Measuring perception on multimedia-based agro-advisory: A scale construction


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Authors

  • SONALI MALLICK ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, West Bengal 743 329, India image/svg+xml
  • RAJARSHI ROY BURMAN Indian Council of Agricultural Research, New Delhi image/svg+xml
  • RABINDRA NATH PADARIA ICAR-Indian Agricultural Research Institute, New Delhi image/svg+xml
  • GIRIJESH SINGH MAHRA Indian Council of Agricultural Research, New Delhi image/svg+xml
  • KAUSTAV ADITYA ICAR-Indian Agricultural Statistics Research Institute, New Delhi image/svg+xml
  • KAPILA SHEKHAWAT ICAR-Indian Agricultural Research Institute, New Delhi image/svg+xml
  • SUSHMITA SAINI ICAR-Indian Agricultural Research Institute, New Delhi image/svg+xml
  • RAHUL SINGH ICAR-Indian Agricultural Research Institute, New Delhi image/svg+xml
  • SWEETY MUKHERJEE ICAR-Indian Agricultural Research Institute, New Delhi image/svg+xml

https://doi.org/10.56093/ijas.v94i3.148669

Keywords:

Agro-advisory, Multimedia, Perception, Principal component analysis

Abstract

Access to information and effective delivery can be improved by using multimedia as a tool for advisory services. Various factors contribute to the development of an effective multimedia-based agro-advisory model. Stakeholders’ perception plays a major role to design and validate it properly. To measure stakeholders’ perception towards multimedia-based agro-advsiory (Pusa Samachar), a multi-dimensional perception scale was developed using Polychoric Principal Component Analysis (PCA). The data pertaining to this study were collected from 150 farmers using Google forms in 2021 and from 225 farmers in 2022. These farmers were sampled using stratified two-stage sampling from five districts each from Uttar Pradesh, Haryana and Punjab states. The majority of the farmers (68.6%) reported watching full weekly episode of agro-advisory telecasted as Pusa Samachar. Notably, farmers of Uttar Pradesh (54.67%) and Haryana (60.0%) showed affirmative perception; while Punjab (50.83%) had neutral perception towards Pusa Samachar model. Analysis of average perception score of farmers revealed that technical factor ranked I followed by linguistic factor (II), content and design factor (III) and timeliness factor (IV). Audio-visual quality, graphics, time duration of content, language, accent, and style of presentation with quality content could be considered as prime parameters for developing multimedia-based content. Location-specific, farmers’ centric language-based, and farmer participatory multimedia-based content should be created for better information availability and acceptance among farming community.

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References

Antwi-Agyei P and Stringer L C. 2021. Improving the effectiveness of agricultural extension services in supporting farmers to adapt to climate change: Insights from north-eastern Ghana. Climate Risk Management 32: 100304.

Babu S C, Glendenning C J, Okyere K A and Govindarajan S K. 2012. Farmers' information needs and search behaviours: Case study in Tamil Nadu, India (No. 1007-2016 79468).

Bhattacharyya S, Burman R R, Sharma J P, Padaria R N, Paul S, Datta A, Venkatesh P, Singh L, Prasad Y G, Nalkar S, Kumari AL, Rao N V, Kumar N K and Roy P. 2021. Measuring stakeholders' perception of Sansad Adarsh Gram Yojana. The Indian Journal of Agricultural Sciences 91(10): 1476–481.

Burman R R, Mahra G S, Singh A K, Mallick S, Anand A, Vashisth A, Mishra G, Shekhawat K, Somvanshi V, Rudra S G, Sangwan S, Kumar B and Das A K. 2022. Pusa Samachar: An innovative multimedia-based extension advisory model. Current Science 123(4): 574.

Cecchini S and Scott C.2003. Can information and communications technology applications contribute to poverty reduction? Lessons from rural India. Information Technology for Development 10(2): 73–84.

DFI. 2017. Empowering the Farmers Through Extension and Knowledge Dissemination, Vol. 11. Doubling Farmers’ Income Committee, Department of Agriculture, Cooperation and Farmers’ Welfare, Ministry of Agriculture & Farmers’ Welfare, New Delhi.

Dossani R, Misra D C and Jhaveri R. 2005. Enabling ICT for rural India. Asia Pacific Research Centre, Stanford University and National Informatics Centre.

Ganesan M, Karthikeyan K, Prashant S and Umadikar J. 2013. Use of mobile multimedia agricultural advisory systems by Indian farmers. Results of a survey. Journal of Agricultural Extension and Rural Development 5(4): 89–99.

Gharis L W, Bardon R E, Evans J L, Hubbard W G and Taylor E. 2014. Expanding the reach of extension through social media. Journal of Extension 52(3): 1–11.

Guo P J, Kim J and Rubin R. 2014. How video production affects student engagement: An empirical study of MOOC videos. (In) Proceedings of the First ACM Conference on Learning@ Scale Conference, March 2014, pp. 41–50.

Khan M Z, Nawab K, Ullah J, Khatam A, Qasim M, Ayub G and Nawaz N. 2012. Communication gap and training needs of Pakistan’s agricultural extension agents in horticulture. Sarhad Journal of Agriculture 28(1): 129–135.

Kolenikov S and Angeles G. 2004. The use of discrete data in PCA: Theory, simulations, and applications to socio-economic indices. Chapel Hill: Carolina Population Centre, University of North Carolina 20: 1–59.

Kumar G S and Popat M N. 2016. Development of a scale to measure farmers’ perceptions on quality of groundnut. Indian Research Journal of Extension Education 9(1): 11–13.

Mayer R E and Johnson C I. 2008. Revising the redundancy principle in multimedia learning. Journal of Educational Psychology 100(2): 380.

Morrow K F Nielsen and C Wettasinha. 2002. Changing information flows. LEISA 182: 4–5.

Nikam V, Kumar S, Kingsly I M and Roy M. 2020. Farmers mobile use pattern, information sources and perception about mobile app for grapes. Indian Journal of Extension Education 56(1): 77–83.

Rickards L, Alexandra J, Jolley C and Frewer T. 2018. Final report: Review of agricultural extension. Australian Centre for International Agricultural Research.

Reddy M M, Rao I S, Srinivasulu M and Kumar G S. 2017. Perception and usefulness of mobile phone based agro- advisories (MBAs). International Journal of Current Microbiology and Applied Sciences 6(7): 866–72.

Shanthy T R and Thiagarajan R. 2011. Interactive multimedia instruction versus traditional training programmes: Analysis of their effectiveness and perception. The Journal of Agricultural Education and Extension 17(5): 459–72.

Sidaty N O, Larabi M C and Saadane A. 2014. Influence of video resolution, viewing device and audio quality on perceived multimedia quality for steaming applications. (In) Proceeding of 5th European Workshop on Visual Information Processing (EUVIP), December 2014, pp. 1–6.

Singh R, Syiem W, Feroze S M, Devarani L, Ray L I, Singh A K, Singh N J and Anurag T S. 2015. Impact assessment of mobile-based agro-advisory: A case study of tribal farmers of Ri-Bhoi district of Meghalaya. Agricultural Economics Research Review 28(347): 183–87.

Som S, Burman R R, Sharma J P, Padaria R N, Iquebal M A and Suresh A. 2018. Construction of multi-dimensional scale for measuring perception towards migration. Journal of Community Mobilization and Sustainable Development 13(2): 279–85.

Subramanian S, Nair S B and Sharma S. 2005. Local content creation and ICT for development: Some experiences. United Nations Educational, Scientific and Cultural Organization, Bangkok.

Thomas P. 2009. Bhoomi, Gyan Ganga, e-governance and the right to information: ICTs and development in India. Telematics and Informatics 26(1): 20–31.

TRAI. 2021. The Indian Telecom Services Performance Indicator, Telecom Regulatory Authority of India, Government of India, 2021.

Tripp R. 2006. Self-sufficient Agriculture: Labour and Knowledge in Small-scale Farming. Routledge, London.

Woods K and Langcuster J C. 2014. The use of digital technology in extension. The Journal of Extension 52(5): 32.

Yee A, Padovano W M, Fox I K, Hill E J, Rowe A G, Brunt L M, Moore A M, Snyder-Warwick A K, Kahn L C, Wood M D and Coert J H. 2020. Video-based learning in surgery: Establishing surgeon engagement and utilization of variable-duration videos. Annals of Surgery 272(6): 1012–019.

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Submitted

2024-02-18

Published

2024-05-08

How to Cite

MALLICK, S. ., BURMAN, R. R. ., PADARIA, R. N. ., MAHRA, G. S. ., ADITYA, K. ., SHEKHAWAT, K. ., SAINI, S. ., SINGH, R. ., & MUKHERJEE, S. . (2024). Measuring perception on multimedia-based agro-advisory: A scale construction. The Indian Journal of Agricultural Sciences, 94(3-1), 109–115. https://doi.org/10.56093/ijas.v94i3.148669
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