7 resultados para Organizational Competitive Intelligence (OCI)

em WestminsterResearch - UK


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Knowledge management theory has struggled with the concept of `knowledge creation'. Since the seminal article of Nonaka in 1991, an industry has grown up seeking to capture the knowledge in the heads and hearts of individuals so as to leverage them for organizational learning and growth. But the process of Socialization, Externalization, Combination and Internalization (SECI) outlined by Nonaka and his colleagues has essentially dealt with knowledge transfer rather than knowledge creation. This paper attempts to fill the gap in the process - from Nonaka's own addition of the need for "ba" to Snowden's suggestion of that we consider "Cynefin" as a space for knowledge creation. Drawing upon a much older theoretical frame - work the Johari Window developed in group dynamics, this paper suggests an alternative concept - latent knowledge - and introduces a different model for the process of knowledge creation.

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This paper considers a large matched employee–employer data set to estimate a model of organizational commitment. In particular, it focuses on the role of firm size and management formality to explain organizational commitment in British small and medium-sized enterprises (SMEs) with high and low levels of employee satisfaction. It is shown that size ‘in itself’ can explain differences in organizational commitment, and that organizational commitment tends to be higher in organizations with high employee satisfaction compared with organizations of similar size with low employee satisfaction. Crucially, the results suggest that formal human resource (HR) practices can be used as important tools to increase commitment and thus, potentially, effort and performance within underperforming SMEs with low employee satisfaction. However, formal HR practices commonly used by large firms may be unnecessary in SMEs which benefit from high employee satisfaction and positive employment relations within a context of informality.

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Marxian thinking following the TSSI (Temporal Single System Interpretation) of Marx is applied to refute the allegation of a tautology in the resource-based view of the firm--paired with providing an explanation of how and why resources create value--, where resources are synonymous with Marx's categories of constant and variable capital. Refuting the allegation naturally leads to the holy grail of resource-based thinking, i.e. the question of what, conceptually, constitutes a firm's competitive advantage within the industry context. The article achieves its objectives by tying the resource-based view into Marx's theory value.

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This paper extends original insights of resource-advantage theory (Hunt & Morgan, 1995) to a specific analysis of the moderators of the capabilities-performance relationship such as market orientation, marketing strategy and organizational power. Using established measures and a representative sample of UK firms drawn from Verhoef and Leeflang’s data (2009), our study tests new hypotheses to explain how different types of marketing capabilities contribute to firm performance. The application of resource-advantage theory advances theorising on both marketing and organisational antecedents of firm performance and the causal mechanisms by which competitive advantage is generated.

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Ashton and colleagues concede in their response (Ashton, Lee, & Visser, in this issue), that neuroimaging methods provide a relatively unambiguous measure of the levels to which cognitive tasks co-recruit dif- ferent functional brain networks (task mixing). It is also evident from their response that they now accept that task mixing differs from the blended models of the classic literature. However, they still have not grasped how the neuroimaging data can help to constrain models of the neural basis of higher order ‘g’. Specifically, they claim that our analyses are invalid as we assume that functional networks have uncorrelated capacities. They use the simple analogy of a set of exercises that recruit multiple muscle groups to varying extents and highlight the fact that individual differences in strength may correlate across muscle groups. Contrary to their claim, we did not assume in the original article (Hampshire, High- field, Parkin, & Owen, 2012) that functional networks had uncorrelated capacities; instead, the analyses were specifically designed to estimate the scale of those correlations, which we referred to as spatially ‘diffuse’ factors

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What makes one person more intellectually able than another? Can the entire distribution of human intelligence be accounted for by just one general factor? Is intelligence supported by a single neural system? Here, we provide a perspective on human intelligence that takes into account how general abilities or ‘‘factors’’ reflect the functional organiza- tion of the brain. By comparing factor models of individual differences in performance with factor models of brain functional organization, we demon- strate that different components of intelligence have their analogs in distinct brain networks. Using simulations based on neuroimaging data, we show that the higher-order factor ‘‘g’’ is accounted for by cognitive tasks corecruiting multiple networks. Finally, we confirm the independence of these com- ponents of intelligence by dissociating them using questionnaire variables. We propose that intelli- gence is an emergent property of anatomically distinct cognitive systems, each of which has its own capacity.