Posted by Kathryn Schwartz on January 26, 2014
Risk Analysis


This study was restricted to listed companies in BSE/NSE operating in the automotive industry India. Among the 146 listed companies, the study has identified 95 companies as the sample frame of this study. The other fifty one companies were excluded due to the companies being relatively new and the decision making being centralised for group of companies. The questionnaire was sent to senior finance professionals (CFO, General Manager-Finance, Vice President-Finance, Controller etc.) of 95 companies. Each questionnaire was sent to senior financial professionals along with signed personalised covering letter. To increase the response rate of the survey, two personalised mailings were sent two months apart. After follow up, 36 completed questionnaires were received (a response rate of 37.89).

The response rate is better than those of previous studies [20 per cent of Ashish Kumar &Bhavin Shah (2006; 15.43 per cent of Manoj Anand (2000)]. Twenty responses were received from the first mailing and sixteen from the second mailing. Five were returned as undeliverable and eight indicated that they do not respond to mail surveys. In addition, this study used one sample’s’ test to test whether the sample companies are truly representative of the population or not with the help of net fixed assets (Shimin Chen, 2008). The test confirmed that the sample of 36 companies is a truly representative of the population (refer table 1).

Table 1: Descriptive Statistics and Results of One Sample t-Test
table1Perceived Environmental Uncertainty-2


In designing the questionnaire, the authors collected the relevant papers (Ho& Pike, 1998; Cohen, 2001). This study has used three variables namely, Risk analysis techniques, Perceived Environmental Uncertainty and Perceived Company Performance. Responses to the questionnaire items were measured on a five-point scale (see annexure).

Risk Analysis Techniques

This term refers to the formal risk analysis techniques viz. Sensitivity Analysis, Probability analysis, Risk simulation, and Capital Asset Pricing Model (CAPM) that are used to assess the risk associated with the investment decisions. This study was used on the four items to measure the extent of use of risk analysis techniques, which were measured on a five point frequency scale ranging from “strongly agree” to ‘strongly disagree”.

Perceived Environmental Uncertainty

This study used dynamism (uncertainty of environment) to assess the organisational environment of automotive manufacturing companies operating in India. The term uncertainty of environment refers to seven items (competitors, customers, suppliers, financial markets, government regulations, trade unions, and technological changes) that are likely to influence a business organisation. The degree of uncertainty of these seven forces were measured on a five-point scale ranging from “predictable” to “Never predictable”.

Perceived Company Performance

In order to measure the overall performance of a company, previous studies had used two types of measures viz. perceptual measures gathered from the respondents and objective/accounting measures derived from mandatory accounting reports. This study has used perceptual measure. This study gathered data from the respondents on six items (sales, profitability, cash flow generating ability, market share position, potential efficiency, and competitive position) that are likely to influence the risk analysis in SIDs. The agreeableness of these six forces was measured on a five-point scale ranging from “Strongly agree” to “Strongly disagree”, the extent to which their performance was improved over the past three years.

The next logical step in data analysis is the test of reliability and validity of the instrument items. The most common method of reliability test viz. Cronbach’s alpha coefficient for assessing reliability of the constructs has been used in this study. The reliability of the constructs is above the minimum threshold level for a construct (Nunnally 1978) and hence all the constructs have good reliability (see Table 2). During the measurement model, three of the items had loadings below the acceptable level of 0.50 and subsequently from the associated factor namely perceived environmental uncertainty (See Table 2). After verifying the reliability, it is important to examine the validity of the constructs. The measures included in the study have the content, predictive, convergent and discriminant validity. The content validity is examined through pilot study involving senior finance professionals, consultants and academics. The convergent validity of each construct, modelled in the reflective mode, is verified by examining the “Average variance extracted (AVE)” values. AVE values are greater than the minimum threshold level i.e. 0.50 (Hair et al, 2006) or was close enough to 0.50 to be acceptable (Cohen 2001). All constructs had exceeded the minimum AVE value or were close enough to the acceptable level (See table 2). The discriminant validity can be tested by examining the square root of a construct’s AVE, which should be greater than the correlation between one construct and the other constructs used in the model or squared correlation between the constructs which should be less than the AVE (see Fornell Larcker, 1981, Hair et al 2006). The AVE values are greater than the R-square value (0.472), which indicates that all the constructs have good discriminant validity (See table 3).

Table 2: Measurement Variables Used in the Model
table2Perceived Environmental Uncertainty-3

Table 3: Correlation among the Construct Scores
table3Perceived Environmental Uncertainty-5

Note: AVE for each construct is greater than the correlation between the construct and any other construct in the model

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