However, in Thailand's construction industry, drones may only be used to monitor the progress of construction work. This research also examines the adoption of drone technology in part of the Heavy-Duty Lifting Process by users with limited or no experience of using drone technology within the Thai construction industry. The numerical data was used to identify the factors influencing technology adoption for drone technology adoption in the construction industry.
In the Thai construction industry, drones can only be used to monitor the progress of work. Regarding the benefits of using drones in the construction industry, this study aims to determine the intention to use drone technology in part of the heavy lifting process in the Thai construction industry. The purpose of the study is to determine users' perceptions of the effects of using drone technology on business performance on the company's core functions.
Additionally, this study aims to better understand user motivations in using drone technologies in part of the heavy lifting process. This research examines the acceptance of drone technology in parts of the heavy-duty lifting process of users who have limited or no experience with drone technology in the Thai construction industry. The study explores the relationship between theory and research empirically in terms of how drone technology is beneficial to the construction industry and how users perceive the use of the technology.
LITERATURE REVIEW
- History of Drones
- Applications of Drones
- Construction Drones
- Diffusion of Innovation Theory
- Technology Acceptance Model (TAM)
- The Theoretical Model of This Study
- Research Hypothesis
- The Reference Questions from TAM Survey in Several Research
- The Original Questions of TAM Survey (Davis et al., 1989) In Term of Perceived Usefulness (PU)
The Diffusion and Innovation Theory classifies the type of adopters into five categories based on the innovation using a bell curve (Rogers et al., 2002). Moreover, social influence may play a role in the development of adoption intentions (Davis et al., 1989). Defined as a level of a personal confidence that the use of technology will make it easier to complete their jobs (Venkatesh et al.,2003) Defined as the extent to which individuals believe that technology will support his or her job performance in terms of the benefits of using a technology, especially to improve productivity, efficiency and performance. Defined as the level of acceptance and use of information technology, including word processing software (Davis et al., 1989), spreadsheet software (Mathieson, 1991), and various end-user productivity software (Adams et al., 1992).
In the consumer's part, the motivations for online retail shopping are found the positive relationship between PU of the new interactive media and ATU these media (Childers et al., 2001). H1: The higher the PU on drone technology, the more positive effect on ATU in drone technology. Defined as a level of confidence by individuals that they can use technology and move forward towards implementation (Venkatesh et al.,2003) Defined as the degree of individuals believe technology will be effortless, which will lead to the behavioral intention to use people different levels of capacity have in adopting the new technology due to different learning capacity (Davis et al., 1989) Contrary to the perceived complexity by Rogers (1983), the more complex a technology is, the more difficult it is to to understand, and the less likely to be adopted (Attewell et al., 1992).
Both PU and PEOU have been used to accurately predict the BI of applications such as an office automation suite (Davis et al., 1989), smart card payment system (Plouffe et al., 2001) and the use of microcomputer (Igbaria et al. , 1995). In the consumer context, PEOU has a significant positive effect on ATU touchscreen self-service (Dabholkar and Bagozzi et al., 2002) and online media shopping (Childers et al. 2001). H3: The higher PEOU in drone technology, the more positive effect on PU in drone technology.
H4: The higher the PEOU on drone technology, the more positive effect on ATU in drone technology. Refers to the evaluative judgment in the adoption of technology, and when the adoption occurs in voluntary settings, ATU has been shown to have a high correlation with BI (Davis et al. 1989). H5: The higher the ATU on drone technology, the more positive effect on BI in drone technology.
Defined as an individual's desire to do or do something (Miftah and Wulandari et al., 2015). It represents the key to PU and PEOU in terms of technology use, and the impact score is expected to be similar to perceived usefulness (Compeau, Higgins, & Huff et al., 1999). H6: The higher the SE in drone technology, the more positive the effect on PU in drone technology.
It is defined as the degree to which individual users perceive an innovation to be consistent with their current values, needs, and past experiences (Moore & Benbasat et al., 1991).
RESEARCH METHOOLOGY
- Research Method
- Population and Sample Selection
- Population
- Sample characteristics
- Sample size
- Research Instrument
- Validity
- Reliability
- Data Collection
- Data Analysis
Taro Yamane's simplified formula was used to determine the sample size for this research. The estimated sample size is determined by using Yamane's formula (Israel, 1992), which is used as an approximation of a population with a 95% confidence level (Office, 2017). PU1 Using (drone technology) in my job would enable me to complete tasks faster.
EU4 Using (drone technology) would be more flexible to do my job than a traditional one. RA1 (Drone technology) would save me time/effort compared to other ways of doing the same tasks. Relative Advantage (RA) RA2 (Drone technology) would enable me to perform many tasks better than.
RA3 (drone technology) would provide more value than other ways of doing the same task. Intent of use (BI) BI2 I increase the number of use cases (of drone technology) in my work. In addition, this study conducted a pilot test for the questionnaire using 30 samples, and it was analyzed by confirmatory factor analysis (CFA) to identify the relationships between individual factors and improve the questionnaire to be more accurate and easy to use. understood by respondents.
The values of factor loading, constructed reliability and average variance extracted from the questionnaire in this study all achieved the criteria mentioned above. Quantitative data could be generated in the numerical data which would be converted into useful information through the statistics. The data was collected in March 2020 by distributing the questionnaires through social media, for example, Line and Facebook, and only people living in Bangkok were allowed to answer the questionnaires.
After collecting the data from the questionnaires, SPSS and AMOS software were used to analyze the data. As a multivariate statistical analysis, the data analysis method of this study is structural equation modeling (SEM) which is the integration of factor analysis, path analysis and multiple regression analysis, to analyze the structural relationships for due to its latent variables.
RESEARCH RESULT
- Validity and Reliability Analysis
- Model Fit Test
- Descriptive Analysis
- The Structural Equation Model (SEM) Analysis
- Hypothesis Test Result
- Standardized Total Effects
Table 4.1 shows that the value of Cronbach's alpha coefficient, Factor Loading and Composite reliability (CR) and Average Variance Extracted (AVE) meet the criteria, which means that the questionnaire and the construction of each variable are reliable. Table 4.2 shows that the consistent values of the structural equation model before adjustment do not meet the criteria. According to Table 4.4, the majority of respondents, as much as 76.4%, already know that drone technology is used in industries.
70% of the variance in Av_ATU is explained by Av_BI which is the highest contributor. From the figure, it is clear that Av_EU (.36) contributes significantly to the variance in Av_BI, and Av_RA (.48) is the most significant contributor to explaining the variance in Av_ATU, followed by. The 65% of variance in Av_EU explained by Av_PU is the second largest contributor in the figure, after Av_RA (.26).
Finally, Av_SE (.36) is the most significant contributor to explaining the variance in Av_RA. Based on the framework of the study, there was also a prediction that Av_PU will have a positive influence on Av_BI. From Figure 6, it is obvious that Av_PU (.25) contributes significantly to the variance in Av_ATU.
H2: The higher the PU on drone technology, the more positive the effect on BI in drone technology. It is evident from Figure 6 that Av_EU (.65) contributes significantly to the variance in Av_PU. It is evident from Figure 6 that Av_ATU (.70) contributes significantly to the variance in Av_BI.
H7: The higher the SE of drone technology, the more positive the effect on PEOU in drone technology. It is evident from Figure 6 that Av_SE (.27) contributes significantly to the variance in Av_EU. From the fit test of the model in Table 4.2 by eliminating some non-significant variables one by one until the model met all the criteria.
From Figure 6, it is clear that Av_RA (.48) contributes significantly to the variance in Av_ATU.
RECOMMENDATION
Conclusion
Limitations
Recommendations
- Management
- Further Research
To gain a better understanding, a qualitative study could therefore be conducted in a more or less similar manner to this study. The further research could explain the acceptance of the technology more deeply and could provide the opportunity for further research on many levels.
A technology acceptance model for empirical testing of new end-user information system: Theory and results (Doctoral Dissertation). An attitude model of technology-based self-service: the moderating effects of consumer characteristics and situational factors. Analysis of the effect of cognitive and personal intention on the use of Internet technology: A case study of Indonesian students.
Retrieved from http://www.bdcnetwork.com/blog/drones-aec-how-every-stage-building-project-can-benefit-drone-technology. Development of an instrument to measure the perceived characteristics of the adoption of an information technology innovation. Affluence versus modesty in modeling technology adoption decisions: Understanding merchant adoption of a smart card payment system.
