How to Make a Smartphone-Based App for Agricultural Advice Attractive: Insights from a Choice Experiment in Mexico
Abstract
:1. Introduction
2. Research Context and Methods
2.1. The AgroTutor App
2.2. Models’ Description
2.2.1. Conditional Logit
2.2.2. Latent Class Model
2.2.3. Attributes and Levels
2.3. Data Collection and Analysis
3. Results and Discussion
3.1. Descriptive Statistic
3.2. Farmers Preferences: Conditional Logit Model Results
3.3. Farmers Valuing Extension Services, Data-Usage Cost and Data Privacy: Latent Class Model Results
4. Limitations
5. Conclusions and Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Explanation of Selected Attributes
- “Support during first-time use” refers to the support provided by an extension agent to the farmer to introduce the app and show how it works. A previous CE study investigated the “input time” attribute in the context of a decision support tool adoption by extension advisers (Kragt and Llewellyn, 2014). The “input time” attribute considers the time spent learning how to use the app. During the field work and workshop preparing this study, the interaction of extension agents with the farmers while introducing the app for the first time was observed as relevant. As well, this attribute is also important to gain insights about the trust in the provider channel. Two levels were suggested: no help and with extension agent help. Additionally, the attribute is consistent with the importance of the credibility of the provided information to the farmers or agricultural professionals in the context of crowdsourcing in agriculture (Minet et al., 2017).
- “Data input requirements” refers to the minimum required frequency with which a farmer needs to register or update information in the mobile phone app. The information required might be the fertiliser use, farm data (input quantities, yields, and cereal type), or the registration of a plot. This attribute was relevant to gain insights in the preferences to update information in the app. Four levels were identified: no requirement, once every 15 days (2 weeks), once every 2 months, and once every productive cycle. If the minimum required is perceived by the farmers as easy to comply with, it may incentivise participation or continuous usage.
- “Data-usage cost” is the cost of internet data-usage every time the farmer accesses the app. A similar attribute has been used previously to assess extension agents’ stated preferences for the cost of a pest management decision support tool (Kragt and Llewellyn, 2014). The cost–benefit perceived by the farmers may be a factor or barrier to farmer participation or continuous usage. The cost was calculated along with the app developers from IIASA considering the cost of megabytes of internet used per time conducting basic actions in the app (register a plot, consult fertilisation advice, weather, and benchmark information). Around 5 megabytes are used per time (1 megabyte = 0.98 MXN); therefore, three levels were proposed: 0 MXN, 5 MXN, and 10 MXN. The 0 MNX level could be considered as an option under which the app will offer free offline features too.
- “Access to training” refers to a nonfinancial utility or compensation for the use of the mobile phone application as special access to trainings and capacity building events in their region. The access to face-to-face knowledge exchanges might be perceived as an incentive for farmers to keep on using an app or provide data entries. Hence, it aims to explore whether some farmers will accept training and capacity building as compensation (nonfinancial utility) and if it will motivate farmers’ preference for the use of the mobile phone app.
- “Data sharing” refers to which actors the farmer prefers to have access to the information registered in the app. Data ownership is an important issue in the context of the current data revolution and big data applications (Wolfert et al., 2017). Choice experiments have been used before to examine privacy trade-offs in smartphone applications (Savage and Waldman, 2015) and also to estimate the value which app users gave to their friends’ information (Pu and Grossklags, 2017). However, this aspect was only recently explored, with farmers looking at their willingness to join a big data platform (Turland and Slade, 2020). In our study, four levels are proposed: only me, everyone including peers, research institutions and government, and private companies. Research institutes and government are together due to the nature of the case study in which the institute developing the app works closely with the government in the region (Section 3.1).
- “Replacing extension services visits” considers the scenarios in which the extension agents keep visiting the farmers or not. The attribute is important to add to have control on the perception that the app might replace the visits. This was a concern raised by the farmers connected to the innovation hub during the preparatory interviews and field work as the extension advisers’ visits are already being conducted in the study area. Two levels are proposed: extension services keep on visiting and no regular extension service visits.
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Attribute | Definitions | Attribute Levels | Comments |
---|---|---|---|
Support during first-time use | Whether farmers use the app by themselves or with an extension agent’s help | Get support from an extensionist to do it yourself | “Input time” attribute considers time spent learning how to use the app. |
Data input requirements | How often farmers are expected to enter or update information | No requirement Once every 2 weeks Once every 2 months Once every production cycle | If farmers perceive minimum required as easy to comply with, it may incentivise participation. |
Data-usage cost | Cost associated with the internet data spent on accessing and conducting basic tasks in the app (each time app is accessed). | 0 MXN 5 MXN 10 MXN | The 0 MNX level is an option to offer offline features too. The cost–benefit perceived by the farmers may be a factor or barrier to farmer participation or continuous usage. |
Access to training | Special access to training and capacity building events in their region, in exchange for using the app. A nonfinancial incentive. | Special access No special access | Allows one to explore whether farmers will accept training and capacity building as compensation (nonfinancial utility) and whether this will motivate farmers’ preference for the use of the mobile phone app. |
Access to shared data | To which extent the data recorded in the app are accessible to others apart from the user farmer. | Only me All other farmers Research institutions and government Private companies | Research institutes and government combined due to the nature of the case study in which the institute developing the app works closely with the government in the region. |
Replacing extension service visits | Whether the farmer prefers to keep (or not) the extension services visits. | Extension service continues to visit No regular extension service visits | Important to examine whether the app might replace the visits. |
Parameters | Total Sample (392) | Parameters | Total Sample (392) |
---|---|---|---|
Socio-economic: | Connection to the innovation hub | ||
Age (average; range) | 55; 23–86 | % farmers linked to the hub | 52% |
Gender (% male/female) | 94%/6% | Time linked to the hub (years) | 3 |
Sample share living w/youth * | 67% | Extension services: | |
Literacy ** | 95% | % of farmers receiving weekly advice | 32% |
Crop production as primary income source (%) | 91% | Mobile ownership and use: | |
Land characteristics: | % owning a mobile (all types) | 82% | |
Land area (average ha; range) | 16.5; 2–600 | % owning a smartphone | 46% |
% with a production contract | 74% | Time using a smartphone (years) | 3.5 |
% owning land | 75% | Time using the phone (min/day) | 43 |
Area of land owned (average ha) | 9 | Mobile credit spent (MXN/month) | 170 *** |
Number of Respondents | All Farmers | Connected Farmers | Non-Connected Farmers | ||||||
---|---|---|---|---|---|---|---|---|---|
392 | 204 | 188 | |||||||
Estimates | Std. error | Estimates | Std. error | Estimates | Std. error | ||||
Support at 1st time use | −0.314 | *** | (0.093) | 0.347 | *** | (0.133) | 0.299 | ** | (0.131) |
Data input requirements ° | |||||||||
Once every 2 weeks | −0.114 | NS | (0.099) | 0.043 | NS | (0.144) | −0.270 | ** | (0.137) |
Once every 2 months | −0.058 | NS | (0.108) | 0.047 | NS | (0.160) | −0.160 | NS | (0.148) |
Once every productive cycle | 0.105 | NS | (0.142) | 0.303 | NS | (0.207) | −0.089 | NS | (0.197) |
Data-usage cost | −0.046 | *** | (0.008) | −0.066 | *** | (0.012) | −0.027 | *** | (0.012) |
Access to trainings | 0.310 | *** | (0.068) | 0.298 | *** | (0.096) | 0.332 | *** | (0.098) |
Access to shared data + | |||||||||
All | −0.025 | NS | (0.103) | −0.074 | NS | (0.147) | 0.014 | NS | (0.144) |
Research institutes and government | −0.061 | NS | (0.087) | −0.113 | NS | (0.130) | −0.042 | NS | (0.120) |
Private companies | −0.213 | ** | (0.085) | −0.290 | ** | (0.124) | −0.136 | NS | (0.119) |
Replacing extension service visits | 0.298 | *** | (0.063) | 0.520 | *** | (0.091) | 0.074 | NS | (0.090) |
ASC † | −0.869 | *** | (0.105) | −0.827 | *** | (0.152) | −0.896 | *** | (0.147) |
Latent Classes | ||||||
---|---|---|---|---|---|---|
Class 1 (50% (n = 197)) Value Extension | Std. Error | Class 2 (11% (n = 45)) Value Learning, Cost-Averse | Std. Error | Class 3 (39% n = 151) Value Data Privacy | Std. Error | |
Preference parameters | ||||||
Support at 1st time use | 1.4547 *** | 0.4523 | 0.4060 | 0.5582 | 0.0566 | 0.1969 |
Data input requirements ° | ||||||
once every 2 weeks | 0.4726 | 0.3276 | 0.0241 | 0.7752 | 0.5280 *** | 0.1777 |
once every 2 months | 0.1944 | 0.3614 | 0.1537 | 0.7458 | 0.3879 ** | 0.1868 |
once every cycle | 0.0653 | 0.6470 | 0.6984 | 0.9075 | 0.1627 | 0.2758 |
Data-usage cost | 0.0644 * | 0.0359 | 0.1659 *** | 0.0567 | 0.0210 | 0.0168 |
Access to trainings | 1.4137 *** | 0.3590 | 0.7436 * | 0.4044 | 0.0883 | 0.1360 |
Access to shared data + | ||||||
All | 0.3464 | 0.3460 | 0.7116 | 0.5665 | 0.1134 | 0.1997 |
Research institutes and government | 0.3114 | 0.3104 | 0.5013 | 0.5865 | 0.0923 | 0.1401 |
Private companies | 0.4392 | 0.2855 | 0.0467 | 0.6283 | 0.6382 *** | 0.1683 |
Extension services | 1.5826 *** | 0.3470 | 0.3974 | 0.4757 | 0.0218 | 0.1255 |
ASC | 0.0999 | 0.4809 | 2.6980 *** | 0.7900 | 2.3486 *** | 0.2473 |
Class assignment | ||||||
Constant | 0.5196 | 0.4244 | 1.4032 ** | 0.6152 | ||
Age (1 if > 55) | 0.2686 | 0.3142 | 0.2173 | 0.4700 | ||
Own mobile (Yes, No) | 0.0437 | 0.4082 | 0.6323 | 0.5639 | ||
Linked to innovation hub (Yes, No) | 0.9523 *** | 0.3053 | 0.5841 | 0.5783 | ||
Behavioural intention (1 if > median) | 0.3485 | 0.4242 | 1.8744 *** | 0.6800 | ||
Mastery approach goal (1 if > median) | 0.6493 | 0.4262 | 0.9156 * | 0.5321 |
Variable | Levels | Total Sample | Class 1 (50%) Value Extension | Class 2 (11%) Value Mastery Cost-Averse | Class 3 (39%) Value Data Privacy | p-Value |
---|---|---|---|---|---|---|
Socio-economic: | ||||||
Age | µ (±σ) | 53 (14.6) | 57.5 (12.8) | 57 (12.7) | 0.044 3 | |
Education level | none | 12% | 22 (11%) | 5 (11%) | 20 (13%) | 0.810 2 |
elementary | 36% | 68 (35%) | 16 (36%) | 56 (37%) | ||
high school | 33% | 63 (32%) | 17 (38%) | 49 (32%) | ||
university | 19% | 44 (22%) | 6 (13%) | 26 (17%) | ||
undergraduate | 5% | 8 (4%) | 3 (6%) | 9 (6%) | ||
Land area | µ mean (±σ) | 15 (28) | 18 (54) | 9 (13) | (1–3) 0.002 2 | |
Land area owned | 9 (28) | 17 (55) | 7 (14) | (2–3)0.031 2 | ||
Extension services: | ||||||
Connected to the innovation hub | No | 48% | 78 (40%) a | 23 (51%) a,b | 88 (58%) b | 0.002 1 |
Yes | 52% | 119 (60%) a | 21 (49%) a,b | 63 (32%) b | ||
Years linked to the hub | µ mean (±σ) | 2.9 (2) | 3.5 (1.8) | 2.5 (1.3) | 0.091 3 | |
Frequency of advice received | Never | 19.4% | 28 (14%) a | 14 (32%) b | 34 (23%) a,b | 0.002 1 |
every 6 months | 13.3% | 20 (10%) a | 4 (9%) a | 28 (19%) a | ||
every 2 months | 7.4% | 14 (7%) a | 1 (2%) a | 14(9%) a | ||
every month | 23% | 49 (25%) a | 9 (20%) a | 32 (21%) a | ||
every week | 32.1% | 74 (38%) a | 16 (36%) a,b | 36 (24%) b | ||
every 3 days | 4.8% | 12 (6%) a | 0 (0%) a | 7 (5%) a | ||
Mobile phone services: | ||||||
Own mobile | No | 17.9% | 31 (16%) a | 13 (30%) b | 26 (17%) a,b | 0.093 1 |
Yes | 82.1% | 166 (84%) a | 31 (70%) b | 125 (83%) a,b | ||
Mobile type | None | 17.9% | 31 (16%) a | 13 (30%) a | 26 (17%) a | <0.001 1 |
Smartphone | 45.9% | 111 (56%) a | 15 (34%) b | 54 (36%) b | ||
Basic | 19.4% | 26 (13%) a | 12 (27%) a,b | 38 (25%) b | ||
Medium | 16.8% | 29 (15%) a | 4 (9%) a | 33 (22%) a | ||
Years using a smartphone | µ (±σ) | 3.9 (2.9) | 3.2 (2.5) | 3.2 (2.7) | 0.262 3 | |
Minutes per day | µ (±σ) | 52 (71) | 36.8 (56) | 32.5 (68) | (1–3) 001 2 | |
Spend in mobile credit/month | µ (±σ) | 181.6 (97.5) | 153 (66.3) | 161.4 (89.8) | (1–3).047 2 |
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Molina-Maturano, J.; Verhulst, N.; Tur-Cardona, J.; Güerena, D.T.; Gardeazábal-Monsalve, A.; Govaerts, B.; De Steur, H.; Speelman, S. How to Make a Smartphone-Based App for Agricultural Advice Attractive: Insights from a Choice Experiment in Mexico. Agronomy 2022, 12, 691. https://doi.org/10.3390/agronomy12030691
Molina-Maturano J, Verhulst N, Tur-Cardona J, Güerena DT, Gardeazábal-Monsalve A, Govaerts B, De Steur H, Speelman S. How to Make a Smartphone-Based App for Agricultural Advice Attractive: Insights from a Choice Experiment in Mexico. Agronomy. 2022; 12(3):691. https://doi.org/10.3390/agronomy12030691
Chicago/Turabian StyleMolina-Maturano, Janet, Nele Verhulst, Juan Tur-Cardona, David T. Güerena, Andrea Gardeazábal-Monsalve, Bram Govaerts, Hans De Steur, and Stijn Speelman. 2022. "How to Make a Smartphone-Based App for Agricultural Advice Attractive: Insights from a Choice Experiment in Mexico" Agronomy 12, no. 3: 691. https://doi.org/10.3390/agronomy12030691