A sample design refers to the plans, process, and methods involved in identifying elements of the research, including identifying the target population and selecting the sample (Kabir, 2016;
Mbwambo, Barongo, & Makuru, 2011). The choice of the sample design for research is, however, dependent on research objectives and the resources available. The detailed explanation of the different aspects of the sample design adopted for this study is discussed in the following sub- sections.
4.9.1 Research Population
A research population is the totality of elements conforming to a set of specific characteristics of interest (Kothari, 2010; Babbie, 2016). In specifying the research population, the researcher made considerations of the nature and aims of the research and the information needed to address the research questions. In this study, the research population constituted residents of the two districts of Dar es Salaam that is, Ilala and Kinondoni, including employees from the four government organisations whose practices for m-government service provisioning were investigated.
Therefore, the population targeted for the adoption challenges identification phase was 2,995,660 million people, from the two districts (NBS, 2018).
The population for the framework evaluation phase comprised ICT experts, which included management and personnel in the ICT departments of the participating organisations, and the academics that hold a PhD in information systems or related fields.
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A research sample is a small collection of elements selected from a larger population for measurement (Saunders, Lewis & Thornhill, 2012). Sampling techniques are the methods or approaches for selecting the elements. Sampling is the process of selecting a small set of elements (Mbwambo, Barongo, & Makuru, 2011; Saunders, Lewis & Thornhill, 2012). Investigating the entire population of approximately 3 million people residing in Ilala and Kinondoni districts is extremely expensive, time-consuming, and somewhat unrealistic. Moreover, the quantitative approach provides an advantage in the ability to work with smaller samples and draw inferences about the larger population (Lubua, 2014; Mbwambo, Barongo, & Makuru, 2011). Hence, it was more practical to work with a sample rather than the population.
Generally, there are two categories of sampling techniques, which are probability and non- probability techniques (Saunders, Lewis & Thornhill, 2012; Mbwambo, Barongo, & Makuru, 2011). In probability sampling, elements of the population have a non-zero equal opportunity of being selected, thus it is regarded as unbiased sampling (Kumar, 2019). Probability sampling techniques employ statistical techniques to determine the inclusion or exclusion of an element to the sample (Saunders, Lewis & Thornhill, 2012; Kumar, 2019). A simple random sampling technique is a type of probability sampling in which each element of the population has an equal chance of being selected in the sample (Saunders, Lewis & Thornhill, 2012). In non-probability sampling, elements are selected subjectively, based on the researcher's judgment towards meeting certain conditions or criteria; thus, elements of the population do not have equal chances of being selected (Saunders, Lewis & Thornhill, 2012). Non-probability sampling techniques include convenience sampling, snowball sampling, and purposive sampling. Convenience sampling entails drawing sample elements from part of the population that is accessible and willing to participate; thus, elements fall under selection only by being situated where the researcher or the research is situated (Saunders, Lewis & Thornhill, 2012). Hence, convenience sampling is affordable, flexible, and easy to conduct (Etikan, Musa & Alkassim, 2016). While purposive sampling refers to the selection of population elements based on specific criteria, snowball sampling is when sample elements are recruited or identified for participation from other sample elements (Mbwambo, Barongo, & Makuru, 2011; Saunders, Lewis & Thornhill, 2012).
This research applied non-probability sampling techniques, specifically purposive sampling and convenience sampling because they are reasonably accessible in mixed-methods research
(Komba, 2012). Purposive sampling techniques were applied in the selection of the participating wards, villages, government organisations and the employees that participated in the research, for the following three reasons. First, the selection of the wards and the villages followed a purposive sampling technique to ensure the inclusion of urban and rural or peri-urban wards and the densely populated wards, either with people or public offices. Second, the four participating organisations were selected based on their role in provisioning m-government services in Tanzania. Thus, the organisations with strategic roles, such as coordination, m-government service development, and provision, were considered for the study. Lastly, a purposive sampling technique was also applied in the selection of respondents from the four participating organisations for qualitative research.
Purposive sampling facilitated the selection of managers and technical personnel, as they are more likely to provide the required information based on their knowledge and experience with the provisioning of m-government services in Tanzania (Oppong, 2013). The organograms of the respective organisations guided the identification of crucial respondents. Fourth, participants for the framework evaluations were also purposively sampled based on their expertise in information systems and technology, as this was a critical requirement to gain an expert opinion on the applicability and relevance of the framework.
A convenience sampling technique was applied in recruiting citizens from the identified districts for the quantitative part of the research. This is because convenience sampling techniques provided the flexibility to recruit citizens in their natural environment, where they are more comfortable to divulge the required information (Etikan et al., 2016). Moreover, the technique allowed the researcher to adjust strategies in the field to ensure the targeted sample was attained, including change physical locations for recruiting citizens, as their participation relied purely on their willingness to participate. Despite using an accidental or convenience sampling technique to recruit participants, the researcher attempted to ensure an equal proportion of participants, that is, youth, elderly, women, and men, in the sample.
4.9.3 Sample Size and Distribution
The sample size is the total number of elements or people taking part in a research study (Saunders, Lewis & Thornhill, 2012). The targeted sample size for the quantitative part of the research, noted in Figure 4.1, was 422 participants, which is between 51 and 54 participants per ward, to maintain an equal contribution of approximately 12% of each ward to the total sample.
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Table 4.1 indicates the sample size and its distribution across the selected wards in the respective districts. According to Krejcie & Morgan’s (1970) table, for a population of 3 million people, a sample size of at least 384 participants is sufficient for quantitative analysis. Also, by applying Hair et al.’s (2006) sample size determination rule of twenty cases to one variable (20:1) rule, scientific quantitative analysis research of thirteen (13) variables requires a minimum sample of 260 observational cases. The conceptual framework discussed in section 3.6.2 raises a total of 13 variables to be investigated. Thus, with making a provision of 10% to cater for non-return and data quality issues such as data omission and incorrect filling of the questionnaire, a sample size of 422 participants is considered sufficient on both conditions.
Table 4.1: Distribution list for administering questionnaires to citizens
District Ward Area Descriptor N %
Ilala
Ilala Urban 54 12.8
Kivukoni Urban 54 12.8
Chanika Peri-Urban/Rural 52 12.3
Kinyerezi Peri-Urban/Rural 51 12.1
Kinondoni
Kinondoni Urban 54 12.8
Kawe Urban 54 12.8
Kunduchi Peri-Urban/Rural 52 12.3
Mbezi Juu Peri-Urban/Rural 51 12.1
Total 422 100
The sample size for the qualitative part of the adoption challenges identification, noted as phase 1 in Figure 4.1, constituted sixteen (16) participants, four from each participating organisation; that is, one (1) management representative, one (1) business or system analyst, one (1) programmer, and one (1) service administrator (Table 4.2). Despite being a small sample, data collection proceeded until there was no new data obtained; that is a saturation point, a necessary condition for qualitative data collection (Mbwambo, Barongo, & Makuru, 2011). Moreover, this sample size is consistent with literatures suggestion of a sufficient sample size for statistically significant
qualitative study to range from five to 50 sample elements (Dworkin, 2021). Understanding provisioning practices coupled with citizens’ perceptions, allows a holistic understanding of the challenges citizens experience when adopting m-government services in Tanzania. The derived information is insightful towards recommending a strategy that overcomes these challenges and thus enhances citizens' adoption.
Table 4.2: List of ICT management and technical personnel for the interview Organisational
Code
Participants Position/Role Number of Respondents
Code Organisation A Business or system analysts 1 Respondent 1
Programmers 1 Respondent 2
Service Administrators 1 Respondent 3
Management Representative 1 Respondent 4
Organisation B Business or system analysts 1 Respondent 5
Programmers 1 Respondent 6
Service Administrators 1 Respondent 7
Management Representative 1 Respondent 8
Organisation C Business or system analysts 1 Respondent 9
Programmers 1 Respondent 10
Service Administrators 1 Respondent 11
Management Representative 1 Respondent 12
Organisation D Business or system analysts 1 Respondent 13
Programmers 1 Respondent 14
Service Administrators 1 Respondent 15
Management Representative 1 Respondent 16
The sample for framework evaluation, noted as phase 2 in Figure 4.1, consisted of twelve (12) purposively sampled participants, as indicated in Table 4.3. The sample included four (4) management representatives, four (4) business analysts (each from the four participating organisations), and four (4) experts/researchers in academia purposively selected across four different universities within and outside Africa. While practitioners' expert opinions are very critical for possible future adoption and implementation of the artefact, the theoretical opinion is equally essential for research artefact development and recommendation, thus the inclusion of participants from academia in the evaluation. Furthermore, participants were identified for
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participation based on their knowledge and experience of the phenomenon being investigated (Sekaran & Bougie, 2013).
Table 4.3: List of participants for framework evaluation
Organisation Expert Type Number of
respondents
Code Organisation A Management Representative 1 Expert 1
Business or system analyst 1 Expert 2
Organisation B Management Representative 1 Expert 3
Business or system analyst 1 Expert 4
Organisation C Management Representative 1 Expert 5
Business or system analyst 1 Expert 6
Organisation D Management Representative 1 Expert 7
Business or system analyst 1 Expert 8
Academia Academic with PhD (from Universities in Tanzania)
1 Expert 9
Academic with PhD (from Universities in
South Africa) 2 Expert 10
Expert 11 Academic with PhD (from Universities
outside Africa)
1 Expert 12