O R I G I N A L R E S E A R C H
Ranking the Factors Influencing e-Trading Usage in Agricultural Marketing
Sanjay Chaudhary1 •P. K. Suri2
Received: 28 December 2020 / Accepted: 4 June 2021 / Published online: 1 July 2021 Global Institute of Flexible Systems Management 2021
Abstract This research article attempts to rank the factors influencing continued e-Trading usage in Indian agricul- ture marketing. The ranking process is the first of its kind application in agricultural e-trading. A review of published literature has helped in identifying key factors influencing e-trading usage in agricultural marketing. The factors are ranked using the efficient Interpretive Ranking Process (IRP) methodology adopted in the context of agricultural marketing. Sixteen expert members, grouped into three different expert panels, have helped generate ranks and validation besides giving suggestions for improving e-trading usage in the context of the National Agriculture Market project (eNAM) in India. It has been found that the top factors influencing e-trading usage are ’Trust’, ’Cost’,
’Perceived Ease of Use’, and ’Facilitating Conditions’, respectively. These factors need to be supported with adequate resources to strengthen eNAM in terms of improved usage among the beneficiaries. It is further revealed that immediate attention is needed on aspects such as transparency, quick information dissemination, adequate quality assurance, uniformity in taxes and market fee, improvement in marketing infrastructure, inter-market trade logistics, conflict resolution, mobility and training.
Further, the e-trading system’s flexibility needs to be enhanced by incorporating modular design options, con- figurable new features, open-source innovation, cloud computing, and progressive artificial intelligence application.
Keywords AgricultureDigitalizationE-commerce E-tradingFlexibilityIRPMarketingSupply chain
Introduction
The Indian agricultural supply chain is fragmented and characterized by several non-value-add intermediaries. The information asymmetry in terms of demand and supply patterns is also high. The main reasons for this are the geographic dispersion of the agricultural trade markets and the lack of adequate agricultural marketing infrastructure.
These markets have little coordination and bear the nega- tive consequences of trader cartels.
One of the most challenging areas in the procure- ment/sourcing stage in the agricultural supply chain is when the buyers of crops (trader/agent/agribusinesses) interact with farmers. The digital solutions in the pro- curement/sourcing stage (Fig.1) are vital to improving the supply chain. Digitalization helps the buyer in terms of transparency, easy monitoring of operations, and making transactions efficient. The farmer benefits from better access to markets, information, and services that help him adopt the recommended agricultural practices and trans- parent trading (Global System for Mobile Communications Association [GSMA],2020).
The e-trading platform is viewed as a game-changer and a valuable instrument to address the supply chain improvement by offering an alternative to the rigid
& Sanjay Chaudhary
one.sanjay@gmail.com P. K. Suri
pksuri@dtu.ac.in
1 Delhi School of Business, Vivekananda Institute of Professional Studies, AU Block, Outer Ring Road, Pitampura, Delhi, India
2 Delhi School of Management, Delhi Technological University, Shahabad Daulatpur, Main Bawana Road, Delhi, India
https://doi.org/10.1007/s40171-021-00276-8
Agricultural Produce Market Committee (APMC) con- trolled markets. It spans geographical boundaries, is eco- nomical, and reduces transaction costs besides improving price discovery (Smart & Harrison,2003; Shirzad & Bell, 2013).
The electronic National Agriculture Market (eNAM) is a unique national e-trading platform. Within four years, it has been launched in 1000 wholesale markets—reaching 14 percent of its Year 2022 target of 7500 markets. As a result, it has emerged as the most effective and govern- ment-supported e-trading platform for agricultural produce in India. In comparison with eNAM, other corporate ini- tiatives are relatively limited in scope and scale.
So far, just 14 percent of India’s farmers have registered for eNAM. Out of these, only about 50 percent have started using eNAM. Thus, transaction growth and continued regular usage are relatively low. The domain experts have identified the need to improve the usage of eNAM among the participants (farmers, traders, corporate agents) to deliver the intended benefits (Hindu, 2019; Naik, 2019;
Sajwan,2020).
Empirically, it is shown that the high user registration and use are critical for deriving value in a B2B e-com- merce system. The critical mass of farmers and traders with many usage transactions contributes to B2B e-market- places, e. g. eNAM. The high number of transactions also enhances market efficiency (Chircu & Kauffman, 2000;
Subramaniam & Shaw,2002; Li & Li, 2005; Engstro¨m &
Salehi-Sangari, 2007; Chordia, Roll, & Subrahmanyam, 2008).
Thus, to ensure the success of an e-trading initiative in agricultural marketing, the factors responsible for increas- ing e-trading use need to be identified. The identified fac- tors would help policymakers and market officials in the prioritization of the requisite incentives to strengthen eNAM in terms of these factors.
So, keeping the facts described above in mind, the research study aims to:
1. Rank the factors influencing the continued use of e-trading in agricultural marketing.
2. Bring out suggestions to improve upon the top-ranked factors for the continued use of e-trading in agricultural marketing in the Indian context.
The research article is organized as follows: The liter- ature review section presents an understanding of factors influencing e-trading use in agricultural marketing and the national agriculture market project. Then, the research methodology section illustrates the Interpretive Ranking Process (IRP) methodology used to achieve the research objectives. Next, the analysis and findings section explains the data analysis undertaken, which is followed by research findings. Next, the discussion and limitations section brings out a few suggestions while mentioning the limitations of the research work. Finally, the article concludes with recommendations.
Literature Review
A brief literature review is conducted under the subsec- tions: traditional agricultural produce wholesale marketing system in India, e-trading in eNAM, flexibility, and deci- sion making. Literature is also reviewed to identify factors in the context of e-trading in agricultural marketing.
Traditional Agricultural Produce Wholesale Marketing System in India
Traditionally, transactions in wholesale agricultural mar- kets are either in the form of an open auction, closed auction, or mutual agreement mode. Thus, only the regis- tered farmers, registered agents, and licensed traders par- ticipate in wholesale trade.
Fig. 1 Digitalization in Procurement ( Source: GSMA, 2020)
A farmer brings his produce to the commission agent (CA) shop in the APMC area, who grades the product’s quality. At open auctions (generally used for perishable produce), traders gather at the CA shop and announce their bid for the lot as per quality. The highest bidder gets the produce. In closed auctions (most popular auctions, gen- erally used for grains), all bidders (buyers) write their bids on slips during the permitted time (2 h to 1 day)—for each lot. The APMC official confidentially selects the highest bid. The APMC official announces the highest bidder (buyer), who collects the produced lot from the CA shop after making the payment. Under mutual negotiation (generally used when there are very few, or a single buyer or the produce is bought by processor/mill), the lot price is mutually decided between the farmer and the trader/agent.
The mutually decided price is subsequently reported to the APMC market office (Aggarwal et al.,2016; Suri,2018).
As per the State APMC Act, the first sale of the notified agricultural commodities in a particular market area (such as wheat, maize, pulses, oilseeds, vegetables, and other food grains) must be made in the APMC controlled market through a licensed agent or a trader, after paying off due fees and tax.
The Indian agricultural supply chain is full of non-value- add intermediaries. Because of many go-betweens, the end consumer price escalation in the supply chain is more than sixty percent. Depending on the nature of produce, the original producer receives between twenty-eight and sev- enty-eight percent of the end consumer price. Thus, many intermediaries add more costs than the value (Patnaik, 2011; Kaur, 2015; National Horticulture Research &
Development Foundation [NHRDF],2018).
E-Trading in eNAM
As part of India’s initiatives for reforming the agricultural marketing sector, India’s Government launched the ’Ag- marknet’ project in the year 2000 (www.agmarknet.
gov.in). This ongoing e-governance project’s objective is limited to empowering farmers with the latest market information (Suri, 2005). Indian Government is now pro gressively deploying an e-trading platform, the ’National Agriculture Market’ (eNAM), in all 2477 regulated whole sale agricultural markets and the linked 4,843 sub-market yards. It has been implemented in 1000 wholesale markets by May 2020, with the registration of 16.6 million farmers, 131 thousand traders, 73 thousand commission agents, and one thousand farmer producer organizations (Suri, 2018;
Ministry of Agriculture and Farmers’ Welfare [MOAFW], 2020; Press Information Bureau [PIB],2020).
The eNAM platform has a virtual electronic trading portal on the front with a physical market (’Mandi’) infrastructure at the back end. The end-to-end e-trading
process activities (Fig.2) include registration of farm- ers/traders/buyers/agents, lot entry at the gate, quantity and quality checks, trading, and payments held online in digital form. At the same time, the actual material movement happens in the physical market. In any market covered under eNAM, the select agricultural commodities are mandatorily traded online on e-NAM.
The e-trading transaction process (Fig.3) is outlined below:
• Online bidding is held on the portal.
• The initial invoice is generated automatically on intra- market or inter-market trade confirmation (by e-NAM software) and shown to traders. The winning bidder also gets an email / SMS.
• The winning bidder deposits the amount online/offline (as per the sale deed, including market and agent’s charges, labour/packaging charges).
• The system sends a confirmation message to the farmer/trader/agent on receiving the funds.
• The delivery happens as per the terms and conditions. It is either on the spot market or through a logistics service provider (arranged by the supplier/buyer) listed on the portal.
• As soon as the buyer or representative accepts the delivery, payment is made to the farmer/trader/agent in online mode through registration. The timing is T?1 business day routed through the e-NAM bank account.
Need for a Flexible E-Trading System
Due consideration must be given to the eNAM e-trading system flexibility. Here, e-trading flexibility is viewed as the capability to add new features as per changing user requirements. Flexibility is vital in all the three-stage of eNAM adoption, implementation, and post-implementation of the system. During the post-implementation stage of eNAM, it is vital to constantly undertake process redesign based on market functionaries’ feedback to establish a versatile e-trading system that is flexible enough to accommodate the changing needs effectively. (Chen et al., 2009; Suri & Sushil,2011; Kar & Rakshit,2015; Evans &
Bahrami,2020).
The unified platform approach adopted by the eNAM e-trading system provides the necessary foundation for achieving increased flexibility in collaboration among farmers, traders, corporate and market officials. As a result, flexibility may help extend the eNAM life cycle and improve its effectiveness, thereby justifying the consider- able investment made in the system (Aulkemeier et al., 2019).
In this research, e-trading system flexibility is viewed as
’perceived ease-of-use’. Prior research has shown that
perceived flexibility significantly directly affects user-per- ceived ease-of-use (Goodwin,1987; Davis,1989; Goeke &
Faley,2007).
The customer care feature ’Facilitating Conditions’, and the quality linked pricing, with an option to do intra-mar- ket/inter-market trade under ’Perceived Usefulness’ also address the flexibility aspect of the e-trading system operations (Bichler et al.,2002; Gorod et al.,2008; Zhang et al.,2010; Sushil,2012; Singh et al.,2019).
Factors in the Context of E-Trading Use in Agricultural Marketing
The six factors are identified based on a review of the scholarly articles, reports, and news articles. The details are mentioned in Table 1
Fig. 2 Complete E-Trading Process ( Source: MOAFW, 2020) Fig. 3 eNAM Transaction Flow
( Source: MOAFW, 2020)
Decision-Making Process and Interpretive Ranking Process (IRP)
Decision making is a problem-solving activity ending with an optional or satisfactory solution. The decision-making process may be intuitive or rational choice-based (Kahne- man, 2011). In the intuitive process, the information is gained through associated learning for storage in long-term memory. During decision making, we access it uncon- sciously to form the basis of a decision, e.g. matchmaker for marriage, car colour or model selection, meal selection while dining out, selecting the book from the best-seller list, choosing a dress from a wardrobe. In the rational choice process: we use a precise, analytical approach in a fact-based decision, e.g. the best solution to increase shop profits.
A large part of the decision-making process involves ranking the alternatives or finding the best option. Espe- cially when multiple criteria (maybe conflicting) are pre- sent at the same time, e.g. investment in various portfolio stocks in a mutual fund (here we seek higher return but low risk), customer satisfaction versus the cost of service (Madhurika & Hemakumara,2015).
Such problems can also be solved using multiple-criteria decision making (MCDM). The multiple criteria (which may involve various stakeholders) can be weighed
implicitly based on intuition or explicitly evaluated while structuring the problem. These alternatives may also be known in advance or unknown (they may be generated using mathematical models). Then, the performance is checked under multiple criteria.
The various competing MCDM methods are the Ana- lytical Hierarchy Process (AHP), the Analytical Network Process (ANP), the Data Envelopment Analysis (DEA), and the Interpretive Ranking Process (IRP) (Govindan et al., 2015).
Initially, within the supply chain domain, the AHP method was preferred as a reliable and flexible tool (Yahya
& Kingsman,1999; Muralidharan et al.,2002; Kar & Pani, 2014). However, during the last decade, the IRP method has been preferred by several researchers. The reason is the IRP method’s capability to extract the expert knowledge and reasoning used for the rating during its pairwise comparison. Expert reasoning lacks expert judgements used during AHP and ANP (Triantaphyllou,2000; Sushil, 2009; Ho et al.,2010).
The IRP methodology mixes the analytical logic (ra- tional choice process) with an intuitive process. The IRP is essentially a knowledge-intensive MCDM method. It may be used in decision making, a process for ranking alter- natives/actions/actors concerning criteria/performance/
process.
Table 1 Factors in e-trading use ( Source: Adapted from Chaudhary & Suri,2021) S.
No
Variable Explanation in Study Context References
1 Perceived Usefulness
The eNAM extends benefits to users in terms of better price and speed of the market transaction
(Tyndell et al.,1998), (Kilpatrick & Factor,2000), (Venkatesh et al.,2003,2012), (Dejen & Solomon,2015), (Nirmal,2017,2019), (Pavithra et al.,2018), (Michels et al.,2019), (Salimi et al.,2020)
2 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)
3 Social Influence
Close friends, leading farmers, associated traders, and community leaders encourage or promote the eNAM
(Ajzen,1991), (Venkatesh et al.,2003,2012), (Dejen &
Solomon,2015), (Engotoit et al.,2016), (Dwivedi et al., 2019)
4 Trust The user’s confidence in the information and trade based on e-trading. It also signifies a belief that the eNAM is reliable and that the management will act in the interest of farmers and other stakeholders
(Moorman et al.,1992), (Killpatrick & Factor,2000), (Ridings et al.,2002), (Kuttainen,2005), (Casalo et al., 2011), (Ramesh et al.,2012), (Bisen & Kumar,2018), (Jayashanker et al.,2018), (Wiyada et al.,2018) 5 Cost The transaction costs in the eNAM platform (Dodd et al.,1991), (Garicano & Kaplan,2001), (Clasen &
Mueller,2006), (Solaymani et al.,2012), (Seifbarghy &
Esfandiari,2013), (Dey,2016), (Nabhani,2016), (Mustaqquim,2017), (Wiyada et al.,2018) 6 Facilitating
Conditions
The infrastructure, including quality testing laboratories, bidding halls, logistics support, IT/product training, and customer care, complements the use of eNAM
(Ajzen,1991), (Bender,2000), (Killpatrick & Factor,2000), (Venkatesh et al.,2003,2012), (Dejen & Solomon,2015), (Tomar et al.,2016), (Dwivedi et al.,2019)
The IRP’s strengths include easily setting the interaction boundaries; the interaction details help in comparison; and quick decision concerning the relative dominance of one interaction with the other. The paired comparison reduces cognitive overload, reduces reliance on the factor or the variable weight, applies to any assemblage of variables, and different influencers with varied interests can be included in the evaluation to prevent preconceived opin- ions. The IRP is not software or calculation intensive. The knowledge generated can be re-used with added informa- tion for future decision making. The limitations of IRP include: the approach is judgemental and interpretive and is subject to biases, given that all criteria are considered as equal, it neglects their relativistic importance, objective validation tests are difficult to be administered, and inter- pretation of a matrix of size beyond ten by ten is very complex given the exponential increase in paired com- parison (Warfield,1974; Sushil,2009).
The IRP method considers the subjectivity in prioritiz- ing various perspectives and the consensus-building rank- ing through a systematic procedure, an essential dimension of group decision-making flexibility (Kar & Pani,2014).
So far, it is used in several research areas, including the public value of e-governance projects, third-party logistics provider, mapping of IS failure factors, a ranking of flex- ibility initiatives, energy security and sustainability, lean implementation manufacturing sector, risks in the business analytics practices adoption, integrated supplier selection, world-class manufacturing, flexible supplier selection, and risk management in the supply chain (Gangotra & Shankar, 2016; Soni et al., 2016; Narkhede et al., 2017; Sushil, 2017a; Gupta & Suri,2018; Kumar & Anbanandam,2020).
Research Methodology
A systematic assessment of the scholarly articles, reports, and news articles relevant to the research has helped in identifying the factors influencing the continued e-trading usage in India’s agricultural marketing. A semi-structured questionnaire for the experts was developed after review- ing recent academic articles.
The factors are ranked using the efficient Interpretive Ranking Process (IRP). The method is in sync with the similar use of an integrated evaluation tool using a fuzzy matrix in the agriculture sector (Girdziute,2012). The IRP method involved developing binary and interpretive matrices, followed by a detailed interpretive logic knowl- edge base (Sushil, 2009). Then, a dominance matrix is prepared for ranking the factors concerning the three ref- erence factors (criterion). The final Interpretive Ranking Model is based on a dominance index derived from the factors adjusted net dominance. Finally, the
recommendations to support the top-ranked factors are made using learning from the case study of the eNAM project in four Indian cities, e.g. Meerut, Aligarh, Bharat- pur, and Kashipur.
This study’s trustworthiness is established using Lincoln and Guba’s Evaluative Criteria of credibility, transferabil- ity, dependability, and conformability (Lincoln & Guba, 1985).
The credibility of qualitative research is established using referential adequacy. Then, a team of six experts from academia and industry is formed to apply the IRP method. After this, no new information is elicited by adding more experts, so a detailed questionnaire response was restricted to six experts in the first panel (Morse,2000;
Berg,2001; Astalin,2013).
The transferability is established using a detailed description (interpretive logic, knowledge base, and con- text for the recommendation) obtained through a National Agriculture Market (eNAM) case study.
The qualitative research’s dependability and confirma- bility are established using the validity check of IRP findings by the five experts in the second panel.
The third panel of five experts deliberated on sugges- tions to improve factors leading to the high usage of the e-trading platform.
In all three expert panels, members are drawn from a mix of Academia, Industry, and Users.
Overall, the total number of 16 expert opinions obtained through questionnaire and semi-structured interviews con- ducted during the year is within the suggested range of 15 to 30 in the information systems research and 5 to 25 for the phenomenological studies (Creswell, 2007; Marshall et al., 2013). The outlined research process is suited to understanding and clarifying current issues and exploring new issues (Bryman,2008).
Analysis and Findings
The Interpretive Ranking Process (IRP) has been applied to influencing e-trading usage in Indian agricultural market- ing. The eight steps in the methodological process are outlined next.
Step 1: Identification of ranking factors concerning the reference factors.
The six ranking factors (Table2) are identified based on a review of the scholarly articles, reports, and news articles relevant to the research, as detailed in Table1. Three ref- erence factors (Table 3), as suggested by experts, are also included.
Step 2: The relationship between the ranking and the reference factors in the context.
If the factor ’Fx’ enhances/influences (in terms of dominance) the factor ’Fy’, the factor ’Fx’ dominates the factor ’Fy’. If it is otherwise, then the factor ’Fx’ is dom- inated by ’Fy’.
Step 3: Development of a cross-interaction matrix (CIM).
The CIM listing the contextual relationship between the ranking and reference factor is shown in Table4. The cell value ’1’ indicates a contextual relationship, that the cor- responding ranking factor ’Fx’ ’enhances/influences’ the reference factor ’Fy’, and ’0’ shows no contextual relationship.
An interpretive matrix (Table 5) is developed by explaining the ’1’ values in the cross-interactions’ matrix (Table4) (Sushil,2005).
Step 4: Dominating interaction matrix.
The pairwise interaction of ranking factors concerning the reference factor (s) listed in Table 4 is shown in Table 6. For each reference factor, the cell value for the pairwise dominance is ’1’ if the ranking factor ’Fx’ dom- inates the ranking factor ’Fy’. If there is no dominance, then the cell value is ’0’.
The overall reference factor-dominated interaction matrices for the three reference factors (C1, C2, C3) are collated (Table7).
Step 5: Development of ranking and interpretation.
The number of dominating interactions in Table 6 is aggregated in a dominance matrix (Table8). The weight of all three reference variables is ’1’: thus, initial column cell values in Table 8 equals ’1*instance values in Table 6’.
The total ’D’ is the weighted sum of the number of instances where the ranking factor(s) dominate other ranking factors. The column ’B’ is the weighted sum of the number of instances in which other ranking factors domi- nate a ranking factor. The difference between ’D’ and ’B’
is termed as a ranking factor’s net dominance. The net dominance is adjusted to the scale by adding the positive value equivalent to the maximum negative net dominance value to each net-dominance value. Then, the index value is determined as the percentage of the total. The ranking factor with maximum dominance index value is ranked first, and so on.
The percentage of each type of paired comparison of ranking factors for a particular reference factor in Table4, leading to the cell value entries in Table 8, is shown in Table 9.
In implicit dominance, if the ranking factor ’Fxi’ has ’1’
entry and ’Fxj’ has ’0’ entry for a reference factor, then cell entry is considered as ’1’ in Table6. Suppose the ranking factor ’Fxi’ has ’0’ entry and ’Fxj’ has ’1’ entry for a reference factor. In that case, cell entry is considered as ’0’
in Table 6. For implicit non-dominance comparison, both ranking factors ’Fxi’ and ’Fxj’ have ’0’ entry for a refer- ence factor, leading to ’0’ cell entry.
Table 2 Ranking factors
Code Variable
F1 Perceived Usefulness
F2 Perceived Ease of Use
F3 Social Influence
F4 Trust
F5 Cost
F6 Facilitating Conditions
Table 3 Reference factors
Code Reference factors
C1 Start actual transactions
C2 Frequency of transactions
C3 Total volume of transactions
Table 4 Cross-interaction matrix
Alternatives (ranking factor) Code Criteria (reference factor)
Start actual transactions Frequency of transactions Total volume of transactions
C1 C2 C3
Perceived Usefulness F1 1 1 1
Perceived Ease of Use F2 1 1 0
Social Influence F3 1 0 0
Trust F4 1 1 1
Cost F5 0 1 1
Facilitating Conditions F6 1 1 0
If both ’Fxi’ and ’Fxj’ cells have ’1’ entries in Table4, we refer to the interpretive matrix (Table 5). If the inter- pretation is the same, then the corresponding entry is ’0’ in Table 6, with implicit non-dominance. However, if the interpretations are different, then the expert opinion is taken. The opinions lead to the interpretive dominance comparison. If ’Fxi’ dominates ’Fxj’, then the corre- sponding cell entry in Table 6 is ’1’, and vice versa.
Sometimes, the cell values’ Fxi’, ’Fxj’, and ’Fxk’, are all
’1’ in Table 4. Then, if ’Fxi’ dominates ’Fxj’, and ’Fxj’
dominates ’Fxk’, ’Fxi’ dominates ’Fxk’ with corresponding paired dominance comparison cell entry ’1’ in Table6and marking it as the transitive dominance comparison in Table9.
The IRP advancements are used as per the efficient IRP method due to the implicit or transitive dominance rela- tionships, weights of reference variables, and computation of dominance index. The experts’ interpretive dominance Table 5 Interpretive matrix
Alternatives (ranking factor)
Code Criteria (reference factor)
Start actual transactions Frequency of transactions Total volume of transactions
C1 C2 C3
Perceived Usefulness
F1 A trial of a useful (possibility of higher price, quick transaction cycle) system
Higher price/return than the offline market average due to quality linked pricing and reduction in trade to payment cycle time
Why not use e-trading for more significant transactions? Even aggregation of produce and e-trade is attempted for small farmers Perceived
Ease of Use
F2 A simple procedure, intuitive website, and the logical graphical user interface of the application. APMC support staff is available to help
The user gains confidence in handling transactions due to familiarity with the website/mobile application. The intuitive and logical graphical user interface in the local language also makes the use easy
Social Influence
F3 Many farmers adopt due to uncertainty reduction and encouragement from influencers as a follower approach Trust F4 Knowledge acquisition/Awareness
Camps build trust in the Government promoted platform
Using knowledge, transparency, and experience for further gains from e-trade
Taking advantage of application expertise, process transparency, and e-trade experience for higher benefits
Cost F5 After comparing with the offline market,
the user makes decisions, and lower transaction cost matters for traders and farmers
An incentive of better margins on higher volume
Facilitating Conditions
F6 Understanding of stakeholders, physical infrastructure commitments, quality laboratories, training, and customer care staff to answer queries and get going on the platform
Increased realization of benefits. The quality linked pricing and inter-market trade possibility both increase trade frequency
Table 6 Dominance interaction matrix for each reference factor
FOR C1 (Weight of reference factor = 1) FOR C2 (Weight of reference factor = 1) FOR C3 (Weight of reference factor = 1)
Code F1 F2 F3 F4 F5 F6 Code F1 F2 F3 F4 F5 F6 Code F1 F2 F3 F4 F5 F6
F1 0 0 0 0 1 1 F1 0 0 1 0 0 0 F1 0 1 1 0 0 1
F2 1 0 1 1 1 0 F2 1 0 1 0 0 0 F2 0 0 0 0 0 0
F3 1 0 0 0 1 0 F3 0 0 0 0 0 0 F3 0 0 0 0 0 0
F4 1 0 0 0 1 0 F4 1 1 1 0 1 0 F4 1 1 1 0 0 1
F5 0 0 0 0 0 0 F5 1 1 1 0 0 0 F5 1 1 1 1 0 1
F6 0 0 0 0 1 0 F6 0 1 1 1 0 0 F6 0 0 0 0 0 0
comparisons are limited to a small set of 33 percent (Table9) or 12 comparisons, which are further detailed for suggestions after discussion with the second set of experts (Sushil,2017b,2020; Parmeshwar et al.,2020).
Step 6: Validation of ranks derived.
The ranking of factors derived from the dominance matrix (Table8) is validated for the confidence-building in the ranking, which is interpretive. The multiple validations (Sushil,2018) done are as follows:
• A structured walk-through for the cross-interaction matrix was done in the expert panel (second) workshop.
All relevant ranking and reference factors were included. Though the list may not be exhaustive, a reference factor may be missed out.
• The interpretations and their wording were corrected based on a structured walk-through in an expert panel discussion.
• For the correct assessment of dominance relationships, the system graphs are drawn for the reference factors.
The arrows in the digraph are expected to be unidirec- tional with no feedback loop/cycle. The feedback loop indicates unclear dominance relationships. The domi- nance interactions of various ranking factors concerning reference factors C1, C2, and C3 are unidirectional, as shown in Figure 4, thereby passing the internal validation test for the paired comparisons and its assessment.
• The cross-validation test is passed as the net dominance values’ sum is zero in the dominance matrix (Table8).
Table 7 Overall reference factor wise dominant interactions matrix
Ranking factor Code F1 F2 F3 F4 F5 F6
F1 C3 C2, C3 C1 C1, C3
F2 C1*, C2 C1, C2 C1 C1
F3 C1 C1
F4 C1, C2*, C3 C2*, C3 C2, C3 C1, C2 C3
F5 C2*, C3* C2, C3 C2, C3 C3 C3
F6 C2* C2 C2 C1
Table 8 Dominance matrix
Ranking factor Code F1 F2 F3 F4 F5 F6 Number Dominating (D)
Net Dominance (D-B)
Adjusted Net Dominance
Dominance Index (%)
Rank Dominating
Perceived Usefulness F1 1 2 0 1 2 6 -2 5 12 4
Perceived Ease of Use F2 2 2 1 1 0 6 0 7 17 3
Social Influence F3 1 0 0 1 0 2 -7 0 0 5
Trust F4 3 2 2 2 1 10 7 14 33 1
Cost F5 2 2 2 1 1 8 2 9 21 2
Facilitating Conditions
F6 0 1 1 1 1 4 0 7 17 3
Number Being Dominated (B)
8 6 9 3 6 4 36 0
Table 9 Different types of dominance comparisons Criteria
(reference factor)
Implicit dominance comparisons
Implicit non- dominance comparisons
Transitive dominance comparisons
Interpretive dominance comparisons
Total comparisons
% Interpretive comparisons
C1 5 0 1 5 11 45
C2 5 0 4 4 13 31
C3 8 0 1 3 12 25
Total 18 0 6 12 36
Percentage 50% 0% 17% 33% 36
• The sensitivity analysis gives different ordinal weights to the reference factors (Sushil, 2020). The results are summarized below (Table10). The sensitivity analysis of ranking is not highly sensitive as the original weights assigned to reference factors changes, and evident modifications are minor. Thus, the efficient IRP ranking is quite robust.
• The rankings obtained were cross-checked in a five- member expert panel discussion.
• The real-life implications of ranking and related suggestions were discussed with a third expert panel.
This includes prioritizing one factor over another. For example, suppose the usage of e-trading must be improved. In that case, the highest-ranking factor,
’Cost’, may be given more management attention, supported with more financial and organizational resources, plus ’lower transaction cost’ benefit may be promoted in public interactions.
Step 7: An ’Interpretive Ranking Model’ diagram.
The ranks of six factors concerning their influence on the usage of e-trading in the Indian agriculture marketing
sector are diagrammatically represented in an ’Interpretive Ranking Model‘‘ (IRM), shown in Fig. 5.
The arrows in the IRM diagram show a reference factor whose ranking factor dominates the other ranking factor.
The interpretation of how a specific factor is influencing various reference factors is also provided. It can also be read in conjunction with the interpretive matrix (Table5).
Step 8: Ranking decision and knowledge base.
The findings section details the IRP model output (Fig. 5). Based on the IRP model’s ranking, one may pri- oritize the higher-ranking factors (Trust, Cost, Facilitating Conditions, Perceived Ease of Use). Suggestions for action are provided in the conclusion section of this research study.
The interpretive logic–knowledge base (Table 5) gen- erated in this research study is the starting point for the conclusion section’s suggestions. In the future, improve- ments may be made by adding more factors and additional learning about relationships.
Fig. 4 Diagraph For Validity Check
Table 10 Comparison of ranks using sensitivity analysis Code Alternative
(ranking factor)
Base Case Case 1 Case 2 Case 3 Case 4
Criteria (reference factor) weights:
C1 = 1, C2 = 1, C3 = 1
Criteria (reference factor) weights:
C1 = 1, C2 = 2, C3 = 3
Criteria (reference factor) weights:
C1 = 3, C2 = 2, C3 = 1
Criteria (reference factor) weights:
C1 = 2, C2 = 3, C3 = 1
Criteria (reference factor) weights:
C1 = 2, C2 = 1, C3 = 3 F1 Perceived
Usefulness
5 3 4 5 4
F2 Perceived Ease of Use
3 5 2 3 3
F3 Social Influence 6 6 6 6 6
F4 Trust 1 1 1 1 1
F5 Cost 2 2 4 4 2
F6 Facilitating Conditions
3 4 3 2 5
Discussion and Limitations
The research findings are aligned with the research objec- tives, and based on the expert panel discussion, a few suggestions are brought out for the strengthening of the e-trading factors influencing the continued use of e-trading in agricultural marketing:
Theoretical Contributions
As per the first research objective of ranking the factors influencing the continued use of e-trading, the IRP model of six factors ranked based on three criteria is shown in Fig. 5. The rank values are taken from the dominance matrix (Table8).
The most important (top) factor is ’Trust’ in the hier- archy. It dominated ten factors-criteria situations and dominated in three, with an adjusted net dominance value of fourteen (highest). It influences criteria ’C1’ (start actual transactions) via knowledge acquisition, criteria ’C2’
(frequency of transactions) through an attempt to further gains from knowledge, transparency, and experience, and criteria ’C3’ (total volume of transactions) in terms of attempts to get more benefits from the market knowledge, openness of process and application experience gained.
The second-ranking factor is ’Cost’ as it dominated eight factor-criteria situations and got dominated in three.
The adjusted net dominance value is nine. The ’Cost’
(transaction costs) consists of search cost, coordination cost, motivation cost, and commitment cost. It influences criteria ’C2’ (frequency of transactions) via cost compar- ison with the open market to get lower transaction cost and
criteria ’C3’ (total volume of transactions) regarding a user seeking better margins on higher volume.
The factors ’Facilitating Conditions’ and ’Perceived Ease of Use’ is at the third position. They dominate four factor-criteria situations and are dominated in four factor- criteria cases, thus having an adjusted net dominance value of seven. The factor ’Facilitating Conditions’ influences the criteria ’C1’ (doing transactions) by understanding stake- holders & infrastructure (IT system, bidding room, mobile application, quality laboratories, training, and support staff) that help to break inertia and try out the system. It influ- ences criteria ’C2’ (frequency of transactions) via trust- building, quality-linked pricing, higher bids in the inter- market trade, and the increasing realization of benefits leading to the system’s frequent use. The factor ’Perceived Ease of Use’ influences criteria ’C1’ (doing transactions) through the easy graphical interface of the website and mobile application, plus support from the staff, and ’C2’
(frequency of transactions) via in terms of gain in confi- dence post-usage and information availability in the local language.
The factor ’Perceived Usefulness’ is in the fourth posi- tion with an adjusted net dominance value of five. The criteria ’C1’ (doing transactions) is influenced in terms of an intent to get a higher price. The criteria ’C2’ (frequency of transactions) is influenced through the possible realiza- tion of higher price due to quality linked pricing and payment realization within a day. The criteria ’C3’ (total volume of transactions) is influenced due to an inherent desire to better the margins or benefits. The factor ’Social Influence takes the fifth spot’. The initial registration and system trial are affected by the influencer farmer/trader or the market official.
RANK 3: F6 (Facilitating Conditions), Influences:
C1, C2 by quality assurance, training, customer care and inter-market trade
RANK 4: F1 (Perceived Usefulness), Influences: C1, C2, C3 by trial in want of benefits, reduction in trade cycle time, quality assurance, and want of increased margins
RANK 5: F3 (Social Influence), Influences: C1 by encouragement from significant/important others, uncertainty reduction
RANK 1: F4 (Trust), Influences: C1, C2, C3 by Knowledge Acquisition, Transparency, Government Sponsorship, and Experience
RANK 3: F2 (Ease of Use), Influences: C1, C2 by simple procedure, intuitive graphical user interface of application, and improvement in confidence with familiarity
RANK 2: F5 (Cost), Influences: C2, C3 by lower transaction cost compared to market, and improve margins of high volume Fig. 5 Interpretive Ranking
Model
Implications for Practice
Discussion with the expert panel is undertaken to achieve the second research objective. Based on the expert panel discussion, the following suggestions are made for the strengthening of the e-trading continued usage in terms of influence factors:
The trust can be improved by incorporating a sound monitoring mechanism for assaying, grading, sorting, delivery, quality checking, and dispute resolution. At pre- sent, quality laboratories in many markets are dysfunc- tional due to a scarcity of equipment or staff. Inclusion and acceptance of authorized private laboratory reports for trade may be a solution. Also, suppose the quality of delivered agri-produce fails to meet the quality standard as per terms and conditions. In that case, a penalty on errant farmer/trader or payment reversal from the escrow account may be provisioned.
In a conventional setup, farmers are compelled to trust the commission agents because of a lack of financing options (e.g. for loans during off-season) and cash pay- ments for products sold. To avoid such a situation, the e-payment in an e-trading transaction is quick on delivery.
Moreover, the ATMs are made available in/near the market complex, from where the farmers can withdraw cash.
Besides, various financing schemes by the banks (Kisan credit card, livestock credit card, Jan-Dhan cards, FPO credit guarantee, export benefits), the government schemes (PM: crop insurance scheme, annual financial support scheme, irrigation scheme, agri-development scheme, pension scheme, livestock insurance scheme) has reduced the dependence of farmers on the agents for financing.
The online cost is expected to be competitive since online transactions reduce transaction costs and informa- tion symmetry. Thus, e-trading provides a cost-effective channel and helps deliver overall welfare. However, the aggregate taxes and market fees at present are high and not uniform across the states. Currently, the taxes and fees at APMC markets (mandis) range from 3 percent (West Bengal) to 19.5 percent (Andhra Pradesh) of Minimum Support Price (Jha, 2007; Subramanian, 2016; Nanda, 2018).
The potential of the e-trading platform is reflected in inter-state and inter-market trade. With uniform taxes and fees, traders with one national license can bid across markets without the need for multiple registrations. In inter-market trade, the market fee goes to the originating/
selling market. Thus, the buyer/trader does not have to search for markets with low/discounted market fees in uniformity. The uniformity will also simplify settlement procedures and reduce conflicts. Thus, taxes and fees may be lowered with uniformity across Indian states to promote inter-market trade.
Also, the timelines to full eNAM implementation and functioning must be monitored and controlled. The moni- toring may be done by appointing dedicated officers as the users are dissatisfied with the agriculture extension officers, as learned during the field visit.
The Facilitating Conditions may be improved by ensuring the availability of the amenities online and in/
around APMCs, e.g. quality assaying labs for grading, electronic weighing, warehousing facility, dedicated user support, and query resolution in coordination with other government schemes.
Smartphone usage is expected to boost access to digital agribusiness, as the rural user base is likely to reach at least 332 million in 2022. Ninety-two percent of the rural users’
have access to the Internet, primarily through mobile phones; thus, the Internet user base in rural India shall be more than 305 million (Klynveld Peat Marwick Goerdeler [KPMG], 2020; Mahapatra, 2020; Sharma et al., 2020).
The existing intuitive eNAM mobile application, when scaled to support all 22 major local languages with a better graphical user interface, is expected to have a broader user base as the local language Internet user base is growing at more than 13 percent annually. In addition, the features such as local language interface, video and image content with inbuilt Agri-dictionary, mobility, basic bank account (Jan-Dhan), and digital identity (Aadhar number) may further help farmers access digital services without inter- mediaries (Aravindh & Karthikeyan, 2018; Dhaygude &
Chakraborty,2020).
Each eNAM market has an e-trading room with com- puters, Internet connectivity, and trained staff (outsourced).
The trained staff helps/handhold farmers visiting markets and organize weekly/monthly eNAM awareness sessions both within market premises and at local fairs. The existing limited information technology literacy efforts by eNAM staff, when integrated with the similar e-Kranti and Prad- han Mantri Gramin Digital Saksharta Abhiyaan (PMGDISHA) initiatives by the Government coupled with support from the gram sevaks, Krishi Vigan Kendras, and common service centres, may help strengthen the trained user base of eNAM (Modekurti, 2016; Raja Lakshmi, 2017; Singh,2017; Department of Agriculture Cooperation and Farmers Welfare [DACFW], 2018; Hindustan Times Digital Content Services,2018).
The e-trading acceptance is more for inter-market and inter-state trades, considering the higher prices expectation due to competition and an increased number of bids. In such cases, the logistics functionality of the e-trading portal may include preferential freight charges and preferential warehousing. The e-trade of aggregated produce by the farmer-producer organization (FPO) may help the marginal farmers to benefit from the e-trade. The mobile-based application’s usage must be promoted to small farmers and
small traders so that e-trading functionality adds to the Mandi premises’ offline presence.
The eNAM e-trading system’s flexibility may be further enhanced by incorporating the system’s capacity to address future uncertainty and risk management. Furthermore, the provision of flexibility in terms of modular design, con- figurable add-on new features, open-source innovation, and cloud computing functionality may increase the life cycle and return on investment in the system. Furthermore, the problem-solving and non-routine tasks may be augmented by artificial intelligence and the Internet of Things. In addition, the improvement in the customer relationship orientation of the eNAM enabled APMCs is expected to enhance their flexibility to adapt to the changing needs and market conditions, which may improve organizational performance (Tsai & Lasminar,2021).
In recent times, data privacy and Internet security have raised concerns among users. In this regard, eNAM has taken due precautions to protect user-information digitally.
The various methods used are access protection via pass- word, web services with basic security profile 1.0, open standards for interoperability, SSL/TLS authentication mechanism for anti-spoofing, data encryption during stor- age, and transfer for confidentiality java-based customized security settings. As per the privacy policy, the user data is used only for digital transactions and shared with autho- rized employees and partners. Such features need to be further enriched from time to time with the emergence of new technological vulnerabilities.
Limitations and Future Research Directions
The ranking process is based on interpretive and judge- mental methods and, thus, subjective. A limited number of factors have been considered for this study to avoid com- plexities associated with paired comparisons.
This research’s efficient IRP process can be modified to include multiple interest groups/experts and academicians, and corporate employees approached for this research. In addition, the ordinal weights assigned to various reference factors may be changed as per the changing management priorities, as shown in the sensitivity analysis. In the cur- rent ranking process, the reference factors are qualitative.
Depending on the situation, a mixed formulation involving both quantitative and qualitative factors can be used. The interpretive ranking may be used for qualitative factors, and the quantitative ranking may be done for quantitative factors.
Conclusion
E-trading is a precursor to the digitalization of the agri- cultural value chain (Agriculture 4.0). With its pan-India presence, eNAM is expected to deliver in terms of a large number of electronically held market transactions and high trade volume.
This research study ranks the factors influencing e-trading usage in Indian agricultural marketing while considering multiple criteria in the framework.
Indian policymakers and managers may consider the six factors (as per their relative priority in terms of ranks) for increasing the usage of e-trading in the Indian agricultural marketing sector. Therefore, the scarce resources and pri- ority actions may be directed towards the top-ranking factors, e.g. lowering the transaction ’Cost’ and increasing
’Trust’ in e-trading while improving ’Facilitating Condi- tions’ and ’Ease of Use’ for ensuring maximum benefit/
desired output.
Declarations
Funding No funds, grants, or other support was received.
Conflict of interest The authors declare that there is no conflict of interest.
References
Aggarwal, N., Jain, S., & Narayanan, S. (2016).The long road to the transformation of agricultural markets in India: Lessons from Karnataka. India. Indira Gandhi Institute of Development Research. http://www.igidr.ac.in/pdf/publication/WP-2016-026.
pdf.
Ajzen, I. (1991). The Theory of Planned Behaviour.Organizational Behaviour and Human Decision Processes, 50(2), 179–211.
Aravindh, K. S., & Karthikeyan, C. (2018). The eminence of mobile agricultural apps in the worldwide Mobile network. Interna- tional Journal of Development Extension, 9(1), 26–38.
Astalin, P. K. (2013). Qualitative research designs: A conceptual framework.International Journal of Social Science & Interdis- ciplinary Research, 2(1), 118–125.
Aulkemeier, F., Iacob, M. E., & Hillegersberg, J. V. (2019). Platform- based collaboration in digital ecosystems. Electronic Markets, 29, 597–608.
Auramo, J., Kauremaa, J., & Tanskanen, K. (2005). Benefits of IT in supply chain management: An explorative study of progressive companies. International Journal of Physical Distribution &
Logistics Management, 35(2), 82–100.
Bender, P. S. (2000). Debunking five supply chain myths. Supply Chain Management Review, 4(1), 52–58.
Berg, B. L. (2001). Qualitative Research Methods for the Social Sciences. Allyn and Bacon.
Bichler, M., Kalagnanam, J., Katircioglu, K., King, A. J., Lawrence, R. D., & Lee, H. S. (2002). Applications of flexible pricing in business-to-business electronic commerce.IBM Systems Journal, 41(2), 287–302.
Bisen, J., & Kumar, R. (2018). Agricultural marketing reforms and national agriculture market (e-NAM) in India: A review.
Agricultural Economics Research Review, 31, 167–176.
Bryman, A. (2008). Social Research Methods. Oxford University Press.
Casalo, L. V., Flavian, C., & Guinaliu,. (2011). The generation of trust in online services and product distribution: The case of Spanish electronic commerce.Journal of Electronic Commerce Research, 12(3), 199–2015.
Chaudhary, S., & Suri, P. K. (2021). Modeling the Enablers of e-Trading Adoption in Agricultural Marketing: A TISM-Based Analysis of eNAM. Advance online publication.https://doi.org/
10.1177/0972262920977979
Chen, R. S., Sun, C. M., Helms, M. M., & Jih, W. J. (2009). Factors influencing information system flexibility: An interpretive flex- ibility perspective.International Journal of Enterprise Informa- tion Systems, 5(1), 32–43.
Chircu, A. M., & Kauffman, R. J. (2000). A framework for performance and value assessment of e-business systems in corporate travel distribution. Working Paper.http://misrc.umn.
edu/workingpapers/fullpapers/2000/0024_112900.pdf.
Chordia, T., Roll, R., & Subrahmanyam, A. (2008). Liquidity and market efficiency. Journal of Financial Economics, 87(2), 249–268.
Clasen, M., & Mueller, R. A. E. (2006). Success factors of agribusiness digital marketplaces. Electronic Markets, 16(4), 349–360.
Creswell, J. W. (2007). Qualitative inquiry and research design:
Choosing among five approaches. Sage Publications Inc.
Davis, F. D. (1989). Perceived usefulness, perceived ease-of-use, and user acceptance of information technology. MIS Quarterly, 13(3), 318–340.
Dejen, A., & Solomon, N. (2015). Mobile information system for small-scale rural farmers.IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR), 79–83.
Department of Agriculture Cooperation and Farmers Welfare (2018).
Agricultural Statistics, at a Glance 2017 Report. Directorate of Economics and Statistics. Ministry of Agriculture and Farmers Welfare.http://agricoop.nic.in/.
Dey, K. (2016). National agricultural market-rationale rollout and ramifications.Economic & Political Weekly, 51(19), 35–39.
Dhaygude, M., & Chakraborty, D. (2020). Rethinking design of digital platforms for emergent users: Findings from a study with rural Indian farmers. India HCI ’20: Proceedings of the 11th Indian Conference on Human-Computer Interaction. https://
dl.acm.org/doi/proceedings/https://doi.org/10.1145/3429290.
Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers.Journal of Marketing Research, 28(3), 307–319.
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Marc, C., & Michael, D. W.
(2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model.
Information System Frontiers, 21, 719–734.
Engotoit, B., Musa, B., Moya, K. G., & Abima, B. (2016). A Mobile- based communication adoption model for agricultural market information dissemination in Uganda.Global Journal of Com- puters & Technology, 5(1), 249–265.
Engstro¨m, A., & Salehi-Sangari, E. (2007).Assessment of Business- to-Business (B2B) e-marketplaces’ Performance[Doctoral The- sis, The Lulea˚ University of Technology]. LUT Online Repos- itory. https://www.diva-portal.org/smash/get/diva2:990718/
FULLTEXT01.pdf.
Evans, S., & Bahrami, H. (2020). Super-Flexibility in practice:
Insights from a crisis. Global Journal of Flexible Systems Management,21(3), 207–214.
Gangotra, A., & Shankar, R. (2016). Strategies in managing risks in the adoption of business analytics practices: A case study of a telecom service provider. Journal of Enterprise Information Management, 29(3), 374–399.
Garicano, L., & Kaplan, S. N. (2001). The effects of business-to- business E-commerce on transaction costs.Journal of Industrial Economics, 49(4), 463–485.
Gebauer, J., & Schober, F. (2006). Information system flexibility and the cost efficiency of business processes. Journal of the Association for Information Systems, 7(3), 122–146.
Girdzˇiu¯t_e, L. (2012). Risks in agriculture and opportunities of their integrated evaluation.Procedia-Social and Behavioral Sciences, 62, 783–790.
Global System for Mobile Communications Association (2020).The GSMA Agritech Toolkit for the Digitisation of Agricultural Value Chains. GSMA Agritech Programme.https://www.gsma.
com/mobilefordevelopment/wp-content/uploads/2020/03/
GSMA_AgriTech_Toolkit_for_the_Digitisation_of_Agricul tural_Value_Chains1.pdf.
Goeke, R. J., & Faley, R. H. (2007). Leveraging the flexibility of your data warehouse.Communications of the ACM, 50(10), 107–111.
Goodwin, N. C. (1987). Functionality and usability.Communications of the ACM, 30(3), 229–233.
Gorod, A., Gandhi, S. J., Sauser, B., & Boardman, J. (2008). The flexibility of the system of systems.Global Journal of Flexible Systems Management, 9(4), 21–31.
Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2015).
Multi-criteria decision-making approaches for green supplier evaluation and selection: A literature review.Journal of Clean Production, 98, 66–83.
Gupta, P. J., & Suri, P. K. (2018). Analyzing the influence of improved situation, capability level of actors, and flexible process workflow on public value of e-governance projects in India.Global Journal of Flexible Systems Management, 19(4), 349–372.
Hindu (2019, July 10). Just 14% of farmers registered on the eNAM platform. The Hindu Business Line. https://www.thehindubusi nessline.com/economy/agri-business/just-14-of-farmers-regis tered-on-enam-platform/article28363454.ece.
Hindustan Times Digital Content Services (2018, April 14). Integrate spot, derivative markets to ensure better prices for farmers:
Panel. The Mint. https://search.proquest.com/docview/
2024675783?accountid=173681.
Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision-making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16–24.
Jayashanker, P., Nilakanta, S., Johnston, W. J., & Gill, P. (2018). IoT adoption in agriculture: The role of trust, perceived value, and risk. Journal of Business & Industrial Marketing, 33(6), 804–821.
Jha, S. (2007). Indian wheat and rice sector policies and the implications of reform. Economic research report. The United States. Dept. of Agriculture. Economic Research Service, 41, 4–5.
Kahneman, D. (2011).Thinking, fast and slow. Farrar, Straus, and Giroux.
Kar, A. K., & Pani, A. K. (2014). How can a group of procurement experts select suppliers? An approach for group decision support.Journal of Enterprise Information Management, 27(4), 337–357.
Kar, A. K., & Rakshit, A. (2015). Flexible pricing models for cloud computing based on group decision making under consensus.
Global Journal of Flexible Systems Management, 16(2), 191–204.
