The qualitative analysis sought to explain and explore in depth the relationships between the variables, as proposed in Chapter 2. It also sought to reveal undiscovered variables through the interesting and insightful perceptions of rice stakeholders in Thailand. A qualitative data analysis is better suited for collecting, analyzing, and interpreting interviewees’ constructions as they are immediate, processual, elaborative, and amenable to intersubjective interpretation. Interestingly, the interview method is likely to yield rich and in-depth knowledge of other possible factors: this method could be useful as a first stage before the model development and testing stages. Hence, this instrument was used to explore and confirm the significant factor that would influence the e-commerce adoption intention of rice farmers. If some variables in the initial conceptual model were missing or had been used in error, they could be added to explore and adjusted in the conceptual model. The data sources triangulation was also applied by comparing and combining the interviews findings of different perspectives of rice stakeholders in Thailand in order to gain better understanding about rice farmers’
e-commerce adoption for rice selling as well as ensuring the finding with the study’s conceptual model which formulated based on the previous studies related to e-commerce adoption. The consistency in overall patterns of data from different sources, and reasonable explanations for differences in data from divergent sources, contribute significantly to the overall credibility of findings (Patton, 1999).
The findings of the in-depth interviews of rice stakeholders in Thailand identified eight influencing factors that could affect rice farmers’ behavioral intention to accept and adopt e-commerce for rice selling. These eight factors were repeatedly stated by rice stakeholders and consisted of: performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk, perceived implementation cost, sufficient IT knowledge and skills, and government support.
The review of the literature, conducted as an exploratory study and described in Chapter
2, identified these influencing factors and drew them as constructs in hypotheses and the initial conceptual model. However, the in-depth interviews of rice stakeholders in Thailand revealed important and significant information that could confirm the influencing factors affects to rice farmers’ prospective behavioral intention to adopt e-commerce as well as their actual adoption of e-commerce for rice selling. It was found that some variables in the initial conceptual model were used in error positions and inaccurate roles. The initial conceptual model was wrong formulated by proposed two significant variables (government support, and sufficient IT knowledge and skills) having a moderating effect to the relationship between six influencing variables and rice farmers’ behavior intention and acceptance and adoption of e-commerce for rice selling. It then resulted in such a complicated initial conceptual model. Furthermore, when considering the original UTAUT model, it was found that there is no main variables being the moderating variables, but there are control variables being moderators, consisting of gender, age, experience and voluntariness of use. Regarding data sources triangulation in qualitative method, the in-depth interviews could then ensure that the initial conceptual model should be revised and modified for the quantitative analysis. These findings, therefore, resulted in the revision of study hypotheses, with some resequencing and renumbering, together with the revised conceptual model for the quantitative data analysis in the next chapter.
As stated above, the major findings from the interviews contributed to revision of the hypotheses and conceptual model. The three constructs affected in the hypotheses and on the conceptual model, and revised in this chapter are: perceived implementation cost, government support, and sufficient IT knowledge and skills, respectively. The first hypothesis revision related to perceived implementation cost which was hypothesized in Chapter 2 as the dependent variable that could provide an influencing effect on rice farmers’ behavioral intention. However, the qualitative analysis finding showed that rice farmers who had already adopted e-commerce for rice selling may perceive this differently to rice farmers who had never adopted e-commerce. The assumption was therefore proposed in the same way as in the previous hypothesis, with some amendment to incorporate the views of rice farmers who had already adopted e-commerce for rice selling. Another revision of a hypothesis related to the two moderators in this study: sufficient IT knowledge and skills and government support.
In Chapter 2, it was hypothesized that these two variables would be influencing moderating factors that would affect rice farmers’ behavioral intention to adopt e-commerce for rice selling. Conversely, the interview findings showed that these two variables were significant influencing factors that could directly affect farmers’
behavioral intention to adopt e-commerce for rice selling. Moreover, the findings showed no relationship between the assumed independent variables and the dependent variable (behavioral intention). These two variables were inaccurately conceptualized being as moderators which was different to the original UTAUT model that no main variables being the moderating variables, but there are control variables being moderators. Then, if there is no revision of conceptual model, these two variables would play inaccurate roles, and it would definitely affect to quantitative data analysis since the conceptual model is too complicated which cannot be computed in any statistical programs. Therefore, these two moderators were revised and changed to become the main independent variables used in conducting the quantitative data analysis in the next chapter.
The first revised variable, e-commerce implementation cost, refers to all costs related to e-commerce adoption or implementation which the adopter would need to pay. Generally, when considering technology adoption, cost appears to be recognized as one of the essential factors for potential users which can lead to a negative impact on behavioral intention. In Chapter 2, it was assumed with this construct that a cost burden would be incurred by rice farmers for e-commerce adoption for rice selling as many related costs would be involved. These costs would include the cost of e-commerce equipment (computer hardware and software), internet service, training, logistics, electricity, etc. Hence, it was hypothesized in H5a that “high e-commerce implementation cost negatively affects Thai rice farmers” behavioral intention to adopt e-commerce’ as presented in Table 4.14. However, after conducting the interviews with rice farmers who had already adopted, and those who had never adopted, e-commerce for rice selling, this finding appears to be different, reflecting the different perceptions of these two groups. The rice farmers who had never adopted e-commerce for rice selling viewed the e-commerce implementation cost as a burden to this adoption as it required a significant amount of funds. On the other hand, the rice farmers who had already adopted e-commerce for rice selling perceived that the cost was low and that
the benefits of e-commerce adoption outweighed the cost constraint. However, in the next chapter, the respondents to the questionnaire-based survey are rice farmers who had adopted e-commerce for rice selling, so it would be reasonable to adapt the assumption to reflect the perceptions of that group. Consequently, the revised hypothesis for perceived implementation cost is ‘low e-commerce implementation cost positively affects Thai rice farmers’ behavioral intention to adopt e-commerce’.
Table 4.14: The revised hypothesis of perceived implementation cost
Previous hypothesis in chapter 2 Final hypothesis for quantitative analysis
H5a: High e-commerce implementation cost negatively affects Thai rice farmers’ behavioral intention to adopt e-commerce.
Low e-commerce implementation cost positively affects Thai rice farmers’
behavioral intention to adopt e-commerce.
The second revised variable is government support which refers to the extent to which the government provides conditions that could facilitate the essential requirements for e-commerce adoption. This could enable increased user awareness of the technology benefits and of the intention to adopt that technology, as well as being required to address some external barriers that could affect e-commerce adoption.
It was assumed that if government support was available for e-commerce adoption, such as providing e-commerce training, financial subsidies, creation of a free public e-commerce website, enactment of laws and regulations to secure e-commerce usage, etc. in agricultural products, and especially online rice selling, this would increase the intention of Thai rice farmers to adopt e-commerce. As shown in Table 4.15, this construct was hypothesized into six hypotheses (H1b to H6b) as the moderator role affecting the relationship of the two dependent variables (behavioral intention and adoption of e-commerce for rice selling) and the six independent variables, comprising performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk, and perceived implementation cost. Nonetheless, after conducting the interviews under the qualitative method, government support and policies were found to be necessary elements for rice farmers’ e-commerce adoption for rice selling,
especially in the early stage of e-commerce adoption in a developing country. It is undeniable that without government support, Thai rice farmers would probably find themselves in difficulties when adopting e-commerce for rice selling. Therefore, government support appears to be a direct influencing factor that could directly affect rice farmers’ behavioral intention to adopt e-commerce for rice selling, rather than acting as a moderator. Due to this finding, no significant sign was found to show the significant effect of the relationship of the aforementioned independent variables with the dependent variable (behavioral intention). The researcher therefore considered it unnecessary to test the moderating effect of government support, but instead changed it to become the main independent variable directly influencing rice farmers’ behavioral intention to adopt e-commerce for rice selling. Therefore, the proposed hypothesis, to be tested in the quantitative data analysis in the next chapter, is ‘government support positively affects Thai rice farmers’ behavioral intention to adopt e-commerce’.
Table 4.15: The revised hypothesis of government support Previous hypothesis in chapter 2
(H1b to H6b)
Final hypothesis for quantitative analysis H1b: If the government provides support, there
will be a more positive relationship between performance expectancy of e-commerce adoption and the behavioral intention to adopt e- commerce.
H2b: If there is government support for e-commerce, there will be more positive relationship between effort expectation of e-commerce adoption and the behavioral intention to adopt e-commerce.
H3b: If government provide some supports, this will provide a more positive relationship between social influence on the e-commerce
The government support positively affect Thai rice farmers’ behavioral intention to adopt e-commerce.
Previous hypothesis in chapter 2 (H1b to H6b)
Final hypothesis for quantitative analysis adoption and the behavioral intention to adopt
e-commerce.
H4b: If there is government support for insufficient facilitating conditions, there will be positively related to the Thai rice farmers’
acceptance and adoption of e-commerce.
H5b: If there is government support for e-commerce implementation cost, this will be positively related to the behavioral intention to adopt e-commerce.
H6b: If there is government support for e-commerce, this will be positively related to the behavioral intention to adopt e-commerce.
The third revised variable is sufficient IT knowledge and skills: this refers to the individual’s perception of the e-commerce benefits and their capabilities to use an IT system for e-commerce adoption in an effective and efficient way. It was stated in the literature that e-commerce adoption was mostly related to the capabilities of IT usage and computer literacy, whereas, in contrast, limitations of this knowledge, these skills and the awareness of their benefits are the main concerns hindering e-commerce adoption. As shown in Table 4.16, this construct was hypothesized into three hypotheses (H1c to H3c) as the moderator role affecting the relationship between the dependent variable (behavioral intention) and these three independent variables, comprising performance expectancy, effort expectancy, and perceived risk. However, after conducting the interviews under the qualitative method, it was found that IT knowledge and skills comprise one of the essential issues which could incentivize or diminish the behavioral intention to adopt e-commerce. Accordingly, as sufficient IT knowledge and skills could directly affect rice farmers’ e-commerce adoption for rice selling, this construct should be a main independent variable rather than a moderating
variable that affects the aforementioned independent variable. As respondents to the quantitative data analysis are rice farmers who had adopted e-commerce for rice selling, it can be accepted that these rice farmers have sufficient IT knowledge and skills. Hence, the researcher proposed the new hypothesis with regard to sufficient IT knowledge and skills as follows: “sufficient IT knowledge and skills positively affect Thai rice farmers’ behavioral intention to adopt e-commerce for rice selling”.
Table 4.16: The revised hypothesis of sufficient IT knowledge Previous hypothesis in chapter 2
(H1c to H3c)
Final hypothesis for quantitative analysis H1c: If a Thai rice farmers has sufficient IT
knowledge and skills, there will be a more positive relationship between performance expectation of e-commerce adoption and the behavioral intention to adopt e-commerce.
H2c: If a Thai rice farmers has sufficient IT knowledge and skills, a positive relationship will be found between effort expectation of e-commerce adoption and the behavioral intention to adopt e-commerce.
H3c: If a Thai rice farmers has sufficient IT knowledge and skills, it will reduce perceived risk which contribute to the positive relationship with the behavioral intention to adopt e-commerce.
Sufficient IT knowledge positively affect Thai rice farmers’ behavioral intention to adopt e-commerce for rice selling.