REALIMENTACIÓN A LA GUÍA DE AUTOEVALUACIÓN

P REGUNTAS PARA LA REFLEXIÓN

3.10. REALIMENTACIÓN A LA GUÍA DE AUTOEVALUACIÓN

This section describes the analyses of the data obtained from the survey. The participants in this group were purposively selected for age and education level, since age [Kwon and Chidambaram, 2000; Pedersen, 2003; Kleijnen et al., 2004] and education [Ling and Haddon, 2001; Teo and Pok, 2003a; Nickerson and Isaac, 2006] have been found to influence mobile phone usage.

According to [Kwon and Chidambaram, 2000; Urbaczewski et al., 2002] nationality influences mobile phone use. In contrast, Rice and Katz [2003] found that race does not influence mobile phone usage. Given these contradictory findings, the participants were selected to include different ethnic groups. However, since participation was optional, there was no control over the percentage of each group.

Employment status, income and experience in using the mobile phone are moderating factors influencing people’s mobile phone use [Rice and Katz, 2003; Nickerson and Isaac, 2006]. Selecting students could control employment status and to a degree income, but experience with the mobile phone was a variable that could not be controlled.

Section 7.4.1 deals with the demographic profile of the participants, providing descriptive statistics on age, gender and language of the sample. In order to gain a better understanding of the infrastructural factors that could influence the behaviour of these participants, the infrastructure used, e.g., type of contract, brand of phone and service provider chosen, are considered in section 7.4.2

7.4.1 Demographic profile

There were 138 participants of whom 64 (46%) were male and 74 (54%) female, 69% attended urban schools, while 31% completed their matriculation in a rural area. All participants have successfully completed the matriculation examination and were third-level (third-year) students from two universities in Pretoria, namely the Tshwane University of Technology (60 students) and the University of Pretoria (78 students).

In general the questionnaires had few missing values; only one of the questionnaires was very incomplete and this case was discarded. Further analysis was hence done on 137 cases with missing values reported where appropriate.

Data on the demographic variables of age, mother tongue and gender were captured, and each of these will now be discussed in turn. Age was distributed as depicted by the histogram in Figure 7.2 with an average age of 21.

The mother-tongue language distribution is used as an indication of ethnic origin: most participants had Afrikaans as their mother tongue (36%) with Sotho (24%) in second place, English (16%) in third place and Nguni (15%) in fourth place as depicted in Figure 7.3.

The influence of ethnic culture is not analysed further due to the difficulties in capturing ethnic origin correctly (as discussed in section 7.2.1). Even the use of language (mother tongue), which was found to be the most acceptable indicator of ethnic origin, is open to criticism on practical and ethical grounds.

A model for representing the motivational and cultural factors that influence mobile phone usage variety 122 Another reason is that many of the respondents came from more than one ethnic culture and it would be impossible to categorise them according to ethnic origin.

27.0 25.0 24.0 23.0 22.0 21.0 20.0 19.0 18.0 17.0 Age 40 30 20 10 0

C

ount

1% 2% 3% 5% 12% 28% 22% 16% 7% 4% Age Distribution

Figure 7.2: Age distribution

Figure 7.3 depicts the mother-tongue language distribution. Language is used as an indication of ethnic origin: most participants had Afrikaans as their mother tongue (36%) with Sotho (24%) in second place, English (16%) in third place and Nguni (15%) in fourth place as depicted in Figure 7.3.

Another reason is that many of the respondents came from more than one ethnic culture and it would be impossible to categorise them according to ethnic origin.

The gender split was 46% male and 74% female. On the question of gender, the literature on the subject reported contradictory findings. Some studies found a difference between the mobile phone use of men and women [Ho and Kwok, 2003; Bina and Giaglis, 2005] while other studies on gender were inconclusive [Rice and Katz, 2003; Nickerson and Isaac, 2006].

Gender is not the focus of this research and therefore gender differences are not investigated in all possible ways. Only priorities on buying and technological development were investigated for gender differences since they related to gender differences found in section 3.2.

A model for representing the motivational and cultural factors that influence mobile phone usage variety 123 2 % 4% 4% 24% 15% 16% 36% Other Xitsonga Tshivanda Sotho Nguni English Afrikaans Mother-tongue Mother-tongue distribution

Figure 7.3: Mother-tongue distribution

The results of the independent-samples t-test for priorities on buying, as depicted in Table 7.5, were not significant as far as gender difference is concerned:

• technology/features t(132)=1.18, p=0.24;

• accessibility t(132)=-0.22, p=0.83;

• usability t(132)= -.05, p =0.96 and

• appearance/image t(132)= -1.4, p=0.16.

Equal variances were assumed in all these cases. It follows that p> 0.05 in all these cases, which means there is no significant difference between genders for these participants regarding the priorities of buying tested here.

The technological development (also referred to as ‘technological advancement’) is a measure of the participant’s familiarity with the use of technology. Technological advancement is discussed in section 7.3.1 since it has been identified as a cultural dimension [Baumgartner, 2003], but other studies list it as a demographic variable [Kleijnen et al., 2004].

A model for representing the motivational and cultural factors that influence mobile phone usage variety 124 Another independent-samples t-test (not shown here) was used to analyse the effect of gender on technological development. Technological development was computed by taking the average of computer skills, computer experience, web experience and e-mail experience, and grouping that by gender). On average, men were more technologically advanced (M=4.27, SE=0.134) than women (M= 3.29, SE=0.150). Furthermore, t(135)=4.853), p< 0.0, indicating that gender groups differed significantly regarding their own perceived technological development. The bar chart in Figure 7.4 depicts technological development (TechDev) rating grouped by gender. This leads to the notion that gender may influence certain issues but gender difference cannot be generalised to all dimensions of mobile phone usage.

5.00 4.00 3.00 2.00 1.00 TechDev 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Per cen t Female Male Q2 Gender

Figure 7.4 : Gender distribution and technological development

Sig. t df Sig (p) Mean Difference

SE Difference Technology/Features _.500 _1.18 ₁₃₂ _.240 _.204 _.173 Accessibility _.500 _-.22 ₁₃₂ _.83 _-.038 _.174 Usability _.266 _-.05 ₁₃₂ _.96 _-.008 _.174 Appearance/ Image .907 -1.4 132 .16 -.245 .172

A model for representing the motivational and cultural factors that influence mobile phone usage variety 125

7.4.2 Infrastructure measures of mobile phone usage

The survey questionnaire (Appendix 4) contained questions on mobile phone selection and use. It was found that 34% of participants had a contract, while 66% used pay-as-you-go. Males were distributed equally, i.e. between having a contract and the pay-as-you-go system. Females favoured the latter, with only 20% of the women having a contract. The participants made use of three different service providers and were divided as follows: Cell-C (10%), MTN (33%) and Vodacom (57%).

The length of time for which the participant had been using mobile phones was captured (not the time with the specific phone). The average was 4.6 years with a mode value of 5 years, which means that most participants have had ample exposure to mobile phone use. The questionnaire listed 12 brands, of which only 7 were used by the participants, with Nokia being used by most participants (50%) followed by Motorola in second place (21%). The distribution between brands is depicted in Figure 7.5.

Other Sony Ericsson Samsung Sagem Nokia Motorola LG Brand 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Percent 5% 8% 14% 1% 50% 21% 1%

Distribution between Brands

A model for representing the motivational and cultural factors that influence mobile phone usage variety 126

In document TéCNICA E INSTRUMENTOS DE INVESTIGACIóN (página 33-39)