Inferential Analysis: Multivariate Analyses for Testing the Hypotheses: To establish which of the IVs were significant correlates of the use of the Internet (the DV), a multivariate

tool, namely multiple regression analysis was used. But before fitting the multiple regression model, the IVs were treated as follows: Being continuous variables, “interaction with ICT change agents” (CA in Table 3), ICT training (T in Table 4), cosmopolitanism (from Table 5) and age were used in the model with no modification. However, dummies were created for the gender (0 = female; 1 = male) and the income level (0 = Low; 1 = Medium and high). Table 6 gives the multiple regression analysis of the DV (the use of the Internet, UI) on the IVs, namely the six personal characteristics. Analysis of Variance (F = 2.644) suggested that the model relating the DV and the six IVs was good at the five percent level of significance (p = 0.05), and the adjusted R square (R2 = 0.291) indicated that over 29% of the variation in the DV could be attributed to those six IVs. However, the t values suggested that of the six IVs, only age (Beta, β = - 0.575) was a significant negative correlate (β < 0) of the DV at the one percent level of significance ( ׀t ׀> 2.58). In other words, multivariate regression analysis led to the rejection of all the six research hypotheses, save the fourth one (H4).

Table 6 Regression of use of Internet on individual adopter characteristics

Individual adopter characteristic β t

Interaction with ICT change agents - 0.017 -0.056

ICT training 0.220 -0.600

Cosmopolitanism 0.217 1.138

Age of respondent (yrs) -0.575 -2.919

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Income level dummy (0 = Low; 1 = Medium and High) 0.382 1.394 F = 2.644 (p = 0.051); Adjusted R square = 0.291

6 Discussion

The study did not support the first research hypothesis (H1) to the effect that interaction with ICT change agents was positively related to the use of the Internet. This finding was not only inconsistent with many other studies (e.g. El-Gayar et al., 2011), it was also at odds with theoretical assertions such as that by Stuart et al. (2009) to the effect that a potential adopter who has more contacts with a relevant change agent is more likely to use the pertinent innovation than those with fewer contacts. The possible explanation for the unexpected finding could still be found in Stuart et al. who observe that the mere presence of champions is inadequate unless the champions “communicate a clear vision of an innovation, display enthusiasm for the innovation, demonstrate commitment and involve others in supporting it”; unless they exude “confidence, persistence, energy and risk-taking” (p. 734). May be the ICT champions in Kampala International University (KIU) lack these attributes. If that be the case, then the finding leads to one major conclusion, namely that the ICT change agents in KIU such as the University’s Directorate of ICT Support should try to communicate a clear vision of the Internet, display enthusiasm for it, demonstrate commitment and involve others in supporting it. They should endeavour to exude “confidence, persistence, energy and risk- taking” (Stuart et al., 2009) with regard to the Internet.

The study rejected the second research hypothesis (H2) to the effect that ICT training positively correlated with the use of the Internet. This finding was at variance with many other studies (e.g. Gakibayo et al., 2013; Khan et al., 2011; Okello-Obura & Ikoja-Odongo, 2010). It was also at odds with theoretical assertions such as those by Ng’ethe et al. (2012) and Sinha and Sinha (2012) who argue that adaptability to technological advances is a factor of training. This anomalous finding could be as a result of not probing deep enough to know which kind of ICT qualifications the doctoral students in KIU held. May be they held too low ICT qualifications to enhance the use of the Internet as expected. In the mean time, the study laid enough ground for the conclusion that relevant stakeholders such as the University’s Top Management and the Directorate of ICT Support should give all its doctoral students equal exposure and/ or encouragement with respect to Internet, irrespective of their differentials in ICT qualifications.

The third hypothesis (H3) was that there was a significant positive correlation between cosmopolitanism and the use of the Internet, but it was not supported by the study. The finding was at variance with other studies (e.g. Amutabi & Oketch, 2003). The explanation for the anomalous finding could be that while innovations are expected to start from urban or cosmopolitism areas and spread to other areas (Bisaso & Visscher, 2005) both rural and urban areas in Uganda have equally low levels of use of the Internet to the extent that the urban or cosmopolitan ones do not enjoy any advantage. The study finding might be implying that all the doctoral students in the University, whether from a cosmopolitan or rural background should be given equal exposure and/ or encouragement by the change agents such as the Directorate of ICT Support with respect to the Internet.

The study did uphold the fourth hypothesis (H4) to the effect that age was inversely related to the use of the Internet. The study finding was consistent with several past studies (e.g. Bakkabulindi & Kabasiita, 2012) and concurred with theoreticians such as Kok et al. (2011) who observe that age is an important negative correlate of the use of innovations. In

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Proceedings conclusion, it is being recommended that the stakeholders in KIU such as the Top Management and the Directorate of ICT Support give preferential encouragement with respect to the Internet, to the aged and ageing doctoral students. The fifth hypothesis (H5) was that gender related with the use of the Internet, with the males being more frequent users. However the study did not support it, a finding which was also contrary to findings of other studies (e.g. ECAR, 2010; Huang et al., 2013; Sim et al., 2011). The possible explanation for the study finding is that as the Internet is becoming more ubiquitous, it is no longer a male domain (Sang et al., 2010). The study finding thus could be a reasonable basis to suggest that both male and female doctoral students in KIU needed equal exposure and/ or encouragement with respect to Internet resources, and hence the call to the Directorate of ICT Support to afford them the same.

The sixth hypothesis (H6) in the study, namely that the income level positively related with the use of the Internet, was not upheld. This was inconsistent with the findings of several other studies (e.g. Bakkabulindi et al., 2009), and challenged the theoretical assertion that the higher the income, the easier it is for an individual to acquire, or otherwise access, expensive innovations (Rogers, 2003) such as the Internet. The possible explanation for this unexpected finding could be that the doctoral students with more financial ability to access the Internet tended to be older, making their advanced age to militate against their eagerness to go in for the innovation (the Internet) (Schiffman & Kanuk, 2004). The opposite may have been true for the less financially able doctoral students. This inconclusive debate raises a gap for future researchers to consider. In the interim however, the study finding seems to imply that the Directorate of ICT Support and other relevant ICT change agents in the University should give all the doctoral students equal exposure and/ or encouragement with respect to Internet facilities, regardless of their income levels.