for economic growth through competitiveness are as follows: a highly and relevantly educated labour force[1] that together with the appropriate infrastructure can innovate and that through concentration (normally in cities) in the form of clusters generate untradeable spillovers (Berube, 2007). Oprescu (2012) linked national intellectual capital to national competitiveness, and similar work aimed at linking national intellectual capital with economic growth, prosperity and/or national competitiveness have been carried out by[2], e.g. Bontis (2004), Andriessen and Stam (2005), Bounfour and Edvinsson (2005), Ståhle (2007), Edvinsson and Lin (2008), Lin and Edvinsson (2010), Käpylä et al. (2012), Lazuka (2012), Salonius and Lönnqvist (2012), Seleim and Bontis (2013), Januškaitė and Užienė (2015), and Mačerinskienė et al. (2016). Some of the studies aim to explain the dynamics of intellectual capital at the national scale, while others focus on how national intellectual capital can be optimised and guided to enhance economic growth. These approaches and studies all use an indicator-based approach towards capturing intellectual capital, and critical questions aligned with measurement theory can be asked around the selected indicators, e.g. is the chosen set of indicators complete? Are the indicators distinct from each other so that no double counting takes place? Is construct validity in place for the indicators chosen as relates to the construct that is to be captured? And finally, is the non-additive combinatorial behaviour of the indicators captured when they are aggregated? (For a detailed discussion of these issues see Pike and Roos, 2007). Since many of these answers would have to be negative there are questions to be asked around the meaning of the correlations between the intellectual capital indicators found and the national construct studied, that is frequently found. Based on these studies and the critique of the methodologies used it is neither possible to reject the relationship between national intellectual capital and prosperity nor to reject that there is no relationship. It seems likely that many of the indicators are, as far as can be judged from the published studies, also indicators of the previously mentioned known drivers of national prosperity. The way the concentrating and dispersing forces of economic activity change over time impacts the benefit of proximity vs the benefit of dispersion as relates to competition for access markets and to increasingly scarce resources which in turn impacts a nation’s ability to capture the increasingly mobile flow of capital and people. Technological externalities add to the concentrating forces since networks of regionally clustered businesses and institutions provides for both the formal exchanges of knowledge through market relationships, where proximity allows the establishment of closer ties, and the informal exchange of knowledge in social networks of individuals (Döring and Schnellenbach, 2006). Those beneficial aspects of close proximity which firms cannot control or achieve in any other way than through close geographical and specialisation proximity have been named untraded interdependencies by Storper (1995). The importance of agglomerations has found empirical support in work by, e.g., Graham (2006, p. 26) who found that a 10 per cent increase in the level of agglomeration is associated on average with a 1.25 per cent increase in aggregate productivity in the UK.