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decisions in collecting empirical data (Fahmy & Sohani, 2020). As a result, the statistical evidence collected becomes more related to the
study.

A conclusive research design was employed to generate findings and test the hypotheses in this study, and it was used for statistical tests,
advanced analytical techniques, and larger sample sizes compared with exploratory studies. Conclusive research is more likely to use
quantitative rather than qualitative techniques and provide a reliable or representative picture of the population by applying valid research
instruments. In the conclusive research, the techniques used are typically more formal and structured. It provides a solution to verify and
quantify the findings obtained from exploratory research. Conclusive research is subdivided into descriptive and casual research. Leavy
(2017) highlighted the role of descriptive research for researchers who wish to define individuals, groupings, activities, events, or contexts.
Descriptive research creates a description by providing many details, definitions, and contexts.

Moreover, Lima (2011) pointed out that descriptive research is only used to describe the distribution of existing variables, regardless of
causality or other hypotheses. This descriptive research was classified into the cross-sectional design and longitudinal design. According to
Zahner and Steedle (2015), the primary difference between cross-sectional and longitudinal research is that cross-sectional research can
interview extraordinary samples of individuals on any given occasion. In contrast, longitudinal research can only interview an equal sample
of people throughout time. Thus, this study used a conclusive research design, including descriptive and cross-sectional designs, to
investigate the relationship between brand awareness, brand association, perceived quality, brand attitude, and purchase intention of robotic
education.

3.2 POPULATION AND SAMPLING

A population is defined as a set or group of all entities that match a set of criteria (Shukla, 2020). In statistics, a population is a term that
refers to an entire group for which data must be compiled. It is vital to comprise the demographic traits of the population such as age,
ethnicity, socioeconomic reputation, training level, area of residence, and family income status in the research (Majid, 2018).

Data sampling is the basis of the research form in collecting the data (Leavy, 2017). Sampling is a term that refers to selecting a
representative sample from an individual or a large population group for research purposes (Bhardwaj, 2019). According to Bhardwaj (2019),
sampling has several benefits and downsides. The benefits of sampling are that researchers can obtain more accurate results, and sampling
is the most appropriate method when resources are restricted. However, sampling’s shortcomings, such as the selection bias, are unavoidable
(Bhardwaj, 2019).

Besides, Hairet al. (2018) had clarified that the exploratory factor analysis could not be analysed if the sample has less than 50 respondents
because it is still subject to other factors. In contrast, simple regression analysis needs at least 50 samples and generally 100 samples for
most research situations. As a result, the sample size for this study is 200 (Memon et al., 2020). According to Alberta et al. (2016), non-
probability sampling is a type of sampling in which the chance that each member of the population in the sample will be selected is unknown.
Judgemental sampling is a non-probability sampling method used to select the representative from the population for the researchers to
propose. Therefore, this study can use the non-probability sampling technique under a judgmental sampling technique to select the
respondents who would send their kids to robotics education. The questionnaire was distributed using online channels such as Facebook,
Instagram, and WhatsApp to 200 Malaysian parents in Malaysia who have children aged 5 to 15.

3.3 RESEARCH INSTRUMENT

A quantitative study could be a kind of research that collects numerical data and analyses it using statistical methods (Boru, 2017).
Alberta et al. (2016) highlighted that self-report instruments, surveys, observation, and biophysical measures as examples of quantitative
instruments. Therefore, researchers used a questionnaire to investigate the customer’s purchase intention towards robotics education. The
questionnaire in a Google Form was used to collect primary data from respondents through online channels. The questionnaire was designed
based on demographics using the Likert scale to measure factors that influence the purchase intention of the respondents. As a result, the
survey was divided into two sections: Section A and Section B. The ten questions in section A of the questionnaire comprise respondents’
demographic profiles, such as age, gender, ethnicity, and family income. Section B comprises twenty-two questions related to respondents’
brand awareness, brand association, perceived quality and brand attitude, and purchase intention of robotic education (Sudhana et al., 2020).

In addition, four factors affecting consumer purchase intentions, namely brand awareness, brand associations, perceived quality, and
brand attitude, were included in the survey (Shamsudin, 2020). Scales can be used to quantify specific data. For example, a Likert scale
provides precise response options, such as strongly agree, agree, not sure, disagree, strongly disagree (Adams, 2016; Jilcha, 2019). The Likert
scale (1=strongly agree, 5=strongly disagree) was applied in this study, in which respondents expressed their level of agreement with a series
of statements. The highest score assigned to each statement reflected the degree of agreement. Lastly, sifting, processing, and analysis of all
data obtained will be performed using the Statistical Package for the Social Sciences (SPSS).

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