Modelling the Enablers of e-Trading Adoption in Agricultural Marketing: A TISM-Based Analysis of eNAM

Abstract

Purpose:

This research study identifies the enablers of adoption of e-trading of agricultural produce in India. It also proposes an adoption framework for promoting e-trading of the agricultural produce. The total interpretive structural modelling (TISM) method is used to develop hierarchical relationships among enablers. It brings the suggestions to improve the critical enablers for the use of policymakers, e-trading managers and market makers.

Methodology:

The research conducted in two phases, identification of variables, using literature assessment and analysing the relationship between the identified enablers using TISM. A case study of the National Agriculture Market (eNAM) project was undertaken to understand the enablers of the adoption in a common pan-India e-trading platform. Further, cross-impact matrix multiplication applied to classification (MICMAC) analysis explores the driving and the dependence power of the identified enablers.

Findings:

This research proposes an e-trading adoption framework for the Indian agricultural supply chain. The ‘perceived ease of use’, ‘facilitating conditions’, ‘social influence’ and lower ‘cost’ are identified as significant enablers in this study besides ‘trust’ and ‘perceived usefulness’. A few suggestions bought out in this paper are expected to improve these enablers, which are expected to help in enhancing eNAM adoption, thereby improving the supply chain of agricultural produce.

Research Implications:

The well-structured analysis in the hierarchical form helps to get more insight into the influence of the seven enablers identified by TISM technique on the adoption of e-trading in the Indian agricultural marketing and supply chain. The e-trading in Indian agricultural has significant economic and workforce value for the country. The suggestions of this study provide insight into practical improvement step considerations for policymakers and e-trading managers in the agricultural sector and micro, small and medium enterprises.

Keywords

Agriculture Marketing Digital India eNAM e-Trading National Agriculture Market Total Interpretive Structural Modelling (TISM)

Introduction

The Indian agricultural supply chain is fragmented and has lots of non–value-add intermediaries (Figure 1). Because of a high number of go-betweens, the end consumer price escalation in the supply chain is more than 60%. Depending on the nature of produce, the original producer receives between 28% and 78% of the end consumer price. Thus, a high number of intermediaries end up in adding more costs than the value (DAC, 2013; Kaur, 2015; Patnaik, 2011).

Figure 1.

Source: Adapted from DACFW (2019).

For example, the Indian farmer gets about 42% of the end consumer price for tomato in Karnal Agricultural Produce Market Committee (APMC), India (Figure 2). In comparison, the farmer in the USA gets about 65% of the end consumer price (DAC, 2013; NHRDF, 2018).

Figure 2.

Source: Adapted from NHRDF (2018).

The information asymmetry, in terms of a mismatch between products stock availability in the market and the aggregate consumer demand, is high. Thus, the forecast necessary for proper planning may not be accurate. The key reasons for this include lack of coordination among wholesale markets (mandis) located across the Indian geography, the presence of trader groups engaged in price rigging and working of markets in traditional offline mode. It also leads to low price realization by the small farmers because of high transaction costs and lower bargaining power.

The improvement in agriculture value chain efficiency may mean better wages, increased farmers’ prices, decreased consumer prices and increased consumption (Landes & Burfisher, 2009).

Use of digitalization, Internet, mobility and information technology (IT) is gradually making the supply chain more responsive and efficient (Parvez, 2014; Routroy & Behera, 2017). A pan-India e-trading platform is the saviour and an essential tool in promoting value chain efficiency. It has a relatively low transaction cost and takes less time to buy the requisite quality produce in the desired quantity from the primary source (Shirzad & Bell, 2013; Smart & Harrison, 2003).

This need for an e-trading platform in the post-production agricultural supply chain in India is intended to be fulfilled by the electronic National Agriculture Market (eNAM) initiative of the government. eNAM is the largest electronic platform for trading agricultural commodities in India.

So far, only 14% of the farmer base in the country is registered on eNAM, and only 7% trade on eNAM (Hindu Business Line, 2019). The adoption progress is slow. There is a need to improve the adoption of eNAM among the participants (farmers, traders and corporate agents) to deliver the intended benefits.

Research Objectives

The objectives of this research are as follows:

To identify the enablers of adoption of e-trading of agricultural produce in India.

To propose an adoption framework for the ambitious eNAM platform launched by the government of India for promoting e-trading of agricultural produce.

Literature Review

The fragmented supply chain and an inefficient marketing system lead to high food retail costs and loss of food in the value chain of approximately US$ 10 billion per annum (CII-McKinsey, 2013). An e-trading–enabled market may reduce intermediation and procurement costs, thus resulting in the reduced retail cost of food. The low cost, in turn, increases demand and farmer prices. Also, the food loss is prevented by efficient distribution channel between supply and demand markets (Landes & Burfisher, 2009).

Several studies have detailed the beneficial effect of information and communication technology on the agricultural marketing and supply chain efficiency in India (Banerji & Meenakshi, 2004; Durga Prasad, 2012; Modekurti, 2016; Verma & Chaudhuri, 2008). For example, e-trading wholesale prices are 3.3% higher for raw coffee grades, and 2% higher for non-premium coffee grades as compared with the farm-gate prices. Further, farmers and primary traders have more chances to get better rates in e-trading. It has been observed that introduction of information kiosks in several markets in the state of Madhya Pradesh in India resulted in the increased price of soybean by 1% to 3% and reduced price dispersion across markets (Banker et al., 2011; Goyal, 2010).

Over the years, the Indian government has taken several steps in improving agricultural marketing system, as shown in Figure 3. The consistent efforts have culminated in the formation of the new Model Agricultural Produce and Livestock Marketing Act, 2017 by the Central Department of Agriculture Co-operation and Farmers Welfare for its customized adoption by the state governments. The intended vital improvements in agricultural marketing as per the Act include the setting up of private or specialized market yards, a public–private partnership for agricultural market management, direct purchase from the farmer, recognition of farmers producer organization, one-point market fee payment, contract farming and unified licensing. So far, only 18 states and three union territories have amended their state APMC laws in line with the Act, which is a reflection of the slow progress of agricultural marketing and supply chain-related reforms in India.

Figure 3.

Source: Adapted from MOAFW (2017).

As per the Model APMC Act, an e-trading platform, viz. eNAM, is implemented in 1,000 wholesale APMC markets. The government has announced to launch eNAM in all 2,479 APMC market yards and 4,843 APMC-regulated sub-market yards by 2022 (MOAFW, 2020).

The synergetic linkage between APMC and eNAM is well reasoned. In the agricultural supply chain, eNAM provides the forward e-integration with buyers and corporate, whereas the APMC provides a backward integration with farmers. According to the government, the eNAM platform empowers farmers in terms of price information, ability to sell directly to buyers across India, get better prices for quality, and reduce marketing cost (DAC, 2013).

eNAM-based trading is unique because it is an impartial fee-based model. It offers a neutral e-trading platform by providing equal trading opportunities to all the participants.

So far, adoption of right technology by farmers and reaching out to numerous small resource-poor farmers are the two aspects where APMCs have only limited success. In contrast, the e-platforms by AMUL and ITC have done reasonably well. APMCs may learn from their experience. For a sound adoption of the technology platform, effective implementation is a prerequisite. It may emanate from trust and experience with the system (Jain, 2016).

The eNAM is a flagship government scheme of immense significance since it reaches a significant part of India’s population. It is listed as a critical instrument of better market price realization in the government of India’s report on doubling farmers’ income by 100% by the year 2022 (ICFA, 2016).

Overview of eNAM

The eNAM is a virtual e-trading platform with physical market infrastructure and operations. The transaction activities are accomplished electronically. At the same time, material logistics is actualized through the APMC market operations. In the markets where the system has operationalized, the select agricultural produce must mandatorily be traded over eNAM with an option of digital payment (Figure 4). The scheme is operated by the government-linked consortium, with an overall budget allocation of INR 2,000 million.

Figure 4.

Source: Adapted from DACFW (2019).

The entire initial costs of the e-trading platform, including maintenance, are borne by the Ministry of Agriculture and Farmers’ Welfare. However, local operations costs, including local software, quality checks and human resource costs, are met from the per-transaction fee (approximately 2%) charged by APMC. The bifurcation makes high usage of the e-trading platform attractive for the local APMC market.

There is evidence which suggests that the eNAM (e-trading)–enabled markets have helped the farmers in realizing higher prices compared with non-eNAM–enabled markets (APMCs). For example, using eNAM copra and onion, farmers got INR 292 and INR 113 more per quintal prices respectively as compared with prices in select non-eNAM–enabled APMC markets of Karnataka in 2017. A study in Karnataka has found that the eNAM has helped farmers in realizing up to 9% better price in 2016 over 2015 and 13% better price over the previous year, 2014 (Chand, 2016; Gowda et al., 2018; Shalendra, 2013; The Financial Express, 2017).

The eNAM, if implemented effectively, may prove to be a significant shift in the conventional mode of agricultural marketing in India.

Current Situation of eNAM

As of June 30, 2020, the eNAM has 1.66 million farmers, 137 thousand traders, 1,398 farmer producer organizations and 78.3 thousand agents registered on its trading platform. As of June 30, 2019, the platform has recorded overall transactions of 25.8 trillion tonnes of agricultural produce worth INR 710 billion (Hindu Business Line, 2019; MOAFW, 2020).

For quality grading, the eNAM has standard tradable parameters for 150 agricultural commodities so far.

During the last year, the following significant features have been introduced on this e-trading platform (PIB, 2018; DACFW, 2019):

Website content in six vernacular languages

Multiple competitive bidding online on a real-time basis

Warehouse receipt acceptance

Unified payment interface (UPI) through BHIM in regional languages in addition to existing payment channels of RTGS/NEFT, debit card and Internet banking

Multilingual mobile app with features such as gated entry, e-payment, a progress update on lot trading, real-time bid--price updates and payment receipt short message service (SMS) messages and viewing of the assaying quality certificates

Updated website with e-learning module, live commodity prices, information on events, dynamic training calendar and grievance redress feature

Integration of central farmer database in eNAM

MIS dashboard for better decision support

Two-factor authentication and push SMS notifications

Farmer Producer Organization and Logistics portal integration

Interstate trading operationalized with a unified trading licence

Variables of Adoption

The following variables are identified based on an analysis of scholarly research, government reports, analyst reviews and published news articles. The technology adoption context of the study is considered while identifying the variables (Table 1).

Table 1.

Variables in e-Trading Adoption

Code	Variable	Explanation in Study Context	References
F1	Perceived usefulness	The eNAM extends benefits to users in terms of better ‘price’ and ‘speed’ of the market transaction.	Dejen and Solomon (2015), Kilpatrick and Factor (2000), Michels et al. (2019), Nirmal (2017, 2019), Pavithra et al. (2018), Salimi et al. (2020), Tyndall et al. (1998), Venkatesh et al. (2003, 2012).
F2	Perceived Ease of Use	The eNAM is simple in operation and learnt without difficulty.	Auramo et al. (2005), Engotoit et al. (2016), Michels et al. (2019).
F3	Social influence	The community of close friends, leading farmers, associated traders and community leaders, encourage or promote the eNAM.	Ajzen (1991), Dejen and Solomon (2015), Dwivedi et al. (2019), Engotoit et al. (2016), Taylor and Todd (1995), Venkatesh et al. (2003, 2012).
F4	Trust	The user confidence in the information and trade based on eNAM portal. It also signifies a belief that the eNAM is reliable and the management will act in the interest of farmers and other stakeholders.	Aggarwal and Shankar (2003), Bisen and Kumar (2018), Casalo et al. (2011), Jayashanker et al. (2018), Kilpatrick and Factor (2000), Kuttainen (2005), Moorman et al. (1992), Ramesh et al. (2012), Ridings et al. (2002), Wiyada et al. (2018).
F5	Cost	The transaction costs in eNAM platform.	Clasen and Mueller (2006), Dey (2016), Dodds et al. (1991), Garicano and Kaplan (2001), Mustaquim (2017), Nabhani et al. (2016), Solaymani et al. (2012), Wiyada et al. (2018).
F6	Facilitating conditions	The infrastructure, including quality testing laboratories, bidding hall, logistics support, IT/product training and customer care complements the use of eNAM.	Ajzen (1991), Bender (2000), Dejen and Solomon (2015), Dwivedi et al. (2019), Kilpatrick and Factor (2000), Tomar et al. (2016), Taylor and Todd (1995), Venkatesh et al. (2003, 2012).
F7	Behavioural Intention to Adopt	The degree to which a person has made an informed plan to buy/sell on eNAM.	Ajzen (1991), Dwivedi et al. (2019), Salimi et al. (2020), Venkatesh et al. (2003, 2012).
F8	Actual usage	Actual usage of eNAM.	Ajzen (1991), Salimi et al. (2020), Venkatesh et al. (2003, 2012).

Source: Authors’ compilation.

Research Methodology

The variables are identified based on a formal assessment of scholarly research, government reports, analyst reviews and published news articles. These variables are termed as enablers and ranked based on expert panel opinion. Total interpretative structural modelling (TISM) method is used to determine the relationship between them and give interpretations to the relationships (Sushil, 2012).

In theory contribution, the building blocks include figuring out the factors (‘what’) to a phenomenon, establishing an interrelationship (‘how’) between factors and interpreting the causality (‘why’) between relevant factors. TISM helps to carve out a structured model, explaining both the nodes (‘what’) and links (‘how’ and ‘why’) as envisaged by both individuals and groups (Sushil, 2016; Whetten, 1989).

TISM is an improvement over interpretive structural modelling (ISM). The TISM method is used in several management contexts (Sushil, 2017), for example, agile manufacturing, construction labour productivity, cloud computing, e-government, emotional intelligence, enterprise resource planning, green supply chain management, higher technical education, lean implementation in healthcare, lean performance, manufacturing system, marketing and sales, organizational and information systems flexibility, public distribution system, supply chain management, smartphone manufacturing ecosystem, strategy execution, sustainable integrated logistics, sustainable supply chain management, technology strategy, telecom service sector: throughput accounting, TQM and waste management (Bohtan et al., 2017; Dubey et al., 2017; Mohanty & Shankar, 2017; Patri & Suresh, 2017; Sindhwani & Malhotra, 2017).

For applying TISM, a team of ten experts (Team 1), including eight experts from academia and two experts from industry, was formed for an expert opinion as per the inductive process. The deductive process included developing insights about the interpretive logic, causal effects and the development of knowledge base through the study of eNAM, user interviews and opinions of a second expert panel of four members (Team 2). For the identified enablers, the cross-impact matrix multiplication applied to classification (MICMAC) analysis is applied to understand the dominance of enablers via the number of enablers influenced/enhanced by them or vice versa.

The unstructured interviews of farmers, traders and officials were conducted at the Meerut APMC and Aligarh APMC markets in the state of Uttar Pradesh and the Bharatpur APMC in the state of Rajasthan in India.

Total Interpretive Structural Modelling

The methodological steps of TISM, along with its implementation in the study context, are detailed below:

Step 1: Identification of Variables

The following eight variables are identified based on a review of literature in the technology adoption context. The seven variables are termed as enablers by a majority expert panel opinion and ranked as per their importance—the ranking, along with MICMAC analysis used for presenting the findings in a subsequent section (Table 2).

Table 2.

Average Ranking and Importance Score of Enablers

Code	Enabler	Average Importance Ranking by Expert Panel
F1	Perceived usefulness	6
F2	Perceived ease of use	3
F3	Social influence	2
F4	Trust	7
F5	Cost	4
F6	Facilitating conditions	5
F7	Behavioural intention to adopt	1

Source: Authors’ own analysis.

Step 2: Contextual Relationship between Enablers

In TISM, ‘influences/enhances’ is the contextual relationship between enablers. The contextual relationship between eight variables is, therefore, of the form e-trading adoption enabler (i) will influence/enhance e-trading adoption enabler (j), e.g. ‘perceived usefulness’ (job performance improvement) will influence/enhance the ‘behavioural intention to adopt’.

Step 3: Structured Self-Interaction Matrix (SSIM)

The SSIM, as per the expert opinion, is created (Table 3). The symbols represent:

V: enabler ‘i’ influences/enhances ‘j’.

A: enabler ‘j’ influences/enhances ‘i’.

X: enabler ‘i’ and ‘j’ influences/enhances one another.

O: enabler ‘i’ and ‘j’ do not influences/enhances one another.

Table 3.

Structured Self-Interaction Matrix

Enablers i↓ j→	Code	F8	F7	F6	F5	F4	F3	F2	F1
Perceived usefulness	F1	V	V	O	O	X	X	X
Perceived ease of use	F2	V	V	O	O	X	X
Social influence	F3	V	V	V	O	X
Trust	F4	V	V	O	O
Cost	F5	V	O	O
Facilitating conditions	F6	V	O
Behavioural intention to adopt	F7	V
Actual usage	F8

Source: Authors’ own analysis.

Remark : Only, if 60% or more experts, (i.e. six or more out of ten experts) have marked a relationship as ‘Yes’ it is counted, else it is dropped, thus implying no relationship.

Step 4: Reachability Matrix (RM)

For RM, please refer to Table 4. Here each cell of the SSIM matrix (Table 2) is converted into the binary number ‘0’, or ‘1’ and transitivity (e.g. if X enhances/influences Y, and Y enhances/influences Z, then X enhances/influences Z) is incorporated. The conversion to binary numbers is based on the following conversion rules:

(i, j) cell value in SSIM is V, then (i, j) value is 1, and the (j, i) is 0.

(i, j) cell value in SSIM is A, then (i, j) value is 0, and the (j, i) is 1.

(i, j) cell value in SSIM is X, then (i, j) value is 1, and the (j, i) is 1.

(i, j) cell value in SSIM is O, then (i, j) value is 0, and the (j, i) is 0.

Table 4.

Reachability Matrix (RM)

Factors i↓ j→	Alpha-Numeric Code	F1	F2	F3	F4	F5	F6	F7	F8	Driving Power
Perceived usefulness	F1	1	1	1	1			1	1*	6
Perceived ease of use	F2	1	1	1	1			1	1*	6
Social influence	F3	1	1	1	1		1	1	1*	7
Trust	F4	1	1	1	1			1	1*	6
Cost	F5					1			1	2
Facilitating conditions	F6						1		1	2
Behavioural intention to adopt	F7							1	1	2
Actual usage	F8								1	1
Dependence		4	4	4	4	1	2	5	8

Source: Authors’ own analysis.

Remark : Transitivity check: If X enhances/influences Y, and Y enhances/influences Z, then X transitively enhances/influences Z; in such case, if the cell value from X to Z is 0, it is converted to ‘1*’.

The cross-impact matrix multiplication applied to classification (MICMAC) analysis helps in the categorization of enablers in terms of dominance based on the number of enablers influenced/enhanced (driving power) and influenced/enhanced by the number of enablers (dependence power) (Figure 5).

Figure 5.

Source: Authors’ own.

Step 5: Level Partitioning

The RM (Table 4) is iteratively split into levels one to three. The reachability (corresponding row in the RM) and antecedents (corresponding column in the RM) for all enablers are listed. At the same time, the intersection set of the reachability and antecedent sets is found. Level 1 is given to the enabler, which has a similar reachability set and intersection set. In the next iteration, level 1 enablers are taken off, and the process repeated with the remaining enablers. Finally, the level of each enabler is arrived at, as shown in Table 5.

Table 5.

Level Partitioning of RM

Enabler	Reachability	Antecedent	Intersection	Level
I. Iteration 1
F1	F1, F2, F3, F4, F7, F8	F1, F2, F3, F4	F1, F2, F3, F4
F2	F1, F2, F3, F4, F7, F8	F1, F2, F3, F4	F1, F2, F3, F4
F3	F1, F2, F3, F4, F6, F7, F8	F1, F2, F3, F4	F1, F2, F3, F4
F4	F1, F2, F3, F4, F7, F8	F1, F2, F3, F4	F1, F2, F3, F4
F5	F5, F8	F5	F5
F6	F6, F8	F3, F6	F6
F7	F7, F8	F1, F2, F3, F4, F7	F7
F8	F8	F1, F2, F3, F4, F5, F6, F7, F8	F8	1
2. Iteration 2
F1	F1, F2, F3, F4, F7	F1, F2, F3, F4	F1, F2, F3, F4
F2	F1, F2, F3, F4, F7	F1, F2, F3, F4	F1, F2, F3, F4
F3	F1, F2, F3, F4, F6, F7	F1, F2, F3, F4	F1, F2, F3, F4
F4	F1, F2, F3, F4, F7	F1, F2, F3, F4	F1, F2, F3, F4
F5	F5	F5	F5	2
F6	F6	F3, F6	F6	2
F7	F7	F1, F2, F3, F4, F7	F7	2
3. Iteration 3
F1	F1, F2, F3, F4	F1, F2, F3, F4	F1, F2, F3, F4	3
F2	F1, F2, F3, F4	F1, F2, F3, F4	F1, F2, F3, F4	3
F3	F1, F2, F3, F4	F1, F2, F3, F4	F1, F2, F3, F4	3
F4	F1, F2, F3, F4	F1, F2, F3, F4	F1, F2, F3, F4	3

Source: Authors’ own analysis.

Remark : Logically based on expert opinion, if two enablers are firmly connected, then both must list together in the respective intersection set. For example, if the intersection set equals (F4, F7) in enabler F4, then it must repeat for enabler F7.

Step 6: Digraph

The digraph (Figure 6) shows the enablers and their relationship graphically in terms of nodes and edges. The directional arrow between enablers shows the association as per the RM.

Step 7: Binary Interaction Matrix (BIM) and Interpretive Matrix (IM)

The digraph (Figure 6) is transformed into a BIM (Table 6). All the influencers/enhancers are depicted by ‘1’; remaining entries are ‘0’. The indicative interpretive matrix and explained interpretive matrix are developed for all significant 1 in BIM and shown in Tables 7 and 8.

Step 8: Total Interpretive Structural Model

The BIM (Table 6), interpretations (Tables 7 and 8) and the digraph (Figure 6) information are used to bring out a TISM-based framework. The nodes in oval shapes show enablers and the interpretations are on the links—the resultant TISM-based framework presented in Figure 7.

Figure 6.

Source: Authors’ own analysis.

Findings and Suggestions

Since its inception in 2016, only 14% of the farmers are registered on the e-trading platform in the last three years, and only 7% of them are e-trading on the platform (Hindu Business Line, 2019). The low adoption calls for identification and a better understanding of enablers. The enablers of e-trading adoption and usage are presented in the framework (Figure 7) developed using TISM methodology.

Among the enablers (Table 1; Figure 7), based on expert ranking (Table 2), the significant enablers are revealed as ‘social influence’, ‘perceived ease of use’, low ‘cost’ and ‘facilitating conditions’. As per the MICMAC analysis (Figure 5), the service provider and policymakers are expected to give priority to enablers with high driving power (influence), that is, ‘perceived ease of use’, ‘social influence’ and ‘trust’. Thus, it is proposed to accord the highest priority to the common enablers, ‘perceived ease of use’ and ‘social influence’.

These enablers must be improved upon so that the intended adoption of e-trading and requisite usage levels are achieved.

The TISM framework (Figure 7) shows the links (as the contextual relationships) and the direction of the relationships. The nodes of the framework are interpreted with a clear definition of respective elements, leading to a clear picture on enablers (enhancers/influencers) of e-trading adoption among farmers and traders. These enablers and the relationships, as explained in Table 8, need to be paid significant attention while designing agricultural e-trading initiatives such as eNAM.

Table 6.

Binary Interaction Matrix

Factors i↓ j→	Code	F1	F2	F3	F4	F5	F6	F7	F8
F1: Perceived usefulness	F1		0	0	0	0	0	1	0
F2: Perceived ease of use	F2	0		0	0	0	0	1	0
F3: Social influence	F3	0	0		0	0	1	1	0
F4: Trust	F4	0	0	0		0	0	1	0
F5: Cost	F5	0	0	0	0		0	0	1
F6: Facilitating conditions	F6	0	0	0	0	0		0	1
F7: Behavioural intention to adopt	F7	0	0	0	0	0	0		1
F8: Actual usage	F8	0	0	0	0	0	0	0

Source: Authors’ own analysis.

Table 7.

Indicative Interpretive Matrix

Enabler	F8	F7	F6	F5	F4	F3	F2	F1
F1: Perceived usefulness		√
F2: Perceived ease of use		√
F3: Social influence		√	√
F4: Trust		√
F5: Cost	√
F6: Facilitating conditions	√
F7: Behavioural intention to adopt	√
F8: Actual usage

Source: Authors’ own analysis.

Note: Explanation for √ (Interpretations) is provided in the interactive matrix explained (Table 8) in Findings and Suggestions.

Table 8.

Explained Interpretive Matrix

S. No.	Enabler (Influencer/Enhancer)	Enabler (Influenced/Enhanced)	Details of Influence with Reason [Explanation for Symbol √ (Table 7)]
1	F1: Perceived usefulness (The eNAM extends benefits to users in terms of better price and speed of the market transaction.)	F7: Behavioural intention to adopt e-trading	Use of e-trading saves time for the farmer/trader as the gate-entry to payment sequence is completed within a day. The farmer completes the sale and goes back to his home, rather than to wait overnight. The transparent online sale process may yield a higher price due to intra-market and inter-market competition between traders, and higher quality of produce yielding to a higher price.
2	F2: Perceived ease of use (The eNAM is simple in operation and learnt without difficulty.)	F7: Behavioural intention to adopt e-trading	The online e-trading is convenient due to the multilingual mobile application. The trained eNAM staff is available to support the user (farmer/trader) in service usage. It is hassle-free as the competing bids are negotiated online without any fear of collusion or forced selling.
3	F3: Social influence (The community of close friends, leading farmers, associated traders and community leaders encourage or promote the eNAM.)	F7: Behavioural intention to adopt e-trading	The experience of market officials and peer (farmer/trader) in terms of higher price realization and quick trade cycle influences the farmers/traders. The farmer is likely to follow the decision of cooperative/farmer producer organizations/corporate buyer to use e-trading. Similarly, traders in the market go with the decision of the association or APMC secretary.
4	F3: Social influence (Same as above.)	F6: Facilitating conditions	Even though the farmer is not trained on e-trading platform, or do not wish to get e-payment into a bank account (as they prefer cash transaction), he is motivated by the community leader/influencer support to use e-trading platform. The community support may make up for shortcomings in the market infrastructure.
5	F4: Trust (The user confidence in the information and trade based on eNAM portal. It also signifies a belief that the eNAM is reliable and the management will act in the interest of farmers and other stakeholders.)	F7: Behavioural intention to adopt e-trading	The eNAM is government supported and implemented in all large APMCs. The government-mandated quality lab reports and digital payment via banks bring in the safety aspect, which is missing in the offline trade. The produce is sold through intra-market/inter-market competing bids, allaying the fear of trader collusion.
6	F5: Cost (The transaction costs in eNAM platform)	F8: Actual usage/adoption	The cost (transaction costs) consists of search cost, coordination cost, motivation cost and commitment cost. The wholesale e-trading platform is likely to reduce search and coordination costs via increased efficiency due to process improvement, marketplace benefits and indirect improvements. The reduced transaction cost and fixed market fee lead to better margin, thus motivating continued usage of e-trading platform.
7	F6: Facilitating conditions (The infrastructure, including quality testing laboratories, bidding hall, logistics support, IT/product training and customer care complements the use of eNAM.)	F8: Actual usage/adoption	As the desired benefits of better pricing, quick cycle, convenience, logistics tie-ups get realized, it encourages users (farmer/trader) to increase frequency and size of transactions on the e-trading platform. Also, the awareness drives and training camps encourage hesitant users to register and trade on the e-trade platform.
8	F7: Behavioural intention to adopt (The degree to which a person has made an informed plan to buy/sell on eNAM.)	F8: Actual usage/adoption	The intention when stimulated turns into actual usage and then regular usage by the farmer/trader.

Source: Authors’ own analysis of expert opinions.

The TISM-based conceptual eNAM adoption framework (Figure 7) is apparently getting validated in practice as the eNAM project is implemented in 1,000 large APMCs.

Figure 7.

Source: Authors’ own analysis.

Considering analysis conducted, discussions held with farmers and traders registered on eNAM as well as experts and officials in the Meerut, Aligarh and Bharatpur wholesale markets, a few suggestions made for further strengthening of eNAM are as follows:

The ‘perceived usefulness’ may be improved by ramping up the timeline for a unified national market with a single legal framework. The government of India has already made an important announcement for pan-India e-trading through its Ordinance issued on 5 June 2020, and activated inter-market trading in 228 APMCs (Aggarwal et al., 2016; Darbari, 2020). All remaining state governments may sync the respective state Acts with the Model APMC Act, 2017 (Yadav & Sharma, 2017). Eventually, there must be uniform procedures across India for the licenses, charging fee and logistics of an agricultural commodity for intra- and interstate market transactions. The small farmers need to be sensitized to aggregate their produce and trade on eNAM.

The ‘perceived ease of use’ may be improved by making the website easy to navigate, localization of contents and hosting most of the IT infrastructure over the cloud. The recently added logistics support on the eNAM portal is a cross-functional integration step in the right direction. More applications related to other related government schemes for improving the agricultural supply chain can be integrated with the portal, for example, logistics, financial inclusion etc.

The ‘social influence’ may be improved by increasing the involvement of small farmers and traders in eNAM. Promoting awareness, instructions on drawing maximum benefits, and making use of the farmer producer organizations as social influencers can build a favourable environment for eNAM and other similar e-trading initiatives (Tomar et al., 2016). The eNAM service should be made easily discoverable and interoperable with other government and partner applications, like its recent integration with Karnataka state ReMS e-trading platform.

The ‘trust’ may be improved by adding reliable information and providing a link to the credit agencies on the eNAM portal. The increased interaction of eNAM officials with the volunteers and leaders from the farmers’ group may enhance the Agri-community engagement. There must be a proper monitoring mechanism for an appropriate dispute resolution mechanism (Gulati et al., 2017). The dispute resolution is the main roadblock in the case of inter-market trade and may be addressed as soon as possible. For example, in case of any quality-related dispute, the partial payment to the inter-market seller should be withheld or penalty imposed.

The ‘facilitating conditions’ may be improved by ramping up the availability of the amenities in or around APMCs, for example, quality assaying labs for sorting and grading (Rajalakshmi, 2017) storage and logistics facility by involving private companies (DAC, 2013; Singh, 2017). The national system may enrol more traders/commission agents through open and transparent criteria-based registration. The implementing agency is expected to conduct a periodic assessment of skills and competency and regularly organize capacity building programmes. Customer care, problem resolution and conflict management can be improved through a dedicated contact centre and grassroots-level periodic stakeholder meetings with APMC management (HT Digital Content Services, 2018).

Currently, the taxes and fees at APMC markets (mandis) range from 3% (West Bengal) to 19.5% (Andhra Pradesh) of MSP (Subramanian, 2016). The variation between state taxes and fees poses a hindrance in the cross-state transactions. The Model APMC Act proposes to cap APMC Mandi tax at 1% (for food grain) and 2% (for fruits and vegetables). It also pegs the commission agent’s levy at 2% of the total transaction cost (DAC, 2013). The aggregate taxes and numerous fees may be lowered with some level of uniformity across states to address the ‘cost’ concerns. The fear of uninformed deduction may be allayed via a provision by banks to not deduct the NAM e-payment amount for settling loan EMIs unless written consent by the farmer is given. Also, there may be provision for easy access to bank credit for traders and farmers trading on eNAM so that the system can compete against an offline unofficial credit system of agents.

Conclusion

E-trading in the context of agricultural marketing is a subject of national importance. In this research, it has been attempted to arrive at a conceptual framework for the adoption of the ambitious eNAM initiative in India for the benefit of the farming community. The adoption framework has been arrived at using total interpretive structure modelling.

The ‘perceived ease of use’, ‘facilitating conditions’, ‘social influence’ and lower ‘cost’ are identified as significant enablers in this study besides ‘trust’ and ‘perceived usefulness’. A few suggestions bought out in this paper are expected to improve these enablers, which are expected to help in enhancing eNAM adoption, thereby improving the supply chain of agricultural produce.

Compared with other sectors, the number of users (farmers, traders) involved in e-trading agricultural marketing is large but low in terms of the average transaction value. The technological suaveness and awareness of users are low with high variation in adoption rate (IFPRI, 2018). The situation is akin to medium, small and micro-industry (MSME) units in other industries. Thus, findings may also interest micro, small and medium enterprises (MSME) marketers, policymakers and marketplace owners.

The eNAM has the potential to increase price realization and improve the quality of transactions. It may prove to be a beneficial shift in the existing ways for India’s agriculture marketing and supply chain.

Based on e-trading in the form of eNAM, now farmers and traders may consider the expansion measures such as inter-market trading and interstate trading. The concept of one nation–one market is a possibility now in the agricultural sector. Further innovations in agricultural marketing such as selling of produce while it is still at the warehouse, block chain–based traceability and aggregation of produce for marketing can pave the way for further improvement. Once the overall marketing infrastructure is e-enabled, the aware farmers shall be in a position to sell their produce directly bypassing intermediaries and commission agents. Such interventions shall go a long way in solving the primary problem of fragmentation and inefficiency in the agricultural produce supply chain.

In the four years between 2013 and 2017, only 126 private marketing licences were issued with corresponding low turnover (NABARD, 2018). Expectedly, the suggestions help improve the marketing, supply chain process and transaction management of the private markets too.

Limitations

The article presents a conceptual framework of eNAM adoption based on insights developed through a review of literature and interactions held with a panel of a limited number of experts which are synthesized using TISM methodology.

Qualitative checks on the interpreting relationships can be further improved through a more extensive panel review (Sushil, 2016). The linkages arrived at need to be statistically validated by conducting a survey of eNAM users in a few markets.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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