questionnaire as well as finalizing the research conceptual model and hypotheses for the quantitative analysis in the chapter 5.
On the other hand, the quantitative method is based on a positivist philosophy which typically deals with “statistical data, gathered by launching the questionnaire or survey to receive the result of public tendency” (Junaid, 2012, p. 88). This method is deductive in nature which is suitable for hypotheses and theory testing (Alqatawna et al., 2009). To proceed with the quantitative method, the significant variables that can influence the e-commerce adoption intention of rice farmers were firstly confirmed in the interviews; the structured questionnaire regarding e-commerce adoption was then organized; as was the revision of the conceptual model and the study’s hypotheses.
The developed questionnaire underwent pilot testing after its delivery to 30 rice farmers who had already adopted e-commerce for rice selling. The results acquired from the pilot testing subsequently contributed to confirm the questions in the questionnaire.
The survey was then conducted by distributing the revised questionnaire to targeted participants, namely, rice farmers who had already adopted e-commerce for rice selling.
The questionnaire was carefully classified into two parts, with the first part seeking general (personal) information, while the second part focused on the eight influencing factors related to e-commerce adoption: performance expectancy, effort expectancy, social influence, facilitating conditions, perceived e-commerce implementation cost, perceived risk, sufficient IT knowledge/skills, and government support. In each part, approximately three to eight questions were asked in multiple-choice formats and closed-ended questions. In terms of survey channels, the research questionnaire was circulated by hand as a hard-copy paper version to be filled in, and also via online channels, such as Facebook, email and the LINE application. Some participants sought the option of answering the questionnaire by telephone.
study, the research participants were categorized into two groups, with one group taking part in the in-depth interviews, while the other group responded to the survey.
The research participants for the interviews were purposively selected from stakeholders in the rice value chain in Thailand. They were divided into five groups.
The first group comprised rice farmers irrespective of whether or not they had adopted e-commerce or rice selling. This group was open to any rice farmers who either had, or had never had, an experience of selling their products online. In total, six rice farmers from this group participated in interviews, four of whom had adopted e-commerce for rice selling, while the other two had not. The second group comprised the middlemen whose role is accepted as a key player in the rice value chain in Thailand. The current study had a great opportunity to interview a rice mill owner in Thailand, as well as the owner of a paddy rice hub located in the same area as the rice field. These two participants were accepted as key middlemen in the rice value chain. The third group comprised four rice consumers. The fourth group consisted of two agricultural cooperative officers. They were employed by an agency that is accepted as an important player in the development of agricultural production. They assist and help agriculturists, who are their members, by supporting linkages with finance, information, the market, and agricultural inputs and output. The fifth group comprised rice industry-related government officers. It was considered vital to obtain the views of policy makers and supportive providers.
With regard to survey participants, as the current study related to e-commerce adoption for rice selling, the selected sample are only those Thai rice farmers who had already adopted or taken part in e-commerce either by selling or by advertising via their own websites or public websites, Facebook, mobile applications, or other online channels. The rationale of this sampling selection is because this study applied the technology acceptance model to investigate and measure the rice farmers’
actual behavior on the e-commerce adoption, including with to avoid some bias and misleading information from unexperienced e-commerce adoption participants. Thus, other Thai rice farmers who had not yet adopted EC for rice selling were excluded in the quantitative data analysis. The rice farmer, as defined in this research, is a farmer with his/her own harvesting area and rice products available for sale. Thailand’s
agricultural census in 2013 recorded 3.78 million rice farmers, defined as those who held areas for rice cultivation, with these mostly in the Northeast region (2.44 million people), followed by the North region (0.88 million people), the Central region (0.34 million people), and the South region (0.11 million people) (The National Statistical Office, 2014). To reach the targeted sample, rice farmer groups who had already adopted e-commerce for rice selling were approached through online channels, such as Facebook and the LINE application. These rice farmer groups, available on Facebook and via the LINE application, are assembled of many rice farmers who had adopted e-commerce for rice selling. The online survey questionnaire was posted to these sites using an HTML link. In addition, some rice farmers were directly approached by telephone, after being accessed through information and contacts from government agency websites, for instance, the website of the Department of Business Development (DBD) in the Ministry of Commerce that has developed the online rice selling channel through this link: <http://www.dbd.go.th/ewt_news.php?nid=469401325>.
The information on the DBD website consists of the following three lists: (1) rice farmers and rice cooperatives that have adopted e-commerce (approximately 400 people/cooperatives); (2) rice farmer and rice cooperative websites (approximately 40 websites); and (3) Facebook pages of rice farmers and rice cooperatives (approximately 70 user names). Furthermore, rice farmers who sell their products on their own websites or webpages were also approached via their contact details which were mostly available and visible at these sources.
3.3.2 Sample size
The sample size in qualitative method is often smaller than quantitative research method. It is due to the fact that qualitative method are often related to gathering an in-depth understanding of a phenomenon, or focusing on meaning (and heterogeneities in meaning) which are often centered on how and why of a particular issue, process, situation, subculture, scene or set of social interactions (Dworkin, 2012). In-depth interview is not as concerned with making generalizations to a larger population of interest and does not tend to rely on hypothesis testing but rather is more inductive and emergent in its process. With regard to the aim of in-depth interviews, it is to create
“categories from the data and then to analyze relationships between categories” whereas
attending to how the “lived experience” of study participants can be understood (Charmaz, 1990, p. 1162). The most widely used for determining sample size and evaluating its sufficiency is the concept of saturation (Vasileiou, Banett, Thorpe &
Young, 2018) which the data collection process no longer offers any new or relevant data (Dworkin, 2012). Strauss and Corbin (1998, p. 212) stated that the saturation occurs in data collection when: (a) no new or relevant data seem to emerge regarding a category, (b) the category is well developed in terms of its properties and dimensions demonstrating variation, and (c) the relationships among categories are well established and validated. Similarly, Charmaz (2006, p. 113) also mentioned that it can be considered as saturation “when gathering fresh data no longer sparks new theoretical insights, nor reveals new properties of your core theoretical categories”. Hence, the sample size in this research was based on the sufficient data from the interviewee to describe the phenomenon of interest, and address the research question. It comprised of 15 stakeholders in the rice value chain, consisting of six rice farmers (four participants had adopted e-commerce for rice selling, while the other two participants had not), four rice consumers, two agricultural cooperative officers, one rice mill owner, one paddy rice hub owner, and one rice industry-related government officer.
For the quantitative approach, the study population comprised rice farmers in Thailand and the selected sample for data collection and analysis consisted of rice farmers who had already adopted e-commerce for rice selling. According to Thailand’s agricultural census statistics from the National Statistical Office (2014), approximately 3.78 million people are rice farmers who hold areas for rice cultivation. The study’s sample size was determined by applying Taro Yamane’s (1973) formula with a significance level of 95 percent. It was calculated by using 3.78 million as the total population (N) of rice farmers holding areas for rice cultivation. Thus, the resultant number of rice farmers used as the sample for this research was 405, which is above the appropriate sample size of 400, based on Yamane’s (1973) probabilities sampling method. Yamane’s (1973) calculation formula is presented as follows:
Where ：
n = number in sample N = total population e = error (tolerance level)