5 Summary and conclusions
5.3 Limitations and future research
This thesis, like any other, has some limitations. First and foremost, a survey was conducted in this study and there are some constraints to survey studies. For example, the responder may misread the questions, or the respondent may not always be capable of answering them, because of a lack of experience with the content of the survey questions, such as questions about intelligent automation. The factor and Cronbach alpha analyses were used to overcome this constraint. In addition, the dataset’s size may be a constraint. A total of about 192 respondents completed the survey. However, as this thesis only focuses on respondents from Europe, ten observations were removed. As a result, there are 182 observations in the data set.
It is possible that with a larger dataset this research would result in different conclusions.
Furthermore, the survey has mostly been sent to respondents from the Netherlands, and some respondents come from elsewhere in Europe. This indicates that the research is a good representation of how it is in The Netherlands, however, there are too few European observations to say that the results apply to Europe. Furthermore, this research is focused on Europe, so it could lead to different results in other continents.
Despite the limitations of the research, it is important to emphasize that this is the first conducted research that looks at the association between digitalization in Finance, the investment in technical skills of employees, and the effectiveness of the Finance function.
Evidence was found for two of the three expectations, and some evidence was found for the third expectation through performing additional analysis. In addition, also other important findings emerged. According to the findings of this research, companies should use digital technologies in Finance as well as invest in the technical skills of employees as both do contribute to a more effective Finance function. The research suggests that companies can better use data analytics than automation tools to increase the effectiveness of the Finance function. However, when the company also invests in the employees’ technical skills, both digital technologies combined with the skills will increase the effectiveness. Furthermore, it can be concluded that small companies should invest more in digital technologies in Finance because as they do this it will lead to higher effectiveness. As research shows that the effectiveness of the Finance function in the public sector is lower, these companies should use digital technologies and invest in the employees' technical skills because this does increase the effectiveness of the Finance function. As a result, this research contributes to the literature and addresses contemporary issues. It is recommended that similar research should be conducted with a bigger dataset in the future. It is also advised to broaden the survey to more respondents
from Europe. At last, it is recommended to check the measured construct of the effectiveness of the Finance function as it could exclude components of financial tasks of some sectors.
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Appendices
Appendix A – Factor analysis and Cronbach Alpha analysis of the effectiveness of the Finance function
Factor analysis
Cronbach Alpha analysis
Cronbach's alpha
Test scale = mean (unstandardized items)
Number of items in the scale: 6
Scale reliability coefficient: 0,906 Factor analysis
Number of obs = 182
Retained factors = 1
Number of params = 6
Eigenvalue Variance Cumulative
1 4,069 0,678 0,678
2 0,582 0,097 0,775
3 0,447 0,074 0,850
4 0,356 0,059 0,909
5 0,317 0,053 0,962
6 0,229 0,038 1,000
Extraction m ethod: Principal com ponent analysis
Variable Factor
Q16_1 0,856
Q16_2 0,834
Q16_3 0,848
Q16_4 0,850
Q16_5 0,756
Q16_6 0,793
Component
Factor loadings (component matrix)
Appendix B – Factor analysis and Cronbach Alpha analysis of digitalization in Finance
Factor analysis
Cronbach Alpha analysis Cronbach's alpha
Test scale = mean (unstandardized items)
Number of items in the scale: 8
Scale reliability coefficient: 0,919
Factor analysis
Number of obs = 182
Retained factors = 1
Number of params = 8
Eigenvalue Variance Cumulative
1 5,125 0,641 0,641
2 0,971 0,121 0,762
3 0,446 0,056 0,818
4 0,394 0,049 0,867
5 0,344 0,043 0,910
6 0,309 0,039 0,949
7 0,256 0,032 0,981
8 0,155 0,019 1,000
Extraction m ethod: Principal com ponent analysis
Variable Factor
Q12_1 0,763
Q12_2 0,847
Q12_3 0,806
Q12_4 0,811
Q12_6 0,788
Q12_7 0,808
Q12_8 0,781
Q12_9 0,798
Component
Factor loadings (component matrix)
Appendix C – Factor analysis and Cronbach Alpha analysis of investment in technical skills of Finance professionals
Factor analysis
Cronbach Alpha analysis Factor analysis
Number of obs = 182
Retained factors = 1
Number of params = 3
Eigenvalue Variance Cumulative
1 2,200 0,733 0,733
2 0,492 0,164 0,897
3 0,308 0,103 1,000
Extraction m ethod: Principal com ponent analysis
Variable Factor Q13_1 0,810 Q13_2 0,885 Q13_3 0,872
Component
Factor loadings (component matrix)
Cronbach's alpha
Test scale = mean (unstandardized items)
Number of items in the scale: 3
Scale reliability coefficient: 0,818
Appendix D – Spearman correlation test
Table D Spearman correlation Spearman Correlation
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
(1) EFF_FIN 1,00
(2) DIG_FIN 0,61 1,00
(3) INV_SKILLS 0,63 0,69 1,00
(4) D_MANUFACTURING 0,05 0,05 0,02 1,00
(5) D_FSERVICES 0,11 0,03 0,07 -0,30 1,00
(6) D_NFSERVICES 0,07 0,07 0,03 -0,55 -0,27 1,00
(7) D_PUBLICSEC -0,27 -0,19 -0,14 -0,34 -0,17 -0,31 1,00
(8) FIRM_SIZE 0,14 0,24 0,16 0,10 -0,06 -0,02 -0,05 1,00
(9) DEG_DIG 0,48 0,57 0,59 -0,04 0,19 0,07 -0,20 0,12 1,00
(10) ANN_GROW 0,09 0,09 0,14 0,04 0,15 -0,00 -0,19 -0,05 0,20 1,00
** p<0,01, * p<0,05
Variables
Appendix E – VIF values multicollinearity
The VIF values for each hypothesis are shown in this appendix which is done for multicollinearity. For none of the items is multicollinearity an issue.
Collinearity statistics
Variable VIF
DIG_FIN 1,577
D_MANUFACTURING 1,347
D_FSERVICES 1,246
D_PUBLICSEC 1,316
FIRM_SIZE 1,082
DEG_DIG 1,567
ANN_GROW 1,068
Mean VIF 1,315
Table E.1 VIF value hypothesis I
Collinearity statistics
Variable VIF
INV_SKILL 1,600
D_MANUFACTURING 1,347
D_FSERVICES 1,240
D_PUBLICSEC 1,314
FIRM_SIZE 1,052
DEG_DIG 1,631
ANN_GROW 1,070
Mean VIF 1,322
Table E.2 VIF value hypothesis II
Table E.3 VIF value hypothesis III
Collinearity statistics
Variable VIF
DIG_FIN 2,365
INV_SKILL 2,169
INT_DIGFIN_INVSKILL 1,169
D_MANUFACTURING 1,348
D_FSERVICES 1,249
D_PUBLICSEC 1,333
FIRM_SIZE 1,084
DEG_DIG 1,817
ANN_GROW 1,083
Mean VIF 1,513
Appendix F – Factor analysis split-up of DIG_FIN
Rotated Component Matrix
Component
1 2
DIG_AUT (1) 0,737
DIG_AUT (2) 0,828
DIG_AUT (3) 0,882
DIG_AUT (4) 0,807
DIG_DAT (5) 0,854
DIG_DAT (6) 0,820
DIG_DAT (7) 0,818
DIG_DAT (8) 0,710
Table F Factor analysis DIG_FIN
Appendix G – Robustness check
In the robustness check, the regression analysis has been performed with the factor of the variables.
Table G.1 Regression analysis hypothesis I
Table G.2 Regression analysis hypothesis II
Regression hypothesis I
Unstand. B Std. Err. Coeff. Beta t-value p-value Sig
CONSTANT 0,095 0,196 0,484 0,629
DIG_FIN 0,507 0,070 0,507 7,272 0,000 **
D_MANUFACTURING 0,042 0,135 0,020 0,310 0,757
D_FSERVICES 0,170 0,190 0,057 0,896 0,372
D_PUBLICSEC -0,465 0,178 -0,171 -2,615 0,010 **
FIRM_SIZE 0,001 0,025 0,003 0,049 0,961
DEG_DIG 0,152 0,069 0,057 0,896 0,372
ANN_GROW -0,026 0,054 -0,028 -0,481 0,631
** p<0,01, * p<0,05
Adjusted R2 0,42 N 182
R2 0,44 F-test 19,47 **
EFF_FIN
Regression hypothesis II
Unstand. B Std. Err. Coeff. Beta t-value p-value Sig
CONSTANT -0,326 0,319 -1,022 0,308
INV_SKILL 0,529 0,071 0,529 7,465 0,000 **
D_MANUFACTURING 0,004 0,134 0,002 0,028 0,978
D_FSERVICES 0,102 0,188 0,034 0,544 0,587
D_PUBLICSEC -0,512 0,175 -0,188 -2,920 0,004 **
FIRM_SIZE 0,013 0,024 0,031 0,543 0,588
DEG_DIG 0,087 0,052 0,119 1,662 0,098
ANN_GROW -0,034 0,053 -0,038 -0,647 0,519
** p<0,01, * p<0,05
Adjusted R2 0,43 N 182
R2 0,45 F-test 20,51 **
EFF_FIN