Unit of Analysis
When developing a research project, it is crucial to have a clear understanding of a unit of analysis. A unit of analysis and a unit of observation are imperative for efficient data collection and evaluation. Essentially, a unit of analysis is the focal point of any study since it is its main focus. Jornet and Damsa (2019, p. 1) define it as “a methodological staple in the construction of any learning theory, determining how different frameworks lead to different kinds of empirical observation.” It is the entity that is being observed and expected to produce conclusions about the research’s thesis.
Therefore, while attempting to uncover insights about the study’s unit of analysis, researchers focus on a unit of observation, which refers to the items they examine and measure. Typical units of analysis are “individuals (the most common), groups, social organisations and social artifacts” (Gorichanaz, Latham, and Wood, 2018, p. 884). The studies used in the literature review utilize relatively the same units of analysis. For instance, Indarsin and Ali (2017) examines Ikens Group customers in Jakarta, Yoo et al. (2017) assessed Korean smartphone users, and Rachman and Napitupulu (2018) gathered data on farmers and extension workers. In my research, the unit of analysis and the unit of observation are the same. It tries to gain more knowledge about the customers in the tourism setting in the United Arab Emirates by collecting data on them.
In order to make observations about the selected unit of analysis, there is a need to choose a sample. It is impossible to analyze the entire population, which is why researchers have to apply sampling strategies to decrease the amount of cases. Taherdoost (2016) identifies two categories of sampling strategies, including probability sampling and non-probability sampling techniques. The first group is comprised of “simple random, stratified random, cluster sampling, systematic sampling, multi state sampling” (Taherdoost, 2016, p. 20). The second category includes quota, snowball, judgement, and convenience strategies for selecting a sample. In the literature review, Balouchi et al. (2017) used snowball sampling, which meant that the respondents answered the questionnaire and then shared the link with other individuals deemed suitable. Tubaishat (2017) opted for cluster sampling in order to choose hospitals, which would differ in location and type. When utilizing thus strategy, researchers divide the population considered for the study into smaller groups (clusters), and a random sample is picked from each group.
In my research, cluster sampling is used, which involves a specific set of steps. First, researchers would decide on cluster grouping for the chosen sampling frame. Then, they would number each group and pick sample via random sampling. There are a number of reasons as to why cluster sampling is utilized in my research. It is time- and cost-efficient due to its ability to ease data collection from the population, which is fragmented over a massive geographical area such as the whole country. Marchi et al. (2017, p. 186) note that “to match cost reduction, (…) sampling efforts may be undertaken using statistical rules to control estimation errors,” which is what cluster sampling facilitates. In addition, this approach requires less resources and ensures higher feasibility (Etikan and Bala, 2017). Furthermore, it is important to recognize the opportunity not to rely on user intervention if a mistake occurs, which cluster sampling provides (Sharma, 2017). Thus, I consider cluster sampling to be the most appropriate sampling strategy for the proposed research.
Quantitative Data Collection and Analysis Methods
There are a variety of data collection techniques that can be used in quantitative research. Despite that, my research is going to utilize surveys. This method is relatively cost-efficient as it is inexpensive to distribute online surveys (Regmi et al., 2016). The cost per respondent is low, which makes it more suitable for the proposed project. Moreover, surveys are extensive due to the opportunity they provide to gather data from a large population. Arguably, no other technique can ensure such broad results (Paradis et al., 2016). Furthermore, this approach is flexible and practical since researchers can easily adapt the mode in which surveys are distributed in a matter of minutes (Cantuaria and Blanes-Vidal, 2019; Siva Durga Prasad Nayak and Narayan, 2019). Finally, surveys are reliable, particularly because of their ability to make respondents open up and answer honestly (Rice et al., 2017). This is achieved through the promise of anonymity surveys usually provide.
As for the literature review, the most common quantitative data analysis techniques include cross-tabulation and MaxDiff analysis. However, Sunny, Patrick, and Rob (2018) analyzed data via exploratory and confirmatory factor analysis. Honarzade, Mahmoudinia, and Anari (2018) chose the equation modeling method, which is a combination of the aforementioned technique and multiple regression analysis. In my research, a conjoint analysis technique. The main reason is the fact that it would enable my team to gain in-depth insights about the population’s purchasing decisions. As the purpose of my study is to examine consumers’ intentions and perception, conjoint analysis seems to be the most appropriate method.
Qualitative Data Collection and Analysis Methods
In regards to the qualitative data collection methods, the ones used frequently are observations, either individual or group interviews, as well as focus groups. When it comes to interviews, the main two types are open and semi-structured ones. An unstructured (open) interview is “based on a single question, with the interviewer and interviewer then shaping the conversation in real time” (Barrett and Twycross, 2018, p. 63). On the other hand, a semi-structured interview ensures the interviewer asks specific questions about the phenomenon being studied, while allowing flexibility as well (Kallio et al., 2016). The main reason for using semi-structured interviews is the opportunity interviewers have to organize the process beforehand and guide the conversation in the right direction. This technique ensures the respondents are allowed to give open-ended answers, which lead to useful insights. DeJonckheere and Vaughn (2019) emphasize the importance of semi-structures interviews. Such a technique is an attempt “to understand the world from the subjects’ point of view, to unfold the meaning of people’s experiences” (as cited in DeJonckheere and Vaughn, 2019, p. 1). Thus, semi-structured interviews enable researchers to extract themes and models from the quantitative data collected.
As for the literature review, the most common qualitative data analysis techniques are content and narrative analysis. In my research, narrative analysis is going to be utilized. First, it would allow me to make conclusions based on a combination of sources: interviews and sources. Second, this technique would enable the research team to answer the questions posed by the study using the collective experiences of the participants. Thus, narrative analysis presents itself as the most appropriate and practical option to reach the objectives of the proposed research.
Reflection on Ethical Aspects and Risk Assessment
While conducting research dealing with human subjects, it is exceptionally important to minimize risks and demonstrate the respect for the participants’ dignity and autonomy. Taking precautions and ensuring the decision-making process during research is ethical are crucial steps the application of ethical rules into practice (Clark-Kazak, 2019). The proposed study would ensure the participants privacy and confidentiality are prioritized. In addition, the team would follow the rules of informed consent. The main ethical considerations would be voluntary participation, non-maleficence, confidentiality, anonymity, as well as informed consent (Mohd Arifin, 2018). Data protection would be the primary focus in regards to privacy and confidentiality (Saltz and Dewar, 2019). Furthermore, the proposed research would have to maintain a reasonable risk-benefit ratio, particularly in the context of an existing public health emergency. The project should not impede with the authorities’ emergency initiatives related to the COVID-19 pandemic response (Stiles-Shields et al., 2020). Moreover, it would be preferable if the data collection approaches were digitized to minimize human interaction and he associated risks.
Main Deliverables of the Proposed Research
In regards to the value provided by the suggested research project, it would be both practical and theoretical. On the other hand, the study integrates technology readiness into the technology acceptance model. This would contribute to the development of a more holistic view of technology acceptance (TA). Thus, the research would extend the existing TA model. On the other hand, the research could be potentially used for developing a framework to predict the perceptions and intentions of the consumers in the tourism sector in terms of TA.
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