R2=.413 R2=.251 R2= .121 Role Prescription Decision Relevance Credibility UA* Risk Perception FTE* Gender In-Group Collectivism Institutional Collectivism PO* FO* PD*
4.6
RESULTS
In this section, I report the results of the study. The process that I followed to analyze and report the data is as follows:
1) Test and Correct for Cultural Response Bias
Different cultures tend to respond to questionnaires of this type by either avoiding or accentuating the extremes of the scales. Asian cultures tend to avoid the extremes while Mediterranean cultures tend to avoid the middle of the scale (Hanges 2004). It is therefore necessary to test for and correct this bias before doing further analysis (van de Vijver and Leung 1997).
2) Report of Demographics
In this section the demographics of the study are reported. 3) Measurement Model Assessment
In this section, the measurement model is assessed to ensure that the psychometric properties of the survey are appropriate.
4) Cross-Cultural Equivalence Testing
Here the responses are tested to ensure that they are equivalent to each other and are unbiased
5) Manipulation Checks
Here, the Credibility and Role Prescription manipulations are checked to ensure that they worked in all the geographies.
6) Structural Analysis
Finally, I report and interpret results of the PLS structural analysis.
4.6.1 Cultural Response Bias
As described above, culturally induced biases in how the respondents completed the items can cause distortions in the data that would prevent comparisons of the data across cultures. It is therefore necessary to test for these effects and if need be correct them.
The classic procedure is described in Hanges (2004). If a subject is asked to
respond to a large range of constructs, the means and standard deviations of his/her responses lose any construct specific content and reflect only the subject’s response biases. Therefore the procedure is to create z-scores for each respondent using the formula:
Standardization is a linear transformation that neither distorts the distribution of the items nor changes their correlations (Cohen, Cohen, West and Aiken 2003). Scale scores for each construct are calculated based on both the original and standardized scores. If a large correlation exists between the original items and the standardized scores, the items are considered to be relatively free from cultural response bias. I performed this test on the cultural variables, calculated as per GLOBE
specifications, and obtained a correlation of .919 between the two numbers. The results are displayed in table 4.5.
Table 4.5: Results of Cultural Response Bias Test
Country UA FO IC IG PD PO USA -0.016 -0.217 -0.158 0.094 -0.296 -0.198 Germany -0.237 0.098 -0.060 0.215 -0.204 -0.071 Z Scores China 0.239 -0.160 -0.010 -0.403 -0.395 -0.067 USA 3.535 3.185 3.267 3.751 3.013 3.224 Germany 3.228 3.820 3.532 4.001 3.280 3.515 Original Scores China 3.939 3.163 3.468 2.782 2.805 2.891 Correlation Coefficient 0.9191
It can be concluded that there is not an issue with cultural response bias in this sample and therefore I will use the original scores in this analysis.
4.6.2 Demographics
The demographics of the study subjects are reported in figure 4.6.
Table 4.6: Demographics
USA Germany China Total
N 153 139 171 463 Gender (F/M) 74/79 63/76 65/106 202/261 Age (SD) 21.82 (3.571) 21.62 (1.924) 20.28 (1.129) 21.19 (2.503) Full Time Work Experience (SD) 3.98 (3.872) 0.49 (1.093) 0.13 (0.456) 1.51 (2.899) IS Exp 0.69 (2.076) 0.10 (0.542) 0.00 (0.00) 0.26 (1.264)
The demographics show that sample sizes are similar and age is statistically the same. Gender shows more men to women in all the countries. In China, this excess of men is particularly pronounced. Work experience is relatively the same except in the USA where there is a significant amount of work experience. IS experience is very close to zero across the board and since in China all subjects reported no IS experience, this construct will not be used in the analysis.
4.6.3 Measurement Model Assessment
Straub, Boudreau and Gefen (2004) reviewed the existing literature on instrument validation and have provided guidelines for the assessment of the measurement model of studies in the positivist tradition, such as this. They prescribe that the following
validation steps be performed: 1) Construct Validity a. Discriminant Validity b. Convergent Validity c. Factorial Validity 2) Reliability 3) Manipulation Validity
They further recommend that content validity, nomological validity and common methods bias be evaluated.
In the following two sections, I review these validities for the study reported here. Factorial validity is assessed by the Confirmatory Factor Analysis tests reported below.
4.6.3.1.
Evaluating the Reflective Measures
Convergent validity, how the items converge to measure a particular construct, is assessed by examining the individual item validity, construct reliability and average variance extracted (AVE) (Chin 1998). Discriminant validity, how items differentiate between constructs so that one item measures only one construct, can be evaluated by examining the cross loadings between items and constructs and examining the AVEs of the latent constructs to ensure that they are greater than the square of the correlations among the latent constructs (Chin 1998; Henseler, et al. 2008). Factorial validity is discussed in the section on cross loadings.
Convergent Validity. Convergent validity for the reflective constructs was examined by using confirmatory factor analysis; PLS performs a confirmatory factor analysis (CFA) rather than an exploratory one as done in principal components analysis. This means that it does not seek for the proper factors, but rather confirms that the factor structure specified in the model is correct. The benchmark value for the loading value is .707 although in exploratory studies such as this, .5 or .6 is common (Chin 1998). In this study, the CFA in Table 4.7a shows the following results for the USA: