ISSN 2454-7115

Working Paper 287

ARE DAIRY RELATIONS SOCIAL TOO?

SOCIAL NETWORK ANALYSIS OF A DAIRY COOPERATIVE

SOCIETY IN GUJARAT

Shyam Singh and Neha Christie

Working Paper 287

ARE DAIRY RELATIONS SOCIAL TOO?

SOCIAL NETWORK ANALYSIS OF A DAIRY COOPERATIVE

SOCIETY IN GUJARAT

Shyam Singh and Neha Christie

Institute of Rural Management Anand

Post Box No. 60, Anand, Gujarat (India)

Phones: (02692) 263260, 260246, 260391, 261502

Fax: 02692-260188 Email: corpas@irma.ac.in

Website: www.irma.ac.in

December 2018

The purpose of the Working Paper Series (WPS) is to provide an opportunity to IRMA

faculty, visiting fellows, and students to sound out their ideas and research work before

publication and to get feedback and comments from their peer group. Therefore, a

working paper is to be considered as a pre-publication document of the Institute. This is

a pre-publication draft for academic circulation and comments only. The author/s retain

the copyrights of the paper for publication.

This work was supported by the Verghese Kurien Centre of Excellence (VKCoE) under

its sponsored Project “Social Network Analysis of Patronage of Dairy Cooperatives

Members in Gujarat, India”

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Are Dairy Relations Social too?

Social Network Analysis of a Dairy Cooperative Society in Gujarat

Shyam Singh1 and Neha Christie2

Abstract

The process of organising dairy co-operative societies in Gujarat started in the late 1940s.

Several dairy co-operatives, six to seven decades old by now, have been operating with

success. This is a fairly long phase for a collective action to generate bonding capital

keeping its members engaged with the co-operative structure. We conjecture that

individuals who have been part of the social and community life for a long period of time

are likely to use existing social networks for a collective action where the same set of

community members are involved. Maintaining separate networks for the collective

action may incur some additional cost to the members. Hence, the study intends to

investigate if dairy relation networks among the members of dairy co-operatives overlap

with their social relation networks. We do this by studying information sharing in dairy

and social networks. We use social network analysis to analyse the network data collected

from a five-decade-old dairy co-operative society located in the Anand district of Gujarat.

1 Associate Professor in Social Sciences, Institute of Rural Management Anand.

E-mail: shyam@irma.ac.in 2 Research Officer at Verghese Kurien Centre of Excellence at Institute of Rural Management Anand.

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1. INTRODUCTION

The Dairy Cooperative Societies (DCSs), village-level community institutions in Gujarat,

are known to be successful models of collective action (Baviskar and Attwood 1984,

Mascarenhas, 1988). The DCSs were formed to eliminate middlemen in the milk supply

chain who would extract enormous profits from the dairy farmers without incentivising

them. The DCSs not only aim to facilitate greater economic returns to dairy farmers but

also visualise a democratic system enabling farmers to take decisions on their own

without providing any space for individual privileges or power (Kurien 2004). Therefore,

a DCS also carries social significance. It provides opportunities to its members to interact

between themselves more frequently with regard to subjects beyond their day-to-day

lives, including information exchange on various aspects of dairying and animal health

(Chaudhary, Ravikumar and Kumar 2016: 1-2). Such interactions not only help the DCS

manage its operations democratically but they also help generate ‘bonding capital’

(Putnam 2000) responsible for bringing people from different social groups, classes, and

castes together.

The social relevance of DCSs becomes even more relevant in the case of old DCSs that

have been operating successfully for decades. Since the process of forming DCSs in

Gujarat started in the late 1940s many DCSs are now six to seven decades old. This is a

fairly long time for a collective action to generate bonding capital keeping its members

engaged with the co-operative structure. The extant literature affirms that the length of an

individual’s residence has a correlation with his/her increased participation in social and

community affairs (Kasarda and Janowitz 1976, Sampson 1988). Local social processes

shaped by the surrounding social environment foster relational ties among individuals

(Robins et al. 2007, p. 177). This becomes important particularly in the context of an

observation made by Davies et al. (2004) who suggest that collective actions do not

create a new set of social and information networks; instead they use existing ones. This

means that individuals who have been part of the local social and community life for a

long period of time, forming their social networks, are likely to use the same social

relations for other collective activities where the same set of community members are

involved. In other words, collective action networks overlap with existing social

networks. Since we don’t find much literature supporting the observation made by Davies

et al. (2004), we take this observation as an assumption and would like to test it in this

paper.

We test this assumption in the context of information sharing networks with respect to

dairy as well as social matters. We term these networks as Dairy Information Network

(DIN) and Social Information Network (SIN). We aim to see if both the networks overlap

and if not, we wish to explore the extent to which both networks are similar or different.

Existing collective action literature considers information as a resource (Woolcock 1998)

and an indicator of measuring trust and social capital among the members of the

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collective (Ostrom 1998). Dearth of research regarding information sharing in social

networks (Cross et al. 2001) has led us to not only test the assumption stated above, we

also provide useful information in our study regarding the characteristics of information

sharing in collective actions. In this paper, we look at the centrality of actors who are

sought out for getting information and the actors who store the information in both

contexts. We compare structural properties of both networks in order to determine how

similar or different they are.

The paper is structured as follows:

After the introduction and motivation of the study is presented in the first two sections,

we discuss a detailed methodological plan in the third section describing the study context

and spelling out the data collection and data analysis scheme. This section describes the

study context, defines network boundaries, and spells out data collection and analysis

plan. We discuss the analysis and results in the fourth section. Section five presents a

detailed discussion of the findings emerging from the analysis. The last section presents

conclusion, limitations of the study, and prospects for further research.

2. MOTIVATION OF THE STUDY

This study is an attempt to address questions regarding sustainability of the DCSs in

Gujarat. The DCSs in Gujarat, organised after the ‘Anand Pattern’ (Kurien, 2004), are

construed as successful models of collection action in the dairy sector. Studies comparing

this model to various other co-operative structures including sugar co-operatives in

Maharashtra (Baviskar and Attwood 1987) and agricultural co-operatives in Gujarat

(Ebrahim 2000) found that dairy co-operatives have shown enormous success while

sustaining their operations. While there are various institutional, managerial, and

technical aspects that explain sustainability of the ‘Anand Pattern’ or DCSs in Gujarat

(Shah 1991; Raju 1995; Shah et al. 1995) this study applies relational sociology and

social network framework to explain sustainability of the DCSs. Since the DCS is a

collective action based organisation which has been functional for decades, it would be

useful to see how communities adapt to such formal initiatives in their informal social

milieux.

3. METHODOLOGY

3.1 Study context

The emergence of DCSs in Gujarat was a reaction to exploitative informal market

relations between dairy farmers and private entities, mostly local middlemen or

contractors. The first step in this direction was the formation of Kaira District Co-

operative Milk Producers Union (popularly known as AMUL Dairy) in late 1946 with a

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resolve to organise local milk farmers under village-level dairy co-operative societies

while enabling them to market milk on their own.

Village-level DCSs are formed by milk producers in the villages. Memberships to the

DCS are open for anyone who is willing to get associated with it. The members elect a

governing body from among themselves to run the administration of the DCS. The

general body consists of all members of the DCS. Every member of the DCS, irrespective

of volume of milk he/she produces or power and wealth that he/she enjoys, is eligible to

exercise one vote during the decision-making process in the general body. The profit

(bonus) is distributed between the members as per volume of milk they have sold to the

DCS. Such processes help avoid elite capture.

The DCSs are organised under a district-level milk union, which processes the milk

procured from all the DCSs under its umbrella and produces various milk products. As of

now, there are 18 district milk unions in Gujarat. The milk unions help the DCS

strengthen its processes and systems. Milk unions extend various benefits to the DCS and

its members including veterinary services, provisioning mineral mixtures for animals at

subsidised rates, artificial insemination services for improving animal breeds, facilitating

loans from nationalised banks and, in some cases, providing advance payments and

educational scholarships to the children of DCS members. Milk unions have been

engaged in capacity building for DCS staff and milk producers. District-level milk unions

have been federated to the Gujarat Co-operative Milk Marketing Federation (GCMMF),

which is responsible for marketing milk and milk products.

This study is based on a five decade old DCS situated in the Anand district of Gujarat.

The DCS comes under the Kaira Milk Union. It has 340 members comprising about 95

percent of total households in the village. Able to collect about an average of 700 litres of

milk every day, the DCS has been providing bonuses to its members in the range of 20-24

percent over the last four to five years. These achievements have brought this DCS under

the successful co-operative category. The village is numerically dominated by one caste,

Parmar, which claims more than 95 percent of the total population of the village.

Therefore, the village does not observe inter-caste dynamics. However, the village does

exhibit economic and human capital based disparities. Since most households (about 90

percent) in the village fall under the marginal or small farmer category, dairying is an

important source of livelihood in the village.

3.2 Network boundaries

While the DCS has 340 members not all members sell milk to it all the time due to

various reasons. The members who sell milk to the DCS during the entire lactation period

of the animal are categorised as ‘active members’. At the time of data collection, the DCS

had 100 active members. The network of active members at the time of data collection

was considered for this study. The network reflects the following variations: 1) new and

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old DCS members and 2) a mix of small, medium, and large milk producer members. The

active members provide a fair representation of the overall membership of the DCS in

terms of the above mentioned variations.

3.3 Data collection and analysis

The data, based on a network survey, was collected from 85 active members leaving out

15 who were either not accessible or available in the village during the data collection

exercise that lasted three days. The data was collected on information sharing relations

among the members along with the members’ socio-economic attributes (please see table

1). The survey also combined semi-structured interviews in order to probe dimensions

emerging from the former.

Table 1: Network survey

Survey Segments Information type Questions

Dairy Information

Network (DIN)

Information sharing on dairy related

matters (Vet services, animal health,

mineral mixture, bonus on milk sale

provided by DCS, loans, scholarships,

etc.)

If you have some information

related to dairying or DCS, with

whom among the DCS and

governing body members would

you share?

Social Information

Network (SIN)

Information sharing on social matters

(marriage, functions, friendship,

personal life, cultural events,

events/incidents taking place in the

village, etc.)

With whom among the DCS and

governing body members do you

share your personal and family

related (on non-dairy matters)

information?

Socio-economic

attributes of the

DCS members

Information related to age, gender,

education, age of membership, dairy

income, other income, association

with other collective actions, etc.

Close-ended questions with

ordinal, interval-level and

continuous response

The data we collected is binary (1 or 0) and asymmetric in nature. One (1) indicates a

relationship between a pair of actors while zero (0) indicates otherwise. The binary data is

also known as non-valued data, which means the data is not able to reveal, in the event of

a relationship between a pair of actors, what would be the intensity/extent of that

relationship. An asymmetric relationship means that the member shares information with

other members but the receiver never shares any information with the sender in return.

The data has been analysed using the Social Network Analysis (SNA) method. SNA uses

sociomatrix and visualisation tools (Scott and Carrington, 2011) to explain relational

network data. We used the UCINET software (version 6.652) for analysing the network

data which we visualised through Netdraw (Borgatti et al., 2002). We analysed two types

of network measures: overall network measures and actor-centric measures. Overall

network measures help to understand the structural characteristics of the network while

actor-centric measures explain the dynamics of relationships between the actors of the

network. Table 2 explains both types of network measures.

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Table 2: Network measures

Measures Definition

Network level measures

Density In a binary network, density is the ratio of actual ties to all possible ties.

Density indicates what level of cohesion or social capital a network has.

Degree

Centralisation

This measure explains how centralised or decentralised a network is. Standard

deviation from the centrality score explains the nature of the centrality of the

network.

Reciprocity Two actors send ties to each other or, in other words, they are mutually

associated.

Diameter Diameter is the largest distance from one actor to another in the network. It

indicates the minimum number of steps one has to walk from one extreme side

to another extreme of the network.

Actor level measures

Degree Degree is the number of ties an actor sends to other actors. Under asymmetric

ties, there are two types of degrees- 1) in-degree: number of ties an actor

receives from other actors and 2) out-degree: number of ties an actor sends to

other actors.

Betweenness This indicates how often an actor lies between the paths of other actors. This

underscores the importance of the actor’s being in a favoured position since

other actors can reach their counterparts through only him/her.

Closeness This measure indicates how early an actor can reach others, or how close an

actor is from other actors.

Source: Hanneman and Riddle (2005)

4. ANALYSIS AND RESULTS

4.1 Network level measures

The overall network measures for both the information networks have been presented in

table 3. At the outset, values of different network measures for both the networks do not

differ much. More so, DIN looks as big and significant as SIN. If the average degree of

DIN is 2.8 then it is 3.2 for SIN. This also makes sense as social relations are broader,

more encompassing, and have been taking shape for centuries. However, it is important to

note that the average degree of DIN is not much lower than that of SIN’s. This may be

attributed to the DCS longevity (53 years). This is a long enough time for any community

institution to become mature in its operations. While the density figures of both networks

(SIN- 3.9% & DIN – 3.4%) are not higher they still hold much significance in large sized

networks (Faust, 2006). Since both networks do not have fractions (sub-groups), density

becomes an important indicator of network cohesiveness (Friedkin, 1891).

Network level measures characterise SIN as a denser and more centralised network and

DIN as an inclusive (no isolates and more reciprocal) and decentralised (lower

centralisation score) network. There is no isolate under DIN, which means that all DCS

members are embedded in the network and dairy information is likely to reach everyone.

The reciprocity is higher in DIN and its centralisation score is lower (1.4%) for DIN than

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for SIN (2.1%). SIN is more tightly knitted than DIN. The diameter of DIN (12) is higher

than that of SIN’s (9). This highlights the fact that DIN is decentralised and something of

a fragmented network. Importantly, reciprocity is somewhat higher for DIN (48%) than

for SIN (34%). This indicates that about 48% ties under DIN tend to be stable and

mutually respected. An asymmetrical (non-reciprocal) relationship faces the danger of

becoming unstable (Hanneman and Riddle, 2005). Therefore, the structural properties of

networks presented in table 3 indicate that while both networks do not differ much in

terms of size, SIN emerges as a more compact network and DIN as more liberal,

cohesive, and accommodating.

Table 3: Network level characteristics

SN Network Measures DIN SIN

1 Total ties 242 279

2 Isolates 0 2

3 Average Degree 2.847 3.282

4 Deg Centralisation 0.014 0.021

5 Density 0.034 0.039

6 Diameter 12 9

7 Dyad Reciprocity 0.485 0.348

Graph 1: Dairy information network (DIN)

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Graph 2: Social information network (SIN)

Node Size: based on degree centrality; Node Colour: Red- governing body members & Yellow-

general members

4.2 Actor level measures

Actor level analysis enables us to understand intra-network processes and dynamics.

Graph 3 exhibits the degree centrality of each DCS member in both networks. Degree

centrality is important for understanding the connectivity of an actor with other actors

within the network. Connectivity is not merely about participating in information

exchanges with other actors it also reflects how central an actor is in the network

(Freeman 1979); in other words, how close he/she is with others and whether he/she can

influence and/or control the process of information exchange. The degree centrality of 14

DCS members is the same for both networks. However, the degree centrality of 54

members is higher for SIN compared to the 17 members who have a higher degree

centrality under DIN. Interestingly, two members whose degree centrality is zero under

SIN have been able to find neighbours under DIN even with a lower degree centrality

under the latter.

Table 4 presents a ranking of top actors in terms of their degree centrality in both

networks. The table indicates that not all actors who occupy top ranks (1 to 5) in one

network maintain the same central position in other networks. This indicates that both the

networks maintain an independent space in the community. Extant literature on

community initiatives and development programmes in India and other countries shows a

propensity towards elite capture, that is, if a person is powerful and enjoys a dominant

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position in the community, he/she can also emerge as a central figure in any institution

dealing with community affairs as well as distribution of welfare benefits. Such a person

is equipped with the potential to influence local politics and power dynamics (Platteau

2004; Dasgupta and Beard 2007; Besle, Pandey and Rao 2012; Bardhan and Mookherjee

2005). However, results indicate another side of this argument. Members of the DCS

governing body seem to have a higher degree centrality under SIN and remain low as far

as DIN is concerned. While there are few actors (S, M66, M74, M60) who occupy central

positions in both networks, these actors work as good disseminators in both. None of

these members has ever held leadership positions in any community or local government

institutions. Besides the Secretary two other actors, M66 and M60, have been DCS

members for the last 20 and 25 years respectively. M74 is a new DCS member (4 years)

who holds rank 3 in both networks. This explains a smooth process of generational

change in the leadership of the DCS and may also be construed as a good sign for the

latter’s sustainability.

The DCS Secretary is responsible for day-to-day operations of the DCS and is the first

point of contact for the members regarding any information to be sought from the DCS

or, conversely, to be purveyed by the latter. Having been a member of the DCS for the

last 20 years the Secretary is able to engage in frequent information exchanges in both

networks.

Table 4: Prominent actors (by degree centrality)

ID DIN Rank SIN Rank

M2 4 8 4

GB1 4 8 4

S 8 2 9 3

M12 7 3 4

M30 6 4 5

GBC 5 8 4

M40 4 10 2

BG8 5 11 1

M59 6 4 5

M60 6 4 7 5

M63 10 1 5

M66 7 3 7 5

M70 3 8 4

M74 7 3 9 3

M75 6 4 4

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Graph 3: Degree centrality of the DCS members

Graph 4 and graph 5 depict other measures of centrality (closeness and betweenness) in

both networks. Both measures inform us about the dependency of the actors on each other

and, thereby, the central positions expressed in terms of the shortest paths or closeness

(Brandes, Borgatti and Freeman 2016). We have divided the actors’ positions into four

quartiles for both information networks. The higher betweenness centrality of an actor

means that he/she is positioned between the paths of two actors. This means that any

information which is shared between two actors has to pass through the actor that has a

higher betweenness. In the graphs 4 & 5, higher values indicate a higher betweenness of

the respective actors. Similarly, the closeness centrality indicates the shortest distance of

an actor with other actors. Therefore, in the graphs 4 & 5, lower values indicate a higher

closeness.

Graph 4 indicates the relationship between closeness and betweenness centrality in the

context of DIN. There are only five actors that come under the quartile Q1 indicating

higher closeness and higher betweenness centrality. Except for GB1, no other actor

figures in the top five ranks of the degree centrality. The vacant space in Q2 in both

graphs may be interpreted easily as we are unlikely to come across anyone with a low

closeness centrality but high betweenness. Higher betweenness also ensures that other

actors are closer, which translates into higher closeness centrality. This may be verified

from graph 4 which shows that the closeness centrality increases when the betweenness

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centrality increases. The third and fourth quartiles of DIN (Q3 & Q4) show a relationship

between betweenness and closeness that follows a specific concave-shaped pattern. This

may be attributed to a limited number of dairy-related subjects that are addressed by the

DCS within the formal pre-articulated processes and structures.

In the case of SIN, while the overall structure of the graph depicts the basic tenet of the

relationship between betweenness and closeness centrality, the shape spreads across

quartiles 3 and 4 in a fragmented way (graph 5). The first quartile of graph 5 has five

actors whose closeness and betweenness centrality is higher. The DCS Secretary is one of

them. Except for these five actors, all others fall in Q3 and Q4. Interestingly, no actor

with higher closeness and betweenness is common to both networks. The actors that fall

in the third quartile of DIN show higher closeness and betweenness under SIN (for

example M74 and M75). This indicates that centrality measures of the DCS members are

quite different for both networks. This also indicates that the channels, through the actors’

exchange information, are different for both networks. Freeman (1979) sees betweenness

as a measure of control (controlling information flow as the actor lies in between many

actors) and closeness as a measure of ‘independence’ and ‘efficiency’. Both networks

indicate different sets of ‘control’, ‘independent’, and ‘efficient’ information channels.

This deduction makes sense as social relations between individuals in a community are

manifested under various individual and collective choices and constraints. There are

diverse and multi-layered interplays of social, economic, political, and cultural

institutions that contribute towards shaping the individuals’ worldview.

Graph 4: Closeness and betweenness centrality in DIN

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Graph 5: Closeness and betweenness centrality in SIN Graph 4 & Graph 5: Q1: higher closeness and higher betweenness; Q2: lower closeness and higher

betweenness; Q3: lower closeness and lower betweenness; Q4: higher closeness and lower betweenness

Finally, we also looked at the actors’ connectedness (with the number of actors a given

actor is connected). Table 5 (in annexure) shows the actor-wise connection for both

networks. Out of 85 connections, 51 are completely distinct in both networks. Only 34

connections have common nodes cut across both networks. Out of 34 connections, 25

have one node common in both networks and the rest nine have two common nodes. In

fact, there are three nodes (M7, M22 & M48) that have no connection under SIN but are

moderately connected under DIN.

5. DISCUSSION

Following the analysis and results presented in the previous section, we can conclude that

even though the two networks do not appear very different in terms of size, the intra-

network structural properties of both are starkly different. DIN appears inclusive while

SIN comes out as fragmented. In order to reach an informed conclusion, we need to

compare the following aspects in both the networks: 1) whether network creators are

different, 2) whether knowledge friends are different from social friends, and 3) whether

social leaders are different from DCS leaders. Discussion on these aspects will enable us

to understand inter-network differences in a more nuanced fashion.

Network creators share information with others (extend ties to others, or out-degree) to a

greater extent while networked are the ones who receive information from many actors

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(in-degree). Our analysis indicates that both the networks have different sets of network

creators and actors who get networked. Under DIN, networked creators are the ones who

have been members of the DCS for many years ranging from 10 to 25 years. On the other

hand, those who get networked represent a fair mix of new members (1-10 years of

membership) and old members (10-25 years of membership). This proves that older

members actively share information with the newer members with the potential of

knowledge transfer from the current generation to the upcoming generation of DCS

members. Under SIN the profiles of networked actors match in terms of longevity of their

association with the DCS, while the profiles of network creators represent a fair mix of

both new and old members of the DCS. Therefore, network creatosvchost.exe different

for both networks.

The profiles of members securing top ranks in information sharing under DIN and SIN

differ a lot from each other, barring one exception- the DCS Secretary. Members have

different ties for sharing dairy-related information and operate different relational

networks when it comes to sharing information on social matters. Therefore, DCS

members do have “knowledge” (on dairy matters) friends who are different from social

friends.

Surprisingly, no DCS leader is included within the network creators under DIN. This

could be because members of the governing body have no role in providing DCS-related

information. In fact, DCS members have access to the DCS Secretary twice in a day when

they go to the DCS office to pour milk. Therefore, the DCS Secretary appears central in

information sharing under DIN with no leader from the governing body having a role to

play. However, members of the governing body are more active under SIN. There is a

reason for this: in order to become a member of the governing body of the DCS one needs

to have a better social profile demonstrating the ability to garner support whenever

needed. Since information sharing on dairy matters is mostly done through established

channels (Secretary, DCS notice board, DCS staff, etc.), DCS leaders need not to

participate in DIN actively.

6. CONCLUSION

It is clear from the results and discussion presented in the previous section that both

information networks are quite different. Even after 53 years of its inception, the DCS

under study has been able to maintain separate networks. We don’t find much support for

the argument presented by Davies et al. (2004) according to which collective actions use

already existing social networks. The results also indicate that the ability of the DCS to

maintain separate networks keeps it away from societal conflicts and complexities.

Hence, the DCS has been able to sustain its operations for decades with a great turnover

of milk collection and sale. This study also seeks to pave some avenues for those who

want to understand, beyond the managerial and technical aspects of dairy co-operatives’

operations, how community actions remain in force over longer stretches. In order to get

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into the depth of this matter, however, one needs to study intra-network dynamics, which

this study could not offer. This dimension could facilitate further research in this subject.

ACKNOWLEDGEMENT

The authors are thankful to the Verghese Kurien Centre of Excellence at Institute of Rural

Management (IRMA), Gujarat, India, for providing financial support to undertake this

study.

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16

ANNEXURE

Table 5: Nodes’ connectedness in DIN and SIN

Member

ID Connected Nodes in DIN Connected Nodes in SIN

No of

Common

Nodes

M1 M28 M36 M36 M64 M74 1

M2 M20 M29

M21 M39 GBC

0

M3 M56 M70

S M65 M70

1

M4 M12 M20 M29 M37 M43 M66

0

GB1 M19 M27 GB8 M75 S M11 M50

0

M5 M34 M63 M66 M11 M34 M35 M63 2

GB2 M1 M49 M55 M10 M15 M41 M47 0

GB3 GB6 M52

M40 M58 M70

0

GB4 M10 M71 M74 M7 M59 M71

1

M6 M9 M21 M68 M3 M73 M74

0

GB5 M15 M73

M18 M34 M39

0

M7 M66 M71 M75 -- --- -- -- -- 0

M8 M31 M35 M60 M35 M60 M67

2

M9 S M44 M51 GB1 M13 M65

0

M10 GB6 M32 M41 M43 GB8 M59 M64 0

S M2 M12 M24 M58 M26 M40 M57 M58 1

M11 M22 M27 M30 M30 M34

1

M12 M4 GB4 M37 M38 GB4 M23 M43

1

M13 M1 M53 M59 M9 S M16 M50 0

M14 M3 M4 M47 M28 M47 M65 M75 1

GB6 M33 M34 M57 M35 M36 M42 M57 1

M15 M1 M32 M53 M1 GB3 M62 M71 1

M16 M17 M31 GBC M71 M17 GBC M40 GB8 M62 2

M17 M8 M63

M8 M63 M62

2

M18 M22 M51 M59 GB1 GB5 M29 M34 0

M19 GB1 M29 M64 M36 GB8 M74

0

M20 M2 M4 M43 GB5 M32 M65

0

M21 M6 M53 M63 M2 M47 M51

0

M22 M11 M18 M28 -- -- -- -- -- 0

M23 M38 M39 M75 M8 M12 M24

0

M24 M36 BG7 M59 S M23 M65 M69 0

M25 M28 M30 M60 M8 M28 M66

1

M26 M49 M73 M74 S M33 GB8

0

M27 M11

M29 M34 M35 M60 0

M28 M30 M33

M30 M33 M60

2

M29 M4 M19 M57 GB4 M18 M27

0

M30 M11 M25 M28 M11 M28 GBC

2

17

Member

ID Connected Nodes in DIN Connected Nodes in SIN

No of

Common

Nodes

M31 M8 M16 M65 GB1 M7 M62 M69 0

M32 M10 M15 M68 GB4 M20 M58

0

M33 GB6 M55 M59 M26 M28 M56 M65 0

M34 M5 M66

M17 M27

0

M35 M8 M60 M67 M8 S GB6 M27 1

M36 M1 M24 M43 M1 M19 M57

1

M37 M12 GB8

M2 M23 M56 GB8 M64 1

M38 M12 M23

M23 M51 M72

1

M39 M23 GBC M55 M2 GB5 GBC M59 1

GBC M12 M30 M37 M17 M30 M40 M75 1

M40 S M47 M53 S GBC GB8 M70 1

M41 M10 BG7 M66 GB2 M40 M70

0

M42 M43 M57 M59 GB6 M54 M70

0

M43 M36 M42 M59 M6 M10 M12 M54 0

M44 M9 M40 M58 M45 M47 M66

0

M45 M44 M65 M74 M44 BG7 M74

2

M46 M2 M66 M75 M2 GB8 M63 M66 2

GB7 M24 M41 M55 M8 M10 M56

0

M47 M40 M72

M21 M44

0

M48 M63 M68

-- -- -- -- -- 0

M49 GB2 S M26 M40 M52 M53

0

M50 M63 M64

GB1 M13 M73

0

M51 M9 M18 M71 M5 M17 M21 M38 0

M52 GB3 S

M44 M49 M61

0

M53 M13 M21 M57 M32 M49 M74

0

M54 M63 M65

M2 GB8 M69

0

M55 M33 M39 BG7 M69 M74 M75

0

M56 M3 M12 M64 M12 M21 M33 M37 BG7 1

M57 M42 M53 M63 S M36 M47 M60 0

BG8 M37 M38 M55 M2 M57 M60

0

M58 S M44

GB3 M32 M69

0

M59 M24 M33 M42 M43 M10 M13 M18 M39 0

M60 M25 M34 M66 GB1 M27 M28 M66 1

M61 M30 M67

M27 M30 M52 M55 1

M62 M17 M30

M17 M30 M34 M52 2

M63 M50 M60 M66 M5 M17 M40

0

M64 M50 M56 M74 M10 M36 M37 M43 0

M65 M45 M54 M74 M24 M33 M74

1

M66 M41 M46 M63 M25 M60 M63

1

M67 M35 M61 M63 M8 M25 M66

0

18

Member

ID Connected Nodes in DIN Connected Nodes in SIN

No of

Common

Nodes

M68 M6 M32 M43 M48 M7 M32 M74 M75 1

M69 M33 M62 M70 M54 M55 M58

0

M70 M3 GB8 M69 M49 M50 M73

0

M71 M16 M51 M74 GB2 GB4 M15 M40 0

M72 GB3 M47 M74 GB3 M6 M38

1

M73 GB5 M26 M41 GB1 GBC M56 M70 0

M74 M64 M65 M71 M72 M2 M6 M65 M68 1

M75 M8 M23 M41 GBC M55 M68 0

Selected Journal Papers in 2017 by IRMA Faculty

Singh PK and Chudasma, S (2017), “Pathways for Drought Resilient Livelihoods Based on

People’s Perception”. Climatic Change, 140:179–193, November. DOI: 10.1007/s10584-

016-1817-8. IF 3.344, H-index 130.

Singh, Pramod K and Chudasma H (2017) ‘Assessing Impacts and Community Preparedness to

Cyclones: A Fuzzy Cognitive Mapping Approach. Climatic Change, doi:

10.1007/s10584-017-2007-z, IF 3.344, H-index 151

Shaikh A, Biswas SN, Yadav V and Mishra, D (2017) “Measuring fairness in franchisor-

franchisee relationship: a confirmatory approach”, International Journal of Retail &

Distribution Management, 54(2), pp: 158-176. DOI: 10.1108/IJRDM-11-2015-0174.

Modi, P., & Sahi, G. K. (2017) Toward a greater understanding of the market orientation and

internal market orientation relationship. Journal of Strategic Marketing, 1-18.

http://dx.doi.org/10.1080/0965254X.2017.1318943

Swati Panda, Satyendra C Pandey (2017), Binge Watching and College Students: Motivations and

Outcomes, Young Consumers, Vol 18, Issue 4, 425-438, https://doi.org/10.1108/YC-07-

2017-00707

Satyendra C Pandey, Pinaki Nandan Pattnaik (2017), Mandatory CSR and Organizational

Compliance in India: Case of Bharti Airtel, Global Business and Organizational

Excellence, Vol 36, Issue 6, 19-24, 10.1002/joe.21810

Venkatesh A and Kushwaha, S (2017) “Measuring technical efficiency of passenger bus

companies in India: a non-radial data envelopment analysis approach”, OPSEARCH, pp:

1-18; February. DOI: 10.1007/s12597-017-0303-z.

Misra, HK (2017), “Managing User Capabilities in Information Systems Life Cycle: Conceptual

Modeling”, International Journal of Information Science and Management, 15 (1), pp.

39-58; January-June.

Misra, H K, (2017), Managing User Capabilities in Information Systems Life Cycle: Conceptual

Modeling, International Journal of Information Science and Management, Vol. 15, No. 1,

39-58

Satyendra C Pandey, Pinaki Nandan Pattnaik, (2017), Follow the founder: case study of action

learning experiment in an MBA program, Development and Learning in Organization, Vol

32 Issue 3, doi.org/10.1108/DLO-05-2017-0042

Stoop, W. A.; Shambu Prasad C. Sabarmatee; Pushpalatha Sivasubramanian; Ravindra, A.;

Debashish Sen; and Thakur, A. K. 2017. Opportunities for ecological intensification:

lessons and insights from the System of Rice/crop Intensification - their implications for

agricultural research and development approaches. CAB Reviews 12: 036, pp 1-19.

Patel, S.S. & Ramachandran, P. Sustainainble Water Resource Management, Springer

International Publishing (2017), pp. 1-14. https://doi.org/10.1007/s40899-017-0124-5

Joshi S, Pandey V and Ross R B (2017) "Asymmetry in Stock Market Reactions to Changes in

Membership of the Dow Jones Sustainability Index", The Journal of Business Inquiry

2017, 16, Issue 1 (Special Issue), 12-35

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situations and rich description of the context in which such

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for IRMA’s academic programmes as well as executive

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