Analysis of research questions 2

By answering this question, this work empirically identified if both, probability of occurrence and impact of every risk type differed significantly in different

a) Project types (Incremental vs. radical). b) Firm sizes (SMEs vs. large firms) c) Industry sector

Because both project type and firm size were binary variable i.e. they have two categories only, I conducted the analysis in the following way.

1) By conducting ANOVA

To determine whether there are any statistically significant differences between the means of two or more independent groups, a one-way analysis of variance (ANOVA) is used. Because one-way ANOVA is an omnibus test statistic and cannot tell which specific groups were significantly different from each other; it only tells that at least two groups were different (Field, 2013). To test this, the null hypothesis is stated as „there are no differences in population means between the groups‟. Mathematically, it is mentioned as

H0: all group population means are equal (i.e., µ1 = µ2 = µ3 = ... = µk) where µ = population mean and k = number of groups.

In the case of this research, the null hypothesis for NPD project type

H0: The means values for NPD project risks in incremental product is equal to the mean

values for NPD project risks in radical products (i.e., µincremental = µradical)

My aim however, is to find evidence against this null hypothesis and accept the alternative hypothesis, which states that there are differences between the group population means

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HA: at least one group population mean is different (i.e., they are not all equal) The one-way ANOVA calculates an F ratio based on the variability between groups versus the variability within groups (Kirk, 1996). The probability (p-value) of finding an F ratio as large as the one calculated by the one-way ANOVA is used to either reject or not reject the null hypothesis. If this probability value is less than .05 (i.e., p < .05), there is a less than 5 in 100 (5%) chance of the F ratio being as large as calculated, given that the null hypothesis is true (Kirk, 1996).

A critical part of the process involves six assumptions that need to be satisfied before one-way ANOVA can be conducted (Rutherford, 2011). The first three assumptions of the one-way ANOVA relate to the study design: first to have a continuous dependent variable; second independent variable is categorical with two or more independent groups and third, independence of observations. All three assumptions are satisfied for the study design. The other three assumptions were related to how empirical data fits the one-way ANOVA model. Among these, the first one is related to outliers in the dataset. Outliers can have a large negative effect on the results because they can exert a large influence (i.e., change) on the mean and standard deviation for that group, which can affect the statistical test results. All outliers were removed from the sample. The second assumption is the normality of data sets. For this research, the sample size assumption of the central limit theorem suggests that all the risk types are normally distributed (Field, 2013; Hair et al., 2011; Tabachnick and Fidell, 2014). The final assumption is associated to the homogeneity of variances which states that the population variance for each group of the independent variable is the same. The assumption of homogeneity of variances is tested using Levene's test of equality of variances, which determine whether the variances between groups for the dependent variable are equal. If Levene's test is statistically significant (i.e., p < .05), then it means groups do not have equal variances and have violated the assumption of homogeneity of variances (i.e. group has heterogeneous variances). On the other hand, if Levene's test is not statistically significant (i.e. p > .05), then there is equal variances, and there is no any violation of the assumption of homogeneity of variances (Field, 2013; Rutherford, 2011).

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2) By conducting binary logistic regression

A binary logistic regression predicts the probability that an observation falls into one of two categories of a dichotomous dependent variable based on one or more independent variable (Cohen, 1990). For the purpose of this research, I decided to use binary logistic regression to predict whether different NPD project risks will occur in a) radical or incremental NPD projects or b) SMEs or large firms. For the illustration purpose, here, the dichotomous dependent variable would be "NPD project types", which has two categories “increment" and "radical" and independent variables are all NPD project risk types. Logistic regression provides a coefficient b which measures each independent variable‟s partial contribution to variations in the dependent variable (Pallant, 2007). The goal is to correctly predict the category of outcome for individual cases using the most parsimonious model (Cohen, 1990). For example, if I consider independent variables to be "all NPD project risk" and the dependent variable to be "NPD project type", a binary logistic regression models the following:

logit(NPD‎project‎type)‎=‎β0 +‎β1Tech.Rap.Risk +‎β2Tech.Cap.Risk +‎β3Mar.Rap.

Risk +‎...+‎ε

Where

β0 is the intercept (also known as the constant), β1 is the slope parameter (also known as the slope coefficient) for technological rapidity risk, and so forth, and ε represents the errors (Laerd Statistics, 2015).

The binary logistic regression analysis was performed to ascertain the effects of all 18 sub-categories of NPD project risks on: i) NPD project types and ii) firm sizes. In the case of NPD project type, I run two logistic models separately for both incremental and radical NPD types. In the first model, radical NPD type was coded as 1 and incremental NPD type as 0. In the second model, incremental NPD type was coded 1

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and radical NPD type as 0. Before running the models, there are six assumptions that should not be violated (Cohen, 1990). The first four assumptions relate to the study design and include (a) dependent variable should be dichotomous; (b) there have to be two or more independent variables, which can be either continuous variables (i.e., an interval or ratio variable) or nominal variables; (c) there should be independence of observations; (c) the categories of the dichotomous dependent variable and all nominal independent variables should be mutually exclusive and exhaustive; and (d) there should be a bare minimum of 15 cases per independent variable. All these four assumptions were satisfied in this case. The other two assumptions relate to the nature of data. Among these, the first one is the assumption of linearity which requires that there is a linear relationship between the continuous independent variables (in this case all the NPD project risks) and the logit transformation of the dependent variable (NPD project types). The linearity of the continuous variables with respect to the logit of the dependent variable was assessed via the Box-Tidwell (1962) procedure (Laerd Statistics, 2015). The second assumption is that there should be not any significant outliers. Once all the assumptions are satisfied, I ran the model.

From the output, there are three important tables that need to be considered to make sense of the analysis (Laerd Statistics, 2015). The first table Omnibus tests of model coefficient provide the overall statistical significance of the model. The model is statistically significant as long as (p < .0005). The second table is for the explanation of variance in the models i.e. how much variation in the dependent variable can be explained by the model (Cohen, 1990). For this, I used Cox and Snell R Square and Nagelkerke R Square values which are both methods of calculating the explained variation (Laerd Statistics, 2015). The third table is titled as the Variables in the Equation table shows the contribution of each independent variable to the model and its statistical significance (Laerd Statistics, 2015). The Wald test is used to determine statistical significance for each of the independent variables. For example, as clear from Appendix 5, there is a strong significant negative association (p <0.015) between the probability of marketing capability risk and radical NPD type. The B coefficients are used in the equation to predict the probability of an event occurring. The output also includes the odds ratios of each of the independent variables. This informs the change in

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the odds for each increase in one unit of the independent variable. For example, a unit increase in the probability/impact of risks decreases the odds of radical NPD to be developed by certain proportion (Appendix 5a & 5b).

For industry sector, I performed the analysis as follow. A one-way analysis of variance (ANOVA) was conducted for which the same procedure for ANOVA test was followed as mentioned in part (7a&7b). Based on the nature of research question, the following hypotheses were proposed.

Null hypothesis

„there is no significant difference in the a)likelihood of occurrence and b) negative impact of different risks within NPD projects associated with different industry types.

Alternative hypothesis

„there is significant difference in the a)likelihood of occurrence and b) negative impact of different risks within NPD projects associated with different industry types.

3.8.4 Analysis of research questions 3: How‎do‎perceptions‎of‎NPD‎projects’‎