Board Intellectual Capital, Board Effectiveness and Corporate Performance: Goodness of the Data
Many factors influence corporate performance and among them, intellectual capital (IC) and corporate governance are the most important determinants. Based on the literature, the direct effect of IC and corporate governance mechanisms on corporate performance have been measured in the past several ye...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Book Section |
| Language: | English |
| Published: |
School of Social Sciences, USM
2017
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| Subjects: | |
| Online Access: | http://eprints.usm.my/40793/1/ART_65.pdf http://eprints.usm.my/40793/ http://www.sspis.usm.my |
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| Summary: | Many factors influence corporate performance and among them, intellectual capital (IC) and corporate governance are the most important determinants. Based on the literature, the direct effect of IC and corporate governance mechanisms on corporate performance have been measured in the past several years. Nevertheless, to empirically test indirect effect of board IC and board effectiveness on corporate performance remains scant. In addition, most of the research in these areas have been conducted in developed countries. It is found that not much research has been carried out in the emerging markets of Middle-East like Iran. The purpose of this paper is to present goodness of data processes in relation to study board IC, board effectiveness and corporate performance of listed companies in Iran. The goodness of data involves screening and purifying of raw data in accordance with the assumptions of multivariate
analysis. Data screening is the process of checking data for errors and correcting them before performing
data analysis. The study employed census method where all listed companies in Tehran Stock Exchange (TSE) were investigated. Data were obtained through the questionnaire survey on 292 board members in TSE. Raw data were keyed into Statistical Package for Social Science (SPSS) version 22. A descriptive statistic, treatment of missing data, univariate assessment and removing of outliers, normality and multicollinearity tests were conducted. The results from data cleaning revealed a significance and the suitability of the data for multivariate analysis. |
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