International journal of Contemporary Business Studies

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International Journal of Contemporary Business Studies Vol: 3, No: 6. June, 2012 ISSN 2156-7506 Available online at http://www.akpinsight.webs.com Regarding the internal validity of this scale of measurement, the reliability coefficient α is 0.795 which means that the measurement scale has good internal validity. The factor analysis extracted one factor whose Eigen value is 2.81 and explains 56, 20% of total variance, which allows us to admit the one-dimensionality of the scale measuring this variable. 3.2.2. Independent variable: Budgetary Participation The choice of instrument for measuring the budgetary participation has been critical in highlighting its effects. In counting the previous studies related to the extent of budgetary participation, Chong et al., (2005) stated that the instrument developed by Milani (1975) was the most used due to its reliability demonstrated by a plethora of research (Brownell, 1982; Dunk, 1993 ...). The instrument consists of six items Milani each of which uses a response on a Likert scale of seven points. The scale is designed for construction of an additive total score (Appendix B). a Table 4. Factor analysis for Budgetary Participation Factor Loading Items First factor Second factor PB1 a 0.695 PB2 0.817 PB3 0.735 PB4 0.888 PB5 0.829 PB6 0.860 Extraction Method: Principal Components Analysis Two dimensions are extracted Cronbach reliability coefficient = 0.828 PB1, …, PB6 are the items measuring the budgetary participation The internal validity of the measurement scale of the variable budget participation is measured by the Cronbach reliability coefficient (α). This value is high at 0.828, which means that this scale of measurement is reliable since α value was well above the limit set by Nunnally (1978) to 0.6. The Kaiser-Meyer-Olkin (KMO) index of this scale of measurement is 0.750. Items can be factorizable when the value exceeds KMO value for 0.5. Note also that, over the index, the higher the number of factor is low. We can therefore conclude that our data lend themselves to a factor analysis. A principal component analysis gave us two main factors. Indeed, a rotation of axes allowed us to eliminate cases where one or more items are several factors in the same proportions. She identified two independent factors, each containing three items. We can conclude that budgetary participation can have two distinct dimensions: "involvement" and "influence". Copyright © 2012. Academy of Knowledge Process 42
International Journal of Contemporary Business Studies Vol: 3, No: 6. June, 2012 ISSN 2156-7506 Available online at http://www.akpinsight.webs.com The first factor (PB1) can be interpreted as the dimension of "influence", it consists of items (see appendix B) 3, 4 and 5 (Milani, 1975; Hassel & Cunningham, 1993, 1996) explains 54, 73% of the total variance. This factor has a Cronbach α value of 0.803. The second factor (PB2) is the “involvement” (items 1, 2 and 6) which explains 16, 66% of the total variance. The reliability coefficient of this factor is 0.771. 3.2.3. The moderator variable "intensity of market competition (COMP) The measurement of this variable was originally made by Khandwalla (1972). However, several criticisms and shortcomings have been addressed to this measure.Mia & Clarke (1999) propose an alternative measure into six items describing the state of competitiveness of the company (Appendix C); the answers are spread on a Likert scale of five points ranging from 1 (very poor) to 5 (very strong). For the internal validity of the construct, the reliability coefficient is 0.451, it is less than 0.6. So, all items constituting the scale of measurement is not reliable. It is advisable to proceed with a measure of adequacy of sampling and factor analysis to find the items or responsible for this anomaly. The study of the scale dimensionality of this variable showed that the measure of sampling adequacy of KMO was 0.426. This value is low, which indicates the absence of the unidimensionality of instrument. Table 5. Factor analysis for Intensity of Market competitiveness Factor Loading a Items First factor Second factor Third factor COMP1 b 0,741 COMP2 0,860 COMP3 0,718 COMP4 0,809 COMP5 0,884 COMP6 0,617 0,549 Extraction Method: Principal Components Analysis Rotation Method: Varimax with Kaiser Normalization Three dimensions are extracted Cronbach Reliability coefficient = 0.451 a Values below 0.5 are eliminated b COMP1, …, COMP6 are the items measuring the intensity of market competitiveness The results of the rotation axis factor analysis showed that the items are divided into three factors. The inverse matrix and anti-image correlation matrix enabled us to retain the factor composed by the following items: 1, 3 and 4 (Competitive Price, Competitive Marketing, Competitiveness market share). Copyright © 2012. Academy of Knowledge Process 43
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