Search results

The purpose of this study is to empirically study the relationship between intellectual capital (IC) components (human, structural, and physical capitals) with the traditional measures of performance of the firm (profitability, productivity and market valuation) within the pharmaceutical sector of Iran.

The empirical data were drawn from pharma companies listed in the Iranian Stock Exchange (ISE), over the six‐year period of 2004 to 2009. The analysis of correlation, simple linear multiple regression and artificial neural networks (ANNs) were applied for analyzing any existing relationship between variables in the present study.

The analysis indicates that the relationships between the performance of a company's IC and conventional performance indicators are varied. The findings suggest that the performance of a company's IC can explain profitability but not productivity and market valuation in Iran. Also the empirical analysis found that physical capital (VACA) was the one which was seen to have the major impact on the profitability of the firms over the period of study, in addition the result of ANN method also confirmed findings of multiple regression.

There is an immediate need for policy makers and corporate managers wake up to the need to start disclosure of the IC of firms. IC measurement is of primary interest for top executives of pharmaceutical firms in Iran.

This is an initial and pioneering study to evaluate the IC and its relationship with the traditional measures of corporate performance in the Iranian pharmaceutical industry. The present study provides a new aspect of performance measurement for research‐based industries in emerging economies and would be a good topic for further research.

This study aims to develop and empirically test a “stocks and flows”-based model of intellectual capital (IC) that examines how human-embodied knowledge (i.e., human capital) can be transformed into organisational non-embodied knowledge (i.e., organisational capital) through the mediating roles of social capital and the knowledge management (KM) process of knowledge transfer.

A structural model was developed and empirically tested using a survey data set of 295 questionnaires collected from the “knowledge-intensive” pharmaceutical manufacturing industry in Jordan.

Empirical results revealed that each of human capital, social capital and knowledge transfer has a positive and significant effect on organizational capital. In particular, knowledge transfer emerged as having the strongest effect. Social capital, on the other hand, emerged as having a positive and significant effect on knowledge transfer. Mediation analysis revealed that while human capital significantly affects organizational capital, such an effect is partially and significantly mediated by each of social capital as well as knowledge transfer.

This study provides senior managers in pharmaceutical manufacturing firms with valuable insights pertaining to the development of their IC, in terms of how to exploit their knowledge stocks (i.e. human-embodied knowledge and organizational non-embodied knowledge) through managing knowledge flows between them. This was shown to be significantly leveraged by the mediating roles of social capital as well as knowledge transfer.

This study provides important theoretical and empirical contributions to the extant literature in a number of ways. It provides better understanding of the intricate linkages among IC dimensions, and how these play complementary roles in organizational capital development. It has also provided important empirical evidence highlighting the vital mediating roles of social capital and knowledge transfer in facilitating knowledge flows, which aid in transforming human-embodied knowledge stocks into organizational-embodied ones.

The purpose of this article is to present an aggregated methodology for construction of the stop word list in Farsi language and generate a generic Farsi stop word list.

The stop word list is extracted based on: syntactic classes, domain dependent, corpus statistic and expert judgments. Some of the main challenges that arise in the Farsi automatic text processing are outlined as well.

Results from the techniques are aggregated and a general Farsi stop word list containing 927 words is generated.

The created stop word list can affect the efficiency and effectiveness of retrieval and indexing process in Farsi information retrieval system, moreover, it can play an important role during Farsi text segmentation.

Our stop word extraction algorithm is a promising technique; it could be applied into other languages that they have ambiguities in automatic text segmentation.