Page 47 - IJSA, Vol. 7, No 1, 2024

International Journal of Science Annals, Vol. 7, No. 1, 2024
рrint ISSN: 2617-2682; online ISSN: 2707-3637; DOI:10.26697/ijsa

As shown in Table 63.3% (187) of participants Furthermore, Table 2 shows that 74.0% (222) of
representing healthcare SMEs were males, whilst 37.7% participants use WhatsApp, 21.3% (64) use Facebook,
(113) were females. Furthermore, the findings in Table and 4.7% (14) use X (formerly Twitter).
2 reveal that 74.0% (222) of participants were between Regression Analysis
the ages of 20 to 30, 21.3% (64) were between 41 to 50 At this stage, regression analysis was utilised to evaluate
and 4.7% (14) were above 50 years. Regarding the degree to which INO, FPM CSE, SHI, DST, IEA,
education, 56.7% (170) of participants had a B-tech, COS, and TTF influence the digitalisation of SMEs to
followed by 19.3% (58) who had a master’s, 15.3% (46) improve the DC of SMEs in the Healthcare sector. As
had a diploma, 6.7% (20) had a matric and only 2.0% (6) shown in Table 3, the regression findings show a
had a PhD. Concerning job position, Table 2 shows that significant value of 0.000, implying that the regression
100% (300) of participants were SMEs managers. model can be used for the digitisation of SMEs to
Moreover, the results in Table 2 show that 100.0% (300) improve the DC. In this present study, the predictor
of SMEs operate in the healthcare sector. Concerning variable accounts for 68.1% of the variance in the
the use of digital technologies (DT), 88.0% (264) of digitalisation of SMEs in the healthcare sector to
2
participants revealed that they are using DTs, whilst improve the DC and the adjusted R Square (R )
12.0% (36) alluded that they are not using DTs. equals=0.681.
Table 3
Regression Statistics*

Note. *Dependent variable – Digitalisation of SMEs to improve the DC; INO – innovation; FPM – firm performance;
CSE – customer service; SHI – sharing information; DST – data security; IEA – internet access; COS – cost savings;
TTF – task-technology fit.

As displayed in Table 3, the results show that INO found that one factor has a negative influence on the
(β=0.125, p<0.05), FPM (β=0.132, p<0.05), and CSE digitalisation of SMEs to improve the DC, namely, COS
(β=0.057, p<0.05) hypotheses positively influence the (β=0.178, p>0.05).
digitalisation of SMEs to improve the DC. Along the The results demonstrated in Table 4 indicate that seven
same lines, this present study confirmed that SHI (7) hypotheses (H1, H2, H3, H4, H5, H6, and H8) are
(β=0.022, p<0.05) and DST (β=0.427, p<0.05) have a supported, whilst H7 is not supported. To check if there
positive effect on the digitisation of SMEs to improve is an existence of multi-collinearity, this present study
the DC. Furthermore, the study proved that IEA employed the Variance Inflation Factor (VIF). As
(β=0.235, p<0.05) and TTF (β=0.052, p<0.05) depicted in Table 3, all the VIF numbers were below 5,
hypotheses positively influence the digitalisation of which implies that there is no existence of multi-
SMEs to improve the DC. However, this present study collinearity.
Table 4
Hypotheses Testing

Note. *p<0.05; **p<0.01; INO – innovation; FPM – firm performance; CSE – customer service; SHI – sharing
information; DST – data security; IEA – internet access; COS – cost savings; TTF – task-technology fit.
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