According to Kothari (2010), there are several data analysis techniques; however, the choice of which technique to use is dependent on the nature of the research objectives. In this study, the nature of the research objectives called for a mixed-method approach in data collection, and subsequently, the analysis method is quantitative and qualitative methods. Quantitative data analysis used both descriptive and inferential statistical techniques to identify determinants of citizens' adoption of m-government services. Descriptive analysis is based on frequencies and percentage distribution of the responses on a particular item. The inferential analysis included chi- square tests, binomial tests and structured equation modelling (SEM) techniques to model factors influencing citizen's adoption decisions. Qualitative data were analysed using thematic analysis methods. By examining the raw qualitative data, various important patterns were identified. The established patterns guided the organisation of the data and coding to develop themes concerning m-government service provisioning practices.
85 5.3.1 Descriptive Statistical Analysis
The descriptive analysis provides an overview or summary of the quantitative data set (Kothari, 2010). It uses statistical indices, represented as frequency distribution tables, pie charts, histograms or bar graphs, to summarize or describe a large data set. It is thus common to commence the presentation of results with descriptive statistics to provide readers with an overview of the data (Kothari, 2010; Wilson, 2014). Therefore, this research used descriptive statistical analysis to provide a demographic description of the data set available before commencing a detailed analysis that addressed specific research questions. The demographic variables that are descriptively analysed included age, gender, education, income, occupation and experience with similar services from the private sector.
5.3.2 Binomial Test
According to McHugh (2013), the binomial test of significance is used to either compute the statistical significance of deviation from an expected outcome or to examine the distributions of a single variable with two mutually exclusive outcomes. In this research, the binomial test was applied to assess the distribution of responses on three variables; experience with similar services, awareness on m-government services, and the nature of access to service whether voluntary or compulsory. Thus, the binomial test examined if a significant proportion of the respondents were aware of the existence of m-government services, their perception of the nature of access, and if they had prior experience with similar services from the private sector.
5.3.3 Chi-square Test
The chi-square test is a non-parametric test commonly used to examine the relationship between categorical variables (Gaunt, Pickett & Reinert, 2017). According to Rana & Singhal (2015), the chi-square goodness of fit test determines how significant the difference is between observed sample distribution and the expected distribution. In this research, the chi-square goodness of fit test was used to determine how significant the proportion of respondents was in selecting a given response. Variables whose responses were tested were frequency of accessing m-government services that ranged from never to daily, and the popularity of media choice for hearing news regarding government innovations, including magazines, televisions, radio and street promotions.
5.3.4 Structural Equation Modeling (SEM)
In examining factors influencing citizens' adoption decision for m-government services, the study made use of the Structural Equation Modeling (SEM) technique. SEM, a multivariate analysis technique, is useful in assessing and confirming structural relationships (Hair et al., 2016). SEM is widely applied in behavioural sciences, more specifically in information systems research, due to its ability to facilitate hypotheses testing and modelling of complex relationships involving latent constructs (Hair et al., 2012; Rahman, Kamarulzaman & Sambasivan, 2015; Ooi & Tan, 2016; Oliveira et al., 2016; Tarhini, Hone & Liu, 2014). According to Kohnke, Cole & Bush (2014), latent constructs are abstract variables that cannot be measured directly or their measurement is error-prone; thus, they are observed through other variables.
SEM involves a combination of multivariate multiple regression or path analysis with confirmatory factor analysis, yielding a compelling analysis that examines the relationship between measured and latent variables while effectively accounting for data multicollinearity and unreliability (Rahman, Kamarulzaman & Sambasivan, 2015). Advantages of SEM compared to multiple regression analysis include flexible assumptions that allow interpretation even in the presence of multicollinearity, reduced measurement error due to the use of confirmatory factors analysis, and its ability to test the overall model with multiple dependents rather than individual coefficients (Shadfar & Malekmohammadi, 2013). This research collected data on latent constructs such as performance expectancy, effort expectancy, hedonic value, attitudinal influence, financial influence, mobile technology influence, and facilitating conditions. For instance, performance expectancy for m-government services is a latent construct measured through its time saving aspect, its usefulness and its assistance in achieving citizens’ goals.
This research applied SEM to examine the relationship between factors influencing citizen's behaviour intention to adopt m-government services. Using the IBM SPSS AMOS 22 program, the SEM analysis was executed and thus modelled the factors influencing citizens' adoption decision against behaviour intention and citizens' use behaviour for m-government services, that is, the measurement model and the structural model. The IBM SPSS AMOS package was chosen due to its wide application in SEM analysis as a result of its availability and accessibility compared to other packages like SAS PROC CALIS, OpenMx, R packages, LISREL, EQS, lavaan, and Mplus (Narayanan, 2012). Likewise, using the same software, the hypothesized relationships between variables were tested and confirmed. However, before commencing any
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analysis, the data was tested using Kaiser-Meyer-Olkin (KMO) and Bartlett tests to see its conformity of the data set to the requirements for factor analysis. KMO and Bartlett’s tests statistically measure sample adequacy for each variable and the comprehensive model (Williams, Onsman, & Brown, 2010). Thus, KMO and Bartlett’s tests were performed on factors influencing citizens’ decision on m-government service adoption to determine the adequacy and appropriateness of the data set available for factor analysis.
5.3.5 Thematic Analysis
According to Clarke & Braun (2013), thematic analysis is a method that identifies themes or patterns within qualitative data. Two reasons led to the selection of thematic analysis technique;
its robustness over a wide range of research questions, and its ability to establish structure in handling qualitative data (Nowell et al., 2017). Babbie (2016) asserts that qualitative analyses are reflexive and interpretive, leading to the generation of common subjective explanations based on responses and the researchers’ objectivity. While quantitative research uses extractive methods, qualitative analysis utilizes inductive approaches to derive a theory, usually from respondents’
lived experiences, captured through interviews or narratives with an individual or a group (Creswell, 2011). Therefore, in this study, thematic analysis was ideal for establishing patterns in m-government service provisioning practices from the interview data collected.
Coding, a technique in thematic analysis, facilitates the identification, analysis, organisation, description, and representation of meaningful themes or patterns to determine the relationship between variables in the data set (Corbin & Strauss, 2015). While coding can be done with the assistance of a computer, this study employed a manual coding process due to its flexibility in applying both inductive and deductive techniques in identifying themes from the qualitative data set (Clarke & Braun, 2013; Nowell et al., 2017). According to Corbin & Strauss (2015), the inductive method involves identification of patterns from the data through the guidance of research questions. Conversely, the deductive approach involves working with a theory whereby hypotheses guide the establishment of themes from the data set (Corbin & Strauss, 2015). This research applied inductive approaches since specific interview questions guided the qualitative data collection, and it is upon these questions that themes from the data were identified. The interview data, which was collected in English, was transcribed to text and stored in a separate Word document before commencing analysis. To protect the identity of interview respondents, pseudonyms, as indicated in Table 4.2, were assigned to each respondent.