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«VOLUM E 1 1, N UM B E R 1 I S SN 2 1 6 8 - 0 6 1 2 F L ASH DR I V E I S SN 1 9 4 1 - 9 5 8 9 ON L I N E T h e In s t it ut e f o r Bu s i n e s s an ...»

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The study covered 212 lecturers from all the colleges of the University of Nairobi, including 104 (49.1%) from the College of Humanities and Social Sciences (CHSS); 19 (9.0%) from the College of Biological and Physical Sciences (CBPS); 24 (11.3%) from the College of Health Sciences (CHS); 29 (13.7%) from the College of Education and External Studies (CEES); 20 (9.44%) from the College of Agriculture and Veterinary Sciences (CAVS); and 16 (7.5%) from the College of Architecture and Engineering (CAE). In terms of gender, lecturers from CHSS included 56 (53.8%) men and 48 (46.2%) women; from CBPS were 16 (84.2%) men and 3 (15.8%) women; while from CHS were 20 (83.3%) men and 4 (16.7%) women. The CEES provided 23 (79.3%) men and 6 (20.7%) women; at CAVS 17 (85.0%) men and 3 (15.0%) women participated; while lecturers from CAE included 14 (87.5%) women and 2 (12.5%) women. In addition, the study involved 96 administrative staff, including 34 (35.4%) administrative assistants, 6 (6.3%) college registrars and 15 (15.6%) assistant registrars; 10 (10.4%) departmental chairpersons; 10 (10.4%) faculty deans and 6 (6.3%) associated deans; as well as 8 (8.3%) directors and 7 (7.3%) deputy directors. The administrative staff included 64 (66.7%) men and 32 (33.3%) women.

ELearning preparedness

Lecturers’ preparedness for eLearning was measured in terms of perceived computing competence, referring to the ability to execute commands and manipulate a range of software applications for various purposes. In this regard, participants were requested to rate their competence on each of the following computing software tools on a scale of 1-10: word processing, spreadsheets, presentation, statistical analysis, internet browsing and e-mailing. The participants’ ratings for each software tool were summed and mean scores determined. Resultant quotients were then rated on a scale of 0-49% and 50-100%.

Participants whose mean scores were less than 50% were considered to be below average; thus, were likely to be unprepared to function in an eLearning environment. Conversely, those whose mean scores were GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 486 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 above 50% were considered to above average, and likely to be prepared for eLearning. Based on the principle, out of 212 participants, 103 (48.6%) had a mean score of 50 percent or higher; while 109 (51.4%) scored less than 50 percent; suggesting that slightly more than one-half of the lecturers were below average in terms of computing competence.

ELearning preparedness and background profile

The results presented in Table 2 show that out of 212 participants, 97 (45.8%) were in the 40 to 49 years age bracket; 4 (25.5%) were aged between 50 and 59 years, while 22 (10.8%) were in the 30 to 39 years bracket. Besides, another 22 (10.8%) reported to be 60 years or higher, while 8 (3.9%) were aged below 30 years. Table 2 further shows that the proportion of lecturers unprepared for eLearning in the 50+ age category was more than the proportion of those prepared in the same age category. Conversely, the proportion of staff prepared for eLearning aged below 40 years was higher than the proportion of those unprepared. The pattern suggests that younger academic staff were likely to be more competent in working with software tools; hence, likely to be better prepared for eLearning than their relatively older colleagues.

Based on this, bivariate analysis obtained a computed Chi-square (χ2) value of 18.026, with 4 degrees of freedom and a p-value of 0.001, which is significant at 0.01 error margin; suggesting up to 99% chance that lecturers’ preparedness for eLearning significantly associated with age. Similar findings on the link between age and computing competence were reported by Venkatesh and Morris (2000) who assessed the role of gender and social influence on technology acceptance behaviour among academic staff of Indian public universities. The study found that younger lecturers were more receptive to new technologies than their older counterparts. In Jordan, Abbad, Morris and Nahlik (2009) found a negative correlation between lecturers’ age and eLearning delivery methods.

Results in Table 2 further show that 146 (68.9%) participants were men and 66 (31.1%) were women.

Besides that proportion of women lecturers prepared for eLearning 34 (33.0%) was marginally higher than the proportion of those unprepared 32 (29.4%). However, the proportion of men prepared for eLearning 69 (67.0%) was lower than the proportion of those unprepared 77 (70.6%). However, the analysis did not find a significant relationship between lecturers’ gender and preparedness for eLearning [computed χ2 = 1.039 (corrected for continuity), df = 1 and p-value = 0. 243]. This suggests that no gender was more competent in computing than the other; hence, none was likely to be more prepared than the other. This is however, inconsistent with the findings of Luan, Aziz, Yunus, Sidek and Bakar (2005), who investigated gender differences in ICT competence among academicians at the Universiti Putra Malaysia. The study noted that female and male academicians were significantly different in the application of software packages such as word processing, spreadsheets and presentation tools. However, in Egypt, Houtz and Gupta (2001) found that male lecturers were more confident and had a greater usage of computers compared to their female counterparts. Besides, Venkatesh and Morris (2000) noted that male lecturers were more likely to accept new technological innovation than their female colleagues.





GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 487 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Table 2: Background profile and preparedness for eLearning

–  –  –

Up to 151 (71.2%) academic staff reported holding PhD degrees, 56 (26.4%) held masters certificates, while 5 (2.4%) had bachelor’s degree qualifications. Besides, the results summarised in Table 2 show that the proportion of PhD holders unprepared for eLearning was higher than the proportion of those prepared.

Conversely, the proportion of masters’ degree holders prepared for eLearning was higher than the proportion of those unprepared. Based on this pattern, a computed Chi-square (χ2) value of 11.031 was obtained, with 2 degrees of freedom and p-value of 0.004, which is significant at 0.01 error margin;

suggesting up to 99% chance that lecturers’ preparedness for eLearning significantly associated with educational attainment. Thus, masters’ degree holders, being relatively younger people, were likely to be more competent in computing; hence, better prepared for eLearning than PhD holders. These findings are consistent with those reported by Roberts, Hutchinson and Little (2003) who assessed barriers to the use of technology for teaching among Dutch universities. The study noted that professors and associate professors were less likely to use ICT tools in their teaching than junior lecturers.

The results in Table 2 further indicate that most participants, 155 (73.1%), were earning KES 90,000 or more; 21 (9.9%) were in the KES 80,000 to 89,000 bracket; 17 (8.0%) averaged at between KES 70,000 and 79,000, while 11 (5.2%) reported an income of KES 60,000 to 69,000. In addition, the proportion of lecturers unprepared for eLearning in the top income bracket was higher than the proportion of those prepared. Contrastingly, the proportion prepared for eLearning in the category of less than KES 60,000 was higher than those unprepared. The analysis yielded a computed Chi-square (χ2) value of 11.707, with 5 degrees of freedom and p-value of 0.039, which is significant at 0.05 error margin; suggesting up to 95% chance that preparedness for eLearning varied significantly across the income categories. More specifically, top earners were less competent in computing than low earners. Similarly, Venkatesh and Morris (2000) found a positive correlation between the frequency of computer use and lecturers’ average income. The study noted that although lecturers in higher income brackets had a greater access to personal computers than those in lower income scales, more than one-half did not use computers consistently to support their work due to limited ICT skills.

GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 488 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Workplace ICT Infrastructure This thematic area focuses on the key workplace infrastructure variables, including access to computers at the workplace, quality of computers at the workplace, frequency of computer use, availability and reliability of Internet connectivity, as well as availability and timeliness of ICT support programme.

Access to computers at the workplace and frequency of use: The results in Table 3 shows that out of 212 participants, 194 (91.5%) had access to functional computers at their workplace; only 18 (8.5%) did not.

The proportion of staff prepared for eLearning was higher among those who had access to computers at the workplace [99 (96.1%)], as opposed to those who did not [95 (87.2%)]. Bivariate analysis revealed a significant relationship between lecturers’ preparedness for eLearning and access to functional computers at the workplace [computed χ2 value = 9.380 (corrected for continuity), df = 1 and p-value = 0.036]. This suggests that participants having access to computers at the workplace were likely to be more competent in computing; thus better prepared to function in an eLearning environment than those lacking such access.

Based on this, the null hypothesis (H01), stating that there is no significant relationship between access to computers at work and lecturers’ preparedness for eLearning, was rejected for inconsistency with empirical results.

Table 3: Access to computers at the workplace and frequency of use

–  –  –

The analysis found that lecturers having access to computers at the workplace were about 2.8 times as likely to be prepared for eLearning as those not having access. Participants noted that access to computers at the workplace provides opportunity for practice and skill improvement, which in turn, enhances discourages anxiety and negative attitudes that may be associated with computer use. Furthermore, although up to 91.5% of the participants reported having access to computers at the workplace, about two-thirds were using personal computers as those provided by the University were inadequate. Access to computers at the workplace has been assessed by various scholars, including Albirini (2006), Gulbahar (2005) and Blankenship (1998). For instance, a study conducted by Albirini (2006) in Syria found that only 33% of the lecturers had access to computers at their places of work, which in turn, influenced the proportion using computers to support teaching activities. The study also indicated that the adequacy of appropriate computers was a key factor influencing lecturers’ preparedness to operate in an eLearning environment.

Regarding the frequency of use, Table 3 shows that 105 (54.1%) participants use workplace computers daily, 44 (20.8%) use them at least once a week; while another 44 (20.8%) do so occasionally. Observation of computer use revealed that 64 (42.1%) participants were consistently using computers for literature search as well as for compiling notes, 47 (30.9%) were using computers occasionally, 12 (7.9%) were rarely using computers, while about one-fifth, 29 (19.1%) were not using computers at all. The analysis showed that frequent computer users were likely to be more competent in computing and better prepared to function in an eLearning environment than infrequent users. In this regard, the analysis obtained a computed ChiGCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 489 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 square (χ2) value of 18.389, with 3 degrees of freedom and a p-value of 0.000, which was significant at 0.01 error margin; suggesting up to 99% chance that consistent computer users were likely to be better prepared for eLearning than their inconsistent colleagues.

More still, workplace computers were used to accomplish various tasks, including communication, 122 (26.6%); data analysis, 105 (22.9%); developing teaching materials, 98 (21.4%); manuscript preparation, 61 (13.3%); personal business, 36 (7.9%); as well as report writing, 36 (7.9%). Word processing and internet browsing software tools were used daily by the largest proportion of participants, 139 (65.6%) and 148 (69.8%), respectively. Contrastingly, the least applied were statistical analysis tools, 13 (6.1%);

presentations, 51 (24.1) and spreadsheets, 53 (25.0%), irrespective of the preparedness for eLearning. The results suggest that preparedness for eLearning significantly associated with the utilisation frequency of all the software tools, including word processing, spreadsheets, presentation, statistical analysis, Internet and emailing.

Participants were requested to indicate their perception about the adequacy and quality of computers at the workplace. In this regard, the results show that 77 (36.3%) felt that the computers were ‘very inadequate’, 79 (37.3%) believed that the computers were ‘inadequate’, 44 (20.8%) hinted that the facilities were ‘adequate’, while 12 (5.7%) indicated ‘very adequate’. The analysis yielded a computed χ2 value of 2.573, with 3 degrees of freedom and a p-value of 0.462, which was not significant; suggesting lack of significant relationship between lecturers’ preparedness for eLearning and perception on the adequacy of workplace computers. Shortage of functional computers was a critical issue cited by most participants, regardless of their competence and preparedness for eLearning. Inadequacy of computers for lecturers may have significant influence on their computing competence and preparedness to function in an e-learning environment, which concurs with the findings of Blankenship (1998) who noted that the integration of eLearning is a function of the number of workplace computers available and accessible to lecturers, learners and the administrative staff.



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