Effects of farmers’ peer influence on the use of ICT-based farm input information in developing countries: a case in Sikasso, Mali

Macire Kante; Christopher Chepken; Robert Oboko

doi:10.34624/jdmi.v1i1.931

Macire Kante Centre National de la Recherche Scientifique et Technologique
Christopher Chepken University of Nairobi
Robert Oboko University of Nairobi

DOI: https://doi.org/10.34624/jdmi.v1i1.931

Keywords: Cereals, Productivity, ICT4D, Developing Countries, Farm Inputs

Abstract

Agriculture, characterised by low productivity and dominated by small-scale cereal farmers constitutes the backbone of developing countries’ economy. Use of agricultural inputs permits the increase of the yield and hence productivity. However, use of such farm inputs relies heavily on the availability of information. Information and Communication Technologies (ICT) play a vital role in the dissemination of agricultural input information. Nevertheless, use of ICT-based farm input information is related to certain conditions such as farmers’ peer influence. This influence was investigated, and its effect on the use of ICTs for more access and use of agricultural input information was identified. We gathered from 300 respondents in Sikasso, Mali. The results showed that farmers’ peer influence explained 80.2% of use ICT-based farm input information. From these results, it is important to take this influence as the main factor determining the utilisation of ICT-based farm input information in the cereal production context.

Downloads

Download data is not yet available.

References

Adegbidi, A., Mensah, R., Vidogbena, F., & Agossou, D. (2012). Determinantsof ICT use by ricefarmers in Benin : from the perception of ICT characteristics to the adoption of the technology. Journal of Research in International Business and Management, 2(11), 273–284.

Aker, J. C. (2011). Dial “ A ” for Agriculture : Using Information and Communication Technologies for Agricultural Extension in Developing Countries. Agricultural Economics, 42(6), 631–647. https://doi.org/10.1111/j.1574-0862.2011.00545.x

Al-Ghaith, W., Sanzogni, L., & Sandhu, K. (2010). Factors Influencing the Adoption and Usage of Online Services in Saudi Arabia. Electronic Journal of Information Systems in Developing Countries, 40(1), 1–32.

AMRTP. (2011). Rapport Annuel 2011. Bamako. Retrieved from http://www.amrtp.ml/pdf/rapport_act/Rapport_2011.pdf

Angelucci, F., Gourichon, H., Mas Aparisi, A., & Witwer, M. (2013). Monitoring and analysing food and agricultural policies in Africa MAFAP Synthesis Report Series.

Aparisi, A. M. (FAO), & Balie, J. (FAO). (2013). Review of Food and Agricultural Policies Mali 2005-2011. Rome.

Atkinson, N. L. (2007). Developing a questionnaire to measure perceived attributes of eHealth innovations. American Journal of Health Behavior, 31, 612–621. https://doi.org/10.5993/AJHB.31.6.6

Bowling, A. (2005). Mode of questionnaire administration can have serious effects on data quality. Journal of Public Health, 27(3), 281–291. https://doi.org/10.1093/pubmed/fdi031

Bwalya, S., Asenso-Okyere, K., & Tefera, W. (2012). Promoting ICT based agricultural knowledge management. In Increasing agricultural productivity and enhancing food security in Africa : New Challenges and Opportunities(p. 39). Addis Ababa: UNDP.

Chung, Y. S. Case study : Tigo Kilimo, Tanzania. (PUBmed, Ed.), 24GSMA 48–51 (2015). London. https://doi.org/10.1016/S1360-8592(98)80013-2

Dandedjrohoun, L., Diagne, a, & Biaou, G. (2012). Determinants of diffusion and adoption of improved technology for rice parboiling in Benin. Review of Agricultural and Environmental Studies, 93(2), 171–191. Retrieved from http://www.raestud.eu/pdf/171-191-ncho.pdf

DRPSIAP. (2011). Rapport de l'enquete sur la reconnaissance rapide des axes et circuits de commercialisation des cereales dans la region de Sikasso. Sikasso. Retrieved from fsg.afre.msu.edu/promisam_2/reconnaissance_bassin_Sikasso.pdf

Flowers, E. P., Freeman, P., & Gladwell, V. F. (2016). A cross-sectional study examining predictors of visit frequency to local green space and the impact this has on physical ... A cross-sectional study examining predictors of visit frequency to local green space and the impact this has on physical activity. BMC Public Health, (December). https://doi.org/10.1186/s12889-016-3050-9

Garson, G. D. (2016). Partial Least Squares: Regression & Structural Equation Models(2016 Editi). Asheboro: Statistical Associates Publishing.

Gefen, D., Straub, D. W., & Boudreau, M.-C. (2000). Structural Equation Modeling and Regression : Guidelines for Research Practice. Communications of the Association for Information Systems, 4(October), 7. https://doi.org/10.1.1.25.781

Groeneveld, R. A., & Meeden, G. (1984). Measuring Skewness and Kurtosis. Journal of the Royal Statistical Society, 33(4), 391–399.

GSMA. Case study: Orange Senekela, Mali (2015). https://doi.org/10.1016/S1360-8592(98)80013-2

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). Partial least squares structural equation modeling (PLS-SEM). In Sage Publisher(Vol. 26, pp. 1–329). Emerald Group Publishing Limited. https://doi.org/10.1108/EBR-10-2013-0128

Hu, T., Zhang, P., Zhang, X., & Dai, H. (2009). Gender Differences in Internet Use : A Logistic Regression Analysis Gender Differences in Internet Use : A Logistic Regression Analysis. In Information Systems. San Francisco: AMCIS.

Kaba, B., Diallo, A., & Plaisent, M. (2006). Explaining the factors influencing cellular phones use in Guinea. The Electronic Journal of Information Systems in Developing Countries, 28(3), 1–7. Retrieved from http://www.ejisdc.org/ojs2../index.php/ejisdc/article/view/341

Kaddu, S. B. (2011). Information and Communication Technologies (ICTs) contribution the access and utilisation of agricultural information by the rural women in Uganda.Makerere University.

Kameswari, V. L. V., Kishore, D., & Gupta, V. (2011). ICTs for agricultural extension: a study in the Indian Himalayan region. Electronic Journal of Information Systems in Developing Countries, 48(3), 1–12.

Kante, M., Chepken, C., & Oboko, R. (2017a).Methods for translating ICTs ’ survey questionnaire into French and Bambara. In Knowledge and Innovation for Social and Economic Development. Njoro: Egerton University.

Kante, M., Chepken, C., & Oboko, R. (2017b). Revisitant les facteurs affectant l'utilistion des Tics sur l'information relative aux intrants agricoles par les paysans dans les pays en voie de developpement d'Afrique et d'Asie. Journal of Information Systems and Technology Management, 14(2), 169–189. https://doi.org/10.4301/S1807-17752017000200003

Kante, M., & Myagro. (2016). Synthese sur Maigro. Bamako.

Kante, M., Oboko, R., & Chepken, C. (2016). Factors affecting the use of ICTs on agricultural input information by farmers in developing countries. AIMS Agriculture and Food, 1(3), 315–329. https://doi.org/10.3934/agrfood.2016.3.315

Kante, M., Oboko, R., & Chepken, C. (2017a). ICTs ’ Model for Cereal Farmers in the Access and Use of Agricultural Input Information in Developing Countries : Questionnaire Validation Using SEM. American Journal ofInformation Systems, 5(1), 1–12. https://doi.org/10.12691/ajis-5-1-1

Kante, M., Oboko, R., & Chepken, C. (2017b). Influence of Perception and Quality of ICT-Based Agricultural Input Information on Use of ICTs by Farmers in Developing Countries: Case of Sikasso in Mali. The Electronic Journal of Information Systems in Developing Countries, 83(1), 1–21. https://doi.org/10.1002/j.1681-4835.2017.tb00617.x

Kante, M., Oboko, R., Chepken, C., & Hamunyela, S. (2017c). Farmers ’ Perceptions of ICTs and its Effects on Access and Use of Agricultural Input Information in Developing Countries : Case of Sikasso , Mali. In P. Cunningham & M. Cunningham (Eds.), IST-Africa Week 2017(pp. 1–8). Windhoek: IIMC International Information Management Corporation. Retrieved from www.IST-Africa.org/Conference2017

Kline, R. B. (2013). Principales and practice of Strutural equation modeling. Guilford Publications(third, Vol. 53). London: THE GUILFORD PRESS. https://doi.org/10.1017/CBO9781107415324.004

KTM. (2013). Kenya Markets Trust -Agricultural Inputs. Retrieved January 18, 2016, from http://www.kenyamarkets.org/what-we-do/the-market-assistance-programme/agricultural-inputs

Li, L. (2010). A critical review of technology acceptance literature. Southwest Decisino Sciences nstitute, 22. Retrieved from http://www.swdsi.org/swdsi2010/SW2010_Preceedings/papers/PA104.pdf

Lim, S., Saldanha, T., Malladi, S., & Melville, N. P. (2009). Theories Used in Information Systems Research : Identifying Theory Networks in Leading IS Journals. In ICIS (Ed.), International Conference on Information Systems(p. 10). AISel. Retrieved from http://aisel.aisnet.org/icis2009

Lundresearch. (2013). Ordinal Regression using SPSS Statistics. Retrieved February 11, 2017, from https://statistics.laerd.com/spss-tutorials/ordinal-regression-using-spss-statistics.php

Lwoga, E. T. (2010). Bridging the agricultural knowledge and information divide: the case of selected telecenters and rural radio in Tanzania. Electronic Journal of Information Systems in Developing Countries, 43(6), 1–14.

Meera, S. N., Jhamtani, A., & Rao, D. U. M. (2004). Information and Communication Technology in agricultural development : A comparative analysis of three projects from IndiaShaik . N . Meera , Anita Jhamtani , and D . U . M . Rao. Agricultural Research & Extension Network, (135), 2–15.

Mittal, S., & Mehar, M. (2012). How Mobile Phones Contribute to Growth of Small Farmers ? Evidence from India. Quarterly Journal of International Agriculture, 51(3), 227–244.

Msoffe, G. E. P., & Ngulube, P. (2016). Agricultural Information Dissemination in Rural Areas of Developing Countries : A Proposed Model for Tanzania. Electronic Journal of Information Systems in Developing Countries, 26(2), 169–187.Myagro. (2016).

Myagro. Retrieved December 2, 2015, from http://www.myagro.org/meet-our-farmers/

Palmer, T. (2015). Orange Sènèkèla Midline. Retrieved from http://www.gsma.com/mobilefordevelopment/wp-content/uploads/2015/09/GSMA_Senekela_Midline.pdf

Pan, Y., & de la Puente, M. (2005). Census Bureau guideline for the translation of data collection instruments and supporting materials: Documentation on how the guideline was developed. Survey Methodology. Washington D.C.

PROMISAM. (2012). Plan de securite alimentaire commune rurale de Zantiebougou 2008-2012, 1–17.

Pshenichnaya, N., & Clause, V. mAgri Case Study: Nokia Life AgricultureService (2013). Retrieved fromhttp://www.gsma.com/mobilefordevelopment/wp-content/uploads/2013/03/GSMAmAgri_Nokia_Case_Study.pdf

Rezaei-Moghaddam, K., & Salehi, S. (2010). Agricultural specialists’ intention toward precision agriculture technologies: Integrating innovation characteristics to technology acceptance model. African Journal of Agricultural Research, 5(March), 1191–1199. https://doi.org/10.5897/AJAR09.506

RGPH. (2013). 4 ème Récensement General de la Population et de l’Habitat du Mali (RGPH). Résultats définitifs. Tome 0: Répertoire des villages. Bamako.

Ringle, C. M., Wende, S., and Becker, J.-M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. Retrieved from http://www.smartpls.com

Rogers, E. M. (1983). Diffusion ofThird Edition(Third Edit). New York: Macmillan Publishing. https://doi.org/82-70998

Rogers, E. M. (1995). Diffusion of innovations. Macmillian Publishing Co.https://doi.org/citeulike-article-id:126680

Simin, M., & Janković, D. (2014). Applicability of Diffusion of Innovation Theory in Organic Agriculture. Economics ofAgriculture, 2014(61), 517–531. https://doi.org/10.5937/ekoPolj1402517T

Singh, M., Bhanotra, A., L, N., Wani, S. A., & Kumar, M. (2016). Mobile Phone Technology-An Eminent ICT Tool for Better Family Farming Mobile Phone Technology-An Eminent ICT Tool forBetter Family Farming. In Family Farming and Rural development(pp. 287–291).

Siraj, M. (2010). A model for ICT based services for agriculture extension in Pakistan. Retrieved from http://r4d.dfid.gov.uk/PDF/Outputs/Misc_Infocomm/60818-extensionmodel-Pakistan.pdforg

Staatz, J., & Temé, B. (2015). What would it take to increase the productivity of those engaged in primary sector in Mali ? In answering the question , we need to consider :Michigan State University. Retrieved from http://fsg.afre.msu.edu/srai2/What_would_it_take_to_increase_the_productivity_5.pdf

Traoré, A., Mamy, S., JBélières, J.-F., & Hilhorst, T. (2011). Quelles évolutions des systèmes de production céréaliers au Mali ?, 54–56.

Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40. https://doi.org/10.1037/0021-9010.90.4.710

Ventkatesh, V., Morris, M. G., Hall, M., Davis, G. B., Davis, F. D., & Walton, S. M. (2003). User Acceptance of information technology: towards a unified view. MIS Quarterly, 27(3), 425–478.

Wood, T. G., & Cowie, R. H. (2001). Assessment of On-Farm Losses in Cereals in Africa Due to Soil Insects. International Journal of Tropical Insect Science, 9(6), 709–716. https://doi.org/10.1017/S1742758400005580

Wulystan, P. M., & Andrew, C. M. (2013). Using Information and Communication Technologies for Enhancing the Accessibility of Agricultural Information for Improved Agricultural Production in Tanzania. Electronic Journal of Information Systems in Developing Countries, 56(1), 1–14.