5.3 Presentation of results
5.3.4 Information culture, electronic records creation and capture
132 After identifying the information behaviours and values, it was necessary to establish whether information culture significantly affects records management. Multiple linear regression was performed to determine the significance of information culture on ERM.
133 5 I send the
records that I created to records management unit
3 3 15 15 23 23 33 33 27 27 0 0 101 101
6 I am aware of procedures for creating and capturing records
2 2 26 26 23 23 25 25 25 25 0 0 101 101
7 I am able to access records created by others
6 6 25 25 20 20 36 36 14 14 0 0 101 101
8 The
organisation enforces proper records creation and capture
6 6 17 17 30 30 25 25 23 23 0 0 101 101
9 There are measures in place to control access to records
5 5 12 12 20 20 45 45 19 19 0 0 101 101
Source: Field data (2021)
Thirty-nine (39%) respondents agreed that policies guide them in creating and capturing records, while 22 (22%) strongly agreed with the statement. Eighteen (18%) neither agreed nor disagreed, 16 (16%) disagreed and five (5%) strongly disagreed.
Regarding knowing the right metadata to be captured for records, the highest number of respondents, 35 (35%) neither agreed nor disagreed, 23 (23%) agreed, 16 (16%) strongly agreed, 23 (23%) disagreed and three (3%) strongly disagreed.
Forty-nine (50%) of the respondents agreed that they were aware of the cost of their lack of adherence to proper records creation and capture and 28 (29%) strongly agreed. Twelve (12%) neither agreed nor disagreed, eight (8%) disagreed and one (1%) strongly disagreed.
134 Thirty-nine (39%) respondents agreed that the records they create are adequately captured while 24 (24%) strongly agreed with the statement. Twenty-seven (27%) neither agreed nor disagreed, eight (8%) disagreed and two (2%) strongly disagreed.
Thirty-three (33%) respondents agreed and 27 (27%) strongly agreed with the statement “I send the records that I created to the records management unit”. Twenty-three (23%) neither agreed nor disagreed, 15 (15%) disagreed and three (3%) strongly disagreed with the statement.
Concerning awareness of procedures for creating and capturing records, 26 (26%) respondents disagreed, two (2%) strongly disagreed, 23 (23%) neither agreed nor disagreed, while 25 (25%) agreed and the same number (25) strongly agreed.
Thirty-six (36%) respondents agreed and 14 (14%) strongly agreed with the statement concerning being able to access records created by others. Twenty (20%) neither agreed nor disagreed, 25 (25%) disagreed and six (6%) strongly disagreed.
Thirty (30%) respondents neither agreed nor disagreed with the statement that the organisation enforces proper records creation and capturing. Twenty-five (25%) agreed, 23 (23%) strongly agreed, 17 (17%) disagreed and six (6%) strongly disagreed.
Regarding controlling access to records, 45 (45%) respondents agreed that there are measures in place to control access to records and 19 (19%) strongly agreed. Twenty (20%) neither agreed nor disagreed, 12 (12%) disagreed and five (5%) strongly disagreed with the statement.
5.3.4.1 Analysis of data using multiple hierarchical linear regression
In carrying out multiple hierarchical linear regression, the information culture types (information sharing, integrity, transparency and proactiveness) were computed in a four-level model as independent variables to predict ERM (dependent variable). These factors were constructed from items following the Information Culture Conceptual Framework (2013), which states that information behaviours and values have a bearing on how organisations handle information. For these factors (information sharing, integrity, transparency and proactiveness), scales were constructed and tested for reliability using Cronbach’s alpha and they were found to be acceptable.
135 Before computing multiple hierarchical linear regression procedures to determine the significance of information culture types on ERM in the selected parastatals in Botswana, the assumptions of parametric statistics were assessed to ascertain the appropriateness of the test statistic in question. The first assumption that the researcher tested for was that of normality. It was important to check if the values were normally distributed across the dependent variable. This assumption was tested using a histogram and was met as the histogram was unimodal and had a bell-shaped curve (see Figure 5.5). A second assumption that was met was that of multicollinearity.
According to Harts (2010), multicollinearity occurs when two or more independent variables are highly correlated. To test for multicollinearity, collinearity statistics from the coefficient table were analysed. The tolerance value has to be greater than zero, and the variance inflation factor (VIF) should be below 10 for the assumption to be met (Allen, Titsworth and Hunt 2008). From the coefficient table, the VIF values ranged from 1.000 – 1.487, thus far below 10. This, therefore, meant that there were no collinearity issues and hence the independent variables were not highly correlated. The tolerance values also showed that there were no collinearity problems as they ranged from .672 – 1.000. The third met assumption was independence, as the researcher ensured that the respondents completed the questionnaires independently, that is, the researcher did not interfere with respondents when they were completing the questionnaire. The assumptions of multivariate normality, homoscedasticity, and independence of errors were also met.
136 Figure 5.5: Histogram for the test of normality
N=101
Source: Field data (2021)
The histogram (Figure 5.5) shows the test of normality to determine if a data set is well-modelled by a normal distribution. The unimodal and bell-shaped curve indicates that the values were normally distributed across the dependent variable.
5.3.4.2 Regression analysis model
Multiple hierarchical linear techniques were computed to test whether information culture has significant effects on ERM. Having met all the assumptions of the test statistic in question, the model which was based on information culture theory, yielded statistically significant results, with only one non-significant predictor, namely, information integrity.
In Table 5.9 Step 1, with one predictor, information sharing, F (1, 99) =16,499, p<.001, R²=.143. Step 2, with two predictors was significant, F(1, 98)=22.63,p< .001, R²=.304. This model was better with an r of .551. Step
137 3, had three predictors and was non-significant, F(1, 97)=1,804, p>.182, R²=.316. This model, although not significant, was even better with an r of .562. In Step 4, Proactiveness slightly added to the regression equation, with a marginal change in R² from .295-.366. This change in R square was significant, F (1, 96) =6.885, p<.010.
Table 5.2: Hierarchical multiple regression analysis predicting information behaviours and values in the selected parastatals
Predictor R R² ∆R² ß Sig
Step 1 Information
sharing .378ª .143 .143 .378 .001
Step 2 Information sharing
transparency .551ᵇ .304 161 .429 .001
Step 3 Information sharing transparency
integrity .562ᶜ .316 .013 .-128 .001 Step 4
Information sharing transparency
proactiveness .602ᵈ .362 .046 .037 .010
Source: Field data (2021)
The results in Table 5.9 reveal that all predictors except one (information integrity) significantly predict ERM in Botswana. Information proactiveness with a variation of 36.2% was found to predict ERM better, over and above other predictors. All predictors had significant standardised regression weights (information sharing ß=.378, t=4.062, p<.001; information transparency ß=.429, t=4.78, p<.001; information integrity ß= -.128, t= - 1.343,p>.182; information proactiveness ß=.037, t= 2.624, p<.010). Therefore, the significant standardised regression weights indicate that the information culture variables have greater significant effects on records management.
138