Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

A Multivariate Analysis of Agricultural Electronic Trading Adoption


Affiliations
1 Delhi Technological University, and Vivekananda Institute of Professional Studies, Pitampura, Delhi 110 034, India
2 Delhi School of Management, Delhi Technological University, Delhi 110 032, India
 

The study addresses the knowledge gap related to the scarce literature on digitalization in India's agricultural marketing. A field survey of five hundred National Agriculture Market users is undertaken to understand the theoretical constructs of wholesale electronic trading adoption in a realistic backdrop of a large digital project. The Partial Least Squares-Structural Equation Modelling (PLS-SEM) methodology is used for the statistical analysis. It demonstrates the positive effect of variables: 'Trust', 'Cost', 'Social Influence', 'Perception-Ease of Use', 'Perception-Usefulness', and 'Facilitating Conditions' on the adoption. The study brings out a simple agricultural wholesale e-trading adoption framework. It extends the existing theoretical knowledge base concerning technology adoption in new contexts (wholesale electronic trading, agriculture, India). It expands the scope of the theory by adding new constructs, 'Trust' and 'Cost'. The study's recommendations are expected to help practitioners in decision-making. It shall help practitioners of developing countries prioritize using scarce resources to deliver the intended benefits to the farming community in terms of administrative ease, user convenience, expanded market reach, faster cycle time,

Keywords

Digitalization, ICT, National agriculture market, PLS-SEM
User
Notifications
Font Size

  • Banker R, Mitra S & Sambamurthy V, The effects of digital trading platforms on commodity prices in agricultural Supply chains, MIS Q, 35(3) (2011) 6–34.

  • Srinivasan G, Quantitative models in operations and supply chain management (PHI Learning, Delhi) 2018.

  • Alstyne M V & Parker G, Platform Business: From Resources to Relationships, NIM Marketing Intelligence Review, 9(1) (2017) 24–29.

  • Department of Agriculture Cooperation and Farmers Welfare, NAM Guidelines, 2021. www.enam.gov.in/NAM/home/ namguidelines (4 January 2022)

  • Chaudhary S & Suri P K, Modelling the Enablers of e-Trading Adoption in Agricultural Marketing: A TISM-Based Analysis of eNAM, Vision, 26(1) (2022) 65–79.

  • Venkatesh V, Morris M G, Davis G B & Davis F D, User acceptance of information technology: Toward a unified view, MIS Q, 27(3) (2003) 425–478.

  • Venkatesh V, Thong J & Xu X, Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology, MIS Q, 36(1) (2012) 157–178.

  • Compeau D R, Higgins C A & Huff S, Social Cognitive Theory, and Individual Reactions to Computing Technology: A Longitudinal Study, MIS Q, 23(2) (1999) 145–158.

  • Davis F D, Bagozzi R P & Warshaw P R, User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Manag Sci, 35(8) (1989) 982–1002.

  • Molina-Maturano J, Verhulst N, Tur-Cardona J, Güereña D T, Gardeazábal-Monsalve A, Govaerts B & Speelman S, Understanding Smallholder Farmers' Intention to Adopt Agricultural Apps: The Role of Mastery Approach and Innovation Hubs in Mexico, Agronomy, 11(2) (2021) 194.

  • Moore G C & Benbasat I, Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation, Inform Syst Res, 2(3) (1991) 192–222.

  • Chaudhary S & Suri P K, Framework for agricultural e-trading platform adoption using neural networks, Int j inf tecnol, 13 (2021) 501–510.

  • Sui X & Geng X, Continuous usage intention to e-transaction cards in wholesale markets of agriproducts: empirical evidence from China, Future Business Journal, 7 (2021) 224–237.

  • Mathieson K, Predicting user intentions: Comparing the technology of planned behaviour, Inform Syst Res, 2(3) (1991) 173–191.

  • Mzomwe Y M, Mariam A T, Margareth A M & Mubarack H K, ICT, and marketing for agricultural products: determinants of mobile phone usage to small scale orange farmers in Tanzania, Business Education Journal, 10(1) (2021) 5–18.

  • Casalo L V, Flavian C & Guinaliu, The generation of trust in online services and product distribution: the case of Spanish electronic commerce, J Electron Commerce Res, 12(3) (2011) 199–215.

  • Kuttainen C, the Role of Trust in B2B Electronic Commerce - Evidence from Two e-Marketplaces, Ph D Thesis, The Luleå University of Technology, Sweden, 2005. CHAUDHARY & SURI: ELECTRONIC TRADING ADOPTION FRAMEWORK 939

  • Ramesh A, Banwet D K & Shankar R, Modelling the barriers of supply chain collaboration, J Model Manag, 5(2) (2010) 176–193

  • Samant S & Dey K, Blockchain technology adoption, architecture, and sustainable agri-food supply chains, J Clean Prod, 284 (2021) 124731.

  • Clasen M & Mueller R A E, Success Factors of Agribusiness Digital Marketplaces, Electron Market, 16(4) (2006) 349-360.

  • Garicano Luis & Kaplan S N, The Effects of Business-toBusiness E-Commerce on Transaction Costs, J Ind Econ, 49(4) (2001) 463–485.

  • Ajzen I, The Theory of Planned Behaviour, Organ Behav Hum Decis Process, 50(2) (1991) 179–211.

  • Kilpatrick J & Factor R, Logistics in Canada survey: tracking the year 2000 supply chain issues and trends, Materials Management and Distribution, 45(1) (2000) 16–20.

  • Tomar G, Chauhan G S & Panigrahi P K, Feasibility of m-governance in agriculture: insights from a multimodal study in rural India, Transform Gov: People Process Policy, 10(3) (2016) 434–456.

  • Hair J F, Hult G T M, Ringle C M & Sarstedt M A, Primer on Partial Least Squares Structural Equation Modelling (Sage, California) 2016.

  • Rožman M, Tominc P & Milfelner B, A Comparative Study Using Two SEM Techniques on Different Samples Sizes for Determining Factors of Older Employees Motivation and Satisfaction, Sustainability, 12 (2020) 2189.

  • De Reuver M, Sørensen C & Basole R C, The digital platform: A research agenda, J Inform Tech, 33(2) (2017) 124–135.

  • Malhotra N K, Marketing Research: An Applied Orientation (Pearson Education, Noida) 2021.

  • Hair J F, Anderson R E, Tatham R L & William C B, Multivariate Data Analysis (Prentice-Hall, New Jersey) 1998.

  • Kline R B, Principles and Practice of Structural Equation Modelling (Guilford Publications, New York) 2015.

  • Diamantopoulos A, Sarstedt M, Fuchs C, Wilczynski P & Kaiser S, Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective, J Acad Market Sci, 40(3) (2012) 434–449.

  • Hair J F, Risher J J, Sarstedt M & Ringle C M, When to use and how to report the results of PLS-SEM, Eur Bus Rev, 31(1) (2019) 2–24.

  • Fornell C G & Larcker D F, Evaluating structural equation models with unobservable variables and measurement error, J Market Res, 18(1) (1981) 39–50.

  • Henseler J, Ringle C M & Sarstedt M, A new criterion for assessing discriminant validity in variance-based structural equation modelling, J Acad Market Sci, 43(1) (2015) 115–135.

  • Hair J F, Ringle C M & Sarstedt M, PLS-SEM: Indeed, a silver bullet, J Mark Theory Pract, 19(2) (2011) 139–151.

  • Ringle CM, Advanced PLS-SEM Topics: PLS Multigroup Analysis, Working paper, The University of Seville, Spain, 2016.

  • Preacher K J, & Hayes A F, Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models, Behav Res Meth, 40 (2008) 879–891.

  • Parker G & Alstyne M V, Innovation, Openness, and Platform Control, Manag Sci, 64(7) (2018) 3015–3032.


Abstract Views: 177

PDF Views: 102




  • A Multivariate Analysis of Agricultural Electronic Trading Adoption

Abstract Views: 177  |  PDF Views: 102

Authors

Sanjay Chaudhary
Delhi Technological University, and Vivekananda Institute of Professional Studies, Pitampura, Delhi 110 034, India
P K Suri
Delhi School of Management, Delhi Technological University, Delhi 110 032, India

Abstract


The study addresses the knowledge gap related to the scarce literature on digitalization in India's agricultural marketing. A field survey of five hundred National Agriculture Market users is undertaken to understand the theoretical constructs of wholesale electronic trading adoption in a realistic backdrop of a large digital project. The Partial Least Squares-Structural Equation Modelling (PLS-SEM) methodology is used for the statistical analysis. It demonstrates the positive effect of variables: 'Trust', 'Cost', 'Social Influence', 'Perception-Ease of Use', 'Perception-Usefulness', and 'Facilitating Conditions' on the adoption. The study brings out a simple agricultural wholesale e-trading adoption framework. It extends the existing theoretical knowledge base concerning technology adoption in new contexts (wholesale electronic trading, agriculture, India). It expands the scope of the theory by adding new constructs, 'Trust' and 'Cost'. The study's recommendations are expected to help practitioners in decision-making. It shall help practitioners of developing countries prioritize using scarce resources to deliver the intended benefits to the farming community in terms of administrative ease, user convenience, expanded market reach, faster cycle time,

Keywords


Digitalization, ICT, National agriculture market, PLS-SEM

References