The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory
Abstract
:1. Introduction
2. Literature Review and Underpinning Theory
2.1. IoT and Its Application in Agriculture
2.2. UTAUT 2 and Hypothesis Formulation
2.2.1. Performance Expectancy (PE)
2.2.2. Government Support
2.2.3. Facilitating Conditions
2.2.4. Social Influence
2.2.5. Hedonic Motivation
2.2.6. Effort Expectancy
2.2.7. Trust
2.2.8. Price Value
2.2.9. Personal Innovativeness
2.2.10. Willingness to Adopt
2.2.11. Willingness to Pay
2.2.12. The Moderation Effect of Facilitating Condition
3. Research Design
3.1. Population, Sample, and Data Collection Procedure
3.2. Instruments of the Study
3.3. Data Analysis
4. Results of the Study
4.1. Demographic Profile
4.2. Measurement Model
4.2.1. Common Method Bias (CMB) Test
4.2.2. Data Normality and Multicollinearity
4.2.3. Reliability and Validity
4.3. Structural Model and Hypotheses Results
5. Discussion
6. Conclusions
6.1. Contribution and Implications
6.1.1. Theoretical Contribution
6.1.2. Practical Implication
6.2. Limitations and Further Research Direction
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Survey Items
Construct | Questions | Reference |
---|---|---|
Willingness to Pay | I will use IoT services in agricultural firming, even if the price increases somewhat. | [91] |
I am interested to pay a higher price for IoT services than similar agricultural technology. | ||
I will use IoT service via information technology devices, even if the price increases. | ||
Hedonic Motivation | IoT system usage is fun. | [19] |
IoT system usage is enjoyable. | ||
IoT system usage is entertaining. | ||
Price Value | The IoT system is reasonably priced. | [19] |
Usually, IoT systems are good value for the money. | ||
With the current price, the IoT system provides good value. | ||
Social Influence | People who matter to me suggest I should utilize the IoT in agriculture. | [92] |
People who shape my behavior suggest I should utilize the IoT in agriculture. | ||
People I respect desire that I employ the IoT in agriculture production. | ||
Government support | The use of the IoT in agricultural production is encouraged and promoted by the government. | [49] |
The Internet infrastructure, including bandwidth, is enough for the IoT. | ||
In agriculture, the government has put in place solid rules and restrictions for the use of IoT systems. | ||
Effort expectancy | The IoT is easy to learn for me. | [19] |
It is simple to become skillful at using the IoT. | ||
I find the IoT simple to use. | ||
Personal Innovativeness | I like to try new things. | [92] |
I would not hesitate to use new agricultural technology. | ||
Among other agri-entrepreneurs, I am usually the first to try out new agricultural technology. | ||
Trust | I believe that using the IoT is safe. | [68] |
I do not doubt the security of the IoT. | ||
The IoT can fulfill its task. | ||
Willingness to Adopt | I intend to use the IoT system in agricultural production. | [90] |
I plan to use IoT systems in agricultural production in the future. | ||
In the future, I believe I will employ an IoT system in agricultural production. | ||
Performance expectancy | I find IoT systems useful in crop yield rate analysis. | [19] |
Using an IoT system will assist in weather forecasting in crop production. | ||
I find IoT systems useful in field mapping using GPS systems in crop production. | ||
Facilitating Condition | I am well equipped to put the IoT to work in agricultural productivity. | [90] |
I know how to apply the IoT in agriculture. | ||
When I encounter challenges in implementing the IoT in agriculture production, I can ask for assistance from others. |
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Application | Functions |
---|---|
Analysis of yield data | Composite layers are created by condensing numerous years’ worth of yield data [36] |
Variable-rate technologies | Allow the application of site-specific agricultural inputs [15] |
Field mapping using GPS | Achieve accurate acreage measurement for fields and roads using maps created by farmers [15] |
Weather forecasts at the field-level | Measurements made by sensors aid in the forecasting of local weather and precipitation [37]. |
Autosteer technology | Allows precision farming machinery to operate on autopilot, improving accuracy and production for farmers [15]. |
Optimization tools for machines | Agriculture inputs are reduced via the use of precise Global Positioning Systems (GPS) and sensors to record agricultural activities [38]. |
Services measuring productivity | Yield monitoring systems collect data from harvesting trucks and sensors on soil conditions, moisture, and crop yields [15]. |
Sources | Research Method/Sample Size/Country | Analysis Tools | Theoretical Framework/ Models | Factors | Limitations |
---|---|---|---|---|---|
[13] | Empirical /Farmers’ interview/220/ India | PLS-SEM | Behavioral reasoning theory | Attitude, reason for, reason against, and value of openness to change | Personal innovations and risk-taking ability could be used as the moderator |
[17] | Literature Review | MICMAC methods | Modified total interpretive structural modeling | Crop management, government initiative, soil quality management, and irrigation management | Results are based on a literature review and are not an empirical research |
[14] | Empirical/ Farm owners and managers’ interview/395/ Thailand | SPSS/ Multiple regression | Technology acceptance model | IoT readiness, e-learning, and institutional support perceived usefulness. | Results are based on only small farms and did not consider government support as a construct |
[15] | Empirical/ Questionnaire survey/492/USA | SEM-STATA | None | Perceived risk, perceived value, trust, age, farm size | Did not consider contextual factors such as price value and other constructs like trust. |
[16] | Empirical/ Face-to-face farmers’ interview/400/Tanzania | Structural Equation Modeling (SEM) (AMOS) | Innovation diffusion theory | Awareness, relative advantages, ease of use, compatibility, visibility | Trust and perceived risk factors could be included to enhance the explanatory power |
[18] | Empirical/company experts/35/China | Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis | Interpretive structural modeling | Cost savings, perceived benefit, external pressure, technical knowledge, executive support, trust, technological compatibility, complexity, scale of the enterprise, and government support. | The study is limited to its analysis method as no robustness was tested using other latest statistical methods |
Current study | Empirical/ Farmer’s interview/345/ Bangladesh | SEM-AMOS | UTAUT 2 | Personal innovativeness, social influence, effort expectancy, willingness to adopt, facilitating condition, performance expectancy, hedonic motivation, price value, government support, and trust. |
Demographics | Classification | Frequency | Percentages |
---|---|---|---|
Gender (out of 345) | Male | 290 | 84 |
Female | 55 | 16 | |
Age (Years) | <25 | 60 | 17 |
25–50 | 192 | 56 | |
>50 | 93 | 27 | |
Types of Premium Fruits Yield | Strawberry | 25 | 7 |
Orange | 20 | 6 | |
Guava | 97 | 28 | |
Jujube (Kul) | 140 | 41 | |
Dragon | 25 | 7 | |
Others | 38 | 11 | |
Farm Size (acres) | <5 | 85 | 25 |
5–10 | 184 | 53 | |
>10 | 76 | 22 |
Construct | Items | Std. Beta | Mean | SD | Skew | Kurt | CA | VIF | |
---|---|---|---|---|---|---|---|---|---|
Tolerance | |||||||||
WTP | WTP1 | 0.985 | 4.060 | 0.792 | −1.552 | 1.632 | 0.875 | 0.865 | 1.155 |
WTP2 | 0.790 | ||||||||
HM | HM1 | 0.818 | 2.058 | 0.826 | 0.887 | 0.344 | 0.737 | 0.937 | 1.067 |
HM2 | 0.706 | ||||||||
HM3 | 0.630 | ||||||||
PV | PV1 | 0.630 | 3.546 | 0.840 | −0.376 | −0.439 | 0.750 | 0.840 | 1.190 |
PV2 | 0.783 | ||||||||
PV3 | 0.724 | ||||||||
SI | SI1 | 0.834 | 3.555 | 0.891 | −0.462 | −0.602 | 0.802 | 0.660 | 1.515 |
SI2 | 0.609 | ||||||||
SI3 | 0.844 | ||||||||
GS | GS1 | 0.719 | 3.336 | 0.951 | −0.376 | −0.844 | 0.778 | 0.943 | 1.061 |
GS2 | 0.871 | ||||||||
GS3 | 0.619 | ||||||||
EE | EE1 | 0.744 | 3.755 | 0.832 | −0.777 | 0.122 | 0.786 | 0.819 | 1.221 |
EE2 | 0.645 | ||||||||
EE3 | 0.836 | ||||||||
PI | PI1 | 0.677 | 3.752 | 0.800 | −0.679 | 0.299 | 0.834 | 0.687 | 1.456 |
PI 2 | 0.936 | ||||||||
PI3 | 0.814 | ||||||||
TT | TT1 | 0.829 | 4.357 | 0.682 | −1.676 | 3.893 | 0.855 | 0.818 | 1.222 |
TT2 | 0.868 | ||||||||
TT3 | 0.755 | ||||||||
WTA | WTA1 | 0.745 | 3.536 | 0.919 | −0.666 | −0.380 | 0.823 | -- | -- |
WTA2 | 0.792 | ||||||||
WTA3 | 0.816 | ||||||||
PE | PE1 | 0.811 | 2.645 | 1.058 | 0.443 | −0.876 | 0.809 | 0.888 | 1.126 |
PE2 | 0.675 | ||||||||
PE3 | 0.812 | ||||||||
FC | FC1 | 0.741 | 2.706 | 0.828 | 0.184 | −0.450 | 0.762 | 0.877 | 1.140 |
FC2 | 0.686 | ||||||||
FC3 | 0.729 |
Variables | CR | AVE | MSV | MaxR(H) | WTP | HM | PV | SI | GS | EE | PI | Trust | WTA | PE | FC |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
WTP | 0.886 | 0.797 | 0.156 | 0.972 | 0.893 | ||||||||||
HM | 0.764 | 0.521 | 0.113 | 0.807 | −0.027 | 0.722 | |||||||||
PV | 0.757 | 0.511 | 0.158 | 0.802 | 0.200 | −0.079 | 0.715 | ||||||||
SI | 0.811 | 0.593 | 0.276 | 0.843 | 0.239 | −0.018 | 0.201 | 0.77 | |||||||
GS | 0.785 | 0.553 | 0.044 | 0.829 | 0.020 | −0.138 | 0.174 | 0.154 | 0.744 | ||||||
EE | 0.788 | 0.557 | 0.260 | 0.811 | 0.145 | −0.059 | 0.106 | 0.510 | 0.045 | 0.746 | |||||
PI | 0.854 | 0.666 | 0.276 | 0.908 | 0.293 | −0.065 | 0.244 | 0.525 | 0.069 | 0.356 | 0.816 | ||||
TT | 0.859 | 0.670 | 0.113 | 0.868 | 0.235 | −0.337 | 0.227 | 0.164 | 0.209 | 0.111 | 0.108 | 0.819 | |||
WTA | 0.828 | 0.616 | 0.156 | 0.831 | 0.396 | 0.028 | 0.303 | 0.350 | 0.185 | 0.328 | 0.322 | 0.172 | 0.785 | ||
PE | 0.812 | 0.591 | 0.053 | 0.825 | 0.069 | 0.104 | 0.107 | 0.194 | −0.073 | 0.186 | 0.142 | −0.223 | 0.230 | 0.769 | |
FC | 0.762 | 0.517 | 0.158 | 0.812 | −0.039 | 0.06 | −0.397 | −0.162 | −0.068 | −0.067 | −0.073 | −0.168 | −0.028 | −0.136 | 0.719 |
WTP | HM | PV | SI | GS | EE | PI | Trust | WTA | PE | FC | R2 |
---|---|---|---|---|---|---|---|---|---|---|---|
WTP | 0.18 | ||||||||||
HM | 0.019 | ||||||||||
PV | 0.189 | 0.074 | |||||||||
SI | 0.244 | 0.013 | 0.235 | ||||||||
GS | 0.030 | 0.123 | 0.190 | 0.165 | |||||||
EE | 0.166 | 0.043 | 0.106 | 0.484 | 0.023 | ||||||
PI | 0.338 | 0.049 | 0.226 | 0.599 | 0.082 | 0.403 | |||||
Trust | 0.212 | 0.316 | 0.249 | 0.172 | 0.185 | 0.091 | 0.09 | 0.01 | |||
WTA | 0.406 | 0.055 | 0.318 | 0.366 | 0.165 | 0.313 | 0.357 | 0.173 | 0.23 | ||
PE | 0.077 | 0.100 | 0.124 | 0.226 | 0.017 | 0.201 | 0.175 | 0.212 | 0.217 | ||
FC | 0.012 | 0.051 | 0.396 | 0.213 | 0.102 | 0.091 | 0.074 | 0.164 | 0.025 | 0.156 |
Hypothesis | STD Beta | STD Error | t-Values | p-Values | Significance (p < 0.05) |
---|---|---|---|---|---|
H1: PE → WTA | 0.190 | 0.039 | 3.736 *** | 0.000 | Supported |
H2: PE → WTP | 0.016 | 0.038 | 0.353 | 0.724 | Not Supported |
H3: GS → WTA | 0.124 | 0.048 | 2.481 ** | 0.013 | Supported |
H4: FC → WTA | 0.126 | 0.055 | 2.428 ** | 0.015 | Supported |
H5: SI → WTA | 0.159 | 0.050 | 3.206 *** | 0.001 | Supported |
H6: HM → WTA | 0.105 | 0.048 | 2.039 ** | 0.041 | Supported |
H7: EE → WTA | 0.173 | 0.048 | 3.379 *** | 0.000 | Supported |
H8: TT → WTA | 0.139 | 0.066 | 2.822 *** | 0.005 | Supported |
H9: TT → WTP | 0.175 | 0.065 | 3.843 *** | 0.000 | Supported |
H10: PV → WTA | 0.224 | 0.059 | 4.220 *** | 0.000 | Supported |
H11: PI → TT | 0.121 | 0.046 | 2.469 ** | 0.014 | Supported |
H12: PI → WTA | 0.145 | 0.060 | 3.070 *** | 0.002 | Supported |
H13: WTA → WTP | 0.354 | 0.053 | 7.101 *** | 0.000 | Supported |
H14: PE × FC → WTA | Supported |
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Shi, Y.; Siddik, A.B.; Masukujjaman, M.; Zheng, G.; Hamayun, M.; Ibrahim, A.M. The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory. Sustainability 2022, 14, 6640. https://doi.org/10.3390/su14116640
Shi Y, Siddik AB, Masukujjaman M, Zheng G, Hamayun M, Ibrahim AM. The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory. Sustainability. 2022; 14(11):6640. https://doi.org/10.3390/su14116640
Chicago/Turabian StyleShi, Yan, Abu Bakkar Siddik, Mohammad Masukujjaman, Guangwen Zheng, Muhammad Hamayun, and Abdullah Mohammed Ibrahim. 2022. "The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory" Sustainability 14, no. 11: 6640. https://doi.org/10.3390/su14116640