This study investigated the factors influencing the adoption of e-commerce in rice sales by Thai rice farmers. 105 Table 4.6: Summary of important points for topic 4 (expected effort) 108 Table 4.7: Summary of important points for topic 5 (perceived implementation. costs) 111.
- Research background
- Statement of problems
- Research objectives
- Research questions
- Implications and contributions of this research
- Outline of thesis
The main theory in the research is the modification and extension of the Unified Theory of Technology Acceptance and Use (UTAUT) in the context of rice sales in Thailand. Thus, this study extends the understanding of the robustness of the UTAUT model in explaining the acceptance and adoption of e-commerce in the context of agricultural products in Thailand.
Thailand’s rice market
- Rice farmers and rice production in Thailand
- Rice market system in Thailand
In the rice milling process, paddy rice would be processed to become polished rice which was then transferred to rice brokers, domestic wholesalers or exporters. The difference is evident in the rice price between paddy rice and processed rice (jasmine, white and glutinous rice).
E-commerce
- Types of e-commerce
- Benefits of e-commerce for company (or seller) and consumer
- E-commerce in agriculture
- E-commerce situation in Thailand
The study by Baourakis, et al. 2002) on the impact of e-commerce on agricultural products in Greece showed that, although agricultural products did not have the same opportunities as digital products, e-commerce can offer crucial advantages, such as lower transaction costs and the ability to the international market. Second, in this digital age, government policy appears to play a significant role in increasing the value of e-commerce through the continuous development of e-commerce infrastructure, such as e-marketing, e-payment, e-logistics, etc.
Theoretical background
Therefore, the current research used a modified and extended UTAUT model in the Thai rice sales context. Therefore, UTUAT, which unifies the views of technology acceptance models, is best suited for modification and application in the current research as it investigates what will influence Thai farmers' perceptions and acceptance of e-commerce adoption.
Technology acceptance-related theories
- Innovation diffusion theory (IDT)
- Theory of reasoned action (TRA)
- Theory of planned behavior (TPB)
- Technology acceptance model (TAM)
- Combined TAM and TPB (C-TAM-TPB)
- Model of PC utilization (MPCU)
- Motivational model (MM)
- Social cognitive theory (SCT)
- Unified Theory of Acceptance and Use of Technology (UTAUT)
Another variable "effort expectancy" is defined as "the degree of ease associated with using the system" (Venkatesh et al., 2003, p. 450). The fourth variable 'facilitating conditions' is defined as "the degree to which an individual believes that there is an organizational and technical infrastructure that supports the use of a system" (Venkatesh et al., 2003, p. 453).
Related studies on technology acceptance and adoption theories/models
Facilitating conditions are highly related to performance expectancy and effort expectancy: when these constructs are presented in the model, it reduces the influence of facilitating conditions. Therefore, the technology acceptance and acceptance models/theories should be applied in the current study.
The initial hypotheses and conceptual model
- The initial Hypotheses
- The initial conceptual model
H1a: The expected performance of e-commerce adoption for rice sales is positively related to Thai rice farmers' behavioral intention to adopt e-commerce. H2a: Expected e-commerce adoption effort for rice sales negatively affects Thai rice farmers' behavioral intention to adopt e-commerce. H3a: The social influence on e-commerce adoption for rice sales positively affects Thai rice farmers' behavioral intention to adopt e-commerce.
H6a: The perceived risk of e-commerce adoption for rice sales negatively affects Thai rice farmers' behavioral intention to adopt e-commerce.
- General research approach
- Research design
- Research participants
- Sample selection
- Sample size
- Research tools and data collection instrument
- Research tools and data collection instrument for qualitative method
- Research tools and data collection instrument for quantitative method
- Measurement
- Data analysis
- Qualitative data analysis
- Quantitative data analysis
- SEM model fit indices criterion
Therefore, a mixed methodology (i.e. the fourth option of the matrix in Table 3.1) was used for this study. The survey participants (i.e., the sample) constitute the segment of the population chosen to represent the entire consumer population (McDaniel & Gates, 2010). The research participants for the interviews were purposively selected from stakeholders in the rice value chain in Thailand.
Thus, other Thai rice farmers who had not yet adopted EC for rice sales were excluded in the quantitative data analysis.
Qualitative data analysis results
- Data collection
- Interviewees
- Interview data analysis results
Therefore, most interviewees believed that the adoption of e-commerce for rice sales is quite good for rice farmers. Accordingly, the performance expectation of adopting e-commerce for rice sales appears to contribute to rice farmers' behavioral intention to adopt e-commerce. As a result, the performance expectation of adopting e-commerce for rice sales is positively related to Thai rice farmers' behavioral intention to adopt e-commerce.
As a result, it could be hypothesized that the perceived risk of e-commerce adoption for rice sales would negatively affect Thai rice farmers' behavioral intention to adopt e-commerce. Therefore, the social influence on e-commerce adoption for rice sales positively affects Thai rice farmers' behavioral intention to adopt e-commerce. C] Government support is massively needed for rice farmers, which may increase their intention to adopt e-commerce for rice sales.
Revision of hypotheses and conceptual model
In Chapter 2, it was hypothesized that these two variables would moderate factors that would influence rice farmers' behavioral intention to adopt e-commerce for rice sales. Therefore, the revised hypothesis for the perceived implementation costs is that “the low implementation costs of e-commerce positively influence Thai rice farmers' behavioral intention to adopt e-commerce.” H2c: If a Thai rice farmer has sufficient IT knowledge and skills, a positive relationship will be found between effort expectation for e-commerce adoption and behavioral intention to adopt e-commerce.
Sufficient IT knowledge has a positive effect on Thai rice farmers' behavioral intention to adopt e-commerce for rice sales.
Final hypotheses and conceptual model
Therefore, the researcher proposed the new hypothesis regarding 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 sales”. H3 The social influence on Thai rice farmers' adoption of e-commerce for rice sales positively affects their behavioral intention to adopt e-commerce. H5 The perceived risk of e-commerce adoption for rice sales negatively affects Thai rice farmers' behavioral intention to adopt e-commerce.
H8 Insufficient facilitating conditions for the introduction of e-commerce have a negative effect on the acceptance and adoption of e-commerce by Thai rice farmers.
Pilot study based on revised hypotheses and conceptual model
- Demographic profile of pilot study
- Validity testing of pilot study
- Reliability testing of pilot study
Adopting e-commerce for selling rice requires electronic banking knowledge, such as registration, online payment or money transfer. Thailand's IT infrastructure is not yet suitable for adopting e-commerce for rice sales. Logistics and facilities especially in peripheral and rural areas still need to be improved to support the adoption of e-commerce for selling rice.
Sufficient IT knowledge and skills can improve the effectiveness of e-commerce adoption for rice sales.
Descriptive statistics
- Gender and marital status
- Age
- Education level
- Household income per month
- Average internet usage and e-commerce usage
- Devices for e-commerce adoption
- Platforms of e-commerce adopted for rice selling
Regarding the frequency of using e-commerce to sell rice, the largest group with 182 respondents (44.9 percent) 'sometimes' used e-commerce to sell rice;. The second group of 76 rice farmers (18.8 percent) were respondents who had used Facebook, the LINE app, and online rice selling websites. The third group of 66 rice farmers (16.3 percent) were respondents who had only used Facebook to sell rice online.
The fourth group of 38 rice farmers (9.4 percent) were the respondents who had used Facebook, the LINE application, and Instagram to sell rice online.
Sampling adequacy: KMO Index, Bartlett’s test, and anti-image correlation
In terms of e-commerce platforms adopted for rice sales, respondents mostly used the following channels: Facebook, the LINE app, Instagram, and websites (government, private, or their own website, such as Shopee, Lazada, etc.). Respondents were categorized into seven groups based on the e-commerce channels each respondent used to sell rice online. The first group consisted of rice farmers who used Facebook and the LINE application (173 respondents, 42 percent).
The fifth to seventh group consisting of 34 rice farmers (8.4 percent); 12 rice farmers (3 percent); and six (6) rice farmers (1.5 percent) were respondents who only used Facebook; four platforms (Facebook, LINE app, Instagram and website); and the website itself.
Factor analysis
- Exploratory factor analysis (EFA)
- Confirmatory factor analysis (CFA)
Therefore, rejection of the null hypothesis would cause the factor model to be considered appropriate. The conceptual model of the study hypothesized that the survey should have 10 uncorrelated factors (latent variables). Factor naming and latent variable interpretability were established by the constructs of the theoretical model.
According to Fornell & Larcker, (1981) inter-construct correlations should be lower than the square root of the average variance extracted from each construct.
Survey reliability and internal consistency
Therefore, to test the discriminant validity, the square roots of the AVE values and their correlation with other factors were compared. As shown in Table 5.8, the square roots of the AVE values were greater than the correlation between the variables, indicating good discriminant validity (Fornell & Larcker, 1981). Note: AA = Acceptance and adoption of e-commerce; EE = expected effort; FC = facilitating conditions; GOV = government support; IC = perceived cost of implementation; BI = behavioral intention; IT = information technology (IT) knowledge and skills; PE = expected performance; PR = perceived risk; and SI = social impact.
As shown in Table 5.9, the resulting value of the Cronbach's alpha (α) coefficient for each variable was greater than 0.7 (performance expectancy 0.706; effort expectancy 0.706; social influence 0.747; .. government support 0.908; sufficient IT knowledge/skills 0.788 ; intention to adopt e-commerce 0.857; and acceptance and adoption of e-commerce 0.822).
Correlation results
Therefore, the consistency of the survey, in general, was confirmed and reliability was established for all variables. Since all 43 items of the questionnaire were formulated positively, it was not necessary to change the result of any item. The results of the correlation analysis of all variables in the study are presented in table 5.10 'Correlations between variables'.
The results in Table 5.4 present the analysis of Pearson's correlations that demonstrate that the values of all variables did not exceed 0.8; Thus, this confirms the independence of the variables from each other (Cooper & Schindler, 2006).
Multiple regression
- Testing the regression model of hypotheses H1 to H7
- Multiple linear regression analysis results (H1 to H7)
- Testing the regression model of hypotheses H8 and H9
- Multiple linear regression analysis results (H8 and H9)
According to Pallant (2010), the p-value, when it is less than 0.05 (usually ≤ 0.05), is statistically significant, as it provides strong evidence against the significance of the null hypothesis. The results show that the R2 value was 0.358, thus implying that the overall model accounted for 35.8 percent of the variance in behavioral intention (BI). This subsection presents the testing of the two study hypotheses, H8 and H9, as shown in Figure 5.3.
In the results presented in Table 5.12, R2 is shown with a value of 0.295, thus implying that the model accounted for 29.5 percent of the variance in the adoption and acceptance (AA) of e-commerce.
Structural equation modeling (SEM)
- SEM model fit evaluation
- Results of structural equation modeling
- Summary of final hypotheses and significant values
This was also consistent with the multiple regression finding of the impact of behavioral intentions on acceptance and adoption of e-commerce with its R2 value of 0.274. H1 The expected performance of e-commerce adoption for rice sales is positively related to Thai rice farmers' behavioral intention to adopt e-commerce. H2 Expected e-commerce adoption effort for rice sales negatively affects Thai rice farmers' behavioral intention to adopt e-commerce.
H9 Behavioral intention to adopt e-commerce positively affects Thai rice farmers' acceptance and adoption of e-commerce.
- General discussion
- Discussion on the influencing factors on e-commerce adoption for rice selling
- Behavioral intention to adopt e-commerce for rice selling
- Performance expectancy or perceived usefulness of e-commerce adoption
- Effort expectancy or perceive ease of use on e-commerce adoption
- Sufficient IT knowledge and skills for e-commerce adoption
- Government support for e-commerce adoption
- Perceived risk on e-commerce adoption
- Social influence on e-commerce adoption
- Perceived implementation cost on e-commerce adoption
- Facilitating conditions for e-commerce adoption
- Theoretical contributions
- Practical implications
- E-commerce adoption knowledge and capabilities
- Incentives, available agricultural tools and financial support
- Support the close collaboration within rice farmer group
- Readiness and availability of e-commerce and IT infrastructures
- Policy recommendations
- E-commerce law and regulatory issues
- Establishment of main focal point and service center
- Research limitations
- Sample selection and sample size
- Research constructs and absence of statistical data
- Self-report perceptions
- Future research
- Conclusion
Eight influencing factors were found to affect the rice farmers. behavioral intention to adopt e-commerce for selling rice and their actual acceptance and adoption of e-commerce. Moreover, behavioral intention was also an independent variable for testing the adoption and acceptance of e-commerce for selling rice. Therefore, the current study assumed that a positive behavioral intention to adopt e-commerce would contribute to e-commerce acceptance and adoption.
In the quantitative data analysis, behavioral intention was hypothesized to predict acceptance and adoption of e-commerce for selling rice.
