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• W3208542076 abstract "Capital expenditure (capex) decisions have been extensively studied except in developing countries (Mansor and Hamidi, 2008). Capex implicitly affects institutional aggregate demand, gross national product and business cycles. This research focuses on the capex decisions by Malaysian public listed companies (PLCs) between the years 2002 to 2006. Using the Pecking Order Hypothesis (POH) and Managerial Hypothesis (MH) frameworks, four independent variables are examined. These are (i) internal cash flows (ICF); (ii) Insider Ownership (I_O); (iii) Investment Opportunities (IOP); and (iv) the interaction effects of the variables. In addition, sales revenue is used as a control variable. Both hypotheses predict a positive effect of (IFC) on capex. However, their arguments differ in relation to the effects of (I_O) on capex. MH argues for a negative relationship but POH proposes no effect between the two. POH suggests a positive effect of (IOP) on capex whereas MH predicts no relationship between them. Using a pool Ordered Logistic Regression (OLR) model, capex is categorized into five ordinal categories and the control variable is divided into four groups, which are ranked to capture the variability of the capex. The result of this study shows that the (ICF) has a positive and significant effect on capex, and this finding supports both studied hypotheses. However, (I_O) and (IOP) show a negative and significant effect on capex. The study also found that there is a conflict of interest between managers and shareholders base on the negative coefficient of (I_O). This finding is consistent with Kim (2006) and supports the MH. This study provides mixed-support for the two hypotheses and reveals interesting characteristics with regards to the managerial style in Malaysian PLCs." @default.
• W3208542076 created "2021-11-08" @default.
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• W3208542076 date "2013-01-01" @default.
• W3208542076 modified "2023-09-23" @default.
• W3208542076 title "Capital expenditure decisions: a study of Malaysian listed companies using an ordered logistic regression analysis" @default.
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• W3208542076 hasPublicationYear "2013" @default.
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